Upload folder using huggingface_hub

v0.19.2/README.md

@@ -0,0 +1,1769 @@
+# Community Examples
+
+> **For more information about community pipelines, please have a look at [this issue](https://github.com/huggingface/diffusers/issues/841).**
+
+**Community** examples consist of both inference and training examples that have been added by the community.
+Please have a look at the following table to get an overview of all community examples. Click on the **Code Example** to get a copy-and-paste ready code example that you can try out.
+If a community doesn't work as expected, please open an issue and ping the author on it.
+
+| Example                                                                                                                               | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Code Example                                                                              | Colab                                                                                                                                                                                                              |                                                        Author |
+|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
+| CLIP Guided Stable Diffusion                                                                                                          | Doing CLIP guidance for text to image generation with Stable Diffusion                                                                                                                                                                                                                                                                                                                                                                                                                                   | [CLIP Guided Stable Diffusion](#clip-guided-stable-diffusion)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) |                [Suraj Patil](https://github.com/patil-suraj/) | 
+| One Step U-Net (Dummy)                                                                                                                | Example showcasing of how to use Community Pipelines (see https://github.com/huggingface/diffusers/issues/841)                                                                                                                                                                                                                                                                                                                                                                                           | [One Step U-Net](#one-step-unet)                                                          | -                                                                                                                                                                                                                  |    [Patrick von Platen](https://github.com/patrickvonplaten/) |
+| Stable Diffusion Interpolation                                                                                                        | Interpolate the latent space of Stable Diffusion between different prompts/seeds                                                                                                                                                                                                                                                                                                                                                                                                                         | [Stable Diffusion Interpolation](#stable-diffusion-interpolation)                         | -                                                                                                                                                                                                                  |                       [Nate Raw](https://github.com/nateraw/) |
+| Stable Diffusion Mega                                                                                                                 | **One** Stable Diffusion Pipeline with all functionalities of [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | [Stable Diffusion Mega](#stable-diffusion-mega)                                           | -                                                                                                                                                                                                                  |    [Patrick von Platen](https://github.com/patrickvonplaten/) |
+| Long Prompt Weighting Stable Diffusion                                                                                                | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt.                                                                                                                                                                                                                                                                                                                                                                                                  | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion)         | -                                                                                                                                                                                                                  |                           [SkyTNT](https://github.com/SkyTNT) |
+| Speech to Image                                                                                                                       | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images                                                                                                                                                                                                                                                                                                                                                                                                            | [Speech to Image](#speech-to-image)                                                       | -                                                                                                                                                                                                                  |             [Mikail Duzenli](https://github.com/MikailINTech) 
+| Wild Card Stable Diffusion                                                                                                            | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values                                                                                                                                                                                                                                                                            | [Wildcard Stable Diffusion](#wildcard-stable-diffusion)                                   | -                                                                                                                                                                                                                  |              [Shyam Sudhakaran](https://github.com/shyamsn97) |
+| [Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | Stable Diffusion Pipeline that supports prompts that contain "|" in prompts (as an AND condition) and weights (separated by "|" as well) to positively / negatively weight prompts.                                                                                                                                                                                                                                                                                                            | [Composable Stable Diffusion](#composable-stable-diffusion)                               | -                                                                                                                                                                                                                  |                      [Mark Rich](https://github.com/MarkRich) |
+| Seed Resizing Stable Diffusion                                                                                                        | Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation.                                                                                                                                                                                                                                                                                                                                                                                       | [Seed Resizing](#seed-resizing)                                                           | -                                                                                                                                                                                                                  |                      [Mark Rich](https://github.com/MarkRich) |
+| Imagic Stable Diffusion                                                                                                               | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image                                                                                                                                                                                                                                                                                                                                                                                                                   | [Imagic Stable Diffusion](#imagic-stable-diffusion)                                       | -                                                                                                                                                                                                                  |                      [Mark Rich](https://github.com/MarkRich) |
+| Multilingual Stable Diffusion                                                                                                         | Stable Diffusion Pipeline that supports prompts in 50 different languages.                                                                                                                                                                                                                                                                                                                                                                                                                               | [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline)                  | -                                                                                                                                                                                                                  |          [Juan Carlos Piñeros](https://github.com/juancopi81) |
+| Image to Image Inpainting Stable Diffusion                                                                                            | Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting                                                                                                                                                                                                                                                                                                                                                                                                            | [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) | -                                                                                                                                                                                                                  |                    [Alex McKinney](https://github.com/vvvm23) |
+| Text Based Inpainting Stable Diffusion                                                                                                | Stable Diffusion Inpainting Pipeline that enables passing a text prompt to generate the mask for inpainting                                                                                                                                                                                                                                                                                                                                                                                              | [Text Based Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion)     | -                                                                                                                                                                                                                  |                   [Dhruv Karan](https://github.com/unography) |
+| Bit Diffusion                                                                                                                         | Diffusion on discrete data                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | [Bit Diffusion](#bit-diffusion)                                                           | -  |                       [Stuti R.](https://github.com/kingstut) |
+| K-Diffusion Stable Diffusion                                                                                                          | Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py)                                                                                                                                                                                                                                                                                                                                                                  | [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion)                   | -  |    [Patrick von Platen](https://github.com/patrickvonplaten/) |
+| Checkpoint Merger Pipeline                                                                                                            | Diffusion Pipeline that enables merging of saved model checkpoints                                                                                                                                                                                                                                                                                                                                                                                                                                       | [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline)                                 | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) | 
+ Stable Diffusion v1.1-1.4 Comparison                                                                                                  | Run all 4 model checkpoints for Stable Diffusion and compare their results together                                                                                                                                                                                                                                                                                                                                                                                                                      | [Stable Diffusion Comparison](#stable-diffusion-comparisons)                              | - |        [Suvaditya Mukherjee](https://github.com/suvadityamuk) |
+ MagicMix                                                                                                                              | Diffusion Pipeline for semantic mixing of an image and a text prompt                                                                                                                                                                                                                                                                                                                                                                                                                                     | [MagicMix](#magic-mix)                                                                    | - |                    [Partho Das](https://github.com/daspartho) |
+| Stable UnCLIP                                                                                                                         | Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `"kakaobrain/karlo-v1-alpha"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `"lambdalabs/sd-image-variations-diffusers"` ).                                                                                                                                                                                                                   | [Stable UnCLIP](#stable-unclip)                                                           | -  |                                [Ray Wang](https://wrong.wang) |
+| UnCLIP Text Interpolation Pipeline                                                                                                    | Diffusion Pipeline that allows passing two prompts and produces images while interpolating between the text-embeddings of the two prompts                                                                                                                                                                                                                                                                                                                                                                | [UnCLIP Text Interpolation Pipeline](#unclip-text-interpolation-pipeline)                 | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) | 
+| UnCLIP Image Interpolation Pipeline                                                                                                   | Diffusion Pipeline that allows passing two images/image_embeddings and produces images while interpolating between their image-embeddings                                                                                                                                                                                                                                                                                                                                                                | [UnCLIP Image Interpolation Pipeline](#unclip-image-interpolation-pipeline)               | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) | 
+| DDIM Noise Comparative Analysis Pipeline                                                                                              | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227))                                                                                                                                                                                                                                                                                                                             | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline)     | - |              [Aengus (Duc-Anh)](https://github.com/aengusng8) |
+| CLIP Guided Img2Img Stable Diffusion Pipeline                                                                                         | Doing CLIP guidance for image to image generation with Stable Diffusion                                                                                                                                                                                                                                                                                                                                                                                                                                  | [CLIP Guided Img2Img Stable Diffusion](#clip-guided-img2img-stable-diffusion)             | - |               [Nipun Jindal](https://github.com/nipunjindal/) | 
+| TensorRT Stable Diffusion Text to Image Pipeline                                                                                                    | Accelerates the Stable Diffusion Text2Image Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Text to Image Pipeline](#tensorrt-text2image-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
+| EDICT Image Editing Pipeline                                                                                                          | Diffusion pipeline for text-guided image editing                                                                                                                                                                                                                                                                                                                                                                                                                                                         | [EDICT Image Editing Pipeline](#edict-image-editing-pipeline)                             | - |                    [Joqsan Azocar](https://github.com/Joqsan) | 
+| Stable Diffusion RePaint                                                                                                              | Stable Diffusion pipeline using [RePaint](https://arxiv.org/abs/2201.0986) for inpainting.                                                                                                                                                                                                                                                                                                                                                                                                               | [Stable Diffusion RePaint](#stable-diffusion-repaint )                                    | - |                  [Markus Pobitzer](https://github.com/Markus-Pobitzer) | 
+| TensorRT Stable Diffusion Image to Image Pipeline                                                                                                    | Accelerates the Stable Diffusion Image2Image Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Image to Image Pipeline](#tensorrt-image2image-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
+| Stable Diffusion IPEX Pipeline | Accelerate Stable Diffusion inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [Stable Diffusion on IPEX](#stable-diffusion-on-ipex) | - | [Yingjie Han](https://github.com/yingjie-han/) | 
+| CLIP Guided Images Mixing Stable Diffusion Pipeline | Сombine images using usual diffusion models. | [CLIP Guided Images Mixing Using Stable Diffusion](#clip-guided-images-mixing-with-stable-diffusion) | - | [Karachev Denis](https://github.com/TheDenk) |  
+| TensorRT Stable Diffusion Inpainting Pipeline                                                                                                    | Accelerates the Stable Diffusion Inpainting Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Inpainting Pipeline](#tensorrt-inpainting-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
+|   IADB Pipeline                                                                                                    | Implementation of [Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [IADB Pipeline](#iadb-pipeline)      | - |              [Thomas Chambon](https://github.com/tchambon) 
+
+To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
+```py
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="filename_in_the_community_folder")
+```
+
+## Example usages
+
+### CLIP Guided Stable Diffusion
+
+CLIP guided stable diffusion can help to generate more realistic images 
+by guiding stable diffusion at every denoising step with an additional CLIP model.
+
+The following code requires roughly 12GB of GPU RAM.
+
+```python
+from diffusers import DiffusionPipeline
+from transformers import CLIPImageProcessor, CLIPModel
+import torch
+
+
+feature_extractor = CLIPImageProcessor.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")
+clip_model = CLIPModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16)
+
+
+guided_pipeline = DiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    custom_pipeline="clip_guided_stable_diffusion",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    
+    torch_dtype=torch.float16,
+)
+guided_pipeline.enable_attention_slicing()
+guided_pipeline = guided_pipeline.to("cuda")
+
+prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
+
+generator = torch.Generator(device="cuda").manual_seed(0)
+images = []
+for i in range(4):
+    image = guided_pipeline(
+        prompt,
+        num_inference_steps=50,
+        guidance_scale=7.5,
+        clip_guidance_scale=100,
+        num_cutouts=4,
+        use_cutouts=False,
+        generator=generator,
+    ).images[0]
+    images.append(image)
+    
+# save images locally
+for i, img in enumerate(images):
+    img.save(f"./clip_guided_sd/image_{i}.png")
+```
+
+The `images` list contains a list of PIL images that can be saved locally or displayed directly in a google colab.
+Generated images tend to be of higher qualtiy than natively using stable diffusion. E.g. the above script generates the following images:
+
+![clip_guidance](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/clip_guidance/merged_clip_guidance.jpg).
+
+### One Step Unet
+
+The dummy "one-step-unet" can be run as follows:
+
+```python
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="one_step_unet")
+pipe()
+```
+
+**Note**: This community pipeline is not useful as a feature, but rather just serves as an example of how community pipelines can be added (see https://github.com/huggingface/diffusers/issues/841).
+
+### Stable Diffusion Interpolation
+
+The following code can be run on a GPU of at least 8GB VRAM and should take approximately 5 minutes.
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    revision='fp16',
+    torch_dtype=torch.float16,
+    safety_checker=None,  # Very important for videos...lots of false positives while interpolating
+    custom_pipeline="interpolate_stable_diffusion",
+).to('cuda')
+pipe.enable_attention_slicing()
+
+frame_filepaths = pipe.walk(
+    prompts=['a dog', 'a cat', 'a horse'],
+    seeds=[42, 1337, 1234],
+    num_interpolation_steps=16,
+    output_dir='./dreams',
+    batch_size=4,
+    height=512,
+    width=512,
+    guidance_scale=8.5,
+    num_inference_steps=50,
+)
+```
+
+The output of the `walk(...)` function returns a list of images saved under the folder as defined in `output_dir`. You can use these images to create videos of stable diffusion. 
+
+> **Please have a look at https://github.com/nateraw/stable-diffusion-videos for more in-detail information on how to create videos using stable diffusion as well as more feature-complete functionality.**
+
+### Stable Diffusion Mega
+
+The Stable Diffusion Mega Pipeline lets you use the main use cases of the stable diffusion pipeline in a single class.
+
+```python
+#!/usr/bin/env python3
+from diffusers import DiffusionPipeline
+import PIL
+import requests
+from io import BytesIO
+import torch
+
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, revision="fp16")
+pipe.to("cuda")
+pipe.enable_attention_slicing()
+
+
+### Text-to-Image
+
+images = pipe.text2img("An astronaut riding a horse").images
+
+### Image-to-Image
+
+init_image = download_image("https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")
+
+prompt = "A fantasy landscape, trending on artstation"
+
+images = pipe.img2img(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
+
+### Inpainting
+
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+init_image = download_image(img_url).resize((512, 512))
+mask_image = download_image(mask_url).resize((512, 512))
+
+prompt = "a cat sitting on a bench"
+images = pipe.inpaint(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.75).images
+```
+
+As shown above this one pipeline can run all both "text-to-image", "image-to-image", and "inpainting" in one pipeline.
+
+### Long Prompt Weighting Stable Diffusion
+Features of this custom pipeline:
+- Input a prompt without the 77 token length limit.
+- Includes tx2img, img2img. and inpainting pipelines.
+- Emphasize/weigh part of your prompt with parentheses as so: `a baby deer with (big eyes)`
+- De-emphasize part of your prompt as so: `a [baby] deer with big eyes`
+- Precisely weigh part of your prompt as so: `a baby deer with (big eyes:1.3)`
+
+Prompt weighting equivalents:
+- `a baby deer with` == `(a baby deer with:1.0)`
+- `(big eyes)` == `(big eyes:1.1)`
+- `((big eyes))` == `(big eyes:1.21)`
+- `[big eyes]` == `(big eyes:0.91)`
+
+You can run this custom pipeline as so:
+
+#### pytorch
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    'hakurei/waifu-diffusion',
+    custom_pipeline="lpw_stable_diffusion",
+    
+    torch_dtype=torch.float16
+)
+pipe=pipe.to("cuda")
+
+prompt = "best_quality (1girl:1.3) bow bride brown_hair closed_mouth frilled_bow frilled_hair_tubes frills (full_body:1.3) fox_ear hair_bow hair_tubes happy hood japanese_clothes kimono long_sleeves red_bow smile solo tabi uchikake white_kimono wide_sleeves cherry_blossoms"
+neg_prompt = "lowres, bad_anatomy, error_body, error_hair, error_arm, error_hands, bad_hands, error_fingers, bad_fingers, missing_fingers, error_legs, bad_legs, multiple_legs, missing_legs, error_lighting, error_shadow, error_reflection, text, error, extra_digit, fewer_digits, cropped, worst_quality, low_quality, normal_quality, jpeg_artifacts, signature, watermark, username, blurry"
+
+pipe.text2img(prompt, negative_prompt=neg_prompt, width=512,height=512,max_embeddings_multiples=3).images[0]
+
+```
+
+#### onnxruntime
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    'CompVis/stable-diffusion-v1-4',
+    custom_pipeline="lpw_stable_diffusion_onnx",
+    revision="onnx",
+    provider="CUDAExecutionProvider"
+)
+
+prompt = "a photo of an astronaut riding a horse on mars, best quality"
+neg_prompt = "lowres, bad anatomy, error body, error hair, error arm, error hands, bad hands, error fingers, bad fingers, missing fingers, error legs, bad legs, multiple legs, missing legs, error lighting, error shadow, error reflection, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
+
+pipe.text2img(prompt,negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]
+
+```
+
+if you see `Token indices sequence length is longer than the specified maximum sequence length for this model ( *** > 77 ) . Running this sequence through the model will result in indexing errors`. Do not worry, it is normal.
+
+### Speech to Image
+
+The following code can generate an image from an audio sample using pre-trained OpenAI whisper-small and Stable Diffusion.
+
+```Python
+import torch
+
+import matplotlib.pyplot as plt
+from datasets import load_dataset
+from diffusers import DiffusionPipeline
+from transformers import (
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+)
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+
+audio_sample = ds[3]
+
+text = audio_sample["text"].lower()
+speech_data = audio_sample["audio"]["array"]
+
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
+processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+
+diffuser_pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="speech_to_image_diffusion",
+    speech_model=model,
+    speech_processor=processor,
+    
+    torch_dtype=torch.float16,
+)
+
+diffuser_pipeline.enable_attention_slicing()
+diffuser_pipeline = diffuser_pipeline.to(device)
+
+output = diffuser_pipeline(speech_data)
+plt.imshow(output.images[0])
+```
+This example produces the following image:
+
+![image](https://user-images.githubusercontent.com/45072645/196901736-77d9c6fc-63ee-4072-90b0-dc8b903d63e3.png)
+
+### Wildcard Stable Diffusion
+Following the great examples from https://github.com/jtkelm2/stable-diffusion-webui-1/blob/master/scripts/wildcards.py and https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts#wildcards, here's a minimal implementation that allows for users to add "wildcards", denoted by `__wildcard__` to prompts that are used as placeholders for randomly sampled values given by either a dictionary or a `.txt` file. For example:
+
+Say we have a prompt:
+
+```
+prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
+```
+
+We can then define possible values to be sampled for `animal`, `object`, and `clothing`. These can either be from a `.txt` with the same name as the category.
+
+The possible values can also be defined / combined by using a dictionary like: `{"animal":["dog", "cat", mouse"]}`.
+
+The actual pipeline works just like `StableDiffusionPipeline`, except the `__call__` method takes in:
+
+`wildcard_files`: list of file paths for wild card replacement
+`wildcard_option_dict`: dict with key as `wildcard` and values as a list of possible replacements
+`num_prompt_samples`: number of prompts to sample, uniformly sampling wildcards
+
+A full example:
+
+create `animal.txt`, with contents like:
+
+```
+dog
+cat
+mouse
+```
+
+create `object.txt`, with contents like:
+
+```
+chair
+sofa
+bench
+```
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="wildcard_stable_diffusion",
+    
+    torch_dtype=torch.float16,
+)
+prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
+out = pipe(
+    prompt,
+    wildcard_option_dict={
+        "clothing":["hat", "shirt", "scarf", "beret"]
+    },
+    wildcard_files=["object.txt", "animal.txt"],
+    num_prompt_samples=1
+)
+```
+
+### Composable Stable diffusion 
+
+[Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) proposes conjunction and negation (negative prompts) operators for compositional generation with conditional diffusion models.
+
+```python
+import torch as th
+import numpy as np
+import torchvision.utils as tvu
+
+from diffusers import DiffusionPipeline
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--prompt", type=str, default="mystical trees | A magical pond | dark",
+                    help="use '|' as the delimiter to compose separate sentences.")
+parser.add_argument("--steps", type=int, default=50)
+parser.add_argument("--scale", type=float, default=7.5)
+parser.add_argument("--weights", type=str, default="7.5 | 7.5 | -7.5")
+parser.add_argument("--seed", type=int, default=2)
+parser.add_argument("--model_path", type=str, default="CompVis/stable-diffusion-v1-4")
+parser.add_argument("--num_images", type=int, default=1)
+args = parser.parse_args()
+
+has_cuda = th.cuda.is_available()
+device = th.device('cpu' if not has_cuda else 'cuda')
+
+prompt = args.prompt
+scale = args.scale
+steps = args.steps
+
+pipe = DiffusionPipeline.from_pretrained(
+    args.model_path,
+    custom_pipeline="composable_stable_diffusion",
+).to(device)
+
+pipe.safety_checker = None
+
+images = []
+generator = th.Generator("cuda").manual_seed(args.seed)
+for i in range(args.num_images):
+    image = pipe(prompt, guidance_scale=scale, num_inference_steps=steps,
+                 weights=args.weights, generator=generator).images[0]
+    images.append(th.from_numpy(np.array(image)).permute(2, 0, 1) / 255.)
+grid = tvu.make_grid(th.stack(images, dim=0), nrow=4, padding=0)
+tvu.save_image(grid, f'{prompt}_{args.weights}' + '.png')
+
+```
+
+### Imagic Stable Diffusion
+Allows you to edit an image using stable diffusion. 
+
+```python
+import requests
+from PIL import Image
+from io import BytesIO
+import torch
+import os
+from diffusers import DiffusionPipeline, DDIMScheduler
+has_cuda = torch.cuda.is_available()
+device = torch.device('cpu' if not has_cuda else 'cuda')
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+        safety_checker=None,
+    use_auth_token=True,
+    custom_pipeline="imagic_stable_diffusion",
+    scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
+).to(device)
+generator = torch.Generator("cuda").manual_seed(0)
+seed = 0
+prompt = "A photo of Barack Obama smiling with a big grin"
+url = 'https://www.dropbox.com/s/6tlwzr73jd1r9yk/obama.png?dl=1'
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+init_image = init_image.resize((512, 512))
+res = pipe.train(
+    prompt,
+    image=init_image,
+    generator=generator)
+res = pipe(alpha=1, guidance_scale=7.5, num_inference_steps=50)
+os.makedirs("imagic", exist_ok=True)
+image = res.images[0]
+image.save('./imagic/imagic_image_alpha_1.png')
+res = pipe(alpha=1.5, guidance_scale=7.5, num_inference_steps=50)
+image = res.images[0]
+image.save('./imagic/imagic_image_alpha_1_5.png')
+res = pipe(alpha=2, guidance_scale=7.5, num_inference_steps=50)
+image = res.images[0]
+image.save('./imagic/imagic_image_alpha_2.png')
+```
+
+### Seed Resizing 
+Test seed resizing. Originally generate an image in 512 by 512, then generate image with same seed at 512 by 592 using seed resizing. Finally, generate 512 by 592 using original stable diffusion pipeline.
+
+```python
+import torch as th
+import numpy as np
+from diffusers import DiffusionPipeline
+
+has_cuda = th.cuda.is_available()
+device = th.device('cpu' if not has_cuda else 'cuda')
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    use_auth_token=True,
+    custom_pipeline="seed_resize_stable_diffusion"
+).to(device)
+
+def dummy(images, **kwargs):
+    return images, False
+
+pipe.safety_checker = dummy
+
+
+images = []
+th.manual_seed(0)
+generator = th.Generator("cuda").manual_seed(0)
+
+seed = 0
+prompt = "A painting of a futuristic cop"
+
+width = 512
+height = 512
+
+res = pipe(
+    prompt,
+    guidance_scale=7.5,
+    num_inference_steps=50,
+    height=height,
+    width=width,
+    generator=generator)
+image = res.images[0]
+image.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))
+
+
+th.manual_seed(0)
+generator = th.Generator("cuda").manual_seed(0)
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    use_auth_token=True,
+    custom_pipeline="/home/mark/open_source/diffusers/examples/community/"
+).to(device)
+
+width = 512
+height = 592
+
+res = pipe(
+    prompt,
+    guidance_scale=7.5,
+    num_inference_steps=50,
+    height=height,
+    width=width,
+    generator=generator)
+image = res.images[0]
+image.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))
+
+pipe_compare = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    use_auth_token=True,
+    custom_pipeline="/home/mark/open_source/diffusers/examples/community/"
+).to(device)
+
+res = pipe_compare(
+    prompt,
+    guidance_scale=7.5,
+    num_inference_steps=50,
+    height=height,
+    width=width,
+    generator=generator
+)
+
+image = res.images[0]
+image.save('./seed_resize/seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height))
+```
+
+### Multilingual Stable Diffusion Pipeline
+
+The following code can generate an images from texts in different languages using the pre-trained [mBART-50 many-to-one multilingual machine translation model](https://huggingface.co/facebook/mbart-large-50-many-to-one-mmt) and Stable Diffusion.
+
+```python
+from PIL import Image
+
+import torch
+
+from diffusers import DiffusionPipeline
+from transformers import (
+    pipeline,
+    MBart50TokenizerFast,
+    MBartForConditionalGeneration,
+)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device_dict = {"cuda": 0, "cpu": -1}
+
+# helper function taken from: https://huggingface.co/blog/stable_diffusion
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows*cols
+
+    w, h = imgs[0].size
+    grid = Image.new('RGB', size=(cols*w, rows*h))
+    grid_w, grid_h = grid.size
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i%cols*w, i//cols*h))
+    return grid
+
+# Add language detection pipeline
+language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection"
+language_detection_pipeline = pipeline("text-classification",
+                                       model=language_detection_model_ckpt,
+                                       device=device_dict[device])
+
+# Add model for language translation
+trans_tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
+trans_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt").to(device)
+
+diffuser_pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="multilingual_stable_diffusion",
+    detection_pipeline=language_detection_pipeline,
+    translation_model=trans_model,
+    translation_tokenizer=trans_tokenizer,
+    
+    torch_dtype=torch.float16,
+)
+
+diffuser_pipeline.enable_attention_slicing()
+diffuser_pipeline = diffuser_pipeline.to(device)
+
+prompt = ["a photograph of an astronaut riding a horse", 
+          "Una casa en la playa",
+          "Ein Hund, der Orange isst",
+          "Un restaurant parisien"]
+
+output = diffuser_pipeline(prompt)
+
+images = output.images
+
+grid = image_grid(images, rows=2, cols=2)
+```
+
+This example produces the following images:
+![image](https://user-images.githubusercontent.com/4313860/198328706-295824a4-9856-4ce5-8e66-278ceb42fd29.png)
+
+### Image to Image Inpainting Stable Diffusion
+
+Similar to the standard stable diffusion inpainting example, except with the addition of an `inner_image` argument.
+
+`image`, `inner_image`, and `mask` should have the same dimensions. `inner_image` should have an alpha (transparency) channel.
+
+The aim is to overlay two images, then mask out the boundary between `image` and `inner_image` to allow stable diffusion to make the connection more seamless.
+For example, this could be used to place a logo on a shirt and make it blend seamlessly.
+
+```python
+import PIL
+import torch
+
+from diffusers import DiffusionPipeline
+
+image_path = "./path-to-image.png"
+inner_image_path = "./path-to-inner-image.png"
+mask_path = "./path-to-mask.png"
+
+init_image = PIL.Image.open(image_path).convert("RGB").resize((512, 512))
+inner_image = PIL.Image.open(inner_image_path).convert("RGBA").resize((512, 512))
+mask_image = PIL.Image.open(mask_path).convert("RGB").resize((512, 512))
+
+pipe = DiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    custom_pipeline="img2img_inpainting",
+    
+    torch_dtype=torch.float16
+)
+pipe = pipe.to("cuda")
+
+prompt = "Your prompt here!"
+image = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]
+```
+
+![2 by 2 grid demonstrating image to image inpainting.](https://user-images.githubusercontent.com/44398246/203506577-ec303be4-887e-4ebd-a773-c83fcb3dd01a.png)
+
+### Text Based Inpainting Stable Diffusion
+
+Use a text prompt to generate the mask for the area to be inpainted.
+Currently uses the CLIPSeg model for mask generation, then calls the standard Stable Diffusion Inpainting pipeline to perform the inpainting.
+
+```python
+from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+from diffusers import DiffusionPipeline
+
+from PIL import Image
+import requests
+
+processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
+
+pipe = DiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    custom_pipeline="text_inpainting",
+    segmentation_model=model,
+    segmentation_processor=processor
+)
+pipe = pipe.to("cuda")
+
+
+url = "https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw).resize((512, 512))
+text = "a glass"  # will mask out this text
+prompt = "a cup"  # the masked out region will be replaced with this
+
+image = pipe(image=image, text=text, prompt=prompt).images[0]
+```
+
+### Bit Diffusion 
+Based https://arxiv.org/abs/2208.04202, this is used for diffusion on discrete data - eg, discreate image data, DNA sequence data. An unconditional discreate image can be generated like this: 
+
+```python
+from diffusers import DiffusionPipeline
+pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="bit_diffusion")
+image = pipe().images[0]
+
+```
+
+### Stable Diffusion with K Diffusion
+
+Make sure you have @crowsonkb's https://github.com/crowsonkb/k-diffusion installed:
+
+```
+pip install k-diffusion
+```
+
+You can use the community pipeline as follows:
+
+```python
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
+pipe = pipe.to("cuda")
+
+prompt = "an astronaut riding a horse on mars"
+pipe.set_scheduler("sample_heun")
+generator = torch.Generator(device="cuda").manual_seed(seed)
+image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]
+
+image.save("./astronaut_heun_k_diffusion.png")
+```
+
+To make sure that K Diffusion and `diffusers` yield the same results:
+
+**Diffusers**:
+```python
+from diffusers import DiffusionPipeline, EulerDiscreteScheduler
+
+seed = 33
+
+pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+generator = torch.Generator(device="cuda").manual_seed(seed)
+image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
+```
+
+![diffusers_euler](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/k_diffusion/astronaut_euler.png)
+
+**K Diffusion**:
+```python
+from diffusers import DiffusionPipeline, EulerDiscreteScheduler
+
+seed = 33
+
+pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+pipe.set_scheduler("sample_euler")
+generator = torch.Generator(device="cuda").manual_seed(seed)
+image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
+```
+
+![diffusers_euler](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/k_diffusion/astronaut_euler_k_diffusion.png)
+
+### Checkpoint Merger Pipeline
+Based on the AUTOMATIC1111/webui for checkpoint merging. This is a custom pipeline that merges upto 3 pretrained model checkpoints as long as they are in the HuggingFace model_index.json format.
+
+The checkpoint merging is currently memory intensive as it modifies the weights of a DiffusionPipeline object in place. Expect atleast 13GB RAM Usage on Kaggle GPU kernels and
+on colab you might run out of the 12GB memory even while merging two checkpoints.
+
+Usage:-
+```python
+from diffusers import DiffusionPipeline
+
+#Return a CheckpointMergerPipeline class that allows you to merge checkpoints. 
+#The checkpoint passed here is ignored. But still pass one of the checkpoints you plan to 
+#merge for convenience
+pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger")
+
+#There are multiple possible scenarios:
+#The pipeline with the merged checkpoints is returned in all the scenarios
+
+#Compatible checkpoints a.k.a matched model_index.json files. Ignores the meta attributes in model_index.json during comparision.( attrs with _ as prefix )
+merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","CompVis/stable-diffusion-v1-2"], interp = "sigmoid", alpha = 0.4)
+
+#Incompatible checkpoints in model_index.json but merge might be possible. Use force = True to ignore model_index.json compatibility
+merged_pipe_1 = pipe.merge(["CompVis/stable-diffusion-v1-4","hakurei/waifu-diffusion"], force = True, interp = "sigmoid", alpha = 0.4)
+
+#Three checkpoint merging. Only "add_difference" method actually works on all three checkpoints. Using any other options will ignore the 3rd checkpoint.
+merged_pipe_2 = pipe.merge(["CompVis/stable-diffusion-v1-4","hakurei/waifu-diffusion","prompthero/openjourney"], force = True, interp = "add_difference", alpha = 0.4)
+
+prompt = "An astronaut riding a horse on Mars"
+
+image = merged_pipe(prompt).images[0]
+
+```
+Some examples along with the merge details:
+
+1. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" ; Sigmoid interpolation; alpha = 0.8 
+
+![Stable plus Waifu Sigmoid 0.8](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/stability_v1_4_waifu_sig_0.8.png)
+
+2. "hakurei/waifu-diffusion" + "prompthero/openjourney" ; Inverse Sigmoid interpolation; alpha = 0.8 
+
+![Stable plus Waifu Sigmoid 0.8](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/waifu_openjourney_inv_sig_0.8.png)
+
+
+3. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" + "prompthero/openjourney"; Add Difference interpolation; alpha = 0.5 
+
+![Stable plus Waifu plus openjourney add_diff 0.5](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/stable_waifu_openjourney_add_diff_0.5.png)
+
+
+### Stable Diffusion Comparisons
+
+This Community Pipeline enables the comparison between the 4 checkpoints that exist for Stable Diffusion. They can be found through the following links:
+1. [Stable Diffusion v1.1](https://huggingface.co/CompVis/stable-diffusion-v1-1)
+2. [Stable Diffusion v1.2](https://huggingface.co/CompVis/stable-diffusion-v1-2)
+3. [Stable Diffusion v1.3](https://huggingface.co/CompVis/stable-diffusion-v1-3)
+4. [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
+
+```python
+from diffusers import DiffusionPipeline
+import matplotlib.pyplot as plt
+
+pipe = DiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', custom_pipeline='suvadityamuk/StableDiffusionComparison')
+pipe.enable_attention_slicing()
+pipe = pipe.to('cuda')
+prompt = "an astronaut riding a horse on mars"
+output = pipe(prompt)
+
+plt.subplots(2,2,1)
+plt.imshow(output.images[0])
+plt.title('Stable Diffusion v1.1')
+plt.axis('off')
+plt.subplots(2,2,2)
+plt.imshow(output.images[1])
+plt.title('Stable Diffusion v1.2')
+plt.axis('off')
+plt.subplots(2,2,3)
+plt.imshow(output.images[2])
+plt.title('Stable Diffusion v1.3')
+plt.axis('off')
+plt.subplots(2,2,4)
+plt.imshow(output.images[3])
+plt.title('Stable Diffusion v1.4')
+plt.axis('off')
+
+plt.show()
+```
+
+As a result, you can look at a grid of all 4 generated images being shown together, that captures a difference the advancement of the training between the 4 checkpoints.
+
+### Magic Mix
+
+Implementation of the [MagicMix: Semantic Mixing with Diffusion Models](https://arxiv.org/abs/2210.16056) paper. This is a Diffusion Pipeline for semantic mixing of an image and a text prompt to create a new concept while preserving the spatial layout and geometry of the subject in the image. The pipeline takes an image that provides the layout semantics and a prompt that provides the content semantics for the mixing process.
+
+There are 3 parameters for the method-
+- `mix_factor`: It is the interpolation constant used in the layout generation phase. The greater the value of `mix_factor`, the greater the influence of the prompt on the layout generation process.
+- `kmax` and `kmin`: These determine the range for the layout and content generation process. A higher value of kmax results in loss of more information about the layout of the original image and a higher value of kmin results in more steps for content generation process.
+
+Here is an example usage-
+
+```python
+from diffusers import DiffusionPipeline, DDIMScheduler
+from PIL import Image
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="magic_mix",
+    scheduler = DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler"),
+).to('cuda')
+
+img = Image.open('phone.jpg')
+mix_img = pipe(
+    img, 
+    prompt = 'bed', 
+    kmin = 0.3,
+    kmax = 0.5,
+    mix_factor = 0.5,
+    )
+mix_img.save('phone_bed_mix.jpg')
+```
+The `mix_img` is a PIL image that can be saved locally or displayed directly in a google colab. Generated image is a mix of the layout semantics of the given image and the content semantics of the prompt.
+
+E.g. the above script generates the following image:
+
+`phone.jpg`
+
+![206903102-34e79b9f-9ed2-4fac-bb38-82871343c655](https://user-images.githubusercontent.com/59410571/209578593-141467c7-d831-4792-8b9a-b17dc5e47816.jpg)
+
+`phone_bed_mix.jpg`
+
+![206903104-913a671d-ef53-4ae4-919d-64c3059c8f67](https://user-images.githubusercontent.com/59410571/209578602-70f323fa-05b7-4dd6-b055-e40683e37914.jpg)
+
+For more example generations check out this [demo notebook](https://github.com/daspartho/MagicMix/blob/main/demo.ipynb).
+
+
+### Stable UnCLIP
+
+UnCLIPPipeline("kakaobrain/karlo-v1-alpha") provide a prior model that can generate clip image embedding from text.
+StableDiffusionImageVariationPipeline("lambdalabs/sd-image-variations-diffusers") provide a decoder model than can generate images from clip image embedding.
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "kakaobrain/karlo-v1-alpha",
+    torch_dtype=torch.float16,
+    custom_pipeline="stable_unclip",
+    decoder_pipe_kwargs=dict(
+        image_encoder=None,
+    ),
+)
+pipeline.to(device)
+
+prompt = "a shiba inu wearing a beret and black turtleneck"
+random_generator = torch.Generator(device=device).manual_seed(1000)
+output = pipeline(
+    prompt=prompt,
+    width=512,
+    height=512,
+    generator=random_generator,
+    prior_guidance_scale=4,
+    prior_num_inference_steps=25,
+    decoder_guidance_scale=8,
+    decoder_num_inference_steps=50,
+)
+
+image = output.images[0]
+image.save("./shiba-inu.jpg")
+
+# debug
+
+# `pipeline.decoder_pipe` is a regular StableDiffusionImageVariationPipeline instance.
+# It is used to convert clip image embedding to latents, then fed into VAE decoder.
+print(pipeline.decoder_pipe.__class__)
+# <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline'>
+
+# this pipeline only use prior module in "kakaobrain/karlo-v1-alpha"
+# It is used to convert clip text embedding to clip image embedding.
+print(pipeline)
+# StableUnCLIPPipeline {
+#   "_class_name": "StableUnCLIPPipeline",
+#   "_diffusers_version": "0.12.0.dev0",
+#   "prior": [
+#     "diffusers",
+#     "PriorTransformer"
+#   ],
+#   "prior_scheduler": [
+#     "diffusers",
+#     "UnCLIPScheduler"
+#   ],
+#   "text_encoder": [
+#     "transformers",
+#     "CLIPTextModelWithProjection"
+#   ],
+#   "tokenizer": [
+#     "transformers",
+#     "CLIPTokenizer"
+#   ]
+# }
+
+# pipeline.prior_scheduler is the scheduler used for prior in UnCLIP.
+print(pipeline.prior_scheduler)
+# UnCLIPScheduler {
+#   "_class_name": "UnCLIPScheduler",
+#   "_diffusers_version": "0.12.0.dev0",
+#   "clip_sample": true,
+#   "clip_sample_range": 5.0,
+#   "num_train_timesteps": 1000,
+#   "prediction_type": "sample",
+#   "variance_type": "fixed_small_log"
+# }
+```
+
+
+`shiba-inu.jpg`
+
+
+![shiba-inu](https://user-images.githubusercontent.com/16448529/209185639-6e5ec794-ce9d-4883-aa29-bd6852a2abad.jpg)
+
+### UnCLIP Text Interpolation Pipeline
+
+This Diffusion Pipeline takes two prompts and interpolates between the two input prompts using spherical interpolation ( slerp ). The input prompts are converted to text embeddings by the pipeline's text_encoder and the interpolation is done on the resulting text_embeddings over the number of steps specified. Defaults to 5 steps. 
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
+
+pipe = DiffusionPipeline.from_pretrained(
+    "kakaobrain/karlo-v1-alpha",
+    torch_dtype=torch.float16,
+    custom_pipeline="unclip_text_interpolation"
+)
+pipe.to(device)
+
+start_prompt = "A photograph of an adult lion"
+end_prompt = "A photograph of a lion cub"
+#For best results keep the prompts close in length to each other. Of course, feel free to try out with differing lengths.
+generator = torch.Generator(device=device).manual_seed(42)
+
+output = pipe(start_prompt, end_prompt, steps = 6, generator = generator, enable_sequential_cpu_offload=False)
+
+for i,image in enumerate(output.images):
+    img.save('result%s.jpg' % i)
+```
+
+The resulting images in order:-
+
+![result_0](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_0.png)
+![result_1](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_1.png)
+![result_2](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_2.png)
+![result_3](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_3.png)
+![result_4](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_4.png)
+![result_5](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_5.png)
+
+### UnCLIP Image Interpolation Pipeline
+
+This Diffusion Pipeline takes two images or an image_embeddings tensor of size 2 and interpolates between their embeddings using spherical interpolation ( slerp ). The input images/image_embeddings are converted to image embeddings by the pipeline's image_encoder and the interpolation is done on the resulting image_embeddings over the number of steps specified. Defaults to 5 steps. 
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+from PIL import Image
+
+device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
+dtype = torch.float16 if torch.cuda.is_available() else torch.bfloat16
+
+pipe = DiffusionPipeline.from_pretrained(
+    "kakaobrain/karlo-v1-alpha-image-variations",
+    torch_dtype=dtype,
+    custom_pipeline="unclip_image_interpolation"
+)
+pipe.to(device)
+
+images = [Image.open('./starry_night.jpg'), Image.open('./flowers.jpg')]
+#For best results keep the prompts close in length to each other. Of course, feel free to try out with differing lengths.
+generator = torch.Generator(device=device).manual_seed(42)
+
+output = pipe(image = images ,steps = 6, generator = generator)
+
+for i,image in enumerate(output.images):
+    image.save('starry_to_flowers_%s.jpg' % i)
+```
+The original images:-
+
+![starry](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_night.jpg)
+![flowers](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/flowers.jpg)
+
+The resulting images in order:-
+
+![result0](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_0.png)
+![result1](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_1.png)
+![result2](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_2.png)
+![result3](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_3.png)
+![result4](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_4.png)
+![result5](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_5.png)
+
+### DDIM Noise Comparative Analysis Pipeline
+#### **Research question: What visual concepts do the diffusion models learn from each noise level during training?**  
+The [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227) paper proposed an approach to answer the above question, which is their second contribution.  
+The approach consists of the following steps:
+
+1. The input is an image x0.
+2. Perturb it to xt using a diffusion process q(xt|x0).
+    - `strength` is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.
+3. Reconstruct the image with the learned denoising process pθ(ˆx0|xt).
+4. Compare x0 and ˆx0 among various t to show how each step contributes to the sample.
+The authors used [openai/guided-diffusion](https://github.com/openai/guided-diffusion) model to denoise images in FFHQ dataset. This pipeline extends their second contribution by investigating DDIM on any input image.
+
+```python
+import torch
+from PIL import Image
+import numpy as np
+
+image_path = "path/to/your/image" # images from CelebA-HQ might be better
+image_pil = Image.open(image_path)
+image_name = image_path.split("/")[-1].split(".")[0]
+
+device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
+pipe = DiffusionPipeline.from_pretrained(
+    "google/ddpm-ema-celebahq-256",
+    custom_pipeline="ddim_noise_comparative_analysis",
+)
+pipe = pipe.to(device)
+
+for strength in np.linspace(0.1, 1, 25):
+    denoised_image, latent_timestep = pipe(
+        image_pil, strength=strength, return_dict=False
+    )
+    denoised_image = denoised_image[0]
+    denoised_image.save(
+        f"noise_comparative_analysis_{image_name}_{latent_timestep}.png"
+    )
+```
+
+Here is the result of this pipeline (which is DDIM) on CelebA-HQ dataset.
+
+![noise-comparative-analysis](https://user-images.githubusercontent.com/67547213/224677066-4474b2ed-56ab-4c27-87c6-de3c0255eb9c.jpeg)
+
+### CLIP Guided Img2Img Stable Diffusion
+
+CLIP guided Img2Img stable diffusion can help to generate more realistic images with an initial image 
+by guiding stable diffusion at every denoising step with an additional CLIP model.
+
+The following code requires roughly 12GB of GPU RAM.
+
+```python
+from io import BytesIO
+import requests
+import torch
+from diffusers import DiffusionPipeline
+from PIL import Image
+from transformers import CLIPFeatureExtractor, CLIPModel
+feature_extractor = CLIPFeatureExtractor.from_pretrained(
+    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
+)
+clip_model = CLIPModel.from_pretrained(
+    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
+)
+guided_pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    # custom_pipeline="clip_guided_stable_diffusion",
+    custom_pipeline="/home/njindal/diffusers/examples/community/clip_guided_stable_diffusion.py",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    torch_dtype=torch.float16,
+)
+guided_pipeline.enable_attention_slicing()
+guided_pipeline = guided_pipeline.to("cuda")
+prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
+url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+image = guided_pipeline(
+    prompt=prompt,
+    num_inference_steps=30,
+    image=init_image,
+    strength=0.75,
+    guidance_scale=7.5,
+    clip_guidance_scale=100,
+    num_cutouts=4,
+    use_cutouts=False,
+).images[0]
+display(image)
+```
+
+Init Image
+
+![img2img_init_clip_guidance](https://huggingface.co/datasets/njindal/images/resolve/main/clip_guided_img2img_init.jpg)
+
+Output Image
+
+![img2img_clip_guidance](https://huggingface.co/datasets/njindal/images/resolve/main/clip_guided_img2img.jpg)
+
+### TensorRT Text2Image Stable Diffusion Pipeline
+
+The TensorRT Pipeline can be used to accelerate the Text2Image Stable Diffusion Inference run.
+
+NOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.
+
+```python
+import torch
+from diffusers import DDIMScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
+
+# Use the DDIMScheduler scheduler here instead
+scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1",
+                                            subfolder="scheduler")
+
+pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1",
+                                                custom_pipeline="stable_diffusion_tensorrt_txt2img",
+                                                revision='fp16',
+                                                torch_dtype=torch.float16,
+                                                scheduler=scheduler,)
+
+# re-use cached folder to save ONNX models and TensorRT Engines
+pipe.set_cached_folder("stabilityai/stable-diffusion-2-1", revision='fp16',)
+
+pipe = pipe.to("cuda")
+
+prompt = "a beautiful photograph of Mt. Fuji during cherry blossom"
+image = pipe(prompt).images[0]
+image.save('tensorrt_mt_fuji.png')
+```
+
+### EDICT Image Editing Pipeline
+
+This pipeline implements the text-guided image editing approach from the paper [EDICT: Exact Diffusion Inversion via Coupled Transformations](https://arxiv.org/abs/2211.12446). You have to pass:
+- (`PIL`) `image` you want to edit.
+- `base_prompt`: the text prompt describing the current image (before editing).
+- `target_prompt`: the text prompt describing with the edits.
+
+```python
+from diffusers import DiffusionPipeline, DDIMScheduler
+from transformers import CLIPTextModel
+import torch, PIL, requests
+from io import BytesIO
+from IPython.display import display
+
+def center_crop_and_resize(im):
+
+    width, height = im.size
+    d = min(width, height)
+    left = (width - d) / 2
+    upper = (height - d) / 2
+    right = (width + d) / 2
+    lower = (height + d) / 2
+
+    return im.crop((left, upper, right, lower)).resize((512, 512))
+
+torch_dtype = torch.float16
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# scheduler and text_encoder param values as in the paper
+scheduler = DDIMScheduler(
+        num_train_timesteps=1000,
+        beta_start=0.00085,
+        beta_end=0.012,
+        beta_schedule="scaled_linear",
+        set_alpha_to_one=False,
+        clip_sample=False,
+)
+
+text_encoder = CLIPTextModel.from_pretrained(
+    pretrained_model_name_or_path="openai/clip-vit-large-patch14",
+    torch_dtype=torch_dtype,
+)
+
+# initialize pipeline
+pipeline = DiffusionPipeline.from_pretrained(
+    pretrained_model_name_or_path="CompVis/stable-diffusion-v1-4",
+    custom_pipeline="edict_pipeline",
+    revision="fp16",
+    scheduler=scheduler,
+    text_encoder=text_encoder,
+    leapfrog_steps=True,
+    torch_dtype=torch_dtype,
+).to(device)
+
+# download image
+image_url = "https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1.jpeg"
+response = requests.get(image_url)
+image = PIL.Image.open(BytesIO(response.content))
+
+# preprocess it
+cropped_image = center_crop_and_resize(image)
+
+# define the prompts
+base_prompt = "A dog"
+target_prompt = "A golden retriever"
+
+# run the pipeline
+result_image = pipeline(
+      base_prompt=base_prompt, 
+      target_prompt=target_prompt, 
+      image=cropped_image,
+)
+
+display(result_image)
+```
+
+Init Image
+
+![img2img_init_edict_text_editing](https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1.jpeg)
+
+Output Image
+
+![img2img_edict_text_editing](https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1_cropped_generated.png)
+
+### Stable Diffusion RePaint
+
+This pipeline uses the [RePaint](https://arxiv.org/abs/2201.09865) logic on the latent space of stable diffusion. It can
+be used similarly to other image inpainting pipelines but does not rely on a specific inpainting model. This means you can use
+models that are not specifically created for inpainting.
+
+Make sure to use the ```RePaintScheduler``` as shown in the example below.
+
+Disclaimer: The mask gets transferred into latent space, this may lead to unexpected changes on the edge of the masked part.
+The inference time is a lot slower.
+
+```py
+import PIL
+import requests
+import torch
+from io import BytesIO
+from diffusers import StableDiffusionPipeline, RePaintScheduler
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+init_image = download_image(img_url).resize((512, 512))
+mask_image = download_image(mask_url).resize((512, 512))
+mask_image = PIL.ImageOps.invert(mask_image)
+pipe = StableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, custom_pipeline="stable_diffusion_repaint",
+)
+pipe.scheduler = RePaintScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
+```
+
+### TensorRT Image2Image Stable Diffusion Pipeline
+
+The TensorRT Pipeline can be used to accelerate the Image2Image Stable Diffusion Inference run.
+
+NOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.
+
+```python
+import requests
+from io import BytesIO
+from PIL import Image
+import torch
+from diffusers import DDIMScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionImg2ImgPipeline
+
+# Use the DDIMScheduler scheduler here instead
+scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1",
+                                            subfolder="scheduler")
+
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-1",
+                                                custom_pipeline="stable_diffusion_tensorrt_img2img",
+                                                revision='fp16',
+                                                torch_dtype=torch.float16,
+                                                scheduler=scheduler,)
+
+# re-use cached folder to save ONNX models and TensorRT Engines
+pipe.set_cached_folder("stabilityai/stable-diffusion-2-1", revision='fp16',)
+
+pipe = pipe.to("cuda")
+
+url = "https://pajoca.com/wp-content/uploads/2022/09/tekito-yamakawa-1.png"
+response = requests.get(url)
+input_image = Image.open(BytesIO(response.content)).convert("RGB")
+
+prompt = "photorealistic new zealand hills"
+image = pipe(prompt, image=input_image, strength=0.75,).images[0]
+image.save('tensorrt_img2img_new_zealand_hills.png')
+```
+
+### Stable Diffusion Reference
+
+This pipeline uses the Reference Control. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).
+
+Based on [this issue](https://github.com/huggingface/diffusers/issues/3566),
+- `EulerAncestralDiscreteScheduler` got poor results.
+
+```py
+import torch
+from diffusers import UniPCMultistepScheduler
+from diffusers.utils import load_image
+
+input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+
+pipe = StableDiffusionReferencePipeline.from_pretrained(
+       "runwayml/stable-diffusion-v1-5",
+       safety_checker=None,
+       torch_dtype=torch.float16
+       ).to('cuda:0')
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+result_img = pipe(ref_image=input_image,
+      prompt="1girl",
+      num_inference_steps=20,
+      reference_attn=True,
+      reference_adain=True).images[0]
+```
+
+Reference Image
+
+![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)
+
+Output Image of `reference_attn=True` and `reference_adain=False`
+
+![output_image](https://github.com/huggingface/diffusers/assets/24734142/813b5c6a-6d89-46ba-b7a4-2624e240eea5)
+
+Output Image of `reference_attn=False` and `reference_adain=True`
+
+![output_image](https://github.com/huggingface/diffusers/assets/24734142/ffc90339-9ef0-4c4d-a544-135c3e5644da)
+
+Output Image of `reference_attn=True` and `reference_adain=True`
+
+![output_image](https://github.com/huggingface/diffusers/assets/24734142/3c5255d6-867d-4d35-b202-8dfd30cc6827)
+
+### Stable Diffusion ControlNet Reference
+
+This pipeline uses the Reference Control with ControlNet. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).
+
+Based on [this issue](https://github.com/huggingface/diffusers/issues/3566),
+- `EulerAncestralDiscreteScheduler` got poor results.
+- `guess_mode=True` works well for ControlNet v1.1
+
+```py
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+from diffusers import UniPCMultistepScheduler
+from diffusers.utils import load_image
+
+input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+
+# get canny image
+image = cv2.Canny(np.array(input_image), 100, 200)
+image = image[:, :, None]
+image = np.concatenate([image, image, image], axis=2)
+canny_image = Image.fromarray(image)
+
+controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
+       "runwayml/stable-diffusion-v1-5",
+       controlnet=controlnet,
+       safety_checker=None,
+       torch_dtype=torch.float16
+       ).to('cuda:0')
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+result_img = pipe(ref_image=input_image,
+      prompt="1girl",
+      image=canny_image,
+      num_inference_steps=20,
+      reference_attn=True,
+      reference_adain=True).images[0]
+```
+
+Reference Image
+
+![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)
+
+Output Image
+
+![output_image](https://github.com/huggingface/diffusers/assets/24734142/7b9a5830-f173-4b92-b0cf-73d0e9c01d60)
+
+
+### Stable Diffusion on IPEX
+
+This diffusion pipeline aims to accelarate the inference of Stable-Diffusion on Intel Xeon CPUs with BF16/FP32 precision using [IPEX](https://github.com/intel/intel-extension-for-pytorch).
+
+To use this pipeline, you need to:
+1. Install [IPEX](https://github.com/intel/intel-extension-for-pytorch)
+
+**Note:** For each PyTorch release, there is a corresponding release of the IPEX. Here is the mapping relationship. It is recommended to install Pytorch/IPEX2.0 to get the best performance.
+
+|PyTorch Version|IPEX Version|
+|--|--|
+|[v2.0.\*](https://github.com/pytorch/pytorch/tree/v2.0.1 "v2.0.1")|[v2.0.\*](https://github.com/intel/intel-extension-for-pytorch/tree/v2.0.100+cpu)|
+|[v1.13.\*](https://github.com/pytorch/pytorch/tree/v1.13.0 "v1.13.0")|[v1.13.\*](https://github.com/intel/intel-extension-for-pytorch/tree/v1.13.100+cpu)|
+
+You can simply use pip to install IPEX with the latest version.
+```python
+python -m pip install intel_extension_for_pytorch
+```
+**Note:** To install a specific version, run with the following command:
+```
+python -m pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
+```
+
+2. After pipeline initialization, `prepare_for_ipex()` should be called to enable IPEX accelaration. Supported inference datatypes are Float32 and BFloat16.
+
+**Note:** The setting of generated image height/width for `prepare_for_ipex()` should be same as the setting of pipeline inference.
+```python
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
+# For Float32
+pipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) #value of image height/width should be consistent with the pipeline inference
+# For BFloat16 
+pipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512) #value of image height/width should be consistent with the pipeline inference
+```
+
+Then you can use the ipex pipeline in a similar way to the default stable diffusion pipeline.
+```python
+# For Float32
+image = pipe(prompt, num_inference_steps=20, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'
+# For BFloat16 
+with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
+    image = pipe(prompt, num_inference_steps=20, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'
+```
+
+The following code compares the performance of the original stable diffusion pipeline with the ipex-optimized pipeline.
+
+```python
+import torch
+import intel_extension_for_pytorch as ipex
+from diffusers import StableDiffusionPipeline
+import time
+
+prompt = "sailing ship in storm by Rembrandt"
+model_id = "runwayml/stable-diffusion-v1-5"
+# Helper function for time evaluation
+def elapsed_time(pipeline, nb_pass=3, num_inference_steps=20):
+    # warmup
+    for _ in range(2):
+        images = pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images
+    #time evaluation
+    start = time.time()
+    for _ in range(nb_pass):
+        pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512)
+    end = time.time()
+    return (end - start) / nb_pass
+
+##############     bf16 inference performance    ###############
+
+# 1. IPEX Pipeline initialization
+pipe = DiffusionPipeline.from_pretrained(model_id, custom_pipeline="stable_diffusion_ipex")
+pipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512)
+
+# 2. Original Pipeline initialization
+pipe2 = StableDiffusionPipeline.from_pretrained(model_id)
+
+# 3. Compare performance between Original Pipeline and IPEX Pipeline
+with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
+    latency = elapsed_time(pipe)
+    print("Latency of StableDiffusionIPEXPipeline--bf16", latency)
+    latency = elapsed_time(pipe2)
+    print("Latency of StableDiffusionPipeline--bf16",latency)
+
+##############     fp32 inference performance    ###############
+
+# 1. IPEX Pipeline initialization
+pipe3 = DiffusionPipeline.from_pretrained(model_id, custom_pipeline="stable_diffusion_ipex")
+pipe3.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512)
+
+# 2. Original Pipeline initialization
+pipe4 = StableDiffusionPipeline.from_pretrained(model_id)
+
+# 3. Compare performance between Original Pipeline and IPEX Pipeline
+latency = elapsed_time(pipe3)
+print("Latency of StableDiffusionIPEXPipeline--fp32", latency)
+latency = elapsed_time(pipe4)
+print("Latency of StableDiffusionPipeline--fp32",latency)
+
+```
+  
+### CLIP Guided Images Mixing With Stable Diffusion
+
+![clip_guided_images_mixing_examples](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/main.png)
+
+CLIP guided stable diffusion images mixing pipline allows to combine two images using standard diffusion models.  
+This approach is using (optional) CoCa model to avoid writing image description.  
+[More code examples](https://github.com/TheDenk/images_mixing)
+
+## Example Images Mixing (with CoCa)
+```python
+import requests
+from io import BytesIO
+
+import PIL
+import torch
+import open_clip
+from open_clip import SimpleTokenizer
+from diffusers import DiffusionPipeline
+from transformers import CLIPFeatureExtractor, CLIPModel
+
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+# Loading additional models
+feature_extractor = CLIPFeatureExtractor.from_pretrained(
+    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
+)
+clip_model = CLIPModel.from_pretrained(
+    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
+)
+coca_model = open_clip.create_model('coca_ViT-L-14', pretrained='laion2B-s13B-b90k').to('cuda')
+coca_model.dtype = torch.float16
+coca_transform = open_clip.image_transform(
+    coca_model.visual.image_size,
+    is_train = False,
+    mean = getattr(coca_model.visual, 'image_mean', None),
+    std = getattr(coca_model.visual, 'image_std', None),
+)
+coca_tokenizer = SimpleTokenizer()
+
+# Pipline creating
+mixing_pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="clip_guided_images_mixing_stable_diffusion",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    coca_model=coca_model,
+    coca_tokenizer=coca_tokenizer,
+    coca_transform=coca_transform,
+    torch_dtype=torch.float16,
+)
+mixing_pipeline.enable_attention_slicing()
+mixing_pipeline = mixing_pipeline.to("cuda")
+
+# Pipline running
+generator = torch.Generator(device="cuda").manual_seed(17) 
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+content_image = download_image("https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir.jpg")
+style_image = download_image("https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/gigachad.jpg")
+
+pipe_images = mixing_pipeline(
+    num_inference_steps=50,
+    content_image=content_image,
+    style_image=style_image,
+    noise_strength=0.65,
+    slerp_latent_style_strength=0.9,
+    slerp_prompt_style_strength=0.1,
+    slerp_clip_image_style_strength=0.1,
+    guidance_scale=9.0,
+    batch_size=1,
+    clip_guidance_scale=100,
+    generator=generator,
+).images
+```
+
+![image_mixing_result](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir_gigachad.png)
+
+### Stable Diffusion Mixture Tiling
+
+This pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.
+    
+```python
+from diffusers import LMSDiscreteScheduler, DiffusionPipeline
+
+# Creater scheduler and model (similar to StableDiffusionPipeline)
+scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
+pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler, custom_pipeline="mixture_tiling")
+pipeline.to("cuda")
+
+# Mixture of Diffusers generation
+image = pipeline(
+    prompt=[[
+        "A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
+        "A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
+        "An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece"
+    ]],
+    tile_height=640,
+    tile_width=640,
+    tile_row_overlap=0,
+    tile_col_overlap=256,
+    guidance_scale=8,
+    seed=7178915308,
+    num_inference_steps=50,
+)["images"][0]
+```
+![mixture_tiling_results](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/mixture_tiling.png)
+
+### TensorRT Inpainting Stable Diffusion Pipeline
+
+The TensorRT Pipeline can be used to accelerate the Inpainting Stable Diffusion Inference run.
+
+NOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.
+
+```python
+import requests
+from io import BytesIO
+from PIL import Image
+import torch
+from diffusers import PNDMScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionImg2ImgPipeline
+
+# Use the PNDMScheduler scheduler here instead
+scheduler = PNDMScheduler.from_pretrained("stabilityai/stable-diffusion-2-inpainting", subfolder="scheduler")
+
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-inpainting",
+    custom_pipeline="stable_diffusion_tensorrt_inpaint",
+    revision='fp16',
+    torch_dtype=torch.float16,
+    scheduler=scheduler,
+    )
+
+# re-use cached folder to save ONNX models and TensorRT Engines
+pipe.set_cached_folder("stabilityai/stable-diffusion-2-inpainting", revision='fp16',)
+
+pipe = pipe.to("cuda")
+
+url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+response = requests.get(url)
+input_image = Image.open(BytesIO(response.content)).convert("RGB")
+
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+response = requests.get(mask_url)
+mask_image = Image.open(BytesIO(response.content)).convert("RGB")
+
+prompt = "a mecha robot sitting on a bench"
+image = pipe(prompt, image=input_image, mask_image=mask_image, strength=0.75,).images[0]
+image.save('tensorrt_inpaint_mecha_robot.png')
+```
+
+### Stable Diffusion Mixture Canvas
+
+This pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.
+    
+```python
+from PIL import Image
+from diffusers import LMSDiscreteScheduler, DiffusionPipeline
+from diffusers.pipelines.pipeline_utils import Image2ImageRegion, Text2ImageRegion, preprocess_image
+
+
+# Load and preprocess guide image
+iic_image = preprocess_image(Image.open("input_image.png").convert("RGB"))
+
+# Creater scheduler and model (similar to StableDiffusionPipeline)
+scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
+pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler).to("cuda:0", custom_pipeline="mixture_canvas")
+pipeline.to("cuda")
+
+# Mixture of Diffusers generation
+output = pipeline(
+    canvas_height=800,
+    canvas_width=352,
+    regions=[
+        Text2ImageRegion(0, 800, 0, 352, guidance_scale=8,
+            prompt=f"best quality, masterpiece, WLOP, sakimichan, art contest winner on pixiv, 8K, intricate details, wet effects, rain drops, ethereal, mysterious, futuristic, UHD, HDR, cinematic lighting, in a beautiful forest, rainy day, award winning, trending on artstation, beautiful confident cheerful young woman, wearing a futuristic sleeveless dress, ultra beautiful detailed  eyes, hyper-detailed face, complex,  perfect, model,  textured,  chiaroscuro, professional make-up, realistic, figure in frame, "),
+        Image2ImageRegion(352-800, 352, 0, 352, reference_image=iic_image, strength=1.0),
+    ],
+    num_inference_steps=100,
+    seed=5525475061,
+)["images"][0]
+```
+![Input_Image](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/input_image.png)
+![mixture_canvas_results](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/canvas.png)
+
+
+### IADB pipeline
+
+This pipeline is the implementation of the [α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486) paper.
+It is a simple and minimalist diffusion model.
+
+The following code shows how to use the IADB pipeline to generate images using a pretrained celebahq-256 model.
+
+```python
+
+pipeline_iadb = DiffusionPipeline.from_pretrained("thomasc4/iadb-celebahq-256", custom_pipeline='iadb')
+
+pipeline_iadb = pipeline_iadb.to('cuda')
+
+output = pipeline_iadb(batch_size=4,num_inference_steps=128)
+for i in range(len(output[0])):
+    plt.imshow(output[0][i])
+    plt.show()
+
+```
+
+Sampling with the IADB formulation is easy, and can be done in a few lines (the pipeline already implements it):
+
+```python
+
+def sample_iadb(model, x0, nb_step):
+    x_alpha = x0
+    for t in range(nb_step):
+        alpha = (t/nb_step)
+        alpha_next =((t+1)/nb_step)
+
+        d = model(x_alpha, torch.tensor(alpha, device=x_alpha.device))['sample']
+        x_alpha = x_alpha + (alpha_next-alpha)*d
+
+    return x_alpha
+
+```
+
+The training loop is also straightforward:
+
+```python
+
+# Training loop
+while True:
+    x0 = sample_noise()
+    x1 = sample_dataset()
+
+    alpha = torch.rand(batch_size)
+
+    # Blend
+    x_alpha = (1-alpha) * x0 + alpha * x1
+
+    # Loss
+    loss = torch.sum((D(x_alpha, alpha)- (x1-x0))**2)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+```

v0.19.2/bit_diffusion.py

@@ -0,0 +1,264 @@
+from typing import Optional, Tuple, Union
+
+import torch
+from einops import rearrange, reduce
+
+from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DConditionModel
+from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
+from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
+
+
+BITS = 8
+
+
+# convert to bit representations and back taken from https://github.com/lucidrains/bit-diffusion/blob/main/bit_diffusion/bit_diffusion.py
+def decimal_to_bits(x, bits=BITS):
+    """expects image tensor ranging from 0 to 1, outputs bit tensor ranging from -1 to 1"""
+    device = x.device
+
+    x = (x * 255).int().clamp(0, 255)
+
+    mask = 2 ** torch.arange(bits - 1, -1, -1, device=device)
+    mask = rearrange(mask, "d -> d 1 1")
+    x = rearrange(x, "b c h w -> b c 1 h w")
+
+    bits = ((x & mask) != 0).float()
+    bits = rearrange(bits, "b c d h w -> b (c d) h w")
+    bits = bits * 2 - 1
+    return bits
+
+
+def bits_to_decimal(x, bits=BITS):
+    """expects bits from -1 to 1, outputs image tensor from 0 to 1"""
+    device = x.device
+
+    x = (x > 0).int()
+    mask = 2 ** torch.arange(bits - 1, -1, -1, device=device, dtype=torch.int32)
+
+    mask = rearrange(mask, "d -> d 1 1")
+    x = rearrange(x, "b (c d) h w -> b c d h w", d=8)
+    dec = reduce(x * mask, "b c d h w -> b c h w", "sum")
+    return (dec / 255).clamp(0.0, 1.0)
+
+
+# modified scheduler step functions for clamping the predicted x_0 between -bit_scale and +bit_scale
+def ddim_bit_scheduler_step(
+    self,
+    model_output: torch.FloatTensor,
+    timestep: int,
+    sample: torch.FloatTensor,
+    eta: float = 0.0,
+    use_clipped_model_output: bool = True,
+    generator=None,
+    return_dict: bool = True,
+) -> Union[DDIMSchedulerOutput, Tuple]:
+    """
+    Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+    process from the learned model outputs (most often the predicted noise).
+    Args:
+        model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+        timestep (`int`): current discrete timestep in the diffusion chain.
+        sample (`torch.FloatTensor`):
+            current instance of sample being created by diffusion process.
+        eta (`float`): weight of noise for added noise in diffusion step.
+        use_clipped_model_output (`bool`): TODO
+        generator: random number generator.
+        return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
+    Returns:
+        [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
+        [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+        returning a tuple, the first element is the sample tensor.
+    """
+    if self.num_inference_steps is None:
+        raise ValueError(
+            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+        )
+
+    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
+    # Ideally, read DDIM paper in-detail understanding
+
+    # Notation (<variable name> -> <name in paper>
+    # - pred_noise_t -> e_theta(x_t, t)
+    # - pred_original_sample -> f_theta(x_t, t) or x_0
+    # - std_dev_t -> sigma_t
+    # - eta -> η
+    # - pred_sample_direction -> "direction pointing to x_t"
+    # - pred_prev_sample -> "x_t-1"
+
+    # 1. get previous step value (=t-1)
+    prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
+
+    # 2. compute alphas, betas
+    alpha_prod_t = self.alphas_cumprod[timestep]
+    alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
+
+    beta_prod_t = 1 - alpha_prod_t
+
+    # 3. compute predicted original sample from predicted noise also called
+    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+
+    # 4. Clip "predicted x_0"
+    scale = self.bit_scale
+    if self.config.clip_sample:
+        pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)
+
+    # 5. compute variance: "sigma_t(η)" -> see formula (16)
+    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
+    variance = self._get_variance(timestep, prev_timestep)
+    std_dev_t = eta * variance ** (0.5)
+
+    if use_clipped_model_output:
+        # the model_output is always re-derived from the clipped x_0 in Glide
+        model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
+
+    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
+
+    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
+
+    if eta > 0:
+        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
+        device = model_output.device if torch.is_tensor(model_output) else "cpu"
+        noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)
+        variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * noise
+
+        prev_sample = prev_sample + variance
+
+    if not return_dict:
+        return (prev_sample,)
+
+    return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
+
+
+def ddpm_bit_scheduler_step(
+    self,
+    model_output: torch.FloatTensor,
+    timestep: int,
+    sample: torch.FloatTensor,
+    prediction_type="epsilon",
+    generator=None,
+    return_dict: bool = True,
+) -> Union[DDPMSchedulerOutput, Tuple]:
+    """
+    Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+    process from the learned model outputs (most often the predicted noise).
+    Args:
+        model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+        timestep (`int`): current discrete timestep in the diffusion chain.
+        sample (`torch.FloatTensor`):
+            current instance of sample being created by diffusion process.
+        prediction_type (`str`, default `epsilon`):
+            indicates whether the model predicts the noise (epsilon), or the samples (`sample`).
+        generator: random number generator.
+        return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
+    Returns:
+        [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:
+        [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+        returning a tuple, the first element is the sample tensor.
+    """
+    t = timestep
+
+    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
+        model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
+    else:
+        predicted_variance = None
+
+    # 1. compute alphas, betas
+    alpha_prod_t = self.alphas_cumprod[t]
+    alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
+    beta_prod_t = 1 - alpha_prod_t
+    beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+    # 2. compute predicted original sample from predicted noise also called
+    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
+    if prediction_type == "epsilon":
+        pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+    elif prediction_type == "sample":
+        pred_original_sample = model_output
+    else:
+        raise ValueError(f"Unsupported prediction_type {prediction_type}.")
+
+    # 3. Clip "predicted x_0"
+    scale = self.bit_scale
+    if self.config.clip_sample:
+        pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)
+
+    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
+    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+    pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
+    current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
+
+    # 5. Compute predicted previous sample µ_t
+    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+    pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
+
+    # 6. Add noise
+    variance = 0
+    if t > 0:
+        noise = torch.randn(
+            model_output.size(), dtype=model_output.dtype, layout=model_output.layout, generator=generator
+        ).to(model_output.device)
+        variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * noise
+
+    pred_prev_sample = pred_prev_sample + variance
+
+    if not return_dict:
+        return (pred_prev_sample,)
+
+    return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
+
+
+class BitDiffusion(DiffusionPipeline):
+    def __init__(
+        self,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, DDPMScheduler],
+        bit_scale: Optional[float] = 1.0,
+    ):
+        super().__init__()
+        self.bit_scale = bit_scale
+        self.scheduler.step = (
+            ddim_bit_scheduler_step if isinstance(scheduler, DDIMScheduler) else ddpm_bit_scheduler_step
+        )
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        height: Optional[int] = 256,
+        width: Optional[int] = 256,
+        num_inference_steps: Optional[int] = 50,
+        generator: Optional[torch.Generator] = None,
+        batch_size: Optional[int] = 1,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        **kwargs,
+    ) -> Union[Tuple, ImagePipelineOutput]:
+        latents = torch.randn(
+            (batch_size, self.unet.config.in_channels, height, width),
+            generator=generator,
+        )
+        latents = decimal_to_bits(latents) * self.bit_scale
+        latents = latents.to(self.device)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        for t in self.progress_bar(self.scheduler.timesteps):
+            # predict the noise residual
+            noise_pred = self.unet(latents, t).sample
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents).prev_sample
+
+        image = bits_to_decimal(latents)
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

v0.19.2/checkpoint_merger.py

@@ -0,0 +1,286 @@
+import glob
+import os
+from typing import Dict, List, Union
+
+import torch
+
+from diffusers.utils import is_safetensors_available
+
+
+if is_safetensors_available():
+    import safetensors.torch
+
+from huggingface_hub import snapshot_download
+
+from diffusers import DiffusionPipeline, __version__
+from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
+from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
+
+
+class CheckpointMergerPipeline(DiffusionPipeline):
+    """
+    A class that that supports merging diffusion models based on the discussion here:
+    https://github.com/huggingface/diffusers/issues/877
+
+    Example usage:-
+
+    pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py")
+
+    merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True)
+
+    merged_pipe.to('cuda')
+
+    prompt = "An astronaut riding a unicycle on Mars"
+
+    results = merged_pipe(prompt)
+
+    ## For more details, see the docstring for the merge method.
+
+    """
+
+    def __init__(self):
+        self.register_to_config()
+        super().__init__()
+
+    def _compare_model_configs(self, dict0, dict1):
+        if dict0 == dict1:
+            return True
+        else:
+            config0, meta_keys0 = self._remove_meta_keys(dict0)
+            config1, meta_keys1 = self._remove_meta_keys(dict1)
+            if config0 == config1:
+                print(f"Warning !: Mismatch in keys {meta_keys0} and {meta_keys1}.")
+                return True
+        return False
+
+    def _remove_meta_keys(self, config_dict: Dict):
+        meta_keys = []
+        temp_dict = config_dict.copy()
+        for key in config_dict.keys():
+            if key.startswith("_"):
+                temp_dict.pop(key)
+                meta_keys.append(key)
+        return (temp_dict, meta_keys)
+
+    @torch.no_grad()
+    def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]], **kwargs):
+        """
+        Returns a new pipeline object of the class 'DiffusionPipeline' with the merged checkpoints(weights) of the models passed
+        in the argument 'pretrained_model_name_or_path_list' as a list.
+
+        Parameters:
+        -----------
+            pretrained_model_name_or_path_list : A list of valid pretrained model names in the HuggingFace hub or paths to locally stored models in the HuggingFace format.
+
+            **kwargs:
+                Supports all the default DiffusionPipeline.get_config_dict kwargs viz..
+
+                cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map.
+
+                alpha - The interpolation parameter. Ranges from 0 to 1.  It affects the ratio in which the checkpoints are merged. A 0.8 alpha
+                    would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
+
+                interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_diff" and None.
+                    Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_diff" is supported.
+
+                force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
+
+        """
+        # Default kwargs from DiffusionPipeline
+        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        resume_download = kwargs.pop("resume_download", False)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+        device_map = kwargs.pop("device_map", None)
+
+        alpha = kwargs.pop("alpha", 0.5)
+        interp = kwargs.pop("interp", None)
+
+        print("Received list", pretrained_model_name_or_path_list)
+        print(f"Combining with alpha={alpha}, interpolation mode={interp}")
+
+        checkpoint_count = len(pretrained_model_name_or_path_list)
+        # Ignore result from model_index_json comparision of the two checkpoints
+        force = kwargs.pop("force", False)
+
+        # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
+        if checkpoint_count > 3 or checkpoint_count < 2:
+            raise ValueError(
+                "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
+                " passed."
+            )
+
+        print("Received the right number of checkpoints")
+        # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
+        # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None
+
+        # Validate that the checkpoints can be merged
+        # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
+        config_dicts = []
+        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
+            config_dict = DiffusionPipeline.load_config(
+                pretrained_model_name_or_path,
+                cache_dir=cache_dir,
+                resume_download=resume_download,
+                force_download=force_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                use_auth_token=use_auth_token,
+                revision=revision,
+            )
+            config_dicts.append(config_dict)
+
+        comparison_result = True
+        for idx in range(1, len(config_dicts)):
+            comparison_result &= self._compare_model_configs(config_dicts[idx - 1], config_dicts[idx])
+            if not force and comparison_result is False:
+                raise ValueError("Incompatible checkpoints. Please check model_index.json for the models.")
+                print(config_dicts[0], config_dicts[1])
+        print("Compatible model_index.json files found")
+        # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
+        cached_folders = []
+        for pretrained_model_name_or_path, config_dict in zip(pretrained_model_name_or_path_list, config_dicts):
+            folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
+            allow_patterns = [os.path.join(k, "*") for k in folder_names]
+            allow_patterns += [
+                WEIGHTS_NAME,
+                SCHEDULER_CONFIG_NAME,
+                CONFIG_NAME,
+                ONNX_WEIGHTS_NAME,
+                DiffusionPipeline.config_name,
+            ]
+            requested_pipeline_class = config_dict.get("_class_name")
+            user_agent = {"diffusers": __version__, "pipeline_class": requested_pipeline_class}
+
+            cached_folder = (
+                pretrained_model_name_or_path
+                if os.path.isdir(pretrained_model_name_or_path)
+                else snapshot_download(
+                    pretrained_model_name_or_path,
+                    cache_dir=cache_dir,
+                    resume_download=resume_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    use_auth_token=use_auth_token,
+                    revision=revision,
+                    allow_patterns=allow_patterns,
+                    user_agent=user_agent,
+                )
+            )
+            print("Cached Folder", cached_folder)
+            cached_folders.append(cached_folder)
+
+        # Step 3:-
+        # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
+        final_pipe = DiffusionPipeline.from_pretrained(
+            cached_folders[0], torch_dtype=torch_dtype, device_map=device_map
+        )
+        final_pipe.to(self.device)
+
+        checkpoint_path_2 = None
+        if len(cached_folders) > 2:
+            checkpoint_path_2 = os.path.join(cached_folders[2])
+
+        if interp == "sigmoid":
+            theta_func = CheckpointMergerPipeline.sigmoid
+        elif interp == "inv_sigmoid":
+            theta_func = CheckpointMergerPipeline.inv_sigmoid
+        elif interp == "add_diff":
+            theta_func = CheckpointMergerPipeline.add_difference
+        else:
+            theta_func = CheckpointMergerPipeline.weighted_sum
+
+        # Find each module's state dict.
+        for attr in final_pipe.config.keys():
+            if not attr.startswith("_"):
+                checkpoint_path_1 = os.path.join(cached_folders[1], attr)
+                if os.path.exists(checkpoint_path_1):
+                    files = [
+                        *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")),
+                        *glob.glob(os.path.join(checkpoint_path_1, "*.bin")),
+                    ]
+                    checkpoint_path_1 = files[0] if len(files) > 0 else None
+                if len(cached_folders) < 3:
+                    checkpoint_path_2 = None
+                else:
+                    checkpoint_path_2 = os.path.join(cached_folders[2], attr)
+                    if os.path.exists(checkpoint_path_2):
+                        files = [
+                            *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")),
+                            *glob.glob(os.path.join(checkpoint_path_2, "*.bin")),
+                        ]
+                        checkpoint_path_2 = files[0] if len(files) > 0 else None
+                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
+                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
+                if checkpoint_path_1 is None and checkpoint_path_2 is None:
+                    print(f"Skipping {attr}: not present in 2nd or 3d model")
+                    continue
+                try:
+                    module = getattr(final_pipe, attr)
+                    if isinstance(module, bool):  # ignore requires_safety_checker boolean
+                        continue
+                    theta_0 = getattr(module, "state_dict")
+                    theta_0 = theta_0()
+
+                    update_theta_0 = getattr(module, "load_state_dict")
+                    theta_1 = (
+                        safetensors.torch.load_file(checkpoint_path_1)
+                        if (is_safetensors_available() and checkpoint_path_1.endswith(".safetensors"))
+                        else torch.load(checkpoint_path_1, map_location="cpu")
+                    )
+                    theta_2 = None
+                    if checkpoint_path_2:
+                        theta_2 = (
+                            safetensors.torch.load_file(checkpoint_path_2)
+                            if (is_safetensors_available() and checkpoint_path_2.endswith(".safetensors"))
+                            else torch.load(checkpoint_path_2, map_location="cpu")
+                        )
+
+                    if not theta_0.keys() == theta_1.keys():
+                        print(f"Skipping {attr}: key mismatch")
+                        continue
+                    if theta_2 and not theta_1.keys() == theta_2.keys():
+                        print(f"Skipping {attr}:y mismatch")
+                except Exception as e:
+                    print(f"Skipping {attr} do to an unexpected error: {str(e)}")
+                    continue
+                print(f"MERGING {attr}")
+
+                for key in theta_0.keys():
+                    if theta_2:
+                        theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key], alpha)
+                    else:
+                        theta_0[key] = theta_func(theta_0[key], theta_1[key], None, alpha)
+
+                del theta_1
+                del theta_2
+                update_theta_0(theta_0)
+
+                del theta_0
+        return final_pipe
+
+    @staticmethod
+    def weighted_sum(theta0, theta1, theta2, alpha):
+        return ((1 - alpha) * theta0) + (alpha * theta1)
+
+    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
+    @staticmethod
+    def sigmoid(theta0, theta1, theta2, alpha):
+        alpha = alpha * alpha * (3 - (2 * alpha))
+        return theta0 + ((theta1 - theta0) * alpha)
+
+    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
+    @staticmethod
+    def inv_sigmoid(theta0, theta1, theta2, alpha):
+        import math
+
+        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
+        return theta0 + ((theta1 - theta0) * alpha)
+
+    @staticmethod
+    def add_difference(theta0, theta1, theta2, alpha):
+        return theta0 + (theta1 - theta2) * (1.0 - alpha)

v0.19.2/clip_guided_images_mixing_stable_diffusion.py

@@ -0,0 +1,456 @@
+# -*- coding: utf-8 -*-
+import inspect
+from typing import Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from torch.nn import functional as F
+from torchvision import transforms
+from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    randn_tensor,
+)
+
+
+def preprocess(image, w, h):
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [image]
+
+    if isinstance(image[0], PIL.Image.Image):
+        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
+
+
+def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
+    if not isinstance(v0, np.ndarray):
+        inputs_are_torch = True
+        input_device = v0.device
+        v0 = v0.cpu().numpy()
+        v1 = v1.cpu().numpy()
+
+    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+    if np.abs(dot) > DOT_THRESHOLD:
+        v2 = (1 - t) * v0 + t * v1
+    else:
+        theta_0 = np.arccos(dot)
+        sin_theta_0 = np.sin(theta_0)
+        theta_t = theta_0 * t
+        sin_theta_t = np.sin(theta_t)
+        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+        s1 = sin_theta_t / sin_theta_0
+        v2 = s0 * v0 + s1 * v1
+
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(input_device)
+
+    return v2
+
+
+def spherical_dist_loss(x, y):
+    x = F.normalize(x, dim=-1)
+    y = F.normalize(y, dim=-1)
+    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
+
+
+def set_requires_grad(model, value):
+    for param in model.parameters():
+        param.requires_grad = value
+
+
+class CLIPGuidedImagesMixingStableDiffusion(DiffusionPipeline):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        clip_model: CLIPModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
+        feature_extractor: CLIPFeatureExtractor,
+        coca_model=None,
+        coca_tokenizer=None,
+        coca_transform=None,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            clip_model=clip_model,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+            coca_model=coca_model,
+            coca_tokenizer=coca_tokenizer,
+            coca_transform=coca_transform,
+        )
+        self.feature_extractor_size = (
+            feature_extractor.size
+            if isinstance(feature_extractor.size, int)
+            else feature_extractor.size["shortest_edge"]
+        )
+        self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
+        set_requires_grad(self.text_encoder, False)
+        set_requires_grad(self.clip_model, False)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        self.enable_attention_slicing(None)
+
+    def freeze_vae(self):
+        set_requires_grad(self.vae, False)
+
+    def unfreeze_vae(self):
+        set_requires_grad(self.vae, True)
+
+    def freeze_unet(self):
+        set_requires_grad(self.unet, False)
+
+    def unfreeze_unet(self):
+        set_requires_grad(self.unet, True)
+
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_latents(self, image, timestep, batch_size, dtype, device, generator=None):
+        if not isinstance(image, torch.Tensor):
+            raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(image)}")
+
+        image = image.to(device=device, dtype=dtype)
+
+        if isinstance(generator, list):
+            init_latents = [
+                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+            ]
+            init_latents = torch.cat(init_latents, dim=0)
+        else:
+            init_latents = self.vae.encode(image).latent_dist.sample(generator)
+
+        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
+        init_latents = 0.18215 * init_latents
+        init_latents = init_latents.repeat_interleave(batch_size, dim=0)
+
+        noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)
+
+        # get latents
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    def get_image_description(self, image):
+        transformed_image = self.coca_transform(image).unsqueeze(0)
+        with torch.no_grad(), torch.cuda.amp.autocast():
+            generated = self.coca_model.generate(transformed_image.to(device=self.device, dtype=self.coca_model.dtype))
+        generated = self.coca_tokenizer.decode(generated[0].cpu().numpy())
+        return generated.split("<end_of_text>")[0].replace("<start_of_text>", "").rstrip(" .,")
+
+    def get_clip_image_embeddings(self, image, batch_size):
+        clip_image_input = self.feature_extractor.preprocess(image)
+        clip_image_features = torch.from_numpy(clip_image_input["pixel_values"][0]).unsqueeze(0).to(self.device).half()
+        image_embeddings_clip = self.clip_model.get_image_features(clip_image_features)
+        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+        image_embeddings_clip = image_embeddings_clip.repeat_interleave(batch_size, dim=0)
+        return image_embeddings_clip
+
+    @torch.enable_grad()
+    def cond_fn(
+        self,
+        latents,
+        timestep,
+        index,
+        text_embeddings,
+        noise_pred_original,
+        original_image_embeddings_clip,
+        clip_guidance_scale,
+    ):
+        latents = latents.detach().requires_grad_()
+
+        latent_model_input = self.scheduler.scale_model_input(latents, timestep)
+
+        # predict the noise residual
+        noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
+
+        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
+            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
+            beta_prod_t = 1 - alpha_prod_t
+            # compute predicted original sample from predicted noise also called
+            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
+
+            fac = torch.sqrt(beta_prod_t)
+            sample = pred_original_sample * (fac) + latents * (1 - fac)
+        elif isinstance(self.scheduler, LMSDiscreteScheduler):
+            sigma = self.scheduler.sigmas[index]
+            sample = latents - sigma * noise_pred
+        else:
+            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
+
+        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
+        sample = 1 / 0.18215 * sample
+        image = self.vae.decode(sample).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        image = transforms.Resize(self.feature_extractor_size)(image)
+        image = self.normalize(image).to(latents.dtype)
+
+        image_embeddings_clip = self.clip_model.get_image_features(image)
+        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+
+        loss = spherical_dist_loss(image_embeddings_clip, original_image_embeddings_clip).mean() * clip_guidance_scale
+
+        grads = -torch.autograd.grad(loss, latents)[0]
+
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents.detach() + grads * (sigma**2)
+            noise_pred = noise_pred_original
+        else:
+            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
+        return noise_pred, latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        style_image: Union[torch.FloatTensor, PIL.Image.Image],
+        content_image: Union[torch.FloatTensor, PIL.Image.Image],
+        style_prompt: Optional[str] = None,
+        content_prompt: Optional[str] = None,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        noise_strength: float = 0.6,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        batch_size: Optional[int] = 1,
+        eta: float = 0.0,
+        clip_guidance_scale: Optional[float] = 100,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        slerp_latent_style_strength: float = 0.8,
+        slerp_prompt_style_strength: float = 0.1,
+        slerp_clip_image_style_strength: float = 0.1,
+    ):
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(f"You have passed {batch_size} batch_size, but only {len(generator)} generators.")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if isinstance(generator, torch.Generator) and batch_size > 1:
+            generator = [generator] + [None] * (batch_size - 1)
+
+        coca_is_none = [
+            ("model", self.coca_model is None),
+            ("tokenizer", self.coca_tokenizer is None),
+            ("transform", self.coca_transform is None),
+        ]
+        coca_is_none = [x[0] for x in coca_is_none if x[1]]
+        coca_is_none_str = ", ".join(coca_is_none)
+        # generate prompts with coca model if prompt is None
+        if content_prompt is None:
+            if len(coca_is_none):
+                raise ValueError(
+                    f"Content prompt is None and CoCa [{coca_is_none_str}] is None."
+                    f"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."
+                )
+            content_prompt = self.get_image_description(content_image)
+        if style_prompt is None:
+            if len(coca_is_none):
+                raise ValueError(
+                    f"Style prompt is None and CoCa [{coca_is_none_str}] is None."
+                    f" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."
+                )
+            style_prompt = self.get_image_description(style_image)
+
+        # get prompt text embeddings for content and style
+        content_text_input = self.tokenizer(
+            content_prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        content_text_embeddings = self.text_encoder(content_text_input.input_ids.to(self.device))[0]
+
+        style_text_input = self.tokenizer(
+            style_prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        style_text_embeddings = self.text_encoder(style_text_input.input_ids.to(self.device))[0]
+
+        text_embeddings = slerp(slerp_prompt_style_strength, content_text_embeddings, style_text_embeddings)
+
+        # duplicate text embeddings for each generation per prompt
+        text_embeddings = text_embeddings.repeat_interleave(batch_size, dim=0)
+
+        # set timesteps
+        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
+        extra_set_kwargs = {}
+        if accepts_offset:
+            extra_set_kwargs["offset"] = 1
+
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        self.scheduler.timesteps.to(self.device)
+
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, noise_strength, self.device)
+        latent_timestep = timesteps[:1].repeat(batch_size)
+
+        # Preprocess image
+        preprocessed_content_image = preprocess(content_image, width, height)
+        content_latents = self.prepare_latents(
+            preprocessed_content_image, latent_timestep, batch_size, text_embeddings.dtype, self.device, generator
+        )
+
+        preprocessed_style_image = preprocess(style_image, width, height)
+        style_latents = self.prepare_latents(
+            preprocessed_style_image, latent_timestep, batch_size, text_embeddings.dtype, self.device, generator
+        )
+
+        latents = slerp(slerp_latent_style_strength, content_latents, style_latents)
+
+        if clip_guidance_scale > 0:
+            content_clip_image_embedding = self.get_clip_image_embeddings(content_image, batch_size)
+            style_clip_image_embedding = self.get_clip_image_embeddings(style_image, batch_size)
+            clip_image_embeddings = slerp(
+                slerp_clip_image_style_strength, content_clip_image_embedding, style_clip_image_embedding
+            )
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            max_length = content_text_input.input_ids.shape[-1]
+            uncond_input = self.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+            # duplicate unconditional embeddings for each generation per prompt
+            uncond_embeddings = uncond_embeddings.repeat_interleave(batch_size, dim=0)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
+        with self.progress_bar(total=num_inference_steps):
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+                # perform classifier free guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # perform clip guidance
+                if clip_guidance_scale > 0:
+                    text_embeddings_for_guidance = (
+                        text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
+                    )
+                    noise_pred, latents = self.cond_fn(
+                        latents,
+                        t,
+                        i,
+                        text_embeddings_for_guidance,
+                        noise_pred,
+                        clip_image_embeddings,
+                        clip_guidance_scale,
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, None)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)

v0.19.2/clip_guided_stable_diffusion.py

@@ -0,0 +1,347 @@
+import inspect
+from typing import List, Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torchvision import transforms
+from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+
+
+class MakeCutouts(nn.Module):
+    def __init__(self, cut_size, cut_power=1.0):
+        super().__init__()
+
+        self.cut_size = cut_size
+        self.cut_power = cut_power
+
+    def forward(self, pixel_values, num_cutouts):
+        sideY, sideX = pixel_values.shape[2:4]
+        max_size = min(sideX, sideY)
+        min_size = min(sideX, sideY, self.cut_size)
+        cutouts = []
+        for _ in range(num_cutouts):
+            size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
+            offsetx = torch.randint(0, sideX - size + 1, ())
+            offsety = torch.randint(0, sideY - size + 1, ())
+            cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
+            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
+        return torch.cat(cutouts)
+
+
+def spherical_dist_loss(x, y):
+    x = F.normalize(x, dim=-1)
+    y = F.normalize(y, dim=-1)
+    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
+
+
+def set_requires_grad(model, value):
+    for param in model.parameters():
+        param.requires_grad = value
+
+
+class CLIPGuidedStableDiffusion(DiffusionPipeline):
+    """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
+    - https://github.com/Jack000/glid-3-xl
+    - https://github.dev/crowsonkb/k-diffusion
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        clip_model: CLIPModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            clip_model=clip_model,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+        )
+
+        self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
+        self.cut_out_size = (
+            feature_extractor.size
+            if isinstance(feature_extractor.size, int)
+            else feature_extractor.size["shortest_edge"]
+        )
+        self.make_cutouts = MakeCutouts(self.cut_out_size)
+
+        set_requires_grad(self.text_encoder, False)
+        set_requires_grad(self.clip_model, False)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        self.enable_attention_slicing(None)
+
+    def freeze_vae(self):
+        set_requires_grad(self.vae, False)
+
+    def unfreeze_vae(self):
+        set_requires_grad(self.vae, True)
+
+    def freeze_unet(self):
+        set_requires_grad(self.unet, False)
+
+    def unfreeze_unet(self):
+        set_requires_grad(self.unet, True)
+
+    @torch.enable_grad()
+    def cond_fn(
+        self,
+        latents,
+        timestep,
+        index,
+        text_embeddings,
+        noise_pred_original,
+        text_embeddings_clip,
+        clip_guidance_scale,
+        num_cutouts,
+        use_cutouts=True,
+    ):
+        latents = latents.detach().requires_grad_()
+
+        latent_model_input = self.scheduler.scale_model_input(latents, timestep)
+
+        # predict the noise residual
+        noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
+
+        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
+            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
+            beta_prod_t = 1 - alpha_prod_t
+            # compute predicted original sample from predicted noise also called
+            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
+
+            fac = torch.sqrt(beta_prod_t)
+            sample = pred_original_sample * (fac) + latents * (1 - fac)
+        elif isinstance(self.scheduler, LMSDiscreteScheduler):
+            sigma = self.scheduler.sigmas[index]
+            sample = latents - sigma * noise_pred
+        else:
+            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
+
+        sample = 1 / self.vae.config.scaling_factor * sample
+        image = self.vae.decode(sample).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        if use_cutouts:
+            image = self.make_cutouts(image, num_cutouts)
+        else:
+            image = transforms.Resize(self.cut_out_size)(image)
+        image = self.normalize(image).to(latents.dtype)
+
+        image_embeddings_clip = self.clip_model.get_image_features(image)
+        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+
+        if use_cutouts:
+            dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
+            dists = dists.view([num_cutouts, sample.shape[0], -1])
+            loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
+        else:
+            loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale
+
+        grads = -torch.autograd.grad(loss, latents)[0]
+
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents.detach() + grads * (sigma**2)
+            noise_pred = noise_pred_original
+        else:
+            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
+        return noise_pred, latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        clip_guidance_scale: Optional[float] = 100,
+        clip_prompt: Optional[Union[str, List[str]]] = None,
+        num_cutouts: Optional[int] = 4,
+        use_cutouts: Optional[bool] = True,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        # get prompt text embeddings
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
+        # duplicate text embeddings for each generation per prompt
+        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+        if clip_guidance_scale > 0:
+            if clip_prompt is not None:
+                clip_text_input = self.tokenizer(
+                    clip_prompt,
+                    padding="max_length",
+                    max_length=self.tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                ).input_ids.to(self.device)
+            else:
+                clip_text_input = text_input.input_ids.to(self.device)
+            text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
+            text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+            # duplicate text embeddings clip for each generation per prompt
+            text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            max_length = text_input.input_ids.shape[-1]
+            uncond_input = self.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+            # duplicate unconditional embeddings for each generation per prompt
+            uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
+        extra_set_kwargs = {}
+        if accepts_offset:
+            extra_set_kwargs["offset"] = 1
+
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform classifier free guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # perform clip guidance
+            if clip_guidance_scale > 0:
+                text_embeddings_for_guidance = (
+                    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
+                )
+                noise_pred, latents = self.cond_fn(
+                    latents,
+                    t,
+                    i,
+                    text_embeddings_for_guidance,
+                    noise_pred,
+                    text_embeddings_clip,
+                    clip_guidance_scale,
+                    num_cutouts,
+                    use_cutouts,
+                )
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+        # scale and decode the image latents with vae
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, None)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)

v0.19.2/clip_guided_stable_diffusion_img2img.py

@@ -0,0 +1,496 @@
+import inspect
+from typing import List, Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torchvision import transforms
+from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    deprecate,
+    randn_tensor,
+)
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```
+        from io import BytesIO
+
+        import requests
+        import torch
+        from diffusers import DiffusionPipeline
+        from PIL import Image
+        from transformers import CLIPFeatureExtractor, CLIPModel
+
+        feature_extractor = CLIPFeatureExtractor.from_pretrained(
+            "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
+        )
+        clip_model = CLIPModel.from_pretrained(
+            "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
+        )
+
+
+        guided_pipeline = DiffusionPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4",
+            # custom_pipeline="clip_guided_stable_diffusion",
+            custom_pipeline="/home/njindal/diffusers/examples/community/clip_guided_stable_diffusion.py",
+            clip_model=clip_model,
+            feature_extractor=feature_extractor,
+            torch_dtype=torch.float16,
+        )
+        guided_pipeline.enable_attention_slicing()
+        guided_pipeline = guided_pipeline.to("cuda")
+
+        prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
+
+        url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+
+        response = requests.get(url)
+        init_image = Image.open(BytesIO(response.content)).convert("RGB")
+
+        image = guided_pipeline(
+            prompt=prompt,
+            num_inference_steps=30,
+            image=init_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            clip_guidance_scale=100,
+            num_cutouts=4,
+            use_cutouts=False,
+        ).images[0]
+        display(image)
+        ```
+"""
+
+
+def preprocess(image, w, h):
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [image]
+
+    if isinstance(image[0], PIL.Image.Image):
+        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
+
+
+class MakeCutouts(nn.Module):
+    def __init__(self, cut_size, cut_power=1.0):
+        super().__init__()
+
+        self.cut_size = cut_size
+        self.cut_power = cut_power
+
+    def forward(self, pixel_values, num_cutouts):
+        sideY, sideX = pixel_values.shape[2:4]
+        max_size = min(sideX, sideY)
+        min_size = min(sideX, sideY, self.cut_size)
+        cutouts = []
+        for _ in range(num_cutouts):
+            size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
+            offsetx = torch.randint(0, sideX - size + 1, ())
+            offsety = torch.randint(0, sideY - size + 1, ())
+            cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
+            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
+        return torch.cat(cutouts)
+
+
+def spherical_dist_loss(x, y):
+    x = F.normalize(x, dim=-1)
+    y = F.normalize(y, dim=-1)
+    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
+
+
+def set_requires_grad(model, value):
+    for param in model.parameters():
+        param.requires_grad = value
+
+
+class CLIPGuidedStableDiffusion(DiffusionPipeline):
+    """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
+    - https://github.com/Jack000/glid-3-xl
+    - https://github.dev/crowsonkb/k-diffusion
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        clip_model: CLIPModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            clip_model=clip_model,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+        )
+
+        self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
+        self.cut_out_size = (
+            feature_extractor.size
+            if isinstance(feature_extractor.size, int)
+            else feature_extractor.size["shortest_edge"]
+        )
+        self.make_cutouts = MakeCutouts(self.cut_out_size)
+
+        set_requires_grad(self.text_encoder, False)
+        set_requires_grad(self.clip_model, False)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        self.enable_attention_slicing(None)
+
+    def freeze_vae(self):
+        set_requires_grad(self.vae, False)
+
+    def unfreeze_vae(self):
+        set_requires_grad(self.vae, True)
+
+    def freeze_unet(self):
+        set_requires_grad(self.unet, False)
+
+    def unfreeze_unet(self):
+        set_requires_grad(self.unet, True)
+
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
+        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+            )
+
+        image = image.to(device=device, dtype=dtype)
+
+        batch_size = batch_size * num_images_per_prompt
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if isinstance(generator, list):
+            init_latents = [
+                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+            ]
+            init_latents = torch.cat(init_latents, dim=0)
+        else:
+            init_latents = self.vae.encode(image).latent_dist.sample(generator)
+
+        init_latents = self.vae.config.scaling_factor * init_latents
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            deprecation_message = (
+                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
+                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
+                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
+                " your script to pass as many initial images as text prompts to suppress this warning."
+            )
+            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        shape = init_latents.shape
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        # get latents
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    @torch.enable_grad()
+    def cond_fn(
+        self,
+        latents,
+        timestep,
+        index,
+        text_embeddings,
+        noise_pred_original,
+        text_embeddings_clip,
+        clip_guidance_scale,
+        num_cutouts,
+        use_cutouts=True,
+    ):
+        latents = latents.detach().requires_grad_()
+
+        latent_model_input = self.scheduler.scale_model_input(latents, timestep)
+
+        # predict the noise residual
+        noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
+
+        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
+            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
+            beta_prod_t = 1 - alpha_prod_t
+            # compute predicted original sample from predicted noise also called
+            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
+
+            fac = torch.sqrt(beta_prod_t)
+            sample = pred_original_sample * (fac) + latents * (1 - fac)
+        elif isinstance(self.scheduler, LMSDiscreteScheduler):
+            sigma = self.scheduler.sigmas[index]
+            sample = latents - sigma * noise_pred
+        else:
+            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
+
+        sample = 1 / self.vae.config.scaling_factor * sample
+        image = self.vae.decode(sample).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        if use_cutouts:
+            image = self.make_cutouts(image, num_cutouts)
+        else:
+            image = transforms.Resize(self.cut_out_size)(image)
+        image = self.normalize(image).to(latents.dtype)
+
+        image_embeddings_clip = self.clip_model.get_image_features(image)
+        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+
+        if use_cutouts:
+            dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
+            dists = dists.view([num_cutouts, sample.shape[0], -1])
+            loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
+        else:
+            loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale
+
+        grads = -torch.autograd.grad(loss, latents)[0]
+
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents.detach() + grads * (sigma**2)
+            noise_pred = noise_pred_original
+        else:
+            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
+        return noise_pred, latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        clip_guidance_scale: Optional[float] = 100,
+        clip_prompt: Optional[Union[str, List[str]]] = None,
+        num_cutouts: Optional[int] = 4,
+        use_cutouts: Optional[bool] = True,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        # get prompt text embeddings
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
+        # duplicate text embeddings for each generation per prompt
+        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+        # set timesteps
+        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
+        extra_set_kwargs = {}
+        if accepts_offset:
+            extra_set_kwargs["offset"] = 1
+
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        self.scheduler.timesteps.to(self.device)
+
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # Preprocess image
+        image = preprocess(image, width, height)
+        latents = self.prepare_latents(
+            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, self.device, generator
+        )
+
+        if clip_guidance_scale > 0:
+            if clip_prompt is not None:
+                clip_text_input = self.tokenizer(
+                    clip_prompt,
+                    padding="max_length",
+                    max_length=self.tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                ).input_ids.to(self.device)
+            else:
+                clip_text_input = text_input.input_ids.to(self.device)
+            text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
+            text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+            # duplicate text embeddings clip for each generation per prompt
+            text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            max_length = text_input.input_ids.shape[-1]
+            uncond_input = self.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+            # duplicate unconditional embeddings for each generation per prompt
+            uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
+        with self.progress_bar(total=num_inference_steps):
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+                # perform classifier free guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # perform clip guidance
+                if clip_guidance_scale > 0:
+                    text_embeddings_for_guidance = (
+                        text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
+                    )
+                    noise_pred, latents = self.cond_fn(
+                        latents,
+                        t,
+                        i,
+                        text_embeddings_for_guidance,
+                        noise_pred,
+                        text_embeddings_clip,
+                        clip_guidance_scale,
+                        num_cutouts,
+                        use_cutouts,
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+        # scale and decode the image latents with vae
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, None)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)

v0.19.2/composable_stable_diffusion.py

@@ -0,0 +1,580 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import deprecate, is_accelerate_available, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class ComposableStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
+        steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+        if self.safety_checker is not None:
+            # TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate
+            # fix by only offloading self.safety_checker for now
+            cpu_offload(self.safety_checker.vision_model, device)
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        text_embeddings = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        text_embeddings = text_embeddings[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            uncond_embeddings = uncond_embeddings[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(self, prompt, height, width, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if latents is None:
+            if device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
+            else:
+                latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        weights: Optional[str] = "",
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        if "|" in prompt:
+            prompt = [x.strip() for x in prompt.split("|")]
+            print(f"composing {prompt}...")
+
+            if not weights:
+                # specify weights for prompts (excluding the unconditional score)
+                print("using equal positive weights (conjunction) for all prompts...")
+                weights = torch.tensor([guidance_scale] * len(prompt), device=self.device).reshape(-1, 1, 1, 1)
+            else:
+                # set prompt weight for each
+                num_prompts = len(prompt) if isinstance(prompt, list) else 1
+                weights = [float(w.strip()) for w in weights.split("|")]
+                # guidance scale as the default
+                if len(weights) < num_prompts:
+                    weights.append(guidance_scale)
+                else:
+                    weights = weights[:num_prompts]
+                assert len(weights) == len(prompt), "weights specified are not equal to the number of prompts"
+                weights = torch.tensor(weights, device=self.device).reshape(-1, 1, 1, 1)
+        else:
+            weights = guidance_scale
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            text_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # composable diffusion
+        if isinstance(prompt, list) and batch_size == 1:
+            # remove extra unconditional embedding
+            # N = one unconditional embed + conditional embeds
+            text_embeddings = text_embeddings[len(prompt) - 1 :]
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                noise_pred = []
+                for j in range(text_embeddings.shape[0]):
+                    noise_pred.append(
+                        self.unet(latent_model_input[:1], t, encoder_hidden_states=text_embeddings[j : j + 1]).sample
+                    )
+                noise_pred = torch.cat(noise_pred, dim=0)
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred[:1], noise_pred[1:]
+                    noise_pred = noise_pred_uncond + (weights * (noise_pred_text - noise_pred_uncond)).sum(
+                        dim=0, keepdims=True
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # 8. Post-processing
+        image = self.decode_latents(latents)
+
+        # 9. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
+
+        # 10. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/ddim_noise_comparative_analysis.py

@@ -0,0 +1,190 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Optional, Tuple, Union
+
+import PIL
+import torch
+from torchvision import transforms
+
+from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from diffusers.schedulers import DDIMScheduler
+from diffusers.utils import randn_tensor
+
+
+trans = transforms.Compose(
+    [
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.5], [0.5]),
+    ]
+)
+
+
+def preprocess(image):
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [image]
+
+    image = [trans(img.convert("RGB")) for img in image]
+    image = torch.stack(image)
+    return image
+
+
+class DDIMNoiseComparativeAnalysisPipeline(DiffusionPipeline):
+    r"""
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Parameters:
+        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
+            [`DDPMScheduler`], or [`DDIMScheduler`].
+    """
+
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        # make sure scheduler can always be converted to DDIM
+        scheduler = DDIMScheduler.from_config(scheduler.config)
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    def check_inputs(self, strength):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_latents(self, image, timestep, batch_size, dtype, device, generator=None):
+        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+            )
+
+        init_latents = image.to(device=device, dtype=dtype)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        shape = init_latents.shape
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        # get latents
+        print("add noise to latents at timestep", timestep)
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        strength: float = 0.8,
+        batch_size: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        eta: float = 0.0,
+        num_inference_steps: int = 50,
+        use_clipped_model_output: Optional[bool] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+        r"""
+        Args:
+            image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
+                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
+                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
+                be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            batch_size (`int`, *optional*, defaults to 1):
+                The number of images to generate.
+            generator (`torch.Generator`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            eta (`float`, *optional*, defaults to 0.0):
+                The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            use_clipped_model_output (`bool`, *optional*, defaults to `None`):
+                if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed
+                downstream to the scheduler. So use `None` for schedulers which don't support this argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is
+            True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
+        """
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(strength)
+
+        # 2. Preprocess image
+        image = preprocess(image)
+
+        # 3. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)
+        latent_timestep = timesteps[:1].repeat(batch_size)
+
+        # 4. Prepare latent variables
+        latents = self.prepare_latents(image, latent_timestep, batch_size, self.unet.dtype, self.device, generator)
+        image = latents
+
+        # 5. Denoising loop
+        for t in self.progress_bar(timesteps):
+            # 1. predict noise model_output
+            model_output = self.unet(image, t).sample
+
+            # 2. predict previous mean of image x_t-1 and add variance depending on eta
+            # eta corresponds to η in paper and should be between [0, 1]
+            # do x_t -> x_t-1
+            image = self.scheduler.step(
+                model_output,
+                t,
+                image,
+                eta=eta,
+                use_clipped_model_output=use_clipped_model_output,
+                generator=generator,
+            ).prev_sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, latent_timestep.item())
+
+        return ImagePipelineOutput(images=image)

v0.19.2/edict_pipeline.py

@@ -0,0 +1,264 @@
+from typing import Optional
+
+import torch
+from PIL import Image
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNet2DConditionModel
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.utils import (
+    deprecate,
+)
+
+
+class EDICTPipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: DDIMScheduler,
+        mixing_coeff: float = 0.93,
+        leapfrog_steps: bool = True,
+    ):
+        self.mixing_coeff = mixing_coeff
+        self.leapfrog_steps = leapfrog_steps
+
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+        )
+
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    def _encode_prompt(
+        self, prompt: str, negative_prompt: Optional[str] = None, do_classifier_free_guidance: bool = False
+    ):
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        prompt_embeds = self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=self.device)
+
+        if do_classifier_free_guidance:
+            uncond_tokens = "" if negative_prompt is None else negative_prompt
+
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            negative_prompt_embeds = self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state
+
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    def denoise_mixing_layer(self, x: torch.Tensor, y: torch.Tensor):
+        x = self.mixing_coeff * x + (1 - self.mixing_coeff) * y
+        y = self.mixing_coeff * y + (1 - self.mixing_coeff) * x
+
+        return [x, y]
+
+    def noise_mixing_layer(self, x: torch.Tensor, y: torch.Tensor):
+        y = (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
+        x = (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff
+
+        return [x, y]
+
+    def _get_alpha_and_beta(self, t: torch.Tensor):
+        # as self.alphas_cumprod is always in cpu
+        t = int(t)
+
+        alpha_prod = self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod
+
+        return alpha_prod, 1 - alpha_prod
+
+    def noise_step(
+        self,
+        base: torch.Tensor,
+        model_input: torch.Tensor,
+        model_output: torch.Tensor,
+        timestep: torch.Tensor,
+    ):
+        prev_timestep = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
+
+        alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep)
+        alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(prev_timestep)
+
+        a_t = (alpha_prod_t_prev / alpha_prod_t) ** 0.5
+        b_t = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
+
+        next_model_input = (base - b_t * model_output) / a_t
+
+        return model_input, next_model_input.to(base.dtype)
+
+    def denoise_step(
+        self,
+        base: torch.Tensor,
+        model_input: torch.Tensor,
+        model_output: torch.Tensor,
+        timestep: torch.Tensor,
+    ):
+        prev_timestep = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
+
+        alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep)
+        alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(prev_timestep)
+
+        a_t = (alpha_prod_t_prev / alpha_prod_t) ** 0.5
+        b_t = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
+        next_model_input = a_t * base + b_t * model_output
+
+        return model_input, next_model_input.to(base.dtype)
+
+    @torch.no_grad()
+    def decode_latents(self, latents: torch.Tensor):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        return image
+
+    @torch.no_grad()
+    def prepare_latents(
+        self,
+        image: Image.Image,
+        text_embeds: torch.Tensor,
+        timesteps: torch.Tensor,
+        guidance_scale: float,
+        generator: Optional[torch.Generator] = None,
+    ):
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        image = image.to(device=self.device, dtype=text_embeds.dtype)
+        latent = self.vae.encode(image).latent_dist.sample(generator)
+
+        latent = self.vae.config.scaling_factor * latent
+
+        coupled_latents = [latent.clone(), latent.clone()]
+
+        for i, t in tqdm(enumerate(timesteps), total=len(timesteps)):
+            coupled_latents = self.noise_mixing_layer(x=coupled_latents[0], y=coupled_latents[1])
+
+            # j - model_input index, k - base index
+            for j in range(2):
+                k = j ^ 1
+
+                if self.leapfrog_steps:
+                    if i % 2 == 0:
+                        k, j = j, k
+
+                model_input = coupled_latents[j]
+                base = coupled_latents[k]
+
+                latent_model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
+
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds).sample
+
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                base, model_input = self.noise_step(
+                    base=base,
+                    model_input=model_input,
+                    model_output=noise_pred,
+                    timestep=t,
+                )
+
+                coupled_latents[k] = model_input
+
+        return coupled_latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        base_prompt: str,
+        target_prompt: str,
+        image: Image.Image,
+        guidance_scale: float = 3.0,
+        num_inference_steps: int = 50,
+        strength: float = 0.8,
+        negative_prompt: Optional[str] = None,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+    ):
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        image = self.image_processor.preprocess(image)
+
+        base_embeds = self._encode_prompt(base_prompt, negative_prompt, do_classifier_free_guidance)
+        target_embeds = self._encode_prompt(target_prompt, negative_prompt, do_classifier_free_guidance)
+
+        self.scheduler.set_timesteps(num_inference_steps, self.device)
+
+        t_limit = num_inference_steps - int(num_inference_steps * strength)
+        fwd_timesteps = self.scheduler.timesteps[t_limit:]
+        bwd_timesteps = fwd_timesteps.flip(0)
+
+        coupled_latents = self.prepare_latents(image, base_embeds, bwd_timesteps, guidance_scale, generator)
+
+        for i, t in tqdm(enumerate(fwd_timesteps), total=len(fwd_timesteps)):
+            # j - model_input index, k - base index
+            for k in range(2):
+                j = k ^ 1
+
+                if self.leapfrog_steps:
+                    if i % 2 == 1:
+                        k, j = j, k
+
+                model_input = coupled_latents[j]
+                base = coupled_latents[k]
+
+                latent_model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
+
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=target_embeds).sample
+
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                base, model_input = self.denoise_step(
+                    base=base,
+                    model_input=model_input,
+                    model_output=noise_pred,
+                    timestep=t,
+                )
+
+                coupled_latents[k] = model_input
+
+            coupled_latents = self.denoise_mixing_layer(x=coupled_latents[0], y=coupled_latents[1])
+
+        # either one is fine
+        final_latent = coupled_latents[0]
+
+        if output_type not in ["latent", "pt", "np", "pil"]:
+            deprecation_message = (
+                f"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
+                "`pil`, `np`, `pt`, `latent`"
+            )
+            deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
+            output_type = "np"
+
+        if output_type == "latent":
+            image = final_latent
+        else:
+            image = self.decode_latents(final_latent)
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        return image

v0.19.2/iadb.py

@@ -0,0 +1,149 @@
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import ConfigMixin
+from diffusers.pipeline_utils import ImagePipelineOutput
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+
+
+class IADBScheduler(SchedulerMixin, ConfigMixin):
+    """
+    IADBScheduler is a scheduler for the Iterative α-(de)Blending denoising method. It is simple and minimalist.
+
+    For more details, see the original paper: https://arxiv.org/abs/2305.03486 and the blog post: https://ggx-research.github.io/publication/2023/05/10/publication-iadb.html
+    """
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: int,
+        x_alpha: torch.FloatTensor,
+    ) -> torch.FloatTensor:
+        """
+        Predict the sample at the previous timestep by reversing the ODE. Core function to propagate the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model. It is the direction from x0 to x1.
+            timestep (`float`): current timestep in the diffusion chain.
+            x_alpha (`torch.FloatTensor`): x_alpha sample for the current timestep
+
+        Returns:
+            `torch.FloatTensor`: the sample at the previous timestep
+
+        """
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
+        alpha = timestep / self.num_inference_steps
+        alpha_next = (timestep + 1) / self.num_inference_steps
+
+        d = model_output
+
+        x_alpha = x_alpha + (alpha_next - alpha) * d
+
+        return x_alpha
+
+    def set_timesteps(self, num_inference_steps: int):
+        self.num_inference_steps = num_inference_steps
+
+    def add_noise(
+        self,
+        original_samples: torch.FloatTensor,
+        noise: torch.FloatTensor,
+        alpha: torch.FloatTensor,
+    ) -> torch.FloatTensor:
+        return original_samples * alpha + noise * (1 - alpha)
+
+    def __len__(self):
+        return self.config.num_train_timesteps
+
+
+class IADBPipeline(DiffusionPipeline):
+    r"""
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Parameters:
+        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
+            [`DDPMScheduler`], or [`DDIMScheduler`].
+    """
+
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        batch_size: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        num_inference_steps: int = 50,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+        r"""
+        Args:
+            batch_size (`int`, *optional*, defaults to 1):
+                The number of images to generate.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is
+            True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        # Sample gaussian noise to begin loop
+        if isinstance(self.unet.config.sample_size, int):
+            image_shape = (
+                batch_size,
+                self.unet.config.in_channels,
+                self.unet.config.sample_size,
+                self.unet.config.sample_size,
+            )
+        else:
+            image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        image = torch.randn(image_shape, generator=generator, device=self.device, dtype=self.unet.dtype)
+
+        # set step values
+        self.scheduler.set_timesteps(num_inference_steps)
+        x_alpha = image.clone()
+        for t in self.progress_bar(range(num_inference_steps)):
+            alpha = t / num_inference_steps
+
+            # 1. predict noise model_output
+            model_output = self.unet(x_alpha, torch.tensor(alpha, device=x_alpha.device)).sample
+
+            # 2. step
+            x_alpha = self.scheduler.step(model_output, t, x_alpha)
+
+        image = (x_alpha * 0.5 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

v0.19.2/imagic_stable_diffusion.py

@@ -0,0 +1,496 @@
+"""
+    modeled after the textual_inversion.py / train_dreambooth.py and the work
+    of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb
+"""
+import inspect
+import warnings
+from typing import List, Optional, Union
+
+import numpy as np
+import PIL
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
+from packaging import version
+from tqdm.auto import tqdm
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import DiffusionPipeline
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import logging
+
+
+if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.Resampling.BILINEAR,
+        "bilinear": PIL.Image.Resampling.BILINEAR,
+        "bicubic": PIL.Image.Resampling.BICUBIC,
+        "lanczos": PIL.Image.Resampling.LANCZOS,
+        "nearest": PIL.Image.Resampling.NEAREST,
+    }
+else:
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.LINEAR,
+        "bilinear": PIL.Image.BILINEAR,
+        "bicubic": PIL.Image.BICUBIC,
+        "lanczos": PIL.Image.LANCZOS,
+        "nearest": PIL.Image.NEAREST,
+    }
+# ------------------------------------------------------------------------------
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def preprocess(image):
+    w, h = image.size
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2.0 * image - 1.0
+
+
+class ImagicStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for imagic image editing.
+    See paper here: https://arxiv.org/pdf/2210.09276.pdf
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offsensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    def train(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        generator: Optional[torch.Generator] = None,
+        embedding_learning_rate: float = 0.001,
+        diffusion_model_learning_rate: float = 2e-6,
+        text_embedding_optimization_steps: int = 500,
+        model_fine_tuning_optimization_steps: int = 1000,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        accelerator = Accelerator(
+            gradient_accumulation_steps=1,
+            mixed_precision="fp16",
+        )
+
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
+
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        # Freeze vae and unet
+        self.vae.requires_grad_(False)
+        self.unet.requires_grad_(False)
+        self.text_encoder.requires_grad_(False)
+        self.unet.eval()
+        self.vae.eval()
+        self.text_encoder.eval()
+
+        if accelerator.is_main_process:
+            accelerator.init_trackers(
+                "imagic",
+                config={
+                    "embedding_learning_rate": embedding_learning_rate,
+                    "text_embedding_optimization_steps": text_embedding_optimization_steps,
+                },
+            )
+
+        # get text embeddings for prompt
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_embeddings = torch.nn.Parameter(
+            self.text_encoder(text_input.input_ids.to(self.device))[0], requires_grad=True
+        )
+        text_embeddings = text_embeddings.detach()
+        text_embeddings.requires_grad_()
+        text_embeddings_orig = text_embeddings.clone()
+
+        # Initialize the optimizer
+        optimizer = torch.optim.Adam(
+            [text_embeddings],  # only optimize the embeddings
+            lr=embedding_learning_rate,
+        )
+
+        if isinstance(image, PIL.Image.Image):
+            image = preprocess(image)
+
+        latents_dtype = text_embeddings.dtype
+        image = image.to(device=self.device, dtype=latents_dtype)
+        init_latent_image_dist = self.vae.encode(image).latent_dist
+        image_latents = init_latent_image_dist.sample(generator=generator)
+        image_latents = 0.18215 * image_latents
+
+        progress_bar = tqdm(range(text_embedding_optimization_steps), disable=not accelerator.is_local_main_process)
+        progress_bar.set_description("Steps")
+
+        global_step = 0
+
+        logger.info("First optimizing the text embedding to better reconstruct the init image")
+        for _ in range(text_embedding_optimization_steps):
+            with accelerator.accumulate(text_embeddings):
+                # Sample noise that we'll add to the latents
+                noise = torch.randn(image_latents.shape).to(image_latents.device)
+                timesteps = torch.randint(1000, (1,), device=image_latents.device)
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
+
+                # Predict the noise residual
+                noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
+
+                loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                accelerator.backward(loss)
+
+                optimizer.step()
+                optimizer.zero_grad()
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+            logs = {"loss": loss.detach().item()}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+            accelerator.log(logs, step=global_step)
+
+        accelerator.wait_for_everyone()
+
+        text_embeddings.requires_grad_(False)
+
+        # Now we fine tune the unet to better reconstruct the image
+        self.unet.requires_grad_(True)
+        self.unet.train()
+        optimizer = torch.optim.Adam(
+            self.unet.parameters(),  # only optimize unet
+            lr=diffusion_model_learning_rate,
+        )
+        progress_bar = tqdm(range(model_fine_tuning_optimization_steps), disable=not accelerator.is_local_main_process)
+
+        logger.info("Next fine tuning the entire model to better reconstruct the init image")
+        for _ in range(model_fine_tuning_optimization_steps):
+            with accelerator.accumulate(self.unet.parameters()):
+                # Sample noise that we'll add to the latents
+                noise = torch.randn(image_latents.shape).to(image_latents.device)
+                timesteps = torch.randint(1000, (1,), device=image_latents.device)
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
+
+                # Predict the noise residual
+                noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
+
+                loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                accelerator.backward(loss)
+
+                optimizer.step()
+                optimizer.zero_grad()
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+            logs = {"loss": loss.detach().item()}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+            accelerator.log(logs, step=global_step)
+
+        accelerator.wait_for_everyone()
+        self.text_embeddings_orig = text_embeddings_orig
+        self.text_embeddings = text_embeddings
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        alpha: float = 1.2,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        guidance_scale: float = 7.5,
+        eta: float = 0.0,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+        if self.text_embeddings is None:
+            raise ValueError("Please run the pipe.train() before trying to generate an image.")
+        if self.text_embeddings_orig is None:
+            raise ValueError("Please run the pipe.train() before trying to generate an image.")
+
+        text_embeddings = alpha * self.text_embeddings_orig + (1 - alpha) * self.text_embeddings
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens = [""]
+            max_length = self.tokenizer.model_max_length
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.view(1, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (1, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if self.device.type == "mps":
+            # randn does not exist on mps
+            latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                self.device
+            )
+        else:
+            latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/img2img_inpainting.py

@@ -0,0 +1,463 @@
+import inspect
+from typing import Callable, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def prepare_mask_and_masked_image(image, mask):
+    image = np.array(image.convert("RGB"))
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+    mask = np.array(mask.convert("L"))
+    mask = mask.astype(np.float32) / 255.0
+    mask = mask[None, None]
+    mask[mask < 0.5] = 0
+    mask[mask >= 0.5] = 1
+    mask = torch.from_numpy(mask)
+
+    masked_image = image * (mask < 0.5)
+
+    return mask, masked_image
+
+
+def check_size(image, height, width):
+    if isinstance(image, PIL.Image.Image):
+        w, h = image.size
+    elif isinstance(image, torch.Tensor):
+        *_, h, w = image.shape
+
+    if h != height or w != width:
+        raise ValueError(f"Image size should be {height}x{width}, but got {h}x{w}")
+
+
+def overlay_inner_image(image, inner_image, paste_offset: Tuple[int] = (0, 0)):
+    inner_image = inner_image.convert("RGBA")
+    image = image.convert("RGB")
+
+    image.paste(inner_image, paste_offset, inner_image)
+    image = image.convert("RGB")
+
+    return image
+
+
+class ImageToImageInpaintingPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-guided image-to-image inpainting using Stable Diffusion. *This is an experimental feature*.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        inner_image: Union[torch.FloatTensor, PIL.Image.Image],
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            inner_image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be overlayed onto `image`. Non-transparent
+                regions of `inner_image` must fit inside white pixels in `mask_image`. Expects four channels, with
+                the last channel representing the alpha channel, which will be used to blend `inner_image` with
+                `image`. If not provided, it will be forcibly cast to RGBA.
+            mask_image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # check if input sizes are correct
+        check_size(image, height, width)
+        check_size(inner_image, height, width)
+        check_size(mask_image, height, width)
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""]
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        num_channels_latents = self.vae.config.latent_channels
+        latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not exist on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # overlay the inner image
+        image = overlay_inner_image(image, inner_image)
+
+        # prepare mask and masked_image
+        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
+        mask = mask.to(device=self.device, dtype=text_embeddings.dtype)
+        masked_image = masked_image.to(device=self.device, dtype=text_embeddings.dtype)
+
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        mask = torch.nn.functional.interpolate(mask, size=(height // 8, width // 8))
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
+        masked_image_latents = 0.18215 * masked_image_latents
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        mask = mask.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
+        masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
+
+        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+        masked_image_latents = (
+            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+        )
+
+        num_channels_mask = mask.shape[1]
+        num_channels_masked_image = masked_image_latents.shape[1]
+
+        if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
+            raise ValueError(
+                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+                f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
+                f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                " `pipeline.unet` or your `mask_image` or `image` input."
+            )
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+            # concat latents, mask, masked_image_latents in the channel dimension
+            latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/interpolate_stable_diffusion.py

@@ -0,0 +1,524 @@
+import inspect
+import time
+from pathlib import Path
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
+    """helper function to spherically interpolate two arrays v1 v2"""
+
+    if not isinstance(v0, np.ndarray):
+        inputs_are_torch = True
+        input_device = v0.device
+        v0 = v0.cpu().numpy()
+        v1 = v1.cpu().numpy()
+
+    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+    if np.abs(dot) > DOT_THRESHOLD:
+        v2 = (1 - t) * v0 + t * v1
+    else:
+        theta_0 = np.arccos(dot)
+        sin_theta_0 = np.sin(theta_0)
+        theta_t = theta_0 * t
+        sin_theta_t = np.sin(theta_t)
+        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+        s1 = sin_theta_t / sin_theta_0
+        v2 = s0 * v0 + s1 * v1
+
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(input_device)
+
+    return v2
+
+
+class StableDiffusionWalkPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        text_embeddings: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*, defaults to `None`):
+                The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
+                Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
+                `prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
+                the supplied `prompt`.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if text_embeddings is None:
+            if isinstance(prompt, str):
+                batch_size = 1
+            elif isinstance(prompt, list):
+                batch_size = len(prompt)
+            else:
+                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+            # get prompt text embeddings
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+
+            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+                removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+                print(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+        else:
+            batch_size = text_embeddings.shape[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = self.tokenizer.model_max_length
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    def embed_text(self, text):
+        """takes in text and turns it into text embeddings"""
+        text_input = self.tokenizer(
+            text,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        with torch.no_grad():
+            embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
+        return embed
+
+    def get_noise(self, seed, dtype=torch.float32, height=512, width=512):
+        """Takes in random seed and returns corresponding noise vector"""
+        return torch.randn(
+            (1, self.unet.config.in_channels, height // 8, width // 8),
+            generator=torch.Generator(device=self.device).manual_seed(seed),
+            device=self.device,
+            dtype=dtype,
+        )
+
+    def walk(
+        self,
+        prompts: List[str],
+        seeds: List[int],
+        num_interpolation_steps: Optional[int] = 6,
+        output_dir: Optional[str] = "./dreams",
+        name: Optional[str] = None,
+        batch_size: Optional[int] = 1,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        guidance_scale: Optional[float] = 7.5,
+        num_inference_steps: Optional[int] = 50,
+        eta: Optional[float] = 0.0,
+    ) -> List[str]:
+        """
+        Walks through a series of prompts and seeds, interpolating between them and saving the results to disk.
+
+        Args:
+            prompts (`List[str]`):
+                List of prompts to generate images for.
+            seeds (`List[int]`):
+                List of seeds corresponding to provided prompts. Must be the same length as prompts.
+            num_interpolation_steps (`int`, *optional*, defaults to 6):
+                Number of interpolation steps to take between prompts.
+            output_dir (`str`, *optional*, defaults to `./dreams`):
+                Directory to save the generated images to.
+            name (`str`, *optional*, defaults to `None`):
+                Subdirectory of `output_dir` to save the generated images to. If `None`, the name will
+                be the current time.
+            batch_size (`int`, *optional*, defaults to 1):
+                Number of images to generate at once.
+            height (`int`, *optional*, defaults to 512):
+                Height of the generated images.
+            width (`int`, *optional*, defaults to 512):
+                Width of the generated images.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+
+        Returns:
+            `List[str]`: List of paths to the generated images.
+        """
+        if not len(prompts) == len(seeds):
+            raise ValueError(
+                f"Number of prompts and seeds must be equalGot {len(prompts)} prompts and {len(seeds)} seeds"
+            )
+
+        name = name or time.strftime("%Y%m%d-%H%M%S")
+        save_path = Path(output_dir) / name
+        save_path.mkdir(exist_ok=True, parents=True)
+
+        frame_idx = 0
+        frame_filepaths = []
+        for prompt_a, prompt_b, seed_a, seed_b in zip(prompts, prompts[1:], seeds, seeds[1:]):
+            # Embed Text
+            embed_a = self.embed_text(prompt_a)
+            embed_b = self.embed_text(prompt_b)
+
+            # Get Noise
+            noise_dtype = embed_a.dtype
+            noise_a = self.get_noise(seed_a, noise_dtype, height, width)
+            noise_b = self.get_noise(seed_b, noise_dtype, height, width)
+
+            noise_batch, embeds_batch = None, None
+            T = np.linspace(0.0, 1.0, num_interpolation_steps)
+            for i, t in enumerate(T):
+                noise = slerp(float(t), noise_a, noise_b)
+                embed = torch.lerp(embed_a, embed_b, t)
+
+                noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise], dim=0)
+                embeds_batch = embed if embeds_batch is None else torch.cat([embeds_batch, embed], dim=0)
+
+                batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
+                if batch_is_ready:
+                    outputs = self(
+                        latents=noise_batch,
+                        text_embeddings=embeds_batch,
+                        height=height,
+                        width=width,
+                        guidance_scale=guidance_scale,
+                        eta=eta,
+                        num_inference_steps=num_inference_steps,
+                    )
+                    noise_batch, embeds_batch = None, None
+
+                    for image in outputs["images"]:
+                        frame_filepath = str(save_path / f"frame_{frame_idx:06d}.png")
+                        image.save(frame_filepath)
+                        frame_filepaths.append(frame_filepath)
+                        frame_idx += 1
+        return frame_filepaths

v0.19.2/lpw_stable_diffusion.py

@@ -0,0 +1,1470 @@
+import inspect
+import re
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    deprecate,
+    is_accelerate_available,
+    is_accelerate_version,
+    logging,
+    randn_tensor,
+)
+
+
+# ------------------------------------------------------------------------------
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+re_attention = re.compile(
+    r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:([+-]?[.\d]+)\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""",
+    re.X,
+)
+
+
+def parse_prompt_attention(text):
+    """
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+    Accepted tokens are:
+      (abc) - increases attention to abc by a multiplier of 1.1
+      (abc:3.12) - increases attention to abc by a multiplier of 3.12
+      [abc] - decreases attention to abc by a multiplier of 1.1
+      \( - literal character '('
+      \[ - literal character '['
+      \) - literal character ')'
+      \] - literal character ']'
+      \\ - literal character '\'
+      anything else - just text
+    >>> parse_prompt_attention('normal text')
+    [['normal text', 1.0]]
+    >>> parse_prompt_attention('an (important) word')
+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+    >>> parse_prompt_attention('(unbalanced')
+    [['unbalanced', 1.1]]
+    >>> parse_prompt_attention('\(literal\]')
+    [['(literal]', 1.0]]
+    >>> parse_prompt_attention('(unnecessary)(parens)')
+    [['unnecessaryparens', 1.1]]
+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+    [['a ', 1.0],
+     ['house', 1.5730000000000004],
+     [' ', 1.1],
+     ['on', 1.0],
+     [' a ', 1.1],
+     ['hill', 0.55],
+     [', sun, ', 1.1],
+     ['sky', 1.4641000000000006],
+     ['.', 1.1]]
+    """
+
+    res = []
+    round_brackets = []
+    square_brackets = []
+
+    round_bracket_multiplier = 1.1
+    square_bracket_multiplier = 1 / 1.1
+
+    def multiply_range(start_position, multiplier):
+        for p in range(start_position, len(res)):
+            res[p][1] *= multiplier
+
+    for m in re_attention.finditer(text):
+        text = m.group(0)
+        weight = m.group(1)
+
+        if text.startswith("\\"):
+            res.append([text[1:], 1.0])
+        elif text == "(":
+            round_brackets.append(len(res))
+        elif text == "[":
+            square_brackets.append(len(res))
+        elif weight is not None and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), float(weight))
+        elif text == ")" and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), round_bracket_multiplier)
+        elif text == "]" and len(square_brackets) > 0:
+            multiply_range(square_brackets.pop(), square_bracket_multiplier)
+        else:
+            res.append([text, 1.0])
+
+    for pos in round_brackets:
+        multiply_range(pos, round_bracket_multiplier)
+
+    for pos in square_brackets:
+        multiply_range(pos, square_bracket_multiplier)
+
+    if len(res) == 0:
+        res = [["", 1.0]]
+
+    # merge runs of identical weights
+    i = 0
+    while i + 1 < len(res):
+        if res[i][1] == res[i + 1][1]:
+            res[i][0] += res[i + 1][0]
+            res.pop(i + 1)
+        else:
+            i += 1
+
+    return res
+
+
+def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int):
+    r"""
+    Tokenize a list of prompts and return its tokens with weights of each token.
+
+    No padding, starting or ending token is included.
+    """
+    tokens = []
+    weights = []
+    truncated = False
+    for text in prompt:
+        texts_and_weights = parse_prompt_attention(text)
+        text_token = []
+        text_weight = []
+        for word, weight in texts_and_weights:
+            # tokenize and discard the starting and the ending token
+            token = pipe.tokenizer(word).input_ids[1:-1]
+            text_token += token
+            # copy the weight by length of token
+            text_weight += [weight] * len(token)
+            # stop if the text is too long (longer than truncation limit)
+            if len(text_token) > max_length:
+                truncated = True
+                break
+        # truncate
+        if len(text_token) > max_length:
+            truncated = True
+            text_token = text_token[:max_length]
+            text_weight = text_weight[:max_length]
+        tokens.append(text_token)
+        weights.append(text_weight)
+    if truncated:
+        logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
+    return tokens, weights
+
+
+def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
+    r"""
+    Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
+    """
+    max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
+    weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
+    for i in range(len(tokens)):
+        tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
+        if no_boseos_middle:
+            weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
+        else:
+            w = []
+            if len(weights[i]) == 0:
+                w = [1.0] * weights_length
+            else:
+                for j in range(max_embeddings_multiples):
+                    w.append(1.0)  # weight for starting token in this chunk
+                    w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
+                    w.append(1.0)  # weight for ending token in this chunk
+                w += [1.0] * (weights_length - len(w))
+            weights[i] = w[:]
+
+    return tokens, weights
+
+
+def get_unweighted_text_embeddings(
+    pipe: DiffusionPipeline,
+    text_input: torch.Tensor,
+    chunk_length: int,
+    no_boseos_middle: Optional[bool] = True,
+):
+    """
+    When the length of tokens is a multiple of the capacity of the text encoder,
+    it should be split into chunks and sent to the text encoder individually.
+    """
+    max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
+    if max_embeddings_multiples > 1:
+        text_embeddings = []
+        for i in range(max_embeddings_multiples):
+            # extract the i-th chunk
+            text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()
+
+            # cover the head and the tail by the starting and the ending tokens
+            text_input_chunk[:, 0] = text_input[0, 0]
+            text_input_chunk[:, -1] = text_input[0, -1]
+            text_embedding = pipe.text_encoder(text_input_chunk)[0]
+
+            if no_boseos_middle:
+                if i == 0:
+                    # discard the ending token
+                    text_embedding = text_embedding[:, :-1]
+                elif i == max_embeddings_multiples - 1:
+                    # discard the starting token
+                    text_embedding = text_embedding[:, 1:]
+                else:
+                    # discard both starting and ending tokens
+                    text_embedding = text_embedding[:, 1:-1]
+
+            text_embeddings.append(text_embedding)
+        text_embeddings = torch.concat(text_embeddings, axis=1)
+    else:
+        text_embeddings = pipe.text_encoder(text_input)[0]
+    return text_embeddings
+
+
+def get_weighted_text_embeddings(
+    pipe: DiffusionPipeline,
+    prompt: Union[str, List[str]],
+    uncond_prompt: Optional[Union[str, List[str]]] = None,
+    max_embeddings_multiples: Optional[int] = 3,
+    no_boseos_middle: Optional[bool] = False,
+    skip_parsing: Optional[bool] = False,
+    skip_weighting: Optional[bool] = False,
+):
+    r"""
+    Prompts can be assigned with local weights using brackets. For example,
+    prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
+    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
+
+    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
+
+    Args:
+        pipe (`DiffusionPipeline`):
+            Pipe to provide access to the tokenizer and the text encoder.
+        prompt (`str` or `List[str]`):
+            The prompt or prompts to guide the image generation.
+        uncond_prompt (`str` or `List[str]`):
+            The unconditional prompt or prompts for guide the image generation. If unconditional prompt
+            is provided, the embeddings of prompt and uncond_prompt are concatenated.
+        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+            The max multiple length of prompt embeddings compared to the max output length of text encoder.
+        no_boseos_middle (`bool`, *optional*, defaults to `False`):
+            If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
+            ending token in each of the chunk in the middle.
+        skip_parsing (`bool`, *optional*, defaults to `False`):
+            Skip the parsing of brackets.
+        skip_weighting (`bool`, *optional*, defaults to `False`):
+            Skip the weighting. When the parsing is skipped, it is forced True.
+    """
+    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+    if isinstance(prompt, str):
+        prompt = [prompt]
+
+    if not skip_parsing:
+        prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
+    else:
+        prompt_tokens = [
+            token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids
+        ]
+        prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens = [
+                token[1:-1]
+                for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
+            ]
+            uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
+
+    # round up the longest length of tokens to a multiple of (model_max_length - 2)
+    max_length = max([len(token) for token in prompt_tokens])
+    if uncond_prompt is not None:
+        max_length = max(max_length, max([len(token) for token in uncond_tokens]))
+
+    max_embeddings_multiples = min(
+        max_embeddings_multiples,
+        (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
+    )
+    max_embeddings_multiples = max(1, max_embeddings_multiples)
+    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+
+    # pad the length of tokens and weights
+    bos = pipe.tokenizer.bos_token_id
+    eos = pipe.tokenizer.eos_token_id
+    pad = getattr(pipe.tokenizer, "pad_token_id", eos)
+    prompt_tokens, prompt_weights = pad_tokens_and_weights(
+        prompt_tokens,
+        prompt_weights,
+        max_length,
+        bos,
+        eos,
+        pad,
+        no_boseos_middle=no_boseos_middle,
+        chunk_length=pipe.tokenizer.model_max_length,
+    )
+    prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
+    if uncond_prompt is not None:
+        uncond_tokens, uncond_weights = pad_tokens_and_weights(
+            uncond_tokens,
+            uncond_weights,
+            max_length,
+            bos,
+            eos,
+            pad,
+            no_boseos_middle=no_boseos_middle,
+            chunk_length=pipe.tokenizer.model_max_length,
+        )
+        uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)
+
+    # get the embeddings
+    text_embeddings = get_unweighted_text_embeddings(
+        pipe,
+        prompt_tokens,
+        pipe.tokenizer.model_max_length,
+        no_boseos_middle=no_boseos_middle,
+    )
+    prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=text_embeddings.device)
+    if uncond_prompt is not None:
+        uncond_embeddings = get_unweighted_text_embeddings(
+            pipe,
+            uncond_tokens,
+            pipe.tokenizer.model_max_length,
+            no_boseos_middle=no_boseos_middle,
+        )
+        uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=uncond_embeddings.device)
+
+    # assign weights to the prompts and normalize in the sense of mean
+    # TODO: should we normalize by chunk or in a whole (current implementation)?
+    if (not skip_parsing) and (not skip_weighting):
+        previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
+        text_embeddings *= prompt_weights.unsqueeze(-1)
+        current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
+        text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
+        if uncond_prompt is not None:
+            previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
+            uncond_embeddings *= uncond_weights.unsqueeze(-1)
+            current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
+            uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
+
+    if uncond_prompt is not None:
+        return text_embeddings, uncond_embeddings
+    return text_embeddings, None
+
+
+def preprocess_image(image, batch_size):
+    w, h = image.size
+    w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
+    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
+    image = np.array(image).astype(np.float32) / 255.0
+    image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)
+    image = torch.from_numpy(image)
+    return 2.0 * image - 1.0
+
+
+def preprocess_mask(mask, batch_size, scale_factor=8):
+    if not isinstance(mask, torch.FloatTensor):
+        mask = mask.convert("L")
+        w, h = mask.size
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
+        mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
+        mask = np.array(mask).astype(np.float32) / 255.0
+        mask = np.tile(mask, (4, 1, 1))
+        mask = np.vstack([mask[None]] * batch_size)
+        mask = 1 - mask  # repaint white, keep black
+        mask = torch.from_numpy(mask)
+        return mask
+
+    else:
+        valid_mask_channel_sizes = [1, 3]
+        # if mask channel is fourth tensor dimension, permute dimensions to pytorch standard (B, C, H, W)
+        if mask.shape[3] in valid_mask_channel_sizes:
+            mask = mask.permute(0, 3, 1, 2)
+        elif mask.shape[1] not in valid_mask_channel_sizes:
+            raise ValueError(
+                f"Mask channel dimension of size in {valid_mask_channel_sizes} should be second or fourth dimension,"
+                f" but received mask of shape {tuple(mask.shape)}"
+            )
+        # (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape
+        mask = mask.mean(dim=1, keepdim=True)
+        h, w = mask.shape[-2:]
+        h, w = (x - x % 8 for x in (h, w))  # resize to integer multiple of 8
+        mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor))
+        return mask
+
+
+class StableDiffusionLongPromptWeightingPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
+    weighting in prompt.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(
+            requires_safety_checker=requires_safety_checker,
+        )
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
+        steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
+        several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu", silence_dtype_warnings=True)
+            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            cpu_offload(cpu_offloaded_model, device)
+
+        if self.safety_checker is not None:
+            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
+    def enable_model_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate import cpu_offload_with_hook
+        else:
+            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu", silence_dtype_warnings=True)
+            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        hook = None
+        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
+            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+        if self.safety_checker is not None:
+            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
+
+        # We'll offload the last model manually.
+        self.final_offload_hook = hook
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        max_embeddings_multiples=3,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+        """
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if negative_prompt_embeds is None:
+            if negative_prompt is None:
+                negative_prompt = [""] * batch_size
+            elif isinstance(negative_prompt, str):
+                negative_prompt = [negative_prompt] * batch_size
+            if batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+        if prompt_embeds is None or negative_prompt_embeds is None:
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+                if do_classifier_free_guidance and negative_prompt_embeds is None:
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, self.tokenizer)
+
+            prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(
+                pipe=self,
+                prompt=prompt,
+                uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
+                max_embeddings_multiples=max_embeddings_multiples,
+            )
+            if prompt_embeds is None:
+                prompt_embeds = prompt_embeds1
+            if negative_prompt_embeds is None:
+                negative_prompt_embeds = negative_prompt_embeds1
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            bs_embed, seq_len, _ = negative_prompt_embeds.shape
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        strength,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def get_timesteps(self, num_inference_steps, strength, device, is_text2img):
+        if is_text2img:
+            return self.scheduler.timesteps.to(device), num_inference_steps
+        else:
+            # get the original timestep using init_timestep
+            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+            t_start = max(num_inference_steps - init_timestep, 0)
+            timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+            return timesteps, num_inference_steps - t_start
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def prepare_latents(
+        self,
+        image,
+        timestep,
+        num_images_per_prompt,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        if image is None:
+            batch_size = batch_size * num_images_per_prompt
+            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            if isinstance(generator, list) and len(generator) != batch_size:
+                raise ValueError(
+                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+                )
+
+            if latents is None:
+                latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            else:
+                latents = latents.to(device)
+
+            # scale the initial noise by the standard deviation required by the scheduler
+            latents = latents * self.scheduler.init_noise_sigma
+            return latents, None, None
+        else:
+            image = image.to(device=self.device, dtype=dtype)
+            init_latent_dist = self.vae.encode(image).latent_dist
+            init_latents = init_latent_dist.sample(generator=generator)
+            init_latents = self.vae.config.scaling_factor * init_latents
+
+            # Expand init_latents for batch_size and num_images_per_prompt
+            init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
+            init_latents_orig = init_latents
+
+            # add noise to latents using the timesteps
+            noise = randn_tensor(init_latents.shape, generator=generator, device=self.device, dtype=dtype)
+            init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+            latents = init_latents
+            return latents, init_latents_orig, noise
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        strength: float = 0.8,
+        num_images_per_prompt: Optional[int] = 1,
+        add_predicted_noise: Optional[bool] = False,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+                noise will be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            add_predicted_noise (`bool`, *optional*, defaults to True):
+                Use predicted noise instead of random noise when constructing noisy versions of the original image in
+                the reverse diffusion process
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+
+        Returns:
+            `None` if cancelled by `is_cancelled_callback`,
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt, height, width, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            max_embeddings_multiples,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+        dtype = prompt_embeds.dtype
+
+        # 4. Preprocess image and mask
+        if isinstance(image, PIL.Image.Image):
+            image = preprocess_image(image, batch_size)
+        if image is not None:
+            image = image.to(device=self.device, dtype=dtype)
+        if isinstance(mask_image, PIL.Image.Image):
+            mask_image = preprocess_mask(mask_image, batch_size, self.vae_scale_factor)
+        if mask_image is not None:
+            mask = mask_image.to(device=self.device, dtype=dtype)
+            mask = torch.cat([mask] * num_images_per_prompt)
+        else:
+            mask = None
+
+        # 5. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        latents, init_latents_orig, noise = self.prepare_latents(
+            image,
+            latent_timestep,
+            num_images_per_prompt,
+            batch_size,
+            self.unet.config.in_channels,
+            height,
+            width,
+            dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                ).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                if mask is not None:
+                    # masking
+                    if add_predicted_noise:
+                        init_latents_proper = self.scheduler.add_noise(
+                            init_latents_orig, noise_pred_uncond, torch.tensor([t])
+                        )
+                    else:
+                        init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
+                    latents = (init_latents_proper * mask) + (latents * (1 - mask))
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if i % callback_steps == 0:
+                        if callback is not None:
+                            callback(i, t, latents)
+                        if is_cancelled_callback is not None and is_cancelled_callback():
+                            return None
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+        elif output_type == "pil":
+            # 9. Post-processing
+            image = self.decode_latents(latents)
+
+            # 10. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+            # 11. Convert to PIL
+            image = self.numpy_to_pil(image)
+        else:
+            # 9. Post-processing
+            image = self.decode_latents(latents)
+
+            # 10. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return image, has_nsfw_concept
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    def text2img(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        r"""
+        Function for text-to-image generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+
+        Returns:
+            `None` if cancelled by `is_cancelled_callback`,
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            is_cancelled_callback=is_cancelled_callback,
+            callback_steps=callback_steps,
+            cross_attention_kwargs=cross_attention_kwargs,
+        )
+
+    def img2img(
+        self,
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        r"""
+        Function for image-to-image generation.
+        Args:
+            image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+                noise will be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter will be modulated by `strength`.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+
+        Returns:
+            `None` if cancelled by `is_cancelled_callback`,
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            is_cancelled_callback=is_cancelled_callback,
+            callback_steps=callback_steps,
+            cross_attention_kwargs=cross_attention_kwargs,
+        )
+
+    def inpaint(
+        self,
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        add_predicted_noise: Optional[bool] = False,
+        eta: Optional[float] = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        r"""
+        Function for inpaint.
+        Args:
+            image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process. This is the image whose masked region will be inpainted.
+            mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
+                is 1, the denoising process will be run on the masked area for the full number of iterations specified
+                in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
+                noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
+                the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            add_predicted_noise (`bool`, *optional*, defaults to True):
+                Use predicted noise instead of random noise when constructing noisy versions of the original image in
+                the reverse diffusion process
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+
+        Returns:
+            `None` if cancelled by `is_cancelled_callback`,
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            mask_image=mask_image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_images_per_prompt=num_images_per_prompt,
+            add_predicted_noise=add_predicted_noise,
+            eta=eta,
+            generator=generator,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            is_cancelled_callback=is_cancelled_callback,
+            callback_steps=callback_steps,
+            cross_attention_kwargs=cross_attention_kwargs,
+        )

v0.19.2/lpw_stable_diffusion_onnx.py

@@ -0,0 +1,1146 @@
+import inspect
+import re
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPTokenizer
+
+import diffusers
+from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import logging
+
+
+try:
+    from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
+except ImportError:
+    ORT_TO_NP_TYPE = {
+        "tensor(bool)": np.bool_,
+        "tensor(int8)": np.int8,
+        "tensor(uint8)": np.uint8,
+        "tensor(int16)": np.int16,
+        "tensor(uint16)": np.uint16,
+        "tensor(int32)": np.int32,
+        "tensor(uint32)": np.uint32,
+        "tensor(int64)": np.int64,
+        "tensor(uint64)": np.uint64,
+        "tensor(float16)": np.float16,
+        "tensor(float)": np.float32,
+        "tensor(double)": np.float64,
+    }
+
+try:
+    from diffusers.utils import PIL_INTERPOLATION
+except ImportError:
+    if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
+        PIL_INTERPOLATION = {
+            "linear": PIL.Image.Resampling.BILINEAR,
+            "bilinear": PIL.Image.Resampling.BILINEAR,
+            "bicubic": PIL.Image.Resampling.BICUBIC,
+            "lanczos": PIL.Image.Resampling.LANCZOS,
+            "nearest": PIL.Image.Resampling.NEAREST,
+        }
+    else:
+        PIL_INTERPOLATION = {
+            "linear": PIL.Image.LINEAR,
+            "bilinear": PIL.Image.BILINEAR,
+            "bicubic": PIL.Image.BICUBIC,
+            "lanczos": PIL.Image.LANCZOS,
+            "nearest": PIL.Image.NEAREST,
+        }
+# ------------------------------------------------------------------------------
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+re_attention = re.compile(
+    r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:([+-]?[.\d]+)\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""",
+    re.X,
+)
+
+
+def parse_prompt_attention(text):
+    """
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+    Accepted tokens are:
+      (abc) - increases attention to abc by a multiplier of 1.1
+      (abc:3.12) - increases attention to abc by a multiplier of 3.12
+      [abc] - decreases attention to abc by a multiplier of 1.1
+      \( - literal character '('
+      \[ - literal character '['
+      \) - literal character ')'
+      \] - literal character ']'
+      \\ - literal character '\'
+      anything else - just text
+    >>> parse_prompt_attention('normal text')
+    [['normal text', 1.0]]
+    >>> parse_prompt_attention('an (important) word')
+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+    >>> parse_prompt_attention('(unbalanced')
+    [['unbalanced', 1.1]]
+    >>> parse_prompt_attention('\(literal\]')
+    [['(literal]', 1.0]]
+    >>> parse_prompt_attention('(unnecessary)(parens)')
+    [['unnecessaryparens', 1.1]]
+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+    [['a ', 1.0],
+     ['house', 1.5730000000000004],
+     [' ', 1.1],
+     ['on', 1.0],
+     [' a ', 1.1],
+     ['hill', 0.55],
+     [', sun, ', 1.1],
+     ['sky', 1.4641000000000006],
+     ['.', 1.1]]
+    """
+
+    res = []
+    round_brackets = []
+    square_brackets = []
+
+    round_bracket_multiplier = 1.1
+    square_bracket_multiplier = 1 / 1.1
+
+    def multiply_range(start_position, multiplier):
+        for p in range(start_position, len(res)):
+            res[p][1] *= multiplier
+
+    for m in re_attention.finditer(text):
+        text = m.group(0)
+        weight = m.group(1)
+
+        if text.startswith("\\"):
+            res.append([text[1:], 1.0])
+        elif text == "(":
+            round_brackets.append(len(res))
+        elif text == "[":
+            square_brackets.append(len(res))
+        elif weight is not None and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), float(weight))
+        elif text == ")" and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), round_bracket_multiplier)
+        elif text == "]" and len(square_brackets) > 0:
+            multiply_range(square_brackets.pop(), square_bracket_multiplier)
+        else:
+            res.append([text, 1.0])
+
+    for pos in round_brackets:
+        multiply_range(pos, round_bracket_multiplier)
+
+    for pos in square_brackets:
+        multiply_range(pos, square_bracket_multiplier)
+
+    if len(res) == 0:
+        res = [["", 1.0]]
+
+    # merge runs of identical weights
+    i = 0
+    while i + 1 < len(res):
+        if res[i][1] == res[i + 1][1]:
+            res[i][0] += res[i + 1][0]
+            res.pop(i + 1)
+        else:
+            i += 1
+
+    return res
+
+
+def get_prompts_with_weights(pipe, prompt: List[str], max_length: int):
+    r"""
+    Tokenize a list of prompts and return its tokens with weights of each token.
+
+    No padding, starting or ending token is included.
+    """
+    tokens = []
+    weights = []
+    truncated = False
+    for text in prompt:
+        texts_and_weights = parse_prompt_attention(text)
+        text_token = []
+        text_weight = []
+        for word, weight in texts_and_weights:
+            # tokenize and discard the starting and the ending token
+            token = pipe.tokenizer(word, return_tensors="np").input_ids[0, 1:-1]
+            text_token += list(token)
+            # copy the weight by length of token
+            text_weight += [weight] * len(token)
+            # stop if the text is too long (longer than truncation limit)
+            if len(text_token) > max_length:
+                truncated = True
+                break
+        # truncate
+        if len(text_token) > max_length:
+            truncated = True
+            text_token = text_token[:max_length]
+            text_weight = text_weight[:max_length]
+        tokens.append(text_token)
+        weights.append(text_weight)
+    if truncated:
+        logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
+    return tokens, weights
+
+
+def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
+    r"""
+    Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
+    """
+    max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
+    weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
+    for i in range(len(tokens)):
+        tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
+        if no_boseos_middle:
+            weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
+        else:
+            w = []
+            if len(weights[i]) == 0:
+                w = [1.0] * weights_length
+            else:
+                for j in range(max_embeddings_multiples):
+                    w.append(1.0)  # weight for starting token in this chunk
+                    w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
+                    w.append(1.0)  # weight for ending token in this chunk
+                w += [1.0] * (weights_length - len(w))
+            weights[i] = w[:]
+
+    return tokens, weights
+
+
+def get_unweighted_text_embeddings(
+    pipe,
+    text_input: np.array,
+    chunk_length: int,
+    no_boseos_middle: Optional[bool] = True,
+):
+    """
+    When the length of tokens is a multiple of the capacity of the text encoder,
+    it should be split into chunks and sent to the text encoder individually.
+    """
+    max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
+    if max_embeddings_multiples > 1:
+        text_embeddings = []
+        for i in range(max_embeddings_multiples):
+            # extract the i-th chunk
+            text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].copy()
+
+            # cover the head and the tail by the starting and the ending tokens
+            text_input_chunk[:, 0] = text_input[0, 0]
+            text_input_chunk[:, -1] = text_input[0, -1]
+
+            text_embedding = pipe.text_encoder(input_ids=text_input_chunk)[0]
+
+            if no_boseos_middle:
+                if i == 0:
+                    # discard the ending token
+                    text_embedding = text_embedding[:, :-1]
+                elif i == max_embeddings_multiples - 1:
+                    # discard the starting token
+                    text_embedding = text_embedding[:, 1:]
+                else:
+                    # discard both starting and ending tokens
+                    text_embedding = text_embedding[:, 1:-1]
+
+            text_embeddings.append(text_embedding)
+        text_embeddings = np.concatenate(text_embeddings, axis=1)
+    else:
+        text_embeddings = pipe.text_encoder(input_ids=text_input)[0]
+    return text_embeddings
+
+
+def get_weighted_text_embeddings(
+    pipe,
+    prompt: Union[str, List[str]],
+    uncond_prompt: Optional[Union[str, List[str]]] = None,
+    max_embeddings_multiples: Optional[int] = 4,
+    no_boseos_middle: Optional[bool] = False,
+    skip_parsing: Optional[bool] = False,
+    skip_weighting: Optional[bool] = False,
+    **kwargs,
+):
+    r"""
+    Prompts can be assigned with local weights using brackets. For example,
+    prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
+    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
+
+    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
+
+    Args:
+        pipe (`OnnxStableDiffusionPipeline`):
+            Pipe to provide access to the tokenizer and the text encoder.
+        prompt (`str` or `List[str]`):
+            The prompt or prompts to guide the image generation.
+        uncond_prompt (`str` or `List[str]`):
+            The unconditional prompt or prompts for guide the image generation. If unconditional prompt
+            is provided, the embeddings of prompt and uncond_prompt are concatenated.
+        max_embeddings_multiples (`int`, *optional*, defaults to `1`):
+            The max multiple length of prompt embeddings compared to the max output length of text encoder.
+        no_boseos_middle (`bool`, *optional*, defaults to `False`):
+            If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
+            ending token in each of the chunk in the middle.
+        skip_parsing (`bool`, *optional*, defaults to `False`):
+            Skip the parsing of brackets.
+        skip_weighting (`bool`, *optional*, defaults to `False`):
+            Skip the weighting. When the parsing is skipped, it is forced True.
+    """
+    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+    if isinstance(prompt, str):
+        prompt = [prompt]
+
+    if not skip_parsing:
+        prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
+    else:
+        prompt_tokens = [
+            token[1:-1]
+            for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True, return_tensors="np").input_ids
+        ]
+        prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens = [
+                token[1:-1]
+                for token in pipe.tokenizer(
+                    uncond_prompt,
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="np",
+                ).input_ids
+            ]
+            uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
+
+    # round up the longest length of tokens to a multiple of (model_max_length - 2)
+    max_length = max([len(token) for token in prompt_tokens])
+    if uncond_prompt is not None:
+        max_length = max(max_length, max([len(token) for token in uncond_tokens]))
+
+    max_embeddings_multiples = min(
+        max_embeddings_multiples,
+        (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
+    )
+    max_embeddings_multiples = max(1, max_embeddings_multiples)
+    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+
+    # pad the length of tokens and weights
+    bos = pipe.tokenizer.bos_token_id
+    eos = pipe.tokenizer.eos_token_id
+    pad = getattr(pipe.tokenizer, "pad_token_id", eos)
+    prompt_tokens, prompt_weights = pad_tokens_and_weights(
+        prompt_tokens,
+        prompt_weights,
+        max_length,
+        bos,
+        eos,
+        pad,
+        no_boseos_middle=no_boseos_middle,
+        chunk_length=pipe.tokenizer.model_max_length,
+    )
+    prompt_tokens = np.array(prompt_tokens, dtype=np.int32)
+    if uncond_prompt is not None:
+        uncond_tokens, uncond_weights = pad_tokens_and_weights(
+            uncond_tokens,
+            uncond_weights,
+            max_length,
+            bos,
+            eos,
+            pad,
+            no_boseos_middle=no_boseos_middle,
+            chunk_length=pipe.tokenizer.model_max_length,
+        )
+        uncond_tokens = np.array(uncond_tokens, dtype=np.int32)
+
+    # get the embeddings
+    text_embeddings = get_unweighted_text_embeddings(
+        pipe,
+        prompt_tokens,
+        pipe.tokenizer.model_max_length,
+        no_boseos_middle=no_boseos_middle,
+    )
+    prompt_weights = np.array(prompt_weights, dtype=text_embeddings.dtype)
+    if uncond_prompt is not None:
+        uncond_embeddings = get_unweighted_text_embeddings(
+            pipe,
+            uncond_tokens,
+            pipe.tokenizer.model_max_length,
+            no_boseos_middle=no_boseos_middle,
+        )
+        uncond_weights = np.array(uncond_weights, dtype=uncond_embeddings.dtype)
+
+    # assign weights to the prompts and normalize in the sense of mean
+    # TODO: should we normalize by chunk or in a whole (current implementation)?
+    if (not skip_parsing) and (not skip_weighting):
+        previous_mean = text_embeddings.mean(axis=(-2, -1))
+        text_embeddings *= prompt_weights[:, :, None]
+        text_embeddings *= (previous_mean / text_embeddings.mean(axis=(-2, -1)))[:, None, None]
+        if uncond_prompt is not None:
+            previous_mean = uncond_embeddings.mean(axis=(-2, -1))
+            uncond_embeddings *= uncond_weights[:, :, None]
+            uncond_embeddings *= (previous_mean / uncond_embeddings.mean(axis=(-2, -1)))[:, None, None]
+
+    # For classifier free guidance, we need to do two forward passes.
+    # Here we concatenate the unconditional and text embeddings into a single batch
+    # to avoid doing two forward passes
+    if uncond_prompt is not None:
+        return text_embeddings, uncond_embeddings
+
+    return text_embeddings
+
+
+def preprocess_image(image):
+    w, h = image.size
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    return 2.0 * image - 1.0
+
+
+def preprocess_mask(mask, scale_factor=8):
+    mask = mask.convert("L")
+    w, h = mask.size
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
+    mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
+    mask = np.array(mask).astype(np.float32) / 255.0
+    mask = np.tile(mask, (4, 1, 1))
+    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
+    mask = 1 - mask  # repaint white, keep black
+    return mask
+
+
+class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
+    weighting in prompt.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+    """
+    if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse("0.9.0"):
+
+        def __init__(
+            self,
+            vae_encoder: OnnxRuntimeModel,
+            vae_decoder: OnnxRuntimeModel,
+            text_encoder: OnnxRuntimeModel,
+            tokenizer: CLIPTokenizer,
+            unet: OnnxRuntimeModel,
+            scheduler: SchedulerMixin,
+            safety_checker: OnnxRuntimeModel,
+            feature_extractor: CLIPImageProcessor,
+            requires_safety_checker: bool = True,
+        ):
+            super().__init__(
+                vae_encoder=vae_encoder,
+                vae_decoder=vae_decoder,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
+                requires_safety_checker=requires_safety_checker,
+            )
+            self.__init__additional__()
+
+    else:
+
+        def __init__(
+            self,
+            vae_encoder: OnnxRuntimeModel,
+            vae_decoder: OnnxRuntimeModel,
+            text_encoder: OnnxRuntimeModel,
+            tokenizer: CLIPTokenizer,
+            unet: OnnxRuntimeModel,
+            scheduler: SchedulerMixin,
+            safety_checker: OnnxRuntimeModel,
+            feature_extractor: CLIPImageProcessor,
+        ):
+            super().__init__(
+                vae_encoder=vae_encoder,
+                vae_decoder=vae_decoder,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
+            )
+            self.__init__additional__()
+
+    def __init__additional__(self):
+        self.unet.config.in_channels = 4
+        self.vae_scale_factor = 8
+
+    def _encode_prompt(
+        self,
+        prompt,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt,
+        max_embeddings_multiples,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        if negative_prompt is None:
+            negative_prompt = [""] * batch_size
+        elif isinstance(negative_prompt, str):
+            negative_prompt = [negative_prompt] * batch_size
+        if batch_size != len(negative_prompt):
+            raise ValueError(
+                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                " the batch size of `prompt`."
+            )
+
+        text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
+            pipe=self,
+            prompt=prompt,
+            uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
+            max_embeddings_multiples=max_embeddings_multiples,
+        )
+
+        text_embeddings = text_embeddings.repeat(num_images_per_prompt, 0)
+        if do_classifier_free_guidance:
+            uncond_embeddings = uncond_embeddings.repeat(num_images_per_prompt, 0)
+            text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def check_inputs(self, prompt, height, width, strength, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def get_timesteps(self, num_inference_steps, strength, is_text2img):
+        if is_text2img:
+            return self.scheduler.timesteps, num_inference_steps
+        else:
+            # get the original timestep using init_timestep
+            offset = self.scheduler.config.get("steps_offset", 0)
+            init_timestep = int(num_inference_steps * strength) + offset
+            init_timestep = min(init_timestep, num_inference_steps)
+
+            t_start = max(num_inference_steps - init_timestep + offset, 0)
+            timesteps = self.scheduler.timesteps[t_start:]
+            return timesteps, num_inference_steps - t_start
+
+    def run_safety_checker(self, image):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(
+                self.numpy_to_pil(image), return_tensors="np"
+            ).pixel_values.astype(image.dtype)
+            # There will throw an error if use safety_checker directly and batchsize>1
+            images, has_nsfw_concept = [], []
+            for i in range(image.shape[0]):
+                image_i, has_nsfw_concept_i = self.safety_checker(
+                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+                )
+                images.append(image_i)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
+            image = np.concatenate(images)
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        # image = self.vae_decoder(latent_sample=latents)[0]
+        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+        image = np.concatenate(
+            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
+        )
+        image = np.clip(image / 2 + 0.5, 0, 1)
+        image = image.transpose((0, 2, 3, 1))
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def prepare_latents(self, image, timestep, batch_size, height, width, dtype, generator, latents=None):
+        if image is None:
+            shape = (
+                batch_size,
+                self.unet.config.in_channels,
+                height // self.vae_scale_factor,
+                width // self.vae_scale_factor,
+            )
+
+            if latents is None:
+                latents = torch.randn(shape, generator=generator, device="cpu").numpy().astype(dtype)
+            else:
+                if latents.shape != shape:
+                    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+
+            # scale the initial noise by the standard deviation required by the scheduler
+            latents = (torch.from_numpy(latents) * self.scheduler.init_noise_sigma).numpy()
+            return latents, None, None
+        else:
+            init_latents = self.vae_encoder(sample=image)[0]
+            init_latents = 0.18215 * init_latents
+            init_latents = np.concatenate([init_latents] * batch_size, axis=0)
+            init_latents_orig = init_latents
+            shape = init_latents.shape
+
+            # add noise to latents using the timesteps
+            noise = torch.randn(shape, generator=generator, device="cpu").numpy().astype(dtype)
+            latents = self.scheduler.add_noise(
+                torch.from_numpy(init_latents), torch.from_numpy(noise), timestep
+            ).numpy()
+            return latents, init_latents_orig, noise
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        image: Union[np.ndarray, PIL.Image.Image] = None,
+        mask_image: Union[np.ndarray, PIL.Image.Image] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        strength: float = 0.8,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[np.ndarray] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            mask_image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+                noise will be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`np.ndarray`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            `None` if cancelled by `is_cancelled_callback`,
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, strength, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            max_embeddings_multiples,
+        )
+        dtype = text_embeddings.dtype
+
+        # 4. Preprocess image and mask
+        if isinstance(image, PIL.Image.Image):
+            image = preprocess_image(image)
+        if image is not None:
+            image = image.astype(dtype)
+        if isinstance(mask_image, PIL.Image.Image):
+            mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
+        if mask_image is not None:
+            mask = mask_image.astype(dtype)
+            mask = np.concatenate([mask] * batch_size * num_images_per_prompt)
+        else:
+            mask = None
+
+        # 5. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+        timestep_dtype = next(
+            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, image is None)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        latents, init_latents_orig, noise = self.prepare_latents(
+            image,
+            latent_timestep,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            dtype,
+            generator,
+            latents,
+        )
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 8. Denoising loop
+        for i, t in enumerate(self.progress_bar(timesteps)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
+            latent_model_input = latent_model_input.numpy()
+
+            # predict the noise residual
+            noise_pred = self.unet(
+                sample=latent_model_input,
+                timestep=np.array([t], dtype=timestep_dtype),
+                encoder_hidden_states=text_embeddings,
+            )
+            noise_pred = noise_pred[0]
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            scheduler_output = self.scheduler.step(
+                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
+            )
+            latents = scheduler_output.prev_sample.numpy()
+
+            if mask is not None:
+                # masking
+                init_latents_proper = self.scheduler.add_noise(
+                    torch.from_numpy(init_latents_orig),
+                    torch.from_numpy(noise),
+                    t,
+                ).numpy()
+                latents = (init_latents_proper * mask) + (latents * (1 - mask))
+
+            # call the callback, if provided
+            if i % callback_steps == 0:
+                if callback is not None:
+                    callback(i, t, latents)
+                if is_cancelled_callback is not None and is_cancelled_callback():
+                    return None
+
+        # 9. Post-processing
+        image = self.decode_latents(latents)
+
+        # 10. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image)
+
+        # 11. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return image, has_nsfw_concept
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    def text2img(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[np.ndarray] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for text-to-image generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`np.ndarray`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    def img2img(
+        self,
+        image: Union[np.ndarray, PIL.Image.Image],
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for image-to-image generation.
+        Args:
+            image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or ndarray representing an image batch, that will be used as the starting point for the
+                process.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+                noise will be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter will be modulated by `strength`.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    def inpaint(
+        self,
+        image: Union[np.ndarray, PIL.Image.Image],
+        mask_image: Union[np.ndarray, PIL.Image.Image],
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for inpaint.
+        Args:
+            image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process. This is the image whose masked region will be inpainted.
+            mask_image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
+                is 1, the denoising process will be run on the masked area for the full number of iterations specified
+                in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
+                noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
+                the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            mask_image=mask_image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )

v0.19.2/magic_mix.py

@@ -0,0 +1,152 @@
+from typing import Union
+
+import torch
+from PIL import Image
+from torchvision import transforms as tfms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+
+
+class MagicMixPipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],
+    ):
+        super().__init__()
+
+        self.register_modules(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
+
+    # convert PIL image to latents
+    def encode(self, img):
+        with torch.no_grad():
+            latent = self.vae.encode(tfms.ToTensor()(img).unsqueeze(0).to(self.device) * 2 - 1)
+            latent = 0.18215 * latent.latent_dist.sample()
+        return latent
+
+    # convert latents to PIL image
+    def decode(self, latent):
+        latent = (1 / 0.18215) * latent
+        with torch.no_grad():
+            img = self.vae.decode(latent).sample
+        img = (img / 2 + 0.5).clamp(0, 1)
+        img = img.detach().cpu().permute(0, 2, 3, 1).numpy()
+        img = (img * 255).round().astype("uint8")
+        return Image.fromarray(img[0])
+
+    # convert prompt into text embeddings, also unconditional embeddings
+    def prep_text(self, prompt):
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_embedding = self.text_encoder(text_input.input_ids.to(self.device))[0]
+
+        uncond_input = self.tokenizer(
+            "",
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        uncond_embedding = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+        return torch.cat([uncond_embedding, text_embedding])
+
+    def __call__(
+        self,
+        img: Image.Image,
+        prompt: str,
+        kmin: float = 0.3,
+        kmax: float = 0.6,
+        mix_factor: float = 0.5,
+        seed: int = 42,
+        steps: int = 50,
+        guidance_scale: float = 7.5,
+    ) -> Image.Image:
+        tmin = steps - int(kmin * steps)
+        tmax = steps - int(kmax * steps)
+
+        text_embeddings = self.prep_text(prompt)
+
+        self.scheduler.set_timesteps(steps)
+
+        width, height = img.size
+        encoded = self.encode(img)
+
+        torch.manual_seed(seed)
+        noise = torch.randn(
+            (1, self.unet.config.in_channels, height // 8, width // 8),
+        ).to(self.device)
+
+        latents = self.scheduler.add_noise(
+            encoded,
+            noise,
+            timesteps=self.scheduler.timesteps[tmax],
+        )
+
+        input = torch.cat([latents] * 2)
+
+        input = self.scheduler.scale_model_input(input, self.scheduler.timesteps[tmax])
+
+        with torch.no_grad():
+            pred = self.unet(
+                input,
+                self.scheduler.timesteps[tmax],
+                encoder_hidden_states=text_embeddings,
+            ).sample
+
+        pred_uncond, pred_text = pred.chunk(2)
+        pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)
+
+        latents = self.scheduler.step(pred, self.scheduler.timesteps[tmax], latents).prev_sample
+
+        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
+            if i > tmax:
+                if i < tmin:  # layout generation phase
+                    orig_latents = self.scheduler.add_noise(
+                        encoded,
+                        noise,
+                        timesteps=t,
+                    )
+
+                    input = (mix_factor * latents) + (
+                        1 - mix_factor
+                    ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
+                    input = torch.cat([input] * 2)
+
+                else:  # content generation phase
+                    input = torch.cat([latents] * 2)
+
+                input = self.scheduler.scale_model_input(input, t)
+
+                with torch.no_grad():
+                    pred = self.unet(
+                        input,
+                        t,
+                        encoder_hidden_states=text_embeddings,
+                    ).sample
+
+                pred_uncond, pred_text = pred.chunk(2)
+                pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)
+
+                latents = self.scheduler.step(pred, t, latents).prev_sample
+
+        return self.decode(latents)

v0.19.2/mixture_canvas.py

@@ -0,0 +1,503 @@
+import re
+from copy import deepcopy
+from dataclasses import asdict, dataclass
+from enum import Enum
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+from numpy import exp, pi, sqrt
+from torchvision.transforms.functional import resize
+from tqdm.auto import tqdm
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+
+
+def preprocess_image(image):
+    from PIL import Image
+
+    """Preprocess an input image
+
+    Same as
+    https://github.com/huggingface/diffusers/blob/1138d63b519e37f0ce04e027b9f4a3261d27c628/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L44
+    """
+    w, h = image.size
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=Image.LANCZOS)
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2.0 * image - 1.0
+
+
+@dataclass
+class CanvasRegion:
+    """Class defining a rectangular region in the canvas"""
+
+    row_init: int  # Region starting row in pixel space (included)
+    row_end: int  # Region end row in pixel space (not included)
+    col_init: int  # Region starting column in pixel space (included)
+    col_end: int  # Region end column in pixel space (not included)
+    region_seed: int = None  # Seed for random operations in this region
+    noise_eps: float = 0.0  # Deviation of a zero-mean gaussian noise to be applied over the latents in this region. Useful for slightly "rerolling" latents
+
+    def __post_init__(self):
+        # Initialize arguments if not specified
+        if self.region_seed is None:
+            self.region_seed = np.random.randint(9999999999)
+        # Check coordinates are non-negative
+        for coord in [self.row_init, self.row_end, self.col_init, self.col_end]:
+            if coord < 0:
+                raise ValueError(
+                    f"A CanvasRegion must be defined with non-negative indices, found ({self.row_init}, {self.row_end}, {self.col_init}, {self.col_end})"
+                )
+        # Check coordinates are divisible by 8, else we end up with nasty rounding error when mapping to latent space
+        for coord in [self.row_init, self.row_end, self.col_init, self.col_end]:
+            if coord // 8 != coord / 8:
+                raise ValueError(
+                    f"A CanvasRegion must be defined with locations divisible by 8, found ({self.row_init}-{self.row_end}, {self.col_init}-{self.col_end})"
+                )
+        # Check noise eps is non-negative
+        if self.noise_eps < 0:
+            raise ValueError(f"A CanvasRegion must be defined noises eps non-negative, found {self.noise_eps}")
+        # Compute coordinates for this region in latent space
+        self.latent_row_init = self.row_init // 8
+        self.latent_row_end = self.row_end // 8
+        self.latent_col_init = self.col_init // 8
+        self.latent_col_end = self.col_end // 8
+
+    @property
+    def width(self):
+        return self.col_end - self.col_init
+
+    @property
+    def height(self):
+        return self.row_end - self.row_init
+
+    def get_region_generator(self, device="cpu"):
+        """Creates a torch.Generator based on the random seed of this region"""
+        # Initialize region generator
+        return torch.Generator(device).manual_seed(self.region_seed)
+
+    @property
+    def __dict__(self):
+        return asdict(self)
+
+
+class MaskModes(Enum):
+    """Modes in which the influence of diffuser is masked"""
+
+    CONSTANT = "constant"
+    GAUSSIAN = "gaussian"
+    QUARTIC = "quartic"  # See https://en.wikipedia.org/wiki/Kernel_(statistics)
+
+
+@dataclass
+class DiffusionRegion(CanvasRegion):
+    """Abstract class defining a region where some class of diffusion process is acting"""
+
+    pass
+
+
+@dataclass
+class Text2ImageRegion(DiffusionRegion):
+    """Class defining a region where a text guided diffusion process is acting"""
+
+    prompt: str = ""  # Text prompt guiding the diffuser in this region
+    guidance_scale: float = 7.5  # Guidance scale of the diffuser in this region. If None, randomize
+    mask_type: MaskModes = MaskModes.GAUSSIAN.value  # Kind of weight mask applied to this region
+    mask_weight: float = 1.0  # Global weights multiplier of the mask
+    tokenized_prompt = None  # Tokenized prompt
+    encoded_prompt = None  # Encoded prompt
+
+    def __post_init__(self):
+        super().__post_init__()
+        # Mask weight cannot be negative
+        if self.mask_weight < 0:
+            raise ValueError(
+                f"A Text2ImageRegion must be defined with non-negative mask weight, found {self.mask_weight}"
+            )
+        # Mask type must be an actual known mask
+        if self.mask_type not in [e.value for e in MaskModes]:
+            raise ValueError(
+                f"A Text2ImageRegion was defined with mask {self.mask_type}, which is not an accepted mask ({[e.value for e in MaskModes]})"
+            )
+        # Randomize arguments if given as None
+        if self.guidance_scale is None:
+            self.guidance_scale = np.random.randint(5, 30)
+        # Clean prompt
+        self.prompt = re.sub(" +", " ", self.prompt).replace("\n", " ")
+
+    def tokenize_prompt(self, tokenizer):
+        """Tokenizes the prompt for this diffusion region using a given tokenizer"""
+        self.tokenized_prompt = tokenizer(
+            self.prompt,
+            padding="max_length",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+    def encode_prompt(self, text_encoder, device):
+        """Encodes the previously tokenized prompt for this diffusion region using a given encoder"""
+        assert self.tokenized_prompt is not None, ValueError(
+            "Prompt in diffusion region must be tokenized before encoding"
+        )
+        self.encoded_prompt = text_encoder(self.tokenized_prompt.input_ids.to(device))[0]
+
+
+@dataclass
+class Image2ImageRegion(DiffusionRegion):
+    """Class defining a region where an image guided diffusion process is acting"""
+
+    reference_image: torch.FloatTensor = None
+    strength: float = 0.8  # Strength of the image
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.reference_image is None:
+            raise ValueError("Must provide a reference image when creating an Image2ImageRegion")
+        if self.strength < 0 or self.strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {self.strength}")
+        # Rescale image to region shape
+        self.reference_image = resize(self.reference_image, size=[self.height, self.width])
+
+    def encode_reference_image(self, encoder, device, generator, cpu_vae=False):
+        """Encodes the reference image for this Image2Image region into the latent space"""
+        # Place encoder in CPU or not following the parameter cpu_vae
+        if cpu_vae:
+            # Note here we use mean instead of sample, to avoid moving also generator to CPU, which is troublesome
+            self.reference_latents = encoder.cpu().encode(self.reference_image).latent_dist.mean.to(device)
+        else:
+            self.reference_latents = encoder.encode(self.reference_image.to(device)).latent_dist.sample(
+                generator=generator
+            )
+        self.reference_latents = 0.18215 * self.reference_latents
+
+    @property
+    def __dict__(self):
+        # This class requires special casting to dict because of the reference_image tensor. Otherwise it cannot be casted to JSON
+
+        # Get all basic fields from parent class
+        super_fields = {key: getattr(self, key) for key in DiffusionRegion.__dataclass_fields__.keys()}
+        # Pack other fields
+        return {**super_fields, "reference_image": self.reference_image.cpu().tolist(), "strength": self.strength}
+
+
+class RerollModes(Enum):
+    """Modes in which the reroll regions operate"""
+
+    RESET = "reset"  # Completely reset the random noise in the region
+    EPSILON = "epsilon"  # Alter slightly the latents in the region
+
+
+@dataclass
+class RerollRegion(CanvasRegion):
+    """Class defining a rectangular canvas region in which initial latent noise will be rerolled"""
+
+    reroll_mode: RerollModes = RerollModes.RESET.value
+
+
+@dataclass
+class MaskWeightsBuilder:
+    """Auxiliary class to compute a tensor of weights for a given diffusion region"""
+
+    latent_space_dim: int  # Size of the U-net latent space
+    nbatch: int = 1  # Batch size in the U-net
+
+    def compute_mask_weights(self, region: DiffusionRegion) -> torch.tensor:
+        """Computes a tensor of weights for a given diffusion region"""
+        MASK_BUILDERS = {
+            MaskModes.CONSTANT.value: self._constant_weights,
+            MaskModes.GAUSSIAN.value: self._gaussian_weights,
+            MaskModes.QUARTIC.value: self._quartic_weights,
+        }
+        return MASK_BUILDERS[region.mask_type](region)
+
+    def _constant_weights(self, region: DiffusionRegion) -> torch.tensor:
+        """Computes a tensor of constant for a given diffusion region"""
+        latent_width = region.latent_col_end - region.latent_col_init
+        latent_height = region.latent_row_end - region.latent_row_init
+        return torch.ones(self.nbatch, self.latent_space_dim, latent_height, latent_width) * region.mask_weight
+
+    def _gaussian_weights(self, region: DiffusionRegion) -> torch.tensor:
+        """Generates a gaussian mask of weights for tile contributions"""
+        latent_width = region.latent_col_end - region.latent_col_init
+        latent_height = region.latent_row_end - region.latent_row_init
+
+        var = 0.01
+        midpoint = (latent_width - 1) / 2  # -1 because index goes from 0 to latent_width - 1
+        x_probs = [
+            exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)
+            for x in range(latent_width)
+        ]
+        midpoint = (latent_height - 1) / 2
+        y_probs = [
+            exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)
+            for y in range(latent_height)
+        ]
+
+        weights = np.outer(y_probs, x_probs) * region.mask_weight
+        return torch.tile(torch.tensor(weights), (self.nbatch, self.latent_space_dim, 1, 1))
+
+    def _quartic_weights(self, region: DiffusionRegion) -> torch.tensor:
+        """Generates a quartic mask of weights for tile contributions
+
+        The quartic kernel has bounded support over the diffusion region, and a smooth decay to the region limits.
+        """
+        quartic_constant = 15.0 / 16.0
+
+        support = (np.array(range(region.latent_col_init, region.latent_col_end)) - region.latent_col_init) / (
+            region.latent_col_end - region.latent_col_init - 1
+        ) * 1.99 - (1.99 / 2.0)
+        x_probs = quartic_constant * np.square(1 - np.square(support))
+        support = (np.array(range(region.latent_row_init, region.latent_row_end)) - region.latent_row_init) / (
+            region.latent_row_end - region.latent_row_init - 1
+        ) * 1.99 - (1.99 / 2.0)
+        y_probs = quartic_constant * np.square(1 - np.square(support))
+
+        weights = np.outer(y_probs, x_probs) * region.mask_weight
+        return torch.tile(torch.tensor(weights), (self.nbatch, self.latent_space_dim, 1, 1))
+
+
+class StableDiffusionCanvasPipeline(DiffusionPipeline):
+    """Stable Diffusion pipeline that mixes several diffusers in the same canvas"""
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def decode_latents(self, latents, cpu_vae=False):
+        """Decodes a given array of latents into pixel space"""
+        # scale and decode the image latents with vae
+        if cpu_vae:
+            lat = deepcopy(latents).cpu()
+            vae = deepcopy(self.vae).cpu()
+        else:
+            lat = latents
+            vae = self.vae
+
+        lat = 1 / 0.18215 * lat
+        image = vae.decode(lat).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        return self.numpy_to_pil(image)
+
+    def get_latest_timestep_img2img(self, num_inference_steps, strength):
+        """Finds the latest timesteps where an img2img strength does not impose latents anymore"""
+        # get the original timestep using init_timestep
+        offset = self.scheduler.config.get("steps_offset", 0)
+        init_timestep = int(num_inference_steps * (1 - strength)) + offset
+        init_timestep = min(init_timestep, num_inference_steps)
+
+        t_start = min(max(num_inference_steps - init_timestep + offset, 0), num_inference_steps - 1)
+        latest_timestep = self.scheduler.timesteps[t_start]
+
+        return latest_timestep
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        canvas_height: int,
+        canvas_width: int,
+        regions: List[DiffusionRegion],
+        num_inference_steps: Optional[int] = 50,
+        seed: Optional[int] = 12345,
+        reroll_regions: Optional[List[RerollRegion]] = None,
+        cpu_vae: Optional[bool] = False,
+        decode_steps: Optional[bool] = False,
+    ):
+        if reroll_regions is None:
+            reroll_regions = []
+        batch_size = 1
+
+        if decode_steps:
+            steps_images = []
+
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
+
+        # Split diffusion regions by their kind
+        text2image_regions = [region for region in regions if isinstance(region, Text2ImageRegion)]
+        image2image_regions = [region for region in regions if isinstance(region, Image2ImageRegion)]
+
+        # Prepare text embeddings
+        for region in text2image_regions:
+            region.tokenize_prompt(self.tokenizer)
+            region.encode_prompt(self.text_encoder, self.device)
+
+        # Create original noisy latents using the timesteps
+        latents_shape = (batch_size, self.unet.config.in_channels, canvas_height // 8, canvas_width // 8)
+        generator = torch.Generator(self.device).manual_seed(seed)
+        init_noise = torch.randn(latents_shape, generator=generator, device=self.device)
+
+        # Reset latents in seed reroll regions, if requested
+        for region in reroll_regions:
+            if region.reroll_mode == RerollModes.RESET.value:
+                region_shape = (
+                    latents_shape[0],
+                    latents_shape[1],
+                    region.latent_row_end - region.latent_row_init,
+                    region.latent_col_end - region.latent_col_init,
+                )
+                init_noise[
+                    :,
+                    :,
+                    region.latent_row_init : region.latent_row_end,
+                    region.latent_col_init : region.latent_col_end,
+                ] = torch.randn(region_shape, generator=region.get_region_generator(self.device), device=self.device)
+
+        # Apply epsilon noise to regions: first diffusion regions, then reroll regions
+        all_eps_rerolls = regions + [r for r in reroll_regions if r.reroll_mode == RerollModes.EPSILON.value]
+        for region in all_eps_rerolls:
+            if region.noise_eps > 0:
+                region_noise = init_noise[
+                    :,
+                    :,
+                    region.latent_row_init : region.latent_row_end,
+                    region.latent_col_init : region.latent_col_end,
+                ]
+                eps_noise = (
+                    torch.randn(
+                        region_noise.shape, generator=region.get_region_generator(self.device), device=self.device
+                    )
+                    * region.noise_eps
+                )
+                init_noise[
+                    :,
+                    :,
+                    region.latent_row_init : region.latent_row_end,
+                    region.latent_col_init : region.latent_col_end,
+                ] += eps_noise
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = init_noise * self.scheduler.init_noise_sigma
+
+        # Get unconditional embeddings for classifier free guidance in text2image regions
+        for region in text2image_regions:
+            max_length = region.tokenized_prompt.input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            region.encoded_prompt = torch.cat([uncond_embeddings, region.encoded_prompt])
+
+        # Prepare image latents
+        for region in image2image_regions:
+            region.encode_reference_image(self.vae, device=self.device, generator=generator)
+
+        # Prepare mask of weights for each region
+        mask_builder = MaskWeightsBuilder(latent_space_dim=self.unet.config.in_channels, nbatch=batch_size)
+        mask_weights = [mask_builder.compute_mask_weights(region).to(self.device) for region in text2image_regions]
+
+        # Diffusion timesteps
+        for i, t in tqdm(enumerate(self.scheduler.timesteps)):
+            # Diffuse each region
+            noise_preds_regions = []
+
+            # text2image regions
+            for region in text2image_regions:
+                region_latents = latents[
+                    :,
+                    :,
+                    region.latent_row_init : region.latent_row_end,
+                    region.latent_col_init : region.latent_col_end,
+                ]
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([region_latents] * 2)
+                # scale model input following scheduler rules
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=region.encoded_prompt)["sample"]
+                # perform guidance
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred_region = noise_pred_uncond + region.guidance_scale * (noise_pred_text - noise_pred_uncond)
+                noise_preds_regions.append(noise_pred_region)
+
+            # Merge noise predictions for all tiles
+            noise_pred = torch.zeros(latents.shape, device=self.device)
+            contributors = torch.zeros(latents.shape, device=self.device)
+            # Add each tile contribution to overall latents
+            for region, noise_pred_region, mask_weights_region in zip(
+                text2image_regions, noise_preds_regions, mask_weights
+            ):
+                noise_pred[
+                    :,
+                    :,
+                    region.latent_row_init : region.latent_row_end,
+                    region.latent_col_init : region.latent_col_end,
+                ] += (
+                    noise_pred_region * mask_weights_region
+                )
+                contributors[
+                    :,
+                    :,
+                    region.latent_row_init : region.latent_row_end,
+                    region.latent_col_init : region.latent_col_end,
+                ] += mask_weights_region
+            # Average overlapping areas with more than 1 contributor
+            noise_pred /= contributors
+            noise_pred = torch.nan_to_num(
+                noise_pred
+            )  # Replace NaNs by zeros: NaN can appear if a position is not covered by any DiffusionRegion
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents).prev_sample
+
+            # Image2Image regions: override latents generated by the scheduler
+            for region in image2image_regions:
+                influence_step = self.get_latest_timestep_img2img(num_inference_steps, region.strength)
+                # Only override in the timesteps before the last influence step of the image (given by its strength)
+                if t > influence_step:
+                    timestep = t.repeat(batch_size)
+                    region_init_noise = init_noise[
+                        :,
+                        :,
+                        region.latent_row_init : region.latent_row_end,
+                        region.latent_col_init : region.latent_col_end,
+                    ]
+                    region_latents = self.scheduler.add_noise(region.reference_latents, region_init_noise, timestep)
+                    latents[
+                        :,
+                        :,
+                        region.latent_row_init : region.latent_row_end,
+                        region.latent_col_init : region.latent_col_end,
+                    ] = region_latents
+
+            if decode_steps:
+                steps_images.append(self.decode_latents(latents, cpu_vae))
+
+        # scale and decode the image latents with vae
+        image = self.decode_latents(latents, cpu_vae)
+
+        output = {"images": image}
+        if decode_steps:
+            output = {**output, "steps_images": steps_images}
+        return output

v0.19.2/mixture_tiling.py

@@ -0,0 +1,405 @@
+import inspect
+from copy import deepcopy
+from enum import Enum
+from typing import List, Optional, Tuple, Union
+
+import torch
+from tqdm.auto import tqdm
+
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import logging
+
+
+try:
+    from ligo.segments import segment
+    from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+except ImportError:
+    raise ImportError("Please install transformers and ligo-segments to use the mixture pipeline")
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> from diffusers import LMSDiscreteScheduler, DiffusionPipeline
+
+        >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
+        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler, custom_pipeline="mixture_tiling")
+        >>> pipeline.to("cuda")
+
+        >>> image = pipeline(
+        >>>     prompt=[[
+        >>>         "A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
+        >>>         "A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
+        >>>         "An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece"
+        >>>     ]],
+        >>>     tile_height=640,
+        >>>     tile_width=640,
+        >>>     tile_row_overlap=0,
+        >>>     tile_col_overlap=256,
+        >>>     guidance_scale=8,
+        >>>     seed=7178915308,
+        >>>     num_inference_steps=50,
+    >>> )["images"][0]
+        ```
+"""
+
+
+def _tile2pixel_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
+    """Given a tile row and column numbers returns the range of pixels affected by that tiles in the overall image
+
+    Returns a tuple with:
+        - Starting coordinates of rows in pixel space
+        - Ending coordinates of rows in pixel space
+        - Starting coordinates of columns in pixel space
+        - Ending coordinates of columns in pixel space
+    """
+    px_row_init = 0 if tile_row == 0 else tile_row * (tile_height - tile_row_overlap)
+    px_row_end = px_row_init + tile_height
+    px_col_init = 0 if tile_col == 0 else tile_col * (tile_width - tile_col_overlap)
+    px_col_end = px_col_init + tile_width
+    return px_row_init, px_row_end, px_col_init, px_col_end
+
+
+def _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end):
+    """Translates coordinates in pixel space to coordinates in latent space"""
+    return px_row_init // 8, px_row_end // 8, px_col_init // 8, px_col_end // 8
+
+
+def _tile2latent_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
+    """Given a tile row and column numbers returns the range of latents affected by that tiles in the overall image
+
+    Returns a tuple with:
+        - Starting coordinates of rows in latent space
+        - Ending coordinates of rows in latent space
+        - Starting coordinates of columns in latent space
+        - Ending coordinates of columns in latent space
+    """
+    px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(
+        tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+    )
+    return _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end)
+
+
+def _tile2latent_exclusive_indices(
+    tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, rows, columns
+):
+    """Given a tile row and column numbers returns the range of latents affected only by that tile in the overall image
+
+    Returns a tuple with:
+        - Starting coordinates of rows in latent space
+        - Ending coordinates of rows in latent space
+        - Starting coordinates of columns in latent space
+        - Ending coordinates of columns in latent space
+    """
+    row_init, row_end, col_init, col_end = _tile2latent_indices(
+        tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+    )
+    row_segment = segment(row_init, row_end)
+    col_segment = segment(col_init, col_end)
+    # Iterate over the rest of tiles, clipping the region for the current tile
+    for row in range(rows):
+        for column in range(columns):
+            if row != tile_row and column != tile_col:
+                clip_row_init, clip_row_end, clip_col_init, clip_col_end = _tile2latent_indices(
+                    row, column, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+                )
+                row_segment = row_segment - segment(clip_row_init, clip_row_end)
+                col_segment = col_segment - segment(clip_col_init, clip_col_end)
+    # return row_init, row_end, col_init, col_end
+    return row_segment[0], row_segment[1], col_segment[0], col_segment[1]
+
+
+class StableDiffusionExtrasMixin:
+    """Mixin providing additional convenience method to Stable Diffusion pipelines"""
+
+    def decode_latents(self, latents, cpu_vae=False):
+        """Decodes a given array of latents into pixel space"""
+        # scale and decode the image latents with vae
+        if cpu_vae:
+            lat = deepcopy(latents).cpu()
+            vae = deepcopy(self.vae).cpu()
+        else:
+            lat = latents
+            vae = self.vae
+
+        lat = 1 / 0.18215 * lat
+        image = vae.decode(lat).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        return self.numpy_to_pil(image)
+
+
+class StableDiffusionTilingPipeline(DiffusionPipeline, StableDiffusionExtrasMixin):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    class SeedTilesMode(Enum):
+        """Modes in which the latents of a particular tile can be re-seeded"""
+
+        FULL = "full"
+        EXCLUSIVE = "exclusive"
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[List[str]]],
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        eta: Optional[float] = 0.0,
+        seed: Optional[int] = None,
+        tile_height: Optional[int] = 512,
+        tile_width: Optional[int] = 512,
+        tile_row_overlap: Optional[int] = 256,
+        tile_col_overlap: Optional[int] = 256,
+        guidance_scale_tiles: Optional[List[List[float]]] = None,
+        seed_tiles: Optional[List[List[int]]] = None,
+        seed_tiles_mode: Optional[Union[str, List[List[str]]]] = "full",
+        seed_reroll_regions: Optional[List[Tuple[int, int, int, int, int]]] = None,
+        cpu_vae: Optional[bool] = False,
+    ):
+        r"""
+        Function to run the diffusion pipeline with tiling support.
+
+        Args:
+            prompt: either a single string (no tiling) or a list of lists with all the prompts to use (one list for each row of tiles). This will also define the tiling structure.
+            num_inference_steps: number of diffusions steps.
+            guidance_scale: classifier-free guidance.
+            seed: general random seed to initialize latents.
+            tile_height: height in pixels of each grid tile.
+            tile_width: width in pixels of each grid tile.
+            tile_row_overlap: number of overlap pixels between tiles in consecutive rows.
+            tile_col_overlap: number of overlap pixels between tiles in consecutive columns.
+            guidance_scale_tiles: specific weights for classifier-free guidance in each tile.
+            guidance_scale_tiles: specific weights for classifier-free guidance in each tile. If None, the value provided in guidance_scale will be used.
+            seed_tiles: specific seeds for the initialization latents in each tile. These will override the latents generated for the whole canvas using the standard seed parameter.
+            seed_tiles_mode: either "full" "exclusive". If "full", all the latents affected by the tile be overriden. If "exclusive", only the latents that are affected exclusively by this tile (and no other tiles) will be overrriden.
+            seed_reroll_regions: a list of tuples in the form (start row, end row, start column, end column, seed) defining regions in pixel space for which the latents will be overriden using the given seed. Takes priority over seed_tiles.
+            cpu_vae: the decoder from latent space to pixel space can require too mucho GPU RAM for large images. If you find out of memory errors at the end of the generation process, try setting this parameter to True to run the decoder in CPU. Slower, but should run without memory issues.
+
+        Examples:
+
+        Returns:
+            A PIL image with the generated image.
+
+        """
+        if not isinstance(prompt, list) or not all(isinstance(row, list) for row in prompt):
+            raise ValueError(f"`prompt` has to be a list of lists but is {type(prompt)}")
+        grid_rows = len(prompt)
+        grid_cols = len(prompt[0])
+        if not all(len(row) == grid_cols for row in prompt):
+            raise ValueError("All prompt rows must have the same number of prompt columns")
+        if not isinstance(seed_tiles_mode, str) and (
+            not isinstance(seed_tiles_mode, list) or not all(isinstance(row, list) for row in seed_tiles_mode)
+        ):
+            raise ValueError(f"`seed_tiles_mode` has to be a string or list of lists but is {type(prompt)}")
+        if isinstance(seed_tiles_mode, str):
+            seed_tiles_mode = [[seed_tiles_mode for _ in range(len(row))] for row in prompt]
+
+        modes = [mode.value for mode in self.SeedTilesMode]
+        if any(mode not in modes for row in seed_tiles_mode for mode in row):
+            raise ValueError(f"Seed tiles mode must be one of {modes}")
+        if seed_reroll_regions is None:
+            seed_reroll_regions = []
+        batch_size = 1
+
+        # create original noisy latents using the timesteps
+        height = tile_height + (grid_rows - 1) * (tile_height - tile_row_overlap)
+        width = tile_width + (grid_cols - 1) * (tile_width - tile_col_overlap)
+        latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
+        generator = torch.Generator("cuda").manual_seed(seed)
+        latents = torch.randn(latents_shape, generator=generator, device=self.device)
+
+        # overwrite latents for specific tiles if provided
+        if seed_tiles is not None:
+            for row in range(grid_rows):
+                for col in range(grid_cols):
+                    if (seed_tile := seed_tiles[row][col]) is not None:
+                        mode = seed_tiles_mode[row][col]
+                        if mode == self.SeedTilesMode.FULL.value:
+                            row_init, row_end, col_init, col_end = _tile2latent_indices(
+                                row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+                            )
+                        else:
+                            row_init, row_end, col_init, col_end = _tile2latent_exclusive_indices(
+                                row,
+                                col,
+                                tile_width,
+                                tile_height,
+                                tile_row_overlap,
+                                tile_col_overlap,
+                                grid_rows,
+                                grid_cols,
+                            )
+                        tile_generator = torch.Generator("cuda").manual_seed(seed_tile)
+                        tile_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
+                        latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
+                            tile_shape, generator=tile_generator, device=self.device
+                        )
+
+        # overwrite again for seed reroll regions
+        for row_init, row_end, col_init, col_end, seed_reroll in seed_reroll_regions:
+            row_init, row_end, col_init, col_end = _pixel2latent_indices(
+                row_init, row_end, col_init, col_end
+            )  # to latent space coordinates
+            reroll_generator = torch.Generator("cuda").manual_seed(seed_reroll)
+            region_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
+            latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
+                region_shape, generator=reroll_generator, device=self.device
+            )
+
+        # Prepare scheduler
+        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
+        extra_set_kwargs = {}
+        if accepts_offset:
+            extra_set_kwargs["offset"] = 1
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents * self.scheduler.sigmas[0]
+
+        # get prompts text embeddings
+        text_input = [
+            [
+                self.tokenizer(
+                    col,
+                    padding="max_length",
+                    max_length=self.tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+                for col in row
+            ]
+            for row in prompt
+        ]
+        text_embeddings = [[self.text_encoder(col.input_ids.to(self.device))[0] for col in row] for row in text_input]
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0  # TODO: also active if any tile has guidance scale
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            for i in range(grid_rows):
+                for j in range(grid_cols):
+                    max_length = text_input[i][j].input_ids.shape[-1]
+                    uncond_input = self.tokenizer(
+                        [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+                    )
+                    uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+                    # For classifier free guidance, we need to do two forward passes.
+                    # Here we concatenate the unconditional and text embeddings into a single batch
+                    # to avoid doing two forward passes
+                    text_embeddings[i][j] = torch.cat([uncond_embeddings, text_embeddings[i][j]])
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # Mask for tile weights strenght
+        tile_weights = self._gaussian_weights(tile_width, tile_height, batch_size)
+
+        # Diffusion timesteps
+        for i, t in tqdm(enumerate(self.scheduler.timesteps)):
+            # Diffuse each tile
+            noise_preds = []
+            for row in range(grid_rows):
+                noise_preds_row = []
+                for col in range(grid_cols):
+                    px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
+                        row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+                    )
+                    tile_latents = latents[:, :, px_row_init:px_row_end, px_col_init:px_col_end]
+                    # expand the latents if we are doing classifier free guidance
+                    latent_model_input = torch.cat([tile_latents] * 2) if do_classifier_free_guidance else tile_latents
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                    # predict the noise residual
+                    noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings[row][col])[
+                        "sample"
+                    ]
+                    # perform guidance
+                    if do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                        guidance = (
+                            guidance_scale
+                            if guidance_scale_tiles is None or guidance_scale_tiles[row][col] is None
+                            else guidance_scale_tiles[row][col]
+                        )
+                        noise_pred_tile = noise_pred_uncond + guidance * (noise_pred_text - noise_pred_uncond)
+                        noise_preds_row.append(noise_pred_tile)
+                noise_preds.append(noise_preds_row)
+            # Stitch noise predictions for all tiles
+            noise_pred = torch.zeros(latents.shape, device=self.device)
+            contributors = torch.zeros(latents.shape, device=self.device)
+            # Add each tile contribution to overall latents
+            for row in range(grid_rows):
+                for col in range(grid_cols):
+                    px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
+                        row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+                    )
+                    noise_pred[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += (
+                        noise_preds[row][col] * tile_weights
+                    )
+                    contributors[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += tile_weights
+            # Average overlapping areas with more than 1 contributor
+            noise_pred /= contributors
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents).prev_sample
+
+        # scale and decode the image latents with vae
+        image = self.decode_latents(latents, cpu_vae)
+
+        return {"images": image}
+
+    def _gaussian_weights(self, tile_width, tile_height, nbatches):
+        """Generates a gaussian mask of weights for tile contributions"""
+        import numpy as np
+        from numpy import exp, pi, sqrt
+
+        latent_width = tile_width // 8
+        latent_height = tile_height // 8
+
+        var = 0.01
+        midpoint = (latent_width - 1) / 2  # -1 because index goes from 0 to latent_width - 1
+        x_probs = [
+            exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)
+            for x in range(latent_width)
+        ]
+        midpoint = latent_height / 2
+        y_probs = [
+            exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)
+            for y in range(latent_height)
+        ]
+
+        weights = np.outer(y_probs, x_probs)
+        return torch.tile(torch.tensor(weights, device=self.device), (nbatches, self.unet.config.in_channels, 1, 1))

v0.19.2/multilingual_stable_diffusion.py

@@ -0,0 +1,436 @@
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTokenizer,
+    MBart50TokenizerFast,
+    MBartForConditionalGeneration,
+    pipeline,
+)
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def detect_language(pipe, prompt, batch_size):
+    """helper function to detect language(s) of prompt"""
+
+    if batch_size == 1:
+        preds = pipe(prompt, top_k=1, truncation=True, max_length=128)
+        return preds[0]["label"]
+    else:
+        detected_languages = []
+        for p in prompt:
+            preds = pipe(p, top_k=1, truncation=True, max_length=128)
+            detected_languages.append(preds[0]["label"])
+
+        return detected_languages
+
+
+def translate_prompt(prompt, translation_tokenizer, translation_model, device):
+    """helper function to translate prompt to English"""
+
+    encoded_prompt = translation_tokenizer(prompt, return_tensors="pt").to(device)
+    generated_tokens = translation_model.generate(**encoded_prompt, max_new_tokens=1000)
+    en_trans = translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+
+    return en_trans[0]
+
+
+class MultilingualStableDiffusion(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion in different languages.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        detection_pipeline ([`pipeline`]):
+            Transformers pipeline to detect prompt's language.
+        translation_model ([`MBartForConditionalGeneration`]):
+            Model to translate prompt to English, if necessary. Please refer to the
+            [model card](https://huggingface.co/docs/transformers/model_doc/mbart) for details.
+        translation_tokenizer ([`MBart50TokenizerFast`]):
+            Tokenizer of the translation model.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        detection_pipeline: pipeline,
+        translation_model: MBartForConditionalGeneration,
+        translation_tokenizer: MBart50TokenizerFast,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            detection_pipeline=detection_pipeline,
+            translation_model=translation_model,
+            translation_tokenizer=translation_tokenizer,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation. Can be in different languages.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # detect language and translate if necessary
+        prompt_language = detect_language(self.detection_pipeline, prompt, batch_size)
+        if batch_size == 1 and prompt_language != "en":
+            prompt = translate_prompt(prompt, self.translation_tokenizer, self.translation_model, self.device)
+
+        if isinstance(prompt, list):
+            for index in range(batch_size):
+                if prompt_language[index] != "en":
+                    p = translate_prompt(
+                        prompt[index], self.translation_tokenizer, self.translation_model, self.device
+                    )
+                    prompt[index] = p
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                # detect language and translate it if necessary
+                negative_prompt_language = detect_language(self.detection_pipeline, negative_prompt, batch_size)
+                if negative_prompt_language != "en":
+                    negative_prompt = translate_prompt(
+                        negative_prompt, self.translation_tokenizer, self.translation_model, self.device
+                    )
+                if isinstance(negative_prompt, str):
+                    uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                # detect language and translate it if necessary
+                if isinstance(negative_prompt, list):
+                    negative_prompt_languages = detect_language(self.detection_pipeline, negative_prompt, batch_size)
+                    for index in range(batch_size):
+                        if negative_prompt_languages[index] != "en":
+                            p = translate_prompt(
+                                negative_prompt[index], self.translation_tokenizer, self.translation_model, self.device
+                            )
+                            negative_prompt[index] = p
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/one_step_unet.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python3
+import torch
+
+from diffusers import DiffusionPipeline
+
+
+class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    def __call__(self):
+        image = torch.randn(
+            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
+        )
+        timestep = 1
+
+        model_output = self.unet(image, timestep).sample
+        scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample
+
+        result = scheduler_output - scheduler_output + torch.ones_like(scheduler_output)
+
+        return result

v0.19.2/sd_text2img_k_diffusion.py

@@ -0,0 +1,475 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import importlib
+import warnings
+from typing import Callable, List, Optional, Union
+
+import torch
+from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
+
+from diffusers import DiffusionPipeline, LMSDiscreteScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import is_accelerate_available, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class ModelWrapper:
+    def __init__(self, model, alphas_cumprod):
+        self.model = model
+        self.alphas_cumprod = alphas_cumprod
+
+    def apply_model(self, *args, **kwargs):
+        if len(args) == 3:
+            encoder_hidden_states = args[-1]
+            args = args[:2]
+        if kwargs.get("cond", None) is not None:
+            encoder_hidden_states = kwargs.pop("cond")
+        return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample
+
+
+class StableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae,
+        text_encoder,
+        tokenizer,
+        unet,
+        scheduler,
+        safety_checker,
+        feature_extractor,
+    ):
+        super().__init__()
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        # get correct sigmas from LMS
+        scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+        model = ModelWrapper(unet, scheduler.alphas_cumprod)
+        if scheduler.config.prediction_type == "v_prediction":
+            self.k_diffusion_model = CompVisVDenoiser(model)
+        else:
+            self.k_diffusion_model = CompVisDenoiser(model)
+
+    def set_sampler(self, scheduler_type: str):
+        warnings.warn("The `set_sampler` method is deprecated, please use `set_scheduler` instead.")
+        return self.set_scheduler(scheduler_type)
+
+    def set_scheduler(self, scheduler_type: str):
+        library = importlib.import_module("k_diffusion")
+        sampling = getattr(library, "sampling")
+        self.sampler = getattr(sampling, scheduler_type)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
+
+        if not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        text_embeddings = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        text_embeddings = text_embeddings[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            uncond_embeddings = uncond_embeddings[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def check_inputs(self, prompt, height, width, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // 8, width // 8)
+        if latents is None:
+            if device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
+            else:
+                latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = True
+        if guidance_scale <= 1.0:
+            raise ValueError("has to use guidance_scale")
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=text_embeddings.device)
+        sigmas = self.scheduler.sigmas
+        sigmas = sigmas.to(text_embeddings.dtype)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            text_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+        latents = latents * sigmas[0]
+        self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
+        self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)
+
+        def model_fn(x, t):
+            latent_model_input = torch.cat([x] * 2)
+
+            noise_pred = self.k_diffusion_model(latent_model_input, t, cond=text_embeddings)
+
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+            return noise_pred
+
+        latents = self.sampler(model_fn, latents, sigmas)
+
+        # 8. Post-processing
+        image = self.decode_latents(latents)
+
+        # 9. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
+
+        # 10. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/seed_resize_stable_diffusion.py

@@ -0,0 +1,366 @@
+"""
+    modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+"""
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import DiffusionPipeline
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class SeedResizeStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        text_embeddings: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+
+        if text_embeddings is None:
+            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""]
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
+        latents_shape_reference = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not exist on mps
+                latents_reference = torch.randn(
+                    latents_shape_reference, generator=generator, device="cpu", dtype=latents_dtype
+                ).to(self.device)
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents_reference = torch.randn(
+                    latents_shape_reference, generator=generator, device=self.device, dtype=latents_dtype
+                )
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents_reference.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents_reference = latents_reference.to(self.device)
+            latents = latents.to(self.device)
+
+        # This is the key part of the pipeline where we
+        # try to ensure that the generated images w/ the same seed
+        # but different sizes actually result in similar images
+        dx = (latents_shape[3] - latents_shape_reference[3]) // 2
+        dy = (latents_shape[2] - latents_shape_reference[2]) // 2
+        w = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
+        h = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
+        tx = 0 if dx < 0 else dx
+        ty = 0 if dy < 0 else dy
+        dx = max(-dx, 0)
+        dy = max(-dy, 0)
+        # import pdb
+        # pdb.set_trace()
+        latents[:, :, ty : ty + h, tx : tx + w] = latents_reference[:, :, dy : dy + h, dx : dx + w]
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/speech_to_image_diffusion.py

@@ -0,0 +1,261 @@
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTokenizer,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+)
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.utils import logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class SpeechToImagePipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        speech_model: WhisperForConditionalGeneration,
+        speech_processor: WhisperProcessor,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            speech_model=speech_model,
+            speech_processor=speech_processor,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        if slice_size == "auto":
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        audio,
+        sampling_rate=16_000,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        inputs = self.speech_processor.feature_extractor(
+            audio, return_tensors="pt", sampling_rate=sampling_rate
+        ).input_features.to(self.device)
+        predicted_ids = self.speech_model.generate(inputs, max_length=480_000)
+
+        prompt = self.speech_processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[
+            0
+        ]
+
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not exist on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return image
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)

v0.19.2/stable_diffusion_comparison.py

@@ -0,0 +1,405 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+
+
+pipe1_model_id = "CompVis/stable-diffusion-v1-1"
+pipe2_model_id = "CompVis/stable-diffusion-v1-2"
+pipe3_model_id = "CompVis/stable-diffusion-v1-3"
+pipe4_model_id = "CompVis/stable-diffusion-v1-4"
+
+
+class StableDiffusionComparisonPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for parallel comparison of Stable Diffusion v1-v4
+    This pipeline inherits from DiffusionPipeline and depends on the use of an Auth Token for
+    downloading pre-trained checkpoints from Hugging Face Hub.
+    If using Hugging Face Hub, pass the Model ID for Stable Diffusion v1.4 as the previous 3 checkpoints will be loaded
+    automatically.
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionMegaSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super()._init_()
+
+        self.pipe1 = StableDiffusionPipeline.from_pretrained(pipe1_model_id)
+        self.pipe2 = StableDiffusionPipeline.from_pretrained(pipe2_model_id)
+        self.pipe3 = StableDiffusionPipeline.from_pretrained(pipe3_model_id)
+        self.pipe4 = StableDiffusionPipeline(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            requires_safety_checker=requires_safety_checker,
+        )
+
+        self.register_modules(pipeline1=self.pipe1, pipeline2=self.pipe2, pipeline3=self.pipe3, pipeline4=self.pipe4)
+
+    @property
+    def layers(self) -> Dict[str, Any]:
+        return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def text2img_sd1_1(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        return self.pipe1(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def text2img_sd1_2(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        return self.pipe2(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def text2img_sd1_3(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        return self.pipe3(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def text2img_sd1_4(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        return self.pipe4(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def _call_(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation. This function will generate 4 results as part
+        of running all the 4 pipelines for SD1.1-1.4 together in a serial-processing, parallel-invocation fashion.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, optional, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, optional, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, optional, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, optional, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            eta (`float`, optional, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, optional):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, optional):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, optional, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, optional, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.to(device)
+
+        # Checks if the height and width are divisible by 8 or not
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` must be divisible by 8 but are {height} and {width}.")
+
+        # Get first result from Stable Diffusion Checkpoint v1.1
+        res1 = self.text2img_sd1_1(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+        # Get first result from Stable Diffusion Checkpoint v1.2
+        res2 = self.text2img_sd1_2(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+        # Get first result from Stable Diffusion Checkpoint v1.3
+        res3 = self.text2img_sd1_3(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+        # Get first result from Stable Diffusion Checkpoint v1.4
+        res4 = self.text2img_sd1_4(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+        # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
+        return StableDiffusionPipelineOutput([res1[0], res2[0], res3[0], res4[0]])

v0.19.2/stable_diffusion_controlnet_img2img.py

@@ -0,0 +1,989 @@
+# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    is_accelerate_available,
+    is_accelerate_version,
+    randn_tensor,
+    replace_example_docstring,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import numpy as np
+        >>> import torch
+        >>> from PIL import Image
+        >>> from diffusers import ControlNetModel, UniPCMultistepScheduler
+        >>> from diffusers.utils import load_image
+
+        >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+
+        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+
+        >>> pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5",
+                controlnet=controlnet,
+                safety_checker=None,
+                torch_dtype=torch.float16
+                )
+
+        >>> pipe_controlnet.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
+        >>> pipe_controlnet.enable_xformers_memory_efficient_attention()
+        >>> pipe_controlnet.enable_model_cpu_offload()
+
+        # using image with edges for our canny controlnet
+        >>> control_image = load_image(
+            "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png")
+
+
+        >>> result_img = pipe_controlnet(controlnet_conditioning_image=control_image,
+                        image=input_image,
+                        prompt="an android robot, cyberpank, digitl art masterpiece",
+                        num_inference_steps=20).images[0]
+
+        >>> result_img.show()
+        ```
+"""
+
+
+def prepare_image(image):
+    if isinstance(image, torch.Tensor):
+        # Batch single image
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+
+        image = image.to(dtype=torch.float32)
+    else:
+        # preprocess image
+        if isinstance(image, (PIL.Image.Image, np.ndarray)):
+            image = [image]
+
+        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+    return image
+
+
+def prepare_controlnet_conditioning_image(
+    controlnet_conditioning_image,
+    width,
+    height,
+    batch_size,
+    num_images_per_prompt,
+    device,
+    dtype,
+    do_classifier_free_guidance,
+):
+    if not isinstance(controlnet_conditioning_image, torch.Tensor):
+        if isinstance(controlnet_conditioning_image, PIL.Image.Image):
+            controlnet_conditioning_image = [controlnet_conditioning_image]
+
+        if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):
+            controlnet_conditioning_image = [
+                np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
+                for i in controlnet_conditioning_image
+            ]
+            controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)
+            controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0
+            controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)
+            controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)
+        elif isinstance(controlnet_conditioning_image[0], torch.Tensor):
+            controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)
+
+    image_batch_size = controlnet_conditioning_image.shape[0]
+
+    if image_batch_size == 1:
+        repeat_by = batch_size
+    else:
+        # image batch size is the same as prompt batch size
+        repeat_by = num_images_per_prompt
+
+    controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)
+
+    controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)
+
+    if do_classifier_free_guidance:
+        controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)
+
+    return controlnet_conditioning_image
+
+
+class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline):
+    """
+    Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        if isinstance(controlnet, (list, tuple)):
+            controlnet = MultiControlNetModel(controlnet)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
+        steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:
+            cpu_offload(cpu_offloaded_model, device)
+
+        if self.safety_checker is not None:
+            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
+
+    def enable_model_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate import cpu_offload_with_hook
+        else:
+            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        hook = None
+        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
+            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+        if self.safety_checker is not None:
+            # the safety checker can offload the vae again
+            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
+
+        # control net hook has be manually offloaded as it alternates with unet
+        cpu_offload_with_hook(self.controlnet, device)
+
+        # We'll offload the last model manually.
+        self.final_offload_hook = hook
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+        """
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_controlnet_conditioning_image(self, image, prompt, prompt_embeds):
+        image_is_pil = isinstance(image, PIL.Image.Image)
+        image_is_tensor = isinstance(image, torch.Tensor)
+        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
+        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
+
+        if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
+            raise TypeError(
+                "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
+            )
+
+        if image_is_pil:
+            image_batch_size = 1
+        elif image_is_tensor:
+            image_batch_size = image.shape[0]
+        elif image_is_pil_list:
+            image_batch_size = len(image)
+        elif image_is_tensor_list:
+            image_batch_size = len(image)
+        else:
+            raise ValueError("controlnet condition image is not valid")
+
+        if prompt is not None and isinstance(prompt, str):
+            prompt_batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            prompt_batch_size = len(prompt)
+        elif prompt_embeds is not None:
+            prompt_batch_size = prompt_embeds.shape[0]
+        else:
+            raise ValueError("prompt or prompt_embeds are not valid")
+
+        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
+            raise ValueError(
+                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
+            )
+
+    def check_inputs(
+        self,
+        prompt,
+        image,
+        controlnet_conditioning_image,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        strength=None,
+        controlnet_guidance_start=None,
+        controlnet_guidance_end=None,
+        controlnet_conditioning_scale=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        # check controlnet condition image
+
+        if isinstance(self.controlnet, ControlNetModel):
+            self.check_controlnet_conditioning_image(controlnet_conditioning_image, prompt, prompt_embeds)
+        elif isinstance(self.controlnet, MultiControlNetModel):
+            if not isinstance(controlnet_conditioning_image, list):
+                raise TypeError("For multiple controlnets: `image` must be type `list`")
+
+            if len(controlnet_conditioning_image) != len(self.controlnet.nets):
+                raise ValueError(
+                    "For multiple controlnets: `image` must have the same length as the number of controlnets."
+                )
+
+            for image_ in controlnet_conditioning_image:
+                self.check_controlnet_conditioning_image(image_, prompt, prompt_embeds)
+        else:
+            assert False
+
+        # Check `controlnet_conditioning_scale`
+
+        if isinstance(self.controlnet, ControlNetModel):
+            if not isinstance(controlnet_conditioning_scale, float):
+                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
+        elif isinstance(self.controlnet, MultiControlNetModel):
+            if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
+                self.controlnet.nets
+            ):
+                raise ValueError(
+                    "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
+                    " the same length as the number of controlnets"
+                )
+        else:
+            assert False
+
+        if isinstance(image, torch.Tensor):
+            if image.ndim != 3 and image.ndim != 4:
+                raise ValueError("`image` must have 3 or 4 dimensions")
+
+            if image.ndim == 3:
+                image_batch_size = 1
+                image_channels, image_height, image_width = image.shape
+            elif image.ndim == 4:
+                image_batch_size, image_channels, image_height, image_width = image.shape
+            else:
+                assert False
+
+            if image_channels != 3:
+                raise ValueError("`image` must have 3 channels")
+
+            if image.min() < -1 or image.max() > 1:
+                raise ValueError("`image` should be in range [-1, 1]")
+
+        if self.vae.config.latent_channels != self.unet.config.in_channels:
+            raise ValueError(
+                f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received"
+                f" latent channels: {self.vae.config.latent_channels},"
+                f" Please verify the config of `pipeline.unet` and the `pipeline.vae`"
+            )
+
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of `strength` should in [0.0, 1.0] but is {strength}")
+
+        if controlnet_guidance_start < 0 or controlnet_guidance_start > 1:
+            raise ValueError(
+                f"The value of `controlnet_guidance_start` should in [0.0, 1.0] but is {controlnet_guidance_start}"
+            )
+
+        if controlnet_guidance_end < 0 or controlnet_guidance_end > 1:
+            raise ValueError(
+                f"The value of `controlnet_guidance_end` should in [0.0, 1.0] but is {controlnet_guidance_end}"
+            )
+
+        if controlnet_guidance_start > controlnet_guidance_end:
+            raise ValueError(
+                "The value of `controlnet_guidance_start` should be less than `controlnet_guidance_end`, but got"
+                f" `controlnet_guidance_start` {controlnet_guidance_start} >= `controlnet_guidance_end` {controlnet_guidance_end}"
+            )
+
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
+        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+            )
+
+        image = image.to(device=device, dtype=dtype)
+
+        batch_size = batch_size * num_images_per_prompt
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if isinstance(generator, list):
+            init_latents = [
+                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+            ]
+            init_latents = torch.cat(init_latents, dim=0)
+        else:
+            init_latents = self.vae.encode(image).latent_dist.sample(generator)
+
+        init_latents = self.vae.config.scaling_factor * init_latents
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        shape = init_latents.shape
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        # get latents
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    def _default_height_width(self, height, width, image):
+        if isinstance(image, list):
+            image = image[0]
+
+        if height is None:
+            if isinstance(image, PIL.Image.Image):
+                height = image.height
+            elif isinstance(image, torch.Tensor):
+                height = image.shape[3]
+
+            height = (height // 8) * 8  # round down to nearest multiple of 8
+
+        if width is None:
+            if isinstance(image, PIL.Image.Image):
+                width = image.width
+            elif isinstance(image, torch.Tensor):
+                width = image.shape[2]
+
+            width = (width // 8) * 8  # round down to nearest multiple of 8
+
+        return height, width
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[torch.Tensor, PIL.Image.Image] = None,
+        controlnet_conditioning_image: Union[
+            torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]
+        ] = None,
+        strength: float = 0.8,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        controlnet_guidance_start: float = 0.0,
+        controlnet_guidance_end: float = 1.0,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            controlnet_conditioning_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
+                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. PIL.Image.Image` can
+                also be accepted as an image. The control image is automatically resized to fit the output image.
+            strength (`float`, *optional*):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
+                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
+                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
+                be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+            controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original unet.
+            controlnet_guidance_start ('float', *optional*, defaults to 0.0):
+                The percentage of total steps the controlnet starts applying. Must be between 0 and 1.
+            controlnet_guidance_end ('float', *optional*, defaults to 1.0):
+                The percentage of total steps the controlnet ends applying. Must be between 0 and 1. Must be greater
+                than `controlnet_guidance_start`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height, width = self._default_height_width(height, width, controlnet_conditioning_image)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            image,
+            controlnet_conditioning_image,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            strength,
+            controlnet_guidance_start,
+            controlnet_guidance_end,
+            controlnet_conditioning_scale,
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
+            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # 4. Prepare image, and controlnet_conditioning_image
+        image = prepare_image(image)
+
+        # condition image(s)
+        if isinstance(self.controlnet, ControlNetModel):
+            controlnet_conditioning_image = prepare_controlnet_conditioning_image(
+                controlnet_conditioning_image=controlnet_conditioning_image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=self.controlnet.dtype,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+            )
+        elif isinstance(self.controlnet, MultiControlNetModel):
+            controlnet_conditioning_images = []
+
+            for image_ in controlnet_conditioning_image:
+                image_ = prepare_controlnet_conditioning_image(
+                    controlnet_conditioning_image=image_,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_images_per_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    device=device,
+                    dtype=self.controlnet.dtype,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                )
+
+                controlnet_conditioning_images.append(image_)
+
+            controlnet_conditioning_image = controlnet_conditioning_images
+        else:
+            assert False
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        latents = self.prepare_latents(
+            image,
+            latent_timestep,
+            batch_size,
+            num_images_per_prompt,
+            prompt_embeds.dtype,
+            device,
+            generator,
+        )
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # compute the percentage of total steps we are at
+                current_sampling_percent = i / len(timesteps)
+
+                if (
+                    current_sampling_percent < controlnet_guidance_start
+                    or current_sampling_percent > controlnet_guidance_end
+                ):
+                    # do not apply the controlnet
+                    down_block_res_samples = None
+                    mid_block_res_sample = None
+                else:
+                    # apply the controlnet
+                    down_block_res_samples, mid_block_res_sample = self.controlnet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        controlnet_cond=controlnet_conditioning_image,
+                        conditioning_scale=controlnet_conditioning_scale,
+                        return_dict=False,
+                    )
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                ).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # If we do sequential model offloading, let's offload unet and controlnet
+        # manually for max memory savings
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.unet.to("cpu")
+            self.controlnet.to("cpu")
+            torch.cuda.empty_cache()
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+        elif output_type == "pil":
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+            # 10. Convert to PIL
+            image = self.numpy_to_pil(image)
+        else:
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/stable_diffusion_controlnet_inpaint.py

@@ -0,0 +1,1138 @@
+# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    is_accelerate_available,
+    is_accelerate_version,
+    randn_tensor,
+    replace_example_docstring,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import numpy as np
+        >>> import torch
+        >>> from PIL import Image
+        >>> from stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
+
+        >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
+        >>> from diffusers import ControlNetModel, UniPCMultistepScheduler
+        >>> from diffusers.utils import load_image
+
+        >>> def ade_palette():
+                return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+                        [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+                        [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+                        [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+                        [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+                        [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+                        [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+                        [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+                        [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+                        [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+                        [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+                        [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+                        [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+                        [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+                        [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+                        [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+                        [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+                        [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+                        [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+                        [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+                        [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+                        [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+                        [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+                        [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+                        [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+                        [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+                        [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+                        [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+                        [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+                        [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+                        [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+                        [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+                        [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+                        [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+                        [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+                        [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+                        [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+                        [102, 255, 0], [92, 0, 255]]
+
+        >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
+        >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
+
+        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)
+
+        >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+            )
+
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        >>> pipe.enable_xformers_memory_efficient_attention()
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> def image_to_seg(image):
+                pixel_values = image_processor(image, return_tensors="pt").pixel_values
+                with torch.no_grad():
+                    outputs = image_segmentor(pixel_values)
+                seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+                color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)  # height, width, 3
+                palette = np.array(ade_palette())
+                for label, color in enumerate(palette):
+                    color_seg[seg == label, :] = color
+                color_seg = color_seg.astype(np.uint8)
+                seg_image = Image.fromarray(color_seg)
+                return seg_image
+
+        >>> image = load_image(
+                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+            )
+
+        >>> mask_image = load_image(
+                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+            )
+
+        >>> controlnet_conditioning_image = image_to_seg(image)
+
+        >>> image = pipe(
+                "Face of a yellow cat, high resolution, sitting on a park bench",
+                image,
+                mask_image,
+                controlnet_conditioning_image,
+                num_inference_steps=20,
+            ).images[0]
+
+        >>> image.save("out.png")
+        ```
+"""
+
+
+def prepare_image(image):
+    if isinstance(image, torch.Tensor):
+        # Batch single image
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+
+        image = image.to(dtype=torch.float32)
+    else:
+        # preprocess image
+        if isinstance(image, (PIL.Image.Image, np.ndarray)):
+            image = [image]
+
+        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+    return image
+
+
+def prepare_mask_image(mask_image):
+    if isinstance(mask_image, torch.Tensor):
+        if mask_image.ndim == 2:
+            # Batch and add channel dim for single mask
+            mask_image = mask_image.unsqueeze(0).unsqueeze(0)
+        elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
+            # Single mask, the 0'th dimension is considered to be
+            # the existing batch size of 1
+            mask_image = mask_image.unsqueeze(0)
+        elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
+            # Batch of mask, the 0'th dimension is considered to be
+            # the batching dimension
+            mask_image = mask_image.unsqueeze(1)
+
+        # Binarize mask
+        mask_image[mask_image < 0.5] = 0
+        mask_image[mask_image >= 0.5] = 1
+    else:
+        # preprocess mask
+        if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):
+            mask_image = [mask_image]
+
+        if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):
+            mask_image = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0)
+            mask_image = mask_image.astype(np.float32) / 255.0
+        elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):
+            mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
+
+        mask_image[mask_image < 0.5] = 0
+        mask_image[mask_image >= 0.5] = 1
+        mask_image = torch.from_numpy(mask_image)
+
+    return mask_image
+
+
+def prepare_controlnet_conditioning_image(
+    controlnet_conditioning_image,
+    width,
+    height,
+    batch_size,
+    num_images_per_prompt,
+    device,
+    dtype,
+    do_classifier_free_guidance,
+):
+    if not isinstance(controlnet_conditioning_image, torch.Tensor):
+        if isinstance(controlnet_conditioning_image, PIL.Image.Image):
+            controlnet_conditioning_image = [controlnet_conditioning_image]
+
+        if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):
+            controlnet_conditioning_image = [
+                np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
+                for i in controlnet_conditioning_image
+            ]
+            controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)
+            controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0
+            controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)
+            controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)
+        elif isinstance(controlnet_conditioning_image[0], torch.Tensor):
+            controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)
+
+    image_batch_size = controlnet_conditioning_image.shape[0]
+
+    if image_batch_size == 1:
+        repeat_by = batch_size
+    else:
+        # image batch size is the same as prompt batch size
+        repeat_by = num_images_per_prompt
+
+    controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)
+
+    controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)
+
+    if do_classifier_free_guidance:
+        controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)
+
+    return controlnet_conditioning_image
+
+
+class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline):
+    """
+    Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        if isinstance(controlnet, (list, tuple)):
+            controlnet = MultiControlNetModel(controlnet)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
+        steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:
+            cpu_offload(cpu_offloaded_model, device)
+
+        if self.safety_checker is not None:
+            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
+
+    def enable_model_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate import cpu_offload_with_hook
+        else:
+            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        hook = None
+        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
+            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+        if self.safety_checker is not None:
+            # the safety checker can offload the vae again
+            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
+
+        # control net hook has be manually offloaded as it alternates with unet
+        cpu_offload_with_hook(self.controlnet, device)
+
+        # We'll offload the last model manually.
+        self.final_offload_hook = hook
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+        """
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_controlnet_conditioning_image(self, image, prompt, prompt_embeds):
+        image_is_pil = isinstance(image, PIL.Image.Image)
+        image_is_tensor = isinstance(image, torch.Tensor)
+        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
+        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
+
+        if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
+            raise TypeError(
+                "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
+            )
+
+        if image_is_pil:
+            image_batch_size = 1
+        elif image_is_tensor:
+            image_batch_size = image.shape[0]
+        elif image_is_pil_list:
+            image_batch_size = len(image)
+        elif image_is_tensor_list:
+            image_batch_size = len(image)
+        else:
+            raise ValueError("controlnet condition image is not valid")
+
+        if prompt is not None and isinstance(prompt, str):
+            prompt_batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            prompt_batch_size = len(prompt)
+        elif prompt_embeds is not None:
+            prompt_batch_size = prompt_embeds.shape[0]
+        else:
+            raise ValueError("prompt or prompt_embeds are not valid")
+
+        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
+            raise ValueError(
+                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
+            )
+
+    def check_inputs(
+        self,
+        prompt,
+        image,
+        mask_image,
+        controlnet_conditioning_image,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        controlnet_conditioning_scale=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        # check controlnet condition image
+        if isinstance(self.controlnet, ControlNetModel):
+            self.check_controlnet_conditioning_image(controlnet_conditioning_image, prompt, prompt_embeds)
+        elif isinstance(self.controlnet, MultiControlNetModel):
+            if not isinstance(controlnet_conditioning_image, list):
+                raise TypeError("For multiple controlnets: `image` must be type `list`")
+            if len(controlnet_conditioning_image) != len(self.controlnet.nets):
+                raise ValueError(
+                    "For multiple controlnets: `image` must have the same length as the number of controlnets."
+                )
+            for image_ in controlnet_conditioning_image:
+                self.check_controlnet_conditioning_image(image_, prompt, prompt_embeds)
+        else:
+            assert False
+
+        # Check `controlnet_conditioning_scale`
+        if isinstance(self.controlnet, ControlNetModel):
+            if not isinstance(controlnet_conditioning_scale, float):
+                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
+        elif isinstance(self.controlnet, MultiControlNetModel):
+            if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
+                self.controlnet.nets
+            ):
+                raise ValueError(
+                    "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
+                    " the same length as the number of controlnets"
+                )
+        else:
+            assert False
+
+        if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor):
+            raise TypeError("if `image` is a tensor, `mask_image` must also be a tensor")
+
+        if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image):
+            raise TypeError("if `image` is a PIL image, `mask_image` must also be a PIL image")
+
+        if isinstance(image, torch.Tensor):
+            if image.ndim != 3 and image.ndim != 4:
+                raise ValueError("`image` must have 3 or 4 dimensions")
+
+            if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4:
+                raise ValueError("`mask_image` must have 2, 3, or 4 dimensions")
+
+            if image.ndim == 3:
+                image_batch_size = 1
+                image_channels, image_height, image_width = image.shape
+            elif image.ndim == 4:
+                image_batch_size, image_channels, image_height, image_width = image.shape
+            else:
+                assert False
+
+            if mask_image.ndim == 2:
+                mask_image_batch_size = 1
+                mask_image_channels = 1
+                mask_image_height, mask_image_width = mask_image.shape
+            elif mask_image.ndim == 3:
+                mask_image_channels = 1
+                mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape
+            elif mask_image.ndim == 4:
+                mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape
+
+            if image_channels != 3:
+                raise ValueError("`image` must have 3 channels")
+
+            if mask_image_channels != 1:
+                raise ValueError("`mask_image` must have 1 channel")
+
+            if image_batch_size != mask_image_batch_size:
+                raise ValueError("`image` and `mask_image` mush have the same batch sizes")
+
+            if image_height != mask_image_height or image_width != mask_image_width:
+                raise ValueError("`image` and `mask_image` must have the same height and width dimensions")
+
+            if image.min() < -1 or image.max() > 1:
+                raise ValueError("`image` should be in range [-1, 1]")
+
+            if mask_image.min() < 0 or mask_image.max() > 1:
+                raise ValueError("`mask_image` should be in range [0, 1]")
+        else:
+            mask_image_channels = 1
+            image_channels = 3
+
+        single_image_latent_channels = self.vae.config.latent_channels
+
+        total_latent_channels = single_image_latent_channels * 2 + mask_image_channels
+
+        if total_latent_channels != self.unet.config.in_channels:
+            raise ValueError(
+                f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received"
+                f" non inpainting latent channels: {single_image_latent_channels},"
+                f" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}."
+                f" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs."
+            )
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        return latents
+
+    def prepare_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+        mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))
+        mask_image = mask_image.to(device=device, dtype=dtype)
+
+        # duplicate mask for each generation per prompt, using mps friendly method
+        if mask_image.shape[0] < batch_size:
+            if not batch_size % mask_image.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1)
+
+        mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image
+
+        mask_image_latents = mask_image
+
+        return mask_image_latents
+
+    def prepare_masked_image_latents(
+        self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+    ):
+        masked_image = masked_image.to(device=device, dtype=dtype)
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            masked_image_latents = [
+                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(batch_size)
+            ]
+            masked_image_latents = torch.cat(masked_image_latents, dim=0)
+        else:
+            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
+        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
+
+        # duplicate masked_image_latents for each generation per prompt, using mps friendly method
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
+
+        masked_image_latents = (
+            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+        )
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+        return masked_image_latents
+
+    def _default_height_width(self, height, width, image):
+        if isinstance(image, list):
+            image = image[0]
+
+        if height is None:
+            if isinstance(image, PIL.Image.Image):
+                height = image.height
+            elif isinstance(image, torch.Tensor):
+                height = image.shape[3]
+
+            height = (height // 8) * 8  # round down to nearest multiple of 8
+
+        if width is None:
+            if isinstance(image, PIL.Image.Image):
+                width = image.width
+            elif isinstance(image, torch.Tensor):
+                width = image.shape[2]
+
+            width = (width // 8) * 8  # round down to nearest multiple of 8
+
+        return height, width
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[torch.Tensor, PIL.Image.Image] = None,
+        mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
+        controlnet_conditioning_image: Union[
+            torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]
+        ] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            mask_image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            controlnet_conditioning_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
+                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. PIL.Image.Image` can
+                also be accepted as an image. The control image is automatically resized to fit the output image.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+            controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original unet.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height, width = self._default_height_width(height, width, controlnet_conditioning_image)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            image,
+            mask_image,
+            controlnet_conditioning_image,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            controlnet_conditioning_scale,
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
+            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # 4. Prepare mask, image, and controlnet_conditioning_image
+        image = prepare_image(image)
+
+        mask_image = prepare_mask_image(mask_image)
+
+        # condition image(s)
+        if isinstance(self.controlnet, ControlNetModel):
+            controlnet_conditioning_image = prepare_controlnet_conditioning_image(
+                controlnet_conditioning_image=controlnet_conditioning_image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=self.controlnet.dtype,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+            )
+        elif isinstance(self.controlnet, MultiControlNetModel):
+            controlnet_conditioning_images = []
+
+            for image_ in controlnet_conditioning_image:
+                image_ = prepare_controlnet_conditioning_image(
+                    controlnet_conditioning_image=image_,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_images_per_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    device=device,
+                    dtype=self.controlnet.dtype,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                )
+                controlnet_conditioning_images.append(image_)
+
+            controlnet_conditioning_image = controlnet_conditioning_images
+        else:
+            assert False
+
+        masked_image = image * (mask_image < 0.5)
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.vae.config.latent_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        mask_image_latents = self.prepare_mask_latents(
+            mask_image,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            do_classifier_free_guidance,
+        )
+
+        masked_image_latents = self.prepare_masked_image_latents(
+            masked_image,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance,
+        )
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                non_inpainting_latent_model_input = (
+                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                )
+
+                non_inpainting_latent_model_input = self.scheduler.scale_model_input(
+                    non_inpainting_latent_model_input, t
+                )
+
+                inpainting_latent_model_input = torch.cat(
+                    [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1
+                )
+
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    non_inpainting_latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    controlnet_cond=controlnet_conditioning_image,
+                    conditioning_scale=controlnet_conditioning_scale,
+                    return_dict=False,
+                )
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    inpainting_latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                ).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # If we do sequential model offloading, let's offload unet and controlnet
+        # manually for max memory savings
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.unet.to("cpu")
+            self.controlnet.to("cpu")
+            torch.cuda.empty_cache()
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+        elif output_type == "pil":
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+            # 10. Convert to PIL
+            image = self.numpy_to_pil(image)
+        else:
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/stable_diffusion_controlnet_inpaint_img2img.py

@@ -0,0 +1,1119 @@
+# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    is_accelerate_available,
+    is_accelerate_version,
+    randn_tensor,
+    replace_example_docstring,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import numpy as np
+        >>> import torch
+        >>> from PIL import Image
+        >>> from stable_diffusion_controlnet_inpaint_img2img import StableDiffusionControlNetInpaintImg2ImgPipeline
+
+        >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
+        >>> from diffusers import ControlNetModel, UniPCMultistepScheduler
+        >>> from diffusers.utils import load_image
+
+        >>> def ade_palette():
+                return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+                        [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+                        [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+                        [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+                        [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+                        [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+                        [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+                        [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+                        [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+                        [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+                        [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+                        [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+                        [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+                        [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+                        [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+                        [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+                        [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+                        [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+                        [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+                        [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+                        [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+                        [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+                        [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+                        [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+                        [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+                        [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+                        [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+                        [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+                        [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+                        [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+                        [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+                        [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+                        [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+                        [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+                        [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+                        [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+                        [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+                        [102, 255, 0], [92, 0, 255]]
+
+        >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
+        >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
+
+        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)
+
+        >>> pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained(
+                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+            )
+
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        >>> pipe.enable_xformers_memory_efficient_attention()
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> def image_to_seg(image):
+                pixel_values = image_processor(image, return_tensors="pt").pixel_values
+                with torch.no_grad():
+                    outputs = image_segmentor(pixel_values)
+                seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+                color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)  # height, width, 3
+                palette = np.array(ade_palette())
+                for label, color in enumerate(palette):
+                    color_seg[seg == label, :] = color
+                color_seg = color_seg.astype(np.uint8)
+                seg_image = Image.fromarray(color_seg)
+                return seg_image
+
+        >>> image = load_image(
+                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+            )
+
+        >>> mask_image = load_image(
+                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+            )
+
+        >>> controlnet_conditioning_image = image_to_seg(image)
+
+        >>> image = pipe(
+                "Face of a yellow cat, high resolution, sitting on a park bench",
+                image,
+                mask_image,
+                controlnet_conditioning_image,
+                num_inference_steps=20,
+            ).images[0]
+
+        >>> image.save("out.png")
+        ```
+"""
+
+
+def prepare_image(image):
+    if isinstance(image, torch.Tensor):
+        # Batch single image
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+
+        image = image.to(dtype=torch.float32)
+    else:
+        # preprocess image
+        if isinstance(image, (PIL.Image.Image, np.ndarray)):
+            image = [image]
+
+        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+    return image
+
+
+def prepare_mask_image(mask_image):
+    if isinstance(mask_image, torch.Tensor):
+        if mask_image.ndim == 2:
+            # Batch and add channel dim for single mask
+            mask_image = mask_image.unsqueeze(0).unsqueeze(0)
+        elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
+            # Single mask, the 0'th dimension is considered to be
+            # the existing batch size of 1
+            mask_image = mask_image.unsqueeze(0)
+        elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
+            # Batch of mask, the 0'th dimension is considered to be
+            # the batching dimension
+            mask_image = mask_image.unsqueeze(1)
+
+        # Binarize mask
+        mask_image[mask_image < 0.5] = 0
+        mask_image[mask_image >= 0.5] = 1
+    else:
+        # preprocess mask
+        if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):
+            mask_image = [mask_image]
+
+        if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):
+            mask_image = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0)
+            mask_image = mask_image.astype(np.float32) / 255.0
+        elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):
+            mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
+
+        mask_image[mask_image < 0.5] = 0
+        mask_image[mask_image >= 0.5] = 1
+        mask_image = torch.from_numpy(mask_image)
+
+    return mask_image
+
+
+def prepare_controlnet_conditioning_image(
+    controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype
+):
+    if not isinstance(controlnet_conditioning_image, torch.Tensor):
+        if isinstance(controlnet_conditioning_image, PIL.Image.Image):
+            controlnet_conditioning_image = [controlnet_conditioning_image]
+
+        if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):
+            controlnet_conditioning_image = [
+                np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
+                for i in controlnet_conditioning_image
+            ]
+            controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)
+            controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0
+            controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)
+            controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)
+        elif isinstance(controlnet_conditioning_image[0], torch.Tensor):
+            controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)
+
+    image_batch_size = controlnet_conditioning_image.shape[0]
+
+    if image_batch_size == 1:
+        repeat_by = batch_size
+    else:
+        # image batch size is the same as prompt batch size
+        repeat_by = num_images_per_prompt
+
+    controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)
+
+    controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)
+
+    return controlnet_conditioning_image
+
+
+class StableDiffusionControlNetInpaintImg2ImgPipeline(DiffusionPipeline):
+    """
+    Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        controlnet: ControlNetModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
+        steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:
+            cpu_offload(cpu_offloaded_model, device)
+
+        if self.safety_checker is not None:
+            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
+
+    def enable_model_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate import cpu_offload_with_hook
+        else:
+            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        hook = None
+        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
+            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+        if self.safety_checker is not None:
+            # the safety checker can offload the vae again
+            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
+
+        # control net hook has be manually offloaded as it alternates with unet
+        cpu_offload_with_hook(self.controlnet, device)
+
+        # We'll offload the last model manually.
+        self.final_offload_hook = hook
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+        """
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        image,
+        mask_image,
+        controlnet_conditioning_image,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        strength=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        controlnet_cond_image_is_pil = isinstance(controlnet_conditioning_image, PIL.Image.Image)
+        controlnet_cond_image_is_tensor = isinstance(controlnet_conditioning_image, torch.Tensor)
+        controlnet_cond_image_is_pil_list = isinstance(controlnet_conditioning_image, list) and isinstance(
+            controlnet_conditioning_image[0], PIL.Image.Image
+        )
+        controlnet_cond_image_is_tensor_list = isinstance(controlnet_conditioning_image, list) and isinstance(
+            controlnet_conditioning_image[0], torch.Tensor
+        )
+
+        if (
+            not controlnet_cond_image_is_pil
+            and not controlnet_cond_image_is_tensor
+            and not controlnet_cond_image_is_pil_list
+            and not controlnet_cond_image_is_tensor_list
+        ):
+            raise TypeError(
+                "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
+            )
+
+        if controlnet_cond_image_is_pil:
+            controlnet_cond_image_batch_size = 1
+        elif controlnet_cond_image_is_tensor:
+            controlnet_cond_image_batch_size = controlnet_conditioning_image.shape[0]
+        elif controlnet_cond_image_is_pil_list:
+            controlnet_cond_image_batch_size = len(controlnet_conditioning_image)
+        elif controlnet_cond_image_is_tensor_list:
+            controlnet_cond_image_batch_size = len(controlnet_conditioning_image)
+
+        if prompt is not None and isinstance(prompt, str):
+            prompt_batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            prompt_batch_size = len(prompt)
+        elif prompt_embeds is not None:
+            prompt_batch_size = prompt_embeds.shape[0]
+
+        if controlnet_cond_image_batch_size != 1 and controlnet_cond_image_batch_size != prompt_batch_size:
+            raise ValueError(
+                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {controlnet_cond_image_batch_size}, prompt batch size: {prompt_batch_size}"
+            )
+
+        if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor):
+            raise TypeError("if `image` is a tensor, `mask_image` must also be a tensor")
+
+        if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image):
+            raise TypeError("if `image` is a PIL image, `mask_image` must also be a PIL image")
+
+        if isinstance(image, torch.Tensor):
+            if image.ndim != 3 and image.ndim != 4:
+                raise ValueError("`image` must have 3 or 4 dimensions")
+
+            if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4:
+                raise ValueError("`mask_image` must have 2, 3, or 4 dimensions")
+
+            if image.ndim == 3:
+                image_batch_size = 1
+                image_channels, image_height, image_width = image.shape
+            elif image.ndim == 4:
+                image_batch_size, image_channels, image_height, image_width = image.shape
+
+            if mask_image.ndim == 2:
+                mask_image_batch_size = 1
+                mask_image_channels = 1
+                mask_image_height, mask_image_width = mask_image.shape
+            elif mask_image.ndim == 3:
+                mask_image_channels = 1
+                mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape
+            elif mask_image.ndim == 4:
+                mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape
+
+            if image_channels != 3:
+                raise ValueError("`image` must have 3 channels")
+
+            if mask_image_channels != 1:
+                raise ValueError("`mask_image` must have 1 channel")
+
+            if image_batch_size != mask_image_batch_size:
+                raise ValueError("`image` and `mask_image` mush have the same batch sizes")
+
+            if image_height != mask_image_height or image_width != mask_image_width:
+                raise ValueError("`image` and `mask_image` must have the same height and width dimensions")
+
+            if image.min() < -1 or image.max() > 1:
+                raise ValueError("`image` should be in range [-1, 1]")
+
+            if mask_image.min() < 0 or mask_image.max() > 1:
+                raise ValueError("`mask_image` should be in range [0, 1]")
+        else:
+            mask_image_channels = 1
+            image_channels = 3
+
+        single_image_latent_channels = self.vae.config.latent_channels
+
+        total_latent_channels = single_image_latent_channels * 2 + mask_image_channels
+
+        if total_latent_channels != self.unet.config.in_channels:
+            raise ValueError(
+                f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received"
+                f" non inpainting latent channels: {single_image_latent_channels},"
+                f" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}."
+                f" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs."
+            )
+
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
+        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+            )
+
+        image = image.to(device=device, dtype=dtype)
+
+        batch_size = batch_size * num_images_per_prompt
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if isinstance(generator, list):
+            init_latents = [
+                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+            ]
+            init_latents = torch.cat(init_latents, dim=0)
+        else:
+            init_latents = self.vae.encode(image).latent_dist.sample(generator)
+
+        init_latents = self.vae.config.scaling_factor * init_latents
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        shape = init_latents.shape
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        # get latents
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    def prepare_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+        mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))
+        mask_image = mask_image.to(device=device, dtype=dtype)
+
+        # duplicate mask for each generation per prompt, using mps friendly method
+        if mask_image.shape[0] < batch_size:
+            if not batch_size % mask_image.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1)
+
+        mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image
+
+        mask_image_latents = mask_image
+
+        return mask_image_latents
+
+    def prepare_masked_image_latents(
+        self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+    ):
+        masked_image = masked_image.to(device=device, dtype=dtype)
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            masked_image_latents = [
+                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(batch_size)
+            ]
+            masked_image_latents = torch.cat(masked_image_latents, dim=0)
+        else:
+            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
+        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
+
+        # duplicate masked_image_latents for each generation per prompt, using mps friendly method
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
+
+        masked_image_latents = (
+            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+        )
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+        return masked_image_latents
+
+    def _default_height_width(self, height, width, image):
+        if isinstance(image, list):
+            image = image[0]
+
+        if height is None:
+            if isinstance(image, PIL.Image.Image):
+                height = image.height
+            elif isinstance(image, torch.Tensor):
+                height = image.shape[3]
+
+            height = (height // 8) * 8  # round down to nearest multiple of 8
+
+        if width is None:
+            if isinstance(image, PIL.Image.Image):
+                width = image.width
+            elif isinstance(image, torch.Tensor):
+                width = image.shape[2]
+
+            width = (width // 8) * 8  # round down to nearest multiple of 8
+
+        return height, width
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[torch.Tensor, PIL.Image.Image] = None,
+        mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
+        controlnet_conditioning_image: Union[
+            torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]
+        ] = None,
+        strength: float = 0.8,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: float = 1.0,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            mask_image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            controlnet_conditioning_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
+                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. PIL.Image.Image` can
+                also be accepted as an image. The control image is automatically resized to fit the output image.
+            strength (`float`, *optional*):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
+                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
+                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
+                be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+            controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original unet.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height, width = self._default_height_width(height, width, controlnet_conditioning_image)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            image,
+            mask_image,
+            controlnet_conditioning_image,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            strength,
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # 4. Prepare mask, image, and controlnet_conditioning_image
+        image = prepare_image(image)
+
+        mask_image = prepare_mask_image(mask_image)
+
+        controlnet_conditioning_image = prepare_controlnet_conditioning_image(
+            controlnet_conditioning_image,
+            width,
+            height,
+            batch_size * num_images_per_prompt,
+            num_images_per_prompt,
+            device,
+            self.controlnet.dtype,
+        )
+
+        masked_image = image * (mask_image < 0.5)
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        latents = self.prepare_latents(
+            image,
+            latent_timestep,
+            batch_size,
+            num_images_per_prompt,
+            prompt_embeds.dtype,
+            device,
+            generator,
+        )
+
+        mask_image_latents = self.prepare_mask_latents(
+            mask_image,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            do_classifier_free_guidance,
+        )
+
+        masked_image_latents = self.prepare_masked_image_latents(
+            masked_image,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance,
+        )
+
+        if do_classifier_free_guidance:
+            controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                non_inpainting_latent_model_input = (
+                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                )
+
+                non_inpainting_latent_model_input = self.scheduler.scale_model_input(
+                    non_inpainting_latent_model_input, t
+                )
+
+                inpainting_latent_model_input = torch.cat(
+                    [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1
+                )
+
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    non_inpainting_latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    controlnet_cond=controlnet_conditioning_image,
+                    return_dict=False,
+                )
+
+                down_block_res_samples = [
+                    down_block_res_sample * controlnet_conditioning_scale
+                    for down_block_res_sample in down_block_res_samples
+                ]
+                mid_block_res_sample *= controlnet_conditioning_scale
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    inpainting_latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                ).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # If we do sequential model offloading, let's offload unet and controlnet
+        # manually for max memory savings
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.unet.to("cpu")
+            self.controlnet.to("cpu")
+            torch.cuda.empty_cache()
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+        elif output_type == "pil":
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+            # 10. Convert to PIL
+            image = self.numpy_to_pil(image)
+        else:
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/stable_diffusion_controlnet_reference.py

@@ -0,0 +1,834 @@
+# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+
+from diffusers import StableDiffusionControlNetPipeline
+from diffusers.models import ControlNetModel
+from diffusers.models.attention import BasicTransformerBlock
+from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import is_compiled_module, logging, randn_tensor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import cv2
+        >>> import torch
+        >>> import numpy as np
+        >>> from PIL import Image
+        >>> from diffusers import UniPCMultistepScheduler
+        >>> from diffusers.utils import load_image
+
+        >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+
+        >>> # get canny image
+        >>> image = cv2.Canny(np.array(input_image), 100, 200)
+        >>> image = image[:, :, None]
+        >>> image = np.concatenate([image, image, image], axis=2)
+        >>> canny_image = Image.fromarray(image)
+
+        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5",
+                controlnet=controlnet,
+                safety_checker=None,
+                torch_dtype=torch.float16
+                ).to('cuda:0')
+
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
+
+        >>> result_img = pipe(ref_image=input_image,
+                        prompt="1girl",
+                        image=canny_image,
+                        num_inference_steps=20,
+                        reference_attn=True,
+                        reference_adain=True).images[0]
+
+        >>> result_img.show()
+        ```
+"""
+
+
+def torch_dfs(model: torch.nn.Module):
+    result = [model]
+    for child in model.children():
+        result += torch_dfs(child)
+    return result
+
+
+class StableDiffusionControlNetReferencePipeline(StableDiffusionControlNetPipeline):
+    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
+        refimage = refimage.to(device=device, dtype=dtype)
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            ref_image_latents = [
+                self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(batch_size)
+            ]
+            ref_image_latents = torch.cat(ref_image_latents, dim=0)
+        else:
+            ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
+        ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
+
+        # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
+        if ref_image_latents.shape[0] < batch_size:
+            if not batch_size % ref_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
+
+        ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
+        return ref_image_latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[
+            torch.FloatTensor,
+            PIL.Image.Image,
+            np.ndarray,
+            List[torch.FloatTensor],
+            List[PIL.Image.Image],
+            List[np.ndarray],
+        ] = None,
+        ref_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        guess_mode: bool = False,
+        attention_auto_machine_weight: float = 1.0,
+        gn_auto_machine_weight: float = 1.0,
+        style_fidelity: float = 0.5,
+        reference_attn: bool = True,
+        reference_adain: bool = True,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
+                    `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
+                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
+                also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
+                height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
+                specified in init, images must be passed as a list such that each element of the list can be correctly
+                batched for input to a single controlnet.
+            ref_image (`torch.FloatTensor`, `PIL.Image.Image`):
+                The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
+                also be accepted as an image.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
+                corresponding scale as a list.
+            guess_mode (`bool`, *optional*, defaults to `False`):
+                In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
+                you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
+            attention_auto_machine_weight (`float`):
+                Weight of using reference query for self attention's context.
+                If attention_auto_machine_weight=1.0, use reference query for all self attention's context.
+            gn_auto_machine_weight (`float`):
+                Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.
+            style_fidelity (`float`):
+                style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,
+                elif style_fidelity=0.0, prompt more important, else balanced.
+            reference_attn (`bool`):
+                Whether to use reference query for self attention's context.
+            reference_adain (`bool`):
+                Whether to use reference adain.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        assert reference_attn or reference_adain, "`reference_attn` or `reference_adain` must be True."
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            image,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            controlnet_conditioning_scale,
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
+
+        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
+            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
+
+        global_pool_conditions = (
+            controlnet.config.global_pool_conditions
+            if isinstance(controlnet, ControlNetModel)
+            else controlnet.nets[0].config.global_pool_conditions
+        )
+        guess_mode = guess_mode or global_pool_conditions
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+        )
+
+        # 4. Prepare image
+        if isinstance(controlnet, ControlNetModel):
+            image = self.prepare_image(
+                image=image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=controlnet.dtype,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                guess_mode=guess_mode,
+            )
+            height, width = image.shape[-2:]
+        elif isinstance(controlnet, MultiControlNetModel):
+            images = []
+
+            for image_ in image:
+                image_ = self.prepare_image(
+                    image=image_,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_images_per_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    device=device,
+                    dtype=controlnet.dtype,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    guess_mode=guess_mode,
+                )
+
+                images.append(image_)
+
+            image = images
+            height, width = image[0].shape[-2:]
+        else:
+            assert False
+
+        # 5. Preprocess reference image
+        ref_image = self.prepare_image(
+            image=ref_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=prompt_embeds.dtype,
+        )
+
+        # 6. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 7. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 8. Prepare reference latent variables
+        ref_image_latents = self.prepare_ref_latents(
+            ref_image,
+            batch_size * num_images_per_prompt,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance,
+        )
+
+        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 9. Modify self attention and group norm
+        MODE = "write"
+        uc_mask = (
+            torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
+            .type_as(ref_image_latents)
+            .bool()
+        )
+
+        def hacked_basic_transformer_inner_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+            timestep: Optional[torch.LongTensor] = None,
+            cross_attention_kwargs: Dict[str, Any] = None,
+            class_labels: Optional[torch.LongTensor] = None,
+        ):
+            if self.use_ada_layer_norm:
+                norm_hidden_states = self.norm1(hidden_states, timestep)
+            elif self.use_ada_layer_norm_zero:
+                norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                    hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+                )
+            else:
+                norm_hidden_states = self.norm1(hidden_states)
+
+            # 1. Self-Attention
+            cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
+            if self.only_cross_attention:
+                attn_output = self.attn1(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                    attention_mask=attention_mask,
+                    **cross_attention_kwargs,
+                )
+            else:
+                if MODE == "write":
+                    self.bank.append(norm_hidden_states.detach().clone())
+                    attn_output = self.attn1(
+                        norm_hidden_states,
+                        encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                        attention_mask=attention_mask,
+                        **cross_attention_kwargs,
+                    )
+                if MODE == "read":
+                    if attention_auto_machine_weight > self.attn_weight:
+                        attn_output_uc = self.attn1(
+                            norm_hidden_states,
+                            encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
+                            # attention_mask=attention_mask,
+                            **cross_attention_kwargs,
+                        )
+                        attn_output_c = attn_output_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            attn_output_c[uc_mask] = self.attn1(
+                                norm_hidden_states[uc_mask],
+                                encoder_hidden_states=norm_hidden_states[uc_mask],
+                                **cross_attention_kwargs,
+                            )
+                        attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc
+                        self.bank.clear()
+                    else:
+                        attn_output = self.attn1(
+                            norm_hidden_states,
+                            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                            attention_mask=attention_mask,
+                            **cross_attention_kwargs,
+                        )
+            if self.use_ada_layer_norm_zero:
+                attn_output = gate_msa.unsqueeze(1) * attn_output
+            hidden_states = attn_output + hidden_states
+
+            if self.attn2 is not None:
+                norm_hidden_states = (
+                    self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+                )
+
+                # 2. Cross-Attention
+                attn_output = self.attn2(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=encoder_attention_mask,
+                    **cross_attention_kwargs,
+                )
+                hidden_states = attn_output + hidden_states
+
+            # 3. Feed-forward
+            norm_hidden_states = self.norm3(hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+            ff_output = self.ff(norm_hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+            hidden_states = ff_output + hidden_states
+
+            return hidden_states
+
+        def hacked_mid_forward(self, *args, **kwargs):
+            eps = 1e-6
+            x = self.original_forward(*args, **kwargs)
+            if MODE == "write":
+                if gn_auto_machine_weight >= self.gn_weight:
+                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
+                    self.mean_bank.append(mean)
+                    self.var_bank.append(var)
+            if MODE == "read":
+                if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
+                    std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                    mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
+                    var_acc = sum(self.var_bank) / float(len(self.var_bank))
+                    std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                    x_uc = (((x - mean) / std) * std_acc) + mean_acc
+                    x_c = x_uc.clone()
+                    if do_classifier_free_guidance and style_fidelity > 0:
+                        x_c[uc_mask] = x[uc_mask]
+                    x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
+                self.mean_bank = []
+                self.var_bank = []
+            return x
+
+        def hack_CrossAttnDownBlock2D_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        ):
+            eps = 1e-6
+
+            # TODO(Patrick, William) - attention mask is not used
+            output_states = ()
+
+            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+                output_states = output_states + (hidden_states,)
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.downsamplers is not None:
+                for downsampler in self.downsamplers:
+                    hidden_states = downsampler(hidden_states)
+
+                output_states = output_states + (hidden_states,)
+
+            return hidden_states, output_states
+
+        def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
+            eps = 1e-6
+
+            output_states = ()
+
+            for i, resnet in enumerate(self.resnets):
+                hidden_states = resnet(hidden_states, temb)
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+                output_states = output_states + (hidden_states,)
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.downsamplers is not None:
+                for downsampler in self.downsamplers:
+                    hidden_states = downsampler(hidden_states)
+
+                output_states = output_states + (hidden_states,)
+
+            return hidden_states, output_states
+
+        def hacked_CrossAttnUpBlock2D_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            upsample_size: Optional[int] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        ):
+            eps = 1e-6
+            # TODO(Patrick, William) - attention mask is not used
+            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
+                # pop res hidden states
+                res_hidden_states = res_hidden_states_tuple[-1]
+                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.upsamplers is not None:
+                for upsampler in self.upsamplers:
+                    hidden_states = upsampler(hidden_states, upsample_size)
+
+            return hidden_states
+
+        def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
+            eps = 1e-6
+            for i, resnet in enumerate(self.resnets):
+                # pop res hidden states
+                res_hidden_states = res_hidden_states_tuple[-1]
+                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+                hidden_states = resnet(hidden_states, temb)
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.upsamplers is not None:
+                for upsampler in self.upsamplers:
+                    hidden_states = upsampler(hidden_states, upsample_size)
+
+            return hidden_states
+
+        if reference_attn:
+            attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]
+            attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
+
+            for i, module in enumerate(attn_modules):
+                module._original_inner_forward = module.forward
+                module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
+                module.bank = []
+                module.attn_weight = float(i) / float(len(attn_modules))
+
+        if reference_adain:
+            gn_modules = [self.unet.mid_block]
+            self.unet.mid_block.gn_weight = 0
+
+            down_blocks = self.unet.down_blocks
+            for w, module in enumerate(down_blocks):
+                module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
+                gn_modules.append(module)
+
+            up_blocks = self.unet.up_blocks
+            for w, module in enumerate(up_blocks):
+                module.gn_weight = float(w) / float(len(up_blocks))
+                gn_modules.append(module)
+
+            for i, module in enumerate(gn_modules):
+                if getattr(module, "original_forward", None) is None:
+                    module.original_forward = module.forward
+                if i == 0:
+                    # mid_block
+                    module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
+                elif isinstance(module, CrossAttnDownBlock2D):
+                    module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
+                elif isinstance(module, DownBlock2D):
+                    module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
+                elif isinstance(module, CrossAttnUpBlock2D):
+                    module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
+                elif isinstance(module, UpBlock2D):
+                    module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
+                module.mean_bank = []
+                module.var_bank = []
+                module.gn_weight *= 2
+
+        # 11. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # controlnet(s) inference
+                if guess_mode and do_classifier_free_guidance:
+                    # Infer ControlNet only for the conditional batch.
+                    control_model_input = latents
+                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
+                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
+                else:
+                    control_model_input = latent_model_input
+                    controlnet_prompt_embeds = prompt_embeds
+
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    control_model_input,
+                    t,
+                    encoder_hidden_states=controlnet_prompt_embeds,
+                    controlnet_cond=image,
+                    conditioning_scale=controlnet_conditioning_scale,
+                    guess_mode=guess_mode,
+                    return_dict=False,
+                )
+
+                if guess_mode and do_classifier_free_guidance:
+                    # Infered ControlNet only for the conditional batch.
+                    # To apply the output of ControlNet to both the unconditional and conditional batches,
+                    # add 0 to the unconditional batch to keep it unchanged.
+                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+
+                # ref only part
+                noise = randn_tensor(
+                    ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
+                )
+                ref_xt = self.scheduler.add_noise(
+                    ref_image_latents,
+                    noise,
+                    t.reshape(
+                        1,
+                    ),
+                )
+                ref_xt = self.scheduler.scale_model_input(ref_xt, t)
+
+                MODE = "write"
+                self.unet(
+                    ref_xt,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )
+
+                # predict the noise residual
+                MODE = "read"
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # If we do sequential model offloading, let's offload unet and controlnet
+        # manually for max memory savings
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.unet.to("cpu")
+            self.controlnet.to("cpu")
+            torch.cuda.empty_cache()
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/stable_diffusion_ipex.py

@@ -0,0 +1,848 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import intel_extension_for_pytorch as ipex
+import torch
+from packaging import version
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    deprecate,
+    is_accelerate_available,
+    is_accelerate_version,
+    logging,
+    randn_tensor,
+    replace_example_docstring,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import StableDiffusionPipeline
+
+        >>> pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
+
+        >>> # For Float32
+        >>> pipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) #value of image height/width should be consistent with the pipeline inference
+        >>> # For BFloat16
+        >>> pipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512) #value of image height/width should be consistent with the pipeline inference
+
+        >>> prompt = "a photo of an astronaut riding a horse on mars"
+        >>> # For Float32
+        >>> image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'
+        >>> # For BFloat16
+        >>> with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
+        >>>     image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'
+        ```
+"""
+
+
+class StableDiffusionIPEXPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion on IPEX.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    def get_input_example(self, prompt, height=None, width=None, guidance_scale=7.5, num_images_per_prompt=1):
+        prompt_embeds = None
+        negative_prompt_embeds = None
+        negative_prompt = None
+        callback_steps = 1
+        generator = None
+        latents = None
+
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+
+        device = "cpu"
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # 5. Prepare latent variables
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            self.unet.in_channels,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+        dummy = torch.ones(1, dtype=torch.int32)
+        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+        latent_model_input = self.scheduler.scale_model_input(latent_model_input, dummy)
+
+        unet_input_example = (latent_model_input, dummy, prompt_embeds)
+        vae_decoder_input_example = latents
+
+        return unet_input_example, vae_decoder_input_example
+
+    def prepare_for_ipex(self, promt, dtype=torch.float32, height=None, width=None, guidance_scale=7.5):
+        self.unet = self.unet.to(memory_format=torch.channels_last)
+        self.vae.decoder = self.vae.decoder.to(memory_format=torch.channels_last)
+        self.text_encoder = self.text_encoder.to(memory_format=torch.channels_last)
+        if self.safety_checker is not None:
+            self.safety_checker = self.safety_checker.to(memory_format=torch.channels_last)
+
+        unet_input_example, vae_decoder_input_example = self.get_input_example(promt, height, width, guidance_scale)
+
+        # optimize with ipex
+        if dtype == torch.bfloat16:
+            self.unet = ipex.optimize(
+                self.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=unet_input_example
+            )
+            self.vae.decoder = ipex.optimize(self.vae.decoder.eval(), dtype=torch.bfloat16, inplace=True)
+            self.text_encoder = ipex.optimize(self.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)
+            if self.safety_checker is not None:
+                self.safety_checker = ipex.optimize(self.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)
+        elif dtype == torch.float32:
+            self.unet = ipex.optimize(
+                self.unet.eval(),
+                dtype=torch.float32,
+                inplace=True,
+                sample_input=unet_input_example,
+                level="O1",
+                weights_prepack=True,
+                auto_kernel_selection=False,
+            )
+            self.vae.decoder = ipex.optimize(
+                self.vae.decoder.eval(),
+                dtype=torch.float32,
+                inplace=True,
+                level="O1",
+                weights_prepack=True,
+                auto_kernel_selection=False,
+            )
+            self.text_encoder = ipex.optimize(
+                self.text_encoder.eval(),
+                dtype=torch.float32,
+                inplace=True,
+                level="O1",
+                weights_prepack=True,
+                auto_kernel_selection=False,
+            )
+            if self.safety_checker is not None:
+                self.safety_checker = ipex.optimize(
+                    self.safety_checker.eval(),
+                    dtype=torch.float32,
+                    inplace=True,
+                    level="O1",
+                    weights_prepack=True,
+                    auto_kernel_selection=False,
+                )
+        else:
+            raise ValueError(" The value of 'dtype' should be 'torch.bfloat16' or 'torch.float32' !")
+
+        # trace unet model to get better performance on IPEX
+        with torch.cpu.amp.autocast(enabled=dtype == torch.bfloat16), torch.no_grad():
+            unet_trace_model = torch.jit.trace(self.unet, unet_input_example, check_trace=False, strict=False)
+            unet_trace_model = torch.jit.freeze(unet_trace_model)
+        self.unet.forward = unet_trace_model.forward
+
+        # trace vae.decoder model to get better performance on IPEX
+        with torch.cpu.amp.autocast(enabled=dtype == torch.bfloat16), torch.no_grad():
+            ave_decoder_trace_model = torch.jit.trace(
+                self.vae.decoder, vae_decoder_input_example, check_trace=False, strict=False
+            )
+            ave_decoder_trace_model = torch.jit.freeze(ave_decoder_trace_model)
+        self.vae.decoder.forward = ave_decoder_trace_model.forward
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
+        steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
+        several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu", silence_dtype_warnings=True)
+            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            cpu_offload(cpu_offloaded_model, device)
+
+        if self.safety_checker is not None:
+            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
+
+    def enable_model_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate import cpu_offload_with_hook
+        else:
+            raise ImportError("`enable_model_offload` requires `accelerate v0.17.0` or higher.")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu", silence_dtype_warnings=True)
+            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        hook = None
+        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
+            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+        if self.safety_checker is not None:
+            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
+
+        # We'll offload the last model manually.
+        self.final_offload_hook = hook
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+        """
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds)["sample"]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+        elif output_type == "pil":
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+            # 10. Convert to PIL
+            image = self.numpy_to_pil(image)
+        else:
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/stable_diffusion_mega.py

@@ -0,0 +1,227 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import PIL.Image
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionImg2ImgPipeline,
+    StableDiffusionInpaintPipelineLegacy,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.configuration_utils import FrozenDict
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.utils import deprecate, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class StableDiffusionMegaPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionMegaSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    @property
+    def components(self) -> Dict[str, Any]:
+        return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def inpaint(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+    ):
+        # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
+        return StableDiffusionInpaintPipelineLegacy(**self.components)(
+            prompt=prompt,
+            image=image,
+            mask_image=mask_image,
+            strength=strength,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+        )
+
+    @torch.no_grad()
+    def img2img(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
+        return StableDiffusionImg2ImgPipeline(**self.components)(
+            prompt=prompt,
+            image=image,
+            strength=strength,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+        )
+
+    @torch.no_grad()
+    def text2img(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+    ):
+        # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
+        return StableDiffusionPipeline(**self.components)(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+        )

v0.19.2/stable_diffusion_reference.py

@@ -0,0 +1,796 @@
+# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+
+from diffusers import StableDiffusionPipeline
+from diffusers.models.attention import BasicTransformerBlock
+from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg
+from diffusers.utils import PIL_INTERPOLATION, logging, randn_tensor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import UniPCMultistepScheduler
+        >>> from diffusers.utils import load_image
+
+        >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+
+        >>> pipe = StableDiffusionReferencePipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5",
+                safety_checker=None,
+                torch_dtype=torch.float16
+                ).to('cuda:0')
+
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
+
+        >>> result_img = pipe(ref_image=input_image,
+                        prompt="1girl",
+                        num_inference_steps=20,
+                        reference_attn=True,
+                        reference_adain=True).images[0]
+
+        >>> result_img.show()
+        ```
+"""
+
+
+def torch_dfs(model: torch.nn.Module):
+    result = [model]
+    for child in model.children():
+        result += torch_dfs(child)
+    return result
+
+
+class StableDiffusionReferencePipeline(StableDiffusionPipeline):
+    def _default_height_width(self, height, width, image):
+        # NOTE: It is possible that a list of images have different
+        # dimensions for each image, so just checking the first image
+        # is not _exactly_ correct, but it is simple.
+        while isinstance(image, list):
+            image = image[0]
+
+        if height is None:
+            if isinstance(image, PIL.Image.Image):
+                height = image.height
+            elif isinstance(image, torch.Tensor):
+                height = image.shape[2]
+
+            height = (height // 8) * 8  # round down to nearest multiple of 8
+
+        if width is None:
+            if isinstance(image, PIL.Image.Image):
+                width = image.width
+            elif isinstance(image, torch.Tensor):
+                width = image.shape[3]
+
+            width = (width // 8) * 8  # round down to nearest multiple of 8
+
+        return height, width
+
+    def prepare_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
+        if not isinstance(image, torch.Tensor):
+            if isinstance(image, PIL.Image.Image):
+                image = [image]
+
+            if isinstance(image[0], PIL.Image.Image):
+                images = []
+
+                for image_ in image:
+                    image_ = image_.convert("RGB")
+                    image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
+                    image_ = np.array(image_)
+                    image_ = image_[None, :]
+                    images.append(image_)
+
+                image = images
+
+                image = np.concatenate(image, axis=0)
+                image = np.array(image).astype(np.float32) / 255.0
+                image = (image - 0.5) / 0.5
+                image = image.transpose(0, 3, 1, 2)
+                image = torch.from_numpy(image)
+            elif isinstance(image[0], torch.Tensor):
+                image = torch.cat(image, dim=0)
+
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance and not guess_mode:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
+        refimage = refimage.to(device=device, dtype=dtype)
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            ref_image_latents = [
+                self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(batch_size)
+            ]
+            ref_image_latents = torch.cat(ref_image_latents, dim=0)
+        else:
+            ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
+        ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
+
+        # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
+        if ref_image_latents.shape[0] < batch_size:
+            if not batch_size % ref_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
+
+        ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
+        return ref_image_latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        ref_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        attention_auto_machine_weight: float = 1.0,
+        gn_auto_machine_weight: float = 1.0,
+        style_fidelity: float = 0.5,
+        reference_attn: bool = True,
+        reference_adain: bool = True,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            ref_image (`torch.FloatTensor`, `PIL.Image.Image`):
+                The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
+                also be accepted as an image.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.7):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            attention_auto_machine_weight (`float`):
+                Weight of using reference query for self attention's context.
+                If attention_auto_machine_weight=1.0, use reference query for all self attention's context.
+            gn_auto_machine_weight (`float`):
+                Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.
+            style_fidelity (`float`):
+                style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,
+                elif style_fidelity=0.0, prompt more important, else balanced.
+            reference_attn (`bool`):
+                Whether to use reference query for self attention's context.
+            reference_adain (`bool`):
+                Whether to use reference adain.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        assert reference_attn or reference_adain, "`reference_attn` or `reference_adain` must be True."
+
+        # 0. Default height and width to unet
+        height, width = self._default_height_width(height, width, ref_image)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+        )
+
+        # 4. Preprocess reference image
+        ref_image = self.prepare_image(
+            image=ref_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=prompt_embeds.dtype,
+        )
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 7. Prepare reference latent variables
+        ref_image_latents = self.prepare_ref_latents(
+            ref_image,
+            batch_size * num_images_per_prompt,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance,
+        )
+
+        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 9. Modify self attention and group norm
+        MODE = "write"
+        uc_mask = (
+            torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
+            .type_as(ref_image_latents)
+            .bool()
+        )
+
+        def hacked_basic_transformer_inner_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+            timestep: Optional[torch.LongTensor] = None,
+            cross_attention_kwargs: Dict[str, Any] = None,
+            class_labels: Optional[torch.LongTensor] = None,
+        ):
+            if self.use_ada_layer_norm:
+                norm_hidden_states = self.norm1(hidden_states, timestep)
+            elif self.use_ada_layer_norm_zero:
+                norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                    hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+                )
+            else:
+                norm_hidden_states = self.norm1(hidden_states)
+
+            # 1. Self-Attention
+            cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
+            if self.only_cross_attention:
+                attn_output = self.attn1(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                    attention_mask=attention_mask,
+                    **cross_attention_kwargs,
+                )
+            else:
+                if MODE == "write":
+                    self.bank.append(norm_hidden_states.detach().clone())
+                    attn_output = self.attn1(
+                        norm_hidden_states,
+                        encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                        attention_mask=attention_mask,
+                        **cross_attention_kwargs,
+                    )
+                if MODE == "read":
+                    if attention_auto_machine_weight > self.attn_weight:
+                        attn_output_uc = self.attn1(
+                            norm_hidden_states,
+                            encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
+                            # attention_mask=attention_mask,
+                            **cross_attention_kwargs,
+                        )
+                        attn_output_c = attn_output_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            attn_output_c[uc_mask] = self.attn1(
+                                norm_hidden_states[uc_mask],
+                                encoder_hidden_states=norm_hidden_states[uc_mask],
+                                **cross_attention_kwargs,
+                            )
+                        attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc
+                        self.bank.clear()
+                    else:
+                        attn_output = self.attn1(
+                            norm_hidden_states,
+                            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                            attention_mask=attention_mask,
+                            **cross_attention_kwargs,
+                        )
+            if self.use_ada_layer_norm_zero:
+                attn_output = gate_msa.unsqueeze(1) * attn_output
+            hidden_states = attn_output + hidden_states
+
+            if self.attn2 is not None:
+                norm_hidden_states = (
+                    self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+                )
+
+                # 2. Cross-Attention
+                attn_output = self.attn2(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=encoder_attention_mask,
+                    **cross_attention_kwargs,
+                )
+                hidden_states = attn_output + hidden_states
+
+            # 3. Feed-forward
+            norm_hidden_states = self.norm3(hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+            ff_output = self.ff(norm_hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+            hidden_states = ff_output + hidden_states
+
+            return hidden_states
+
+        def hacked_mid_forward(self, *args, **kwargs):
+            eps = 1e-6
+            x = self.original_forward(*args, **kwargs)
+            if MODE == "write":
+                if gn_auto_machine_weight >= self.gn_weight:
+                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
+                    self.mean_bank.append(mean)
+                    self.var_bank.append(var)
+            if MODE == "read":
+                if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
+                    std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                    mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
+                    var_acc = sum(self.var_bank) / float(len(self.var_bank))
+                    std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                    x_uc = (((x - mean) / std) * std_acc) + mean_acc
+                    x_c = x_uc.clone()
+                    if do_classifier_free_guidance and style_fidelity > 0:
+                        x_c[uc_mask] = x[uc_mask]
+                    x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
+                self.mean_bank = []
+                self.var_bank = []
+            return x
+
+        def hack_CrossAttnDownBlock2D_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        ):
+            eps = 1e-6
+
+            # TODO(Patrick, William) - attention mask is not used
+            output_states = ()
+
+            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+                output_states = output_states + (hidden_states,)
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.downsamplers is not None:
+                for downsampler in self.downsamplers:
+                    hidden_states = downsampler(hidden_states)
+
+                output_states = output_states + (hidden_states,)
+
+            return hidden_states, output_states
+
+        def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
+            eps = 1e-6
+
+            output_states = ()
+
+            for i, resnet in enumerate(self.resnets):
+                hidden_states = resnet(hidden_states, temb)
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+                output_states = output_states + (hidden_states,)
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.downsamplers is not None:
+                for downsampler in self.downsamplers:
+                    hidden_states = downsampler(hidden_states)
+
+                output_states = output_states + (hidden_states,)
+
+            return hidden_states, output_states
+
+        def hacked_CrossAttnUpBlock2D_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            upsample_size: Optional[int] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        ):
+            eps = 1e-6
+            # TODO(Patrick, William) - attention mask is not used
+            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
+                # pop res hidden states
+                res_hidden_states = res_hidden_states_tuple[-1]
+                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.upsamplers is not None:
+                for upsampler in self.upsamplers:
+                    hidden_states = upsampler(hidden_states, upsample_size)
+
+            return hidden_states
+
+        def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
+            eps = 1e-6
+            for i, resnet in enumerate(self.resnets):
+                # pop res hidden states
+                res_hidden_states = res_hidden_states_tuple[-1]
+                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+                hidden_states = resnet(hidden_states, temb)
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.upsamplers is not None:
+                for upsampler in self.upsamplers:
+                    hidden_states = upsampler(hidden_states, upsample_size)
+
+            return hidden_states
+
+        if reference_attn:
+            attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]
+            attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
+
+            for i, module in enumerate(attn_modules):
+                module._original_inner_forward = module.forward
+                module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
+                module.bank = []
+                module.attn_weight = float(i) / float(len(attn_modules))
+
+        if reference_adain:
+            gn_modules = [self.unet.mid_block]
+            self.unet.mid_block.gn_weight = 0
+
+            down_blocks = self.unet.down_blocks
+            for w, module in enumerate(down_blocks):
+                module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
+                gn_modules.append(module)
+
+            up_blocks = self.unet.up_blocks
+            for w, module in enumerate(up_blocks):
+                module.gn_weight = float(w) / float(len(up_blocks))
+                gn_modules.append(module)
+
+            for i, module in enumerate(gn_modules):
+                if getattr(module, "original_forward", None) is None:
+                    module.original_forward = module.forward
+                if i == 0:
+                    # mid_block
+                    module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
+                elif isinstance(module, CrossAttnDownBlock2D):
+                    module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
+                elif isinstance(module, DownBlock2D):
+                    module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
+                elif isinstance(module, CrossAttnUpBlock2D):
+                    module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
+                elif isinstance(module, UpBlock2D):
+                    module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
+                module.mean_bank = []
+                module.var_bank = []
+                module.gn_weight *= 2
+
+        # 10. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # ref only part
+                noise = randn_tensor(
+                    ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
+                )
+                ref_xt = self.scheduler.add_noise(
+                    ref_image_latents,
+                    noise,
+                    t.reshape(
+                        1,
+                    ),
+                )
+                ref_xt = self.scheduler.scale_model_input(ref_xt, t)
+
+                MODE = "write"
+                self.unet(
+                    ref_xt,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )
+
+                # predict the noise residual
+                MODE = "read"
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if do_classifier_free_guidance and guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/stable_diffusion_repaint.py

@@ -0,0 +1,956 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel
+from diffusers.configuration_utils import FrozenDict, deprecate
+from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+)
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    is_accelerate_available,
+    is_accelerate_version,
+    logging,
+    randn_tensor,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def prepare_mask_and_masked_image(image, mask):
+    """
+    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
+    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
+    ``image`` and ``1`` for the ``mask``.
+    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
+    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
+    Args:
+        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
+            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
+            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
+        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
+            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
+            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
+    Raises:
+        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
+        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
+        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
+            (ot the other way around).
+    Returns:
+        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
+            dimensions: ``batch x channels x height x width``.
+    """
+    if isinstance(image, torch.Tensor):
+        if not isinstance(mask, torch.Tensor):
+            raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
+
+        # Batch single image
+        if image.ndim == 3:
+            assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
+            image = image.unsqueeze(0)
+
+        # Batch and add channel dim for single mask
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0).unsqueeze(0)
+
+        # Batch single mask or add channel dim
+        if mask.ndim == 3:
+            # Single batched mask, no channel dim or single mask not batched but channel dim
+            if mask.shape[0] == 1:
+                mask = mask.unsqueeze(0)
+
+            # Batched masks no channel dim
+            else:
+                mask = mask.unsqueeze(1)
+
+        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
+        assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
+        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
+
+        # Check image is in [-1, 1]
+        if image.min() < -1 or image.max() > 1:
+            raise ValueError("Image should be in [-1, 1] range")
+
+        # Check mask is in [0, 1]
+        if mask.min() < 0 or mask.max() > 1:
+            raise ValueError("Mask should be in [0, 1] range")
+
+        # Binarize mask
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+
+        # Image as float32
+        image = image.to(dtype=torch.float32)
+    elif isinstance(mask, torch.Tensor):
+        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
+    else:
+        # preprocess image
+        if isinstance(image, (PIL.Image.Image, np.ndarray)):
+            image = [image]
+
+        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+        # preprocess mask
+        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
+            mask = [mask]
+
+        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
+            mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
+            mask = mask.astype(np.float32) / 255.0
+        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
+            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
+
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+        mask = torch.from_numpy(mask)
+
+    # masked_image = image * (mask >= 0.5)
+    masked_image = image
+
+    return mask, masked_image
+
+
+class StableDiffusionRepaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
+    r"""
+    Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+    In addition the pipeline inherits the following loading methods:
+        - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
+        - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
+    as well as the following saving methods:
+        - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
+                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
+                " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
+                " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
+                " Hub, it would be very nice if you could open a Pull request for the"
+                " `scheduler/scheduler_config.json` file"
+            )
+            deprecate(
+                "skip_prk_steps not set",
+                "1.0.0",
+                deprecation_message,
+                standard_warn=False,
+            )
+            new_config = dict(scheduler.config)
+            new_config["skip_prk_steps"] = True
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+        # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
+        if unet.config.in_channels != 4:
+            logger.warning(
+                f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
+                f" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,"
+                ". If you did not intend to modify"
+                " this behavior, please check whether you have loaded the right checkpoint."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu", silence_dtype_warnings=True)
+            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            cpu_offload(cpu_offloaded_model, device)
+
+        if self.safety_checker is not None:
+            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
+    def enable_model_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+        """
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate import cpu_offload_with_hook
+        else:
+            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu", silence_dtype_warnings=True)
+            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        hook = None
+        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
+            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+        if self.safety_checker is not None:
+            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
+
+        # We'll offload the last model manually.
+        self.final_offload_hook = hook
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+        """
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        do_classifier_free_guidance,
+    ):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
+        )
+        mask = mask.to(device=device, dtype=dtype)
+
+        masked_image = masked_image.to(device=device, dtype=dtype)
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            masked_image_latents = [
+                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(batch_size)
+            ]
+            masked_image_latents = torch.cat(masked_image_latents, dim=0)
+        else:
+            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
+        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
+
+        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+        masked_image_latents = (
+            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+        )
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+        return mask, masked_image_latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        jump_length: Optional[int] = 10,
+        jump_n_sample: Optional[int] = 10,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            mask_image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            jump_length (`int`, *optional*, defaults to 10):
+                The number of steps taken forward in time before going backward in time for a single jump ("j" in
+                RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
+            jump_n_sample (`int`, *optional*, defaults to 10):
+                The number of times we will make forward time jump for a given chosen time sample. Take a look at
+                Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
+                is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Examples:
+        ```py
+        >>> import PIL
+        >>> import requests
+        >>> import torch
+        >>> from io import BytesIO
+        >>> from diffusers import StableDiffusionPipeline, RePaintScheduler
+        >>> def download_image(url):
+        ...     response = requests.get(url)
+        ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+        >>> base_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/"
+        >>> img_url = base_url + "overture-creations-5sI6fQgYIuo.png"
+        >>> mask_url = base_url + "overture-creations-5sI6fQgYIuo_mask.png "
+        >>> init_image = download_image(img_url).resize((512, 512))
+        >>> mask_image = download_image(mask_url).resize((512, 512))
+        >>> pipe = DiffusionPipeline.from_pretrained(
+        ...     "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, custom_pipeline="stable_diffusion_repaint",
+        ... )
+        >>> pipe.scheduler = RePaintScheduler.from_config(pipe.scheduler.config)
+        >>> pipe = pipe.to("cuda")
+        >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+        >>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
+        ```
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+
+        if image is None:
+            raise ValueError("`image` input cannot be undefined.")
+
+        if mask_image is None:
+            raise ValueError("`mask_image` input cannot be undefined.")
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # 4. Preprocess mask and image
+        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
+
+        # 5. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, device)
+        self.scheduler.eta = eta
+
+        timesteps = self.scheduler.timesteps
+        # latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.vae.config.latent_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 7. Prepare mask latent variables
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask,
+            masked_image,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance=False,  # We do not need duplicate mask and image
+        )
+
+        # 8. Check that sizes of mask, masked image and latents match
+        # num_channels_mask = mask.shape[1]
+        # num_channels_masked_image = masked_image_latents.shape[1]
+        if num_channels_latents != self.unet.config.in_channels:
+            raise ValueError(
+                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} "
+                f" = Please verify the config of"
+                " `pipeline.unet` or your `mask_image` or `image` input."
+            )
+
+        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        t_last = timesteps[0] + 1
+
+        # 10. Denoising loop
+        with self.progress_bar(total=len(timesteps)) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if t >= t_last:
+                    # compute the reverse: x_t-1 -> x_t
+                    latents = self.scheduler.undo_step(latents, t_last, generator)
+                    progress_bar.update()
+                    t_last = t
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+                # concat latents, mask, masked_image_latents in the channel dimension
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                # latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(
+                    noise_pred,
+                    t,
+                    latents,
+                    masked_image_latents,
+                    mask,
+                    **extra_step_kwargs,
+                ).prev_sample
+
+                # call the callback, if provided
+                progress_bar.update()
+                if callback is not None and i % callback_steps == 0:
+                    callback(i, t, latents)
+
+                t_last = t
+
+        # 11. Post-processing
+        image = self.decode_latents(latents)
+
+        # 12. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+        # 13. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

v0.19.2/stable_diffusion_tensorrt_img2img.py

@@ -0,0 +1,1055 @@
+#
+# Copyright 2023 The HuggingFace Inc. team.
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import os
+from collections import OrderedDict
+from copy import copy
+from typing import List, Optional, Union
+
+import numpy as np
+import onnx
+import onnx_graphsurgeon as gs
+import PIL
+import tensorrt as trt
+import torch
+from huggingface_hub import snapshot_download
+from onnx import shape_inference
+from polygraphy import cuda
+from polygraphy.backend.common import bytes_from_path
+from polygraphy.backend.onnx.loader import fold_constants
+from polygraphy.backend.trt import (
+    CreateConfig,
+    Profile,
+    engine_from_bytes,
+    engine_from_network,
+    network_from_onnx_path,
+    save_engine,
+)
+from polygraphy.backend.trt import util as trt_util
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import (
+    StableDiffusionImg2ImgPipeline,
+    StableDiffusionPipelineOutput,
+    StableDiffusionSafetyChecker,
+)
+from diffusers.schedulers import DDIMScheduler
+from diffusers.utils import DIFFUSERS_CACHE, logging
+
+
+"""
+Installation instructions
+python3 -m pip install --upgrade transformers diffusers>=0.16.0
+python3 -m pip install --upgrade tensorrt>=8.6.1
+python3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com
+python3 -m pip install onnxruntime
+"""
+
+TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+# Map of numpy dtype -> torch dtype
+numpy_to_torch_dtype_dict = {
+    np.uint8: torch.uint8,
+    np.int8: torch.int8,
+    np.int16: torch.int16,
+    np.int32: torch.int32,
+    np.int64: torch.int64,
+    np.float16: torch.float16,
+    np.float32: torch.float32,
+    np.float64: torch.float64,
+    np.complex64: torch.complex64,
+    np.complex128: torch.complex128,
+}
+if np.version.full_version >= "1.24.0":
+    numpy_to_torch_dtype_dict[np.bool_] = torch.bool
+else:
+    numpy_to_torch_dtype_dict[np.bool] = torch.bool
+
+# Map of torch dtype -> numpy dtype
+torch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}
+
+
+def device_view(t):
+    return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])
+
+
+def preprocess_image(image):
+    """
+    image: torch.Tensor
+    """
+    w, h = image.size
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h))
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image).contiguous()
+    return 2.0 * image - 1.0
+
+
+class Engine:
+    def __init__(self, engine_path):
+        self.engine_path = engine_path
+        self.engine = None
+        self.context = None
+        self.buffers = OrderedDict()
+        self.tensors = OrderedDict()
+
+    def __del__(self):
+        [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)]
+        del self.engine
+        del self.context
+        del self.buffers
+        del self.tensors
+
+    def build(
+        self,
+        onnx_path,
+        fp16,
+        input_profile=None,
+        enable_preview=False,
+        enable_all_tactics=False,
+        timing_cache=None,
+        workspace_size=0,
+    ):
+        logger.warning(f"Building TensorRT engine for {onnx_path}: {self.engine_path}")
+        p = Profile()
+        if input_profile:
+            for name, dims in input_profile.items():
+                assert len(dims) == 3
+                p.add(name, min=dims[0], opt=dims[1], max=dims[2])
+
+        config_kwargs = {}
+
+        config_kwargs["preview_features"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
+        if enable_preview:
+            # Faster dynamic shapes made optional since it increases engine build time.
+            config_kwargs["preview_features"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)
+        if workspace_size > 0:
+            config_kwargs["memory_pool_limits"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}
+        if not enable_all_tactics:
+            config_kwargs["tactic_sources"] = []
+
+        engine = engine_from_network(
+            network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),
+            config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),
+            save_timing_cache=timing_cache,
+        )
+        save_engine(engine, path=self.engine_path)
+
+    def load(self):
+        logger.warning(f"Loading TensorRT engine: {self.engine_path}")
+        self.engine = engine_from_bytes(bytes_from_path(self.engine_path))
+
+    def activate(self):
+        self.context = self.engine.create_execution_context()
+
+    def allocate_buffers(self, shape_dict=None, device="cuda"):
+        for idx in range(trt_util.get_bindings_per_profile(self.engine)):
+            binding = self.engine[idx]
+            if shape_dict and binding in shape_dict:
+                shape = shape_dict[binding]
+            else:
+                shape = self.engine.get_binding_shape(binding)
+            dtype = trt.nptype(self.engine.get_binding_dtype(binding))
+            if self.engine.binding_is_input(binding):
+                self.context.set_binding_shape(idx, shape)
+            tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)
+            self.tensors[binding] = tensor
+            self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)
+
+    def infer(self, feed_dict, stream):
+        start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)
+        # shallow copy of ordered dict
+        device_buffers = copy(self.buffers)
+        for name, buf in feed_dict.items():
+            assert isinstance(buf, cuda.DeviceView)
+            device_buffers[name] = buf
+        bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]
+        noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)
+        if not noerror:
+            raise ValueError("ERROR: inference failed.")
+
+        return self.tensors
+
+
+class Optimizer:
+    def __init__(self, onnx_graph):
+        self.graph = gs.import_onnx(onnx_graph)
+
+    def cleanup(self, return_onnx=False):
+        self.graph.cleanup().toposort()
+        if return_onnx:
+            return gs.export_onnx(self.graph)
+
+    def select_outputs(self, keep, names=None):
+        self.graph.outputs = [self.graph.outputs[o] for o in keep]
+        if names:
+            for i, name in enumerate(names):
+                self.graph.outputs[i].name = name
+
+    def fold_constants(self, return_onnx=False):
+        onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)
+        self.graph = gs.import_onnx(onnx_graph)
+        if return_onnx:
+            return onnx_graph
+
+    def infer_shapes(self, return_onnx=False):
+        onnx_graph = gs.export_onnx(self.graph)
+        if onnx_graph.ByteSize() > 2147483648:
+            raise TypeError("ERROR: model size exceeds supported 2GB limit")
+        else:
+            onnx_graph = shape_inference.infer_shapes(onnx_graph)
+
+        self.graph = gs.import_onnx(onnx_graph)
+        if return_onnx:
+            return onnx_graph
+
+
+class BaseModel:
+    def __init__(self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77):
+        self.model = model
+        self.name = "SD Model"
+        self.fp16 = fp16
+        self.device = device
+
+        self.min_batch = 1
+        self.max_batch = max_batch_size
+        self.min_image_shape = 256  # min image resolution: 256x256
+        self.max_image_shape = 1024  # max image resolution: 1024x1024
+        self.min_latent_shape = self.min_image_shape // 8
+        self.max_latent_shape = self.max_image_shape // 8
+
+        self.embedding_dim = embedding_dim
+        self.text_maxlen = text_maxlen
+
+    def get_model(self):
+        return self.model
+
+    def get_input_names(self):
+        pass
+
+    def get_output_names(self):
+        pass
+
+    def get_dynamic_axes(self):
+        return None
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        pass
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        return None
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        return None
+
+    def optimize(self, onnx_graph):
+        opt = Optimizer(onnx_graph)
+        opt.cleanup()
+        opt.fold_constants()
+        opt.infer_shapes()
+        onnx_opt_graph = opt.cleanup(return_onnx=True)
+        return onnx_opt_graph
+
+    def check_dims(self, batch_size, image_height, image_width):
+        assert batch_size >= self.min_batch and batch_size <= self.max_batch
+        assert image_height % 8 == 0 or image_width % 8 == 0
+        latent_height = image_height // 8
+        latent_width = image_width // 8
+        assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape
+        assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape
+        return (latent_height, latent_width)
+
+    def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):
+        min_batch = batch_size if static_batch else self.min_batch
+        max_batch = batch_size if static_batch else self.max_batch
+        latent_height = image_height // 8
+        latent_width = image_width // 8
+        min_image_height = image_height if static_shape else self.min_image_shape
+        max_image_height = image_height if static_shape else self.max_image_shape
+        min_image_width = image_width if static_shape else self.min_image_shape
+        max_image_width = image_width if static_shape else self.max_image_shape
+        min_latent_height = latent_height if static_shape else self.min_latent_shape
+        max_latent_height = latent_height if static_shape else self.max_latent_shape
+        min_latent_width = latent_width if static_shape else self.min_latent_shape
+        max_latent_width = latent_width if static_shape else self.max_latent_shape
+        return (
+            min_batch,
+            max_batch,
+            min_image_height,
+            max_image_height,
+            min_image_width,
+            max_image_width,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        )
+
+
+def getOnnxPath(model_name, onnx_dir, opt=True):
+    return os.path.join(onnx_dir, model_name + (".opt" if opt else "") + ".onnx")
+
+
+def getEnginePath(model_name, engine_dir):
+    return os.path.join(engine_dir, model_name + ".plan")
+
+
+def build_engines(
+    models: dict,
+    engine_dir,
+    onnx_dir,
+    onnx_opset,
+    opt_image_height,
+    opt_image_width,
+    opt_batch_size=1,
+    force_engine_rebuild=False,
+    static_batch=False,
+    static_shape=True,
+    enable_preview=False,
+    enable_all_tactics=False,
+    timing_cache=None,
+    max_workspace_size=0,
+):
+    built_engines = {}
+    if not os.path.isdir(onnx_dir):
+        os.makedirs(onnx_dir)
+    if not os.path.isdir(engine_dir):
+        os.makedirs(engine_dir)
+
+    # Export models to ONNX
+    for model_name, model_obj in models.items():
+        engine_path = getEnginePath(model_name, engine_dir)
+        if force_engine_rebuild or not os.path.exists(engine_path):
+            logger.warning("Building Engines...")
+            logger.warning("Engine build can take a while to complete")
+            onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
+            onnx_opt_path = getOnnxPath(model_name, onnx_dir)
+            if force_engine_rebuild or not os.path.exists(onnx_opt_path):
+                if force_engine_rebuild or not os.path.exists(onnx_path):
+                    logger.warning(f"Exporting model: {onnx_path}")
+                    model = model_obj.get_model()
+                    with torch.inference_mode(), torch.autocast("cuda"):
+                        inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width)
+                        torch.onnx.export(
+                            model,
+                            inputs,
+                            onnx_path,
+                            export_params=True,
+                            opset_version=onnx_opset,
+                            do_constant_folding=True,
+                            input_names=model_obj.get_input_names(),
+                            output_names=model_obj.get_output_names(),
+                            dynamic_axes=model_obj.get_dynamic_axes(),
+                        )
+                    del model
+                    torch.cuda.empty_cache()
+                    gc.collect()
+                else:
+                    logger.warning(f"Found cached model: {onnx_path}")
+
+                # Optimize onnx
+                if force_engine_rebuild or not os.path.exists(onnx_opt_path):
+                    logger.warning(f"Generating optimizing model: {onnx_opt_path}")
+                    onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path))
+                    onnx.save(onnx_opt_graph, onnx_opt_path)
+                else:
+                    logger.warning(f"Found cached optimized model: {onnx_opt_path} ")
+
+    # Build TensorRT engines
+    for model_name, model_obj in models.items():
+        engine_path = getEnginePath(model_name, engine_dir)
+        engine = Engine(engine_path)
+        onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
+        onnx_opt_path = getOnnxPath(model_name, onnx_dir)
+
+        if force_engine_rebuild or not os.path.exists(engine.engine_path):
+            engine.build(
+                onnx_opt_path,
+                fp16=True,
+                input_profile=model_obj.get_input_profile(
+                    opt_batch_size,
+                    opt_image_height,
+                    opt_image_width,
+                    static_batch=static_batch,
+                    static_shape=static_shape,
+                ),
+                enable_preview=enable_preview,
+                timing_cache=timing_cache,
+                workspace_size=max_workspace_size,
+            )
+        built_engines[model_name] = engine
+
+    # Load and activate TensorRT engines
+    for model_name, model_obj in models.items():
+        engine = built_engines[model_name]
+        engine.load()
+        engine.activate()
+
+    return built_engines
+
+
+def runEngine(engine, feed_dict, stream):
+    return engine.infer(feed_dict, stream)
+
+
+class CLIP(BaseModel):
+    def __init__(self, model, device, max_batch_size, embedding_dim):
+        super(CLIP, self).__init__(
+            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
+        )
+        self.name = "CLIP"
+
+    def get_input_names(self):
+        return ["input_ids"]
+
+    def get_output_names(self):
+        return ["text_embeddings", "pooler_output"]
+
+    def get_dynamic_axes(self):
+        return {"input_ids": {0: "B"}, "text_embeddings": {0: "B"}}
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        self.check_dims(batch_size, image_height, image_width)
+        min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(
+            batch_size, image_height, image_width, static_batch, static_shape
+        )
+        return {
+            "input_ids": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        self.check_dims(batch_size, image_height, image_width)
+        return {
+            "input_ids": (batch_size, self.text_maxlen),
+            "text_embeddings": (batch_size, self.text_maxlen, self.embedding_dim),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        self.check_dims(batch_size, image_height, image_width)
+        return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)
+
+    def optimize(self, onnx_graph):
+        opt = Optimizer(onnx_graph)
+        opt.select_outputs([0])  # delete graph output#1
+        opt.cleanup()
+        opt.fold_constants()
+        opt.infer_shapes()
+        opt.select_outputs([0], names=["text_embeddings"])  # rename network output
+        opt_onnx_graph = opt.cleanup(return_onnx=True)
+        return opt_onnx_graph
+
+
+def make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
+
+
+class UNet(BaseModel):
+    def __init__(
+        self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4
+    ):
+        super(UNet, self).__init__(
+            model=model,
+            fp16=fp16,
+            device=device,
+            max_batch_size=max_batch_size,
+            embedding_dim=embedding_dim,
+            text_maxlen=text_maxlen,
+        )
+        self.unet_dim = unet_dim
+        self.name = "UNet"
+
+    def get_input_names(self):
+        return ["sample", "timestep", "encoder_hidden_states"]
+
+    def get_output_names(self):
+        return ["latent"]
+
+    def get_dynamic_axes(self):
+        return {
+            "sample": {0: "2B", 2: "H", 3: "W"},
+            "encoder_hidden_states": {0: "2B"},
+            "latent": {0: "2B", 2: "H", 3: "W"},
+        }
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        (
+            min_batch,
+            max_batch,
+            _,
+            _,
+            _,
+            _,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
+        return {
+            "sample": [
+                (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width),
+                (2 * batch_size, self.unet_dim, latent_height, latent_width),
+                (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width),
+            ],
+            "encoder_hidden_states": [
+                (2 * min_batch, self.text_maxlen, self.embedding_dim),
+                (2 * batch_size, self.text_maxlen, self.embedding_dim),
+                (2 * max_batch, self.text_maxlen, self.embedding_dim),
+            ],
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return {
+            "sample": (2 * batch_size, self.unet_dim, latent_height, latent_width),
+            "encoder_hidden_states": (2 * batch_size, self.text_maxlen, self.embedding_dim),
+            "latent": (2 * batch_size, 4, latent_height, latent_width),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        dtype = torch.float16 if self.fp16 else torch.float32
+        return (
+            torch.randn(
+                2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device
+            ),
+            torch.tensor([1.0], dtype=torch.float32, device=self.device),
+            torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
+        )
+
+
+def make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return UNet(
+        model,
+        fp16=True,
+        device=device,
+        max_batch_size=max_batch_size,
+        embedding_dim=embedding_dim,
+        unet_dim=(9 if inpaint else 4),
+    )
+
+
+class VAE(BaseModel):
+    def __init__(self, model, device, max_batch_size, embedding_dim):
+        super(VAE, self).__init__(
+            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
+        )
+        self.name = "VAE decoder"
+
+    def get_input_names(self):
+        return ["latent"]
+
+    def get_output_names(self):
+        return ["images"]
+
+    def get_dynamic_axes(self):
+        return {"latent": {0: "B", 2: "H", 3: "W"}, "images": {0: "B", 2: "8H", 3: "8W"}}
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        (
+            min_batch,
+            max_batch,
+            _,
+            _,
+            _,
+            _,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
+        return {
+            "latent": [
+                (min_batch, 4, min_latent_height, min_latent_width),
+                (batch_size, 4, latent_height, latent_width),
+                (max_batch, 4, max_latent_height, max_latent_width),
+            ]
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return {
+            "latent": (batch_size, 4, latent_height, latent_width),
+            "images": (batch_size, 3, image_height, image_width),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device)
+
+
+def make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
+
+
+class TorchVAEEncoder(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.vae_encoder = model
+
+    def forward(self, x):
+        return self.vae_encoder.encode(x).latent_dist.sample()
+
+
+class VAEEncoder(BaseModel):
+    def __init__(self, model, device, max_batch_size, embedding_dim):
+        super(VAEEncoder, self).__init__(
+            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
+        )
+        self.name = "VAE encoder"
+
+    def get_model(self):
+        vae_encoder = TorchVAEEncoder(self.model)
+        return vae_encoder
+
+    def get_input_names(self):
+        return ["images"]
+
+    def get_output_names(self):
+        return ["latent"]
+
+    def get_dynamic_axes(self):
+        return {"images": {0: "B", 2: "8H", 3: "8W"}, "latent": {0: "B", 2: "H", 3: "W"}}
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        assert batch_size >= self.min_batch and batch_size <= self.max_batch
+        min_batch = batch_size if static_batch else self.min_batch
+        max_batch = batch_size if static_batch else self.max_batch
+        self.check_dims(batch_size, image_height, image_width)
+        (
+            min_batch,
+            max_batch,
+            min_image_height,
+            max_image_height,
+            min_image_width,
+            max_image_width,
+            _,
+            _,
+            _,
+            _,
+        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
+
+        return {
+            "images": [
+                (min_batch, 3, min_image_height, min_image_width),
+                (batch_size, 3, image_height, image_width),
+                (max_batch, 3, max_image_height, max_image_width),
+            ]
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return {
+            "images": (batch_size, 3, image_height, image_width),
+            "latent": (batch_size, 4, latent_height, latent_width),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        self.check_dims(batch_size, image_height, image_width)
+        return torch.randn(batch_size, 3, image_height, image_width, dtype=torch.float32, device=self.device)
+
+
+def make_VAEEncoder(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return VAEEncoder(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
+
+
+class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
+    r"""
+    Pipeline for image-to-image generation using TensorRT accelerated Stable Diffusion.
+
+    This model inherits from [`StableDiffusionImg2ImgPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: DDIMScheduler,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+        stages=["clip", "unet", "vae", "vae_encoder"],
+        image_height: int = 512,
+        image_width: int = 512,
+        max_batch_size: int = 16,
+        # ONNX export parameters
+        onnx_opset: int = 17,
+        onnx_dir: str = "onnx",
+        # TensorRT engine build parameters
+        engine_dir: str = "engine",
+        build_preview_features: bool = True,
+        force_engine_rebuild: bool = False,
+        timing_cache: str = "timing_cache",
+    ):
+        super().__init__(
+            vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker
+        )
+
+        self.vae.forward = self.vae.decode
+
+        self.stages = stages
+        self.image_height, self.image_width = image_height, image_width
+        self.inpaint = False
+        self.onnx_opset = onnx_opset
+        self.onnx_dir = onnx_dir
+        self.engine_dir = engine_dir
+        self.force_engine_rebuild = force_engine_rebuild
+        self.timing_cache = timing_cache
+        self.build_static_batch = False
+        self.build_dynamic_shape = False
+        self.build_preview_features = build_preview_features
+
+        self.max_batch_size = max_batch_size
+        # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.
+        if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512:
+            self.max_batch_size = 4
+
+        self.stream = None  # loaded in loadResources()
+        self.models = {}  # loaded in __loadModels()
+        self.engine = {}  # loaded in build_engines()
+
+    def __loadModels(self):
+        # Load pipeline models
+        self.embedding_dim = self.text_encoder.config.hidden_size
+        models_args = {
+            "device": self.torch_device,
+            "max_batch_size": self.max_batch_size,
+            "embedding_dim": self.embedding_dim,
+            "inpaint": self.inpaint,
+        }
+        if "clip" in self.stages:
+            self.models["clip"] = make_CLIP(self.text_encoder, **models_args)
+        if "unet" in self.stages:
+            self.models["unet"] = make_UNet(self.unet, **models_args)
+        if "vae" in self.stages:
+            self.models["vae"] = make_VAE(self.vae, **models_args)
+        if "vae_encoder" in self.stages:
+            self.models["vae_encoder"] = make_VAEEncoder(self.vae, **models_args)
+
+    @classmethod
+    def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
+        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+
+        cls.cached_folder = (
+            pretrained_model_name_or_path
+            if os.path.isdir(pretrained_model_name_or_path)
+            else snapshot_download(
+                pretrained_model_name_or_path,
+                cache_dir=cache_dir,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                use_auth_token=use_auth_token,
+                revision=revision,
+            )
+        )
+
+    def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False):
+        super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings)
+
+        self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir)
+        self.engine_dir = os.path.join(self.cached_folder, self.engine_dir)
+        self.timing_cache = os.path.join(self.cached_folder, self.timing_cache)
+
+        # set device
+        self.torch_device = self._execution_device
+        logger.warning(f"Running inference on device: {self.torch_device}")
+
+        # load models
+        self.__loadModels()
+
+        # build engines
+        self.engine = build_engines(
+            self.models,
+            self.engine_dir,
+            self.onnx_dir,
+            self.onnx_opset,
+            opt_image_height=self.image_height,
+            opt_image_width=self.image_width,
+            force_engine_rebuild=self.force_engine_rebuild,
+            static_batch=self.build_static_batch,
+            static_shape=not self.build_dynamic_shape,
+            enable_preview=self.build_preview_features,
+            timing_cache=self.timing_cache,
+        )
+
+        return self
+
+    def __initialize_timesteps(self, timesteps, strength):
+        self.scheduler.set_timesteps(timesteps)
+        offset = self.scheduler.steps_offset if hasattr(self.scheduler, "steps_offset") else 0
+        init_timestep = int(timesteps * strength) + offset
+        init_timestep = min(init_timestep, timesteps)
+        t_start = max(timesteps - init_timestep + offset, 0)
+        timesteps = self.scheduler.timesteps[t_start:].to(self.torch_device)
+        return timesteps, t_start
+
+    def __preprocess_images(self, batch_size, images=()):
+        init_images = []
+        for image in images:
+            image = image.to(self.torch_device).float()
+            image = image.repeat(batch_size, 1, 1, 1)
+            init_images.append(image)
+        return tuple(init_images)
+
+    def __encode_image(self, init_image):
+        init_latents = runEngine(self.engine["vae_encoder"], {"images": device_view(init_image)}, self.stream)[
+            "latent"
+        ]
+        init_latents = 0.18215 * init_latents
+        return init_latents
+
+    def __encode_prompt(self, prompt, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+        """
+        # Tokenize prompt
+        text_input_ids = (
+            self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            .input_ids.type(torch.int32)
+            .to(self.torch_device)
+        )
+
+        text_input_ids_inp = device_view(text_input_ids)
+        # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt
+        text_embeddings = runEngine(self.engine["clip"], {"input_ids": text_input_ids_inp}, self.stream)[
+            "text_embeddings"
+        ].clone()
+
+        # Tokenize negative prompt
+        uncond_input_ids = (
+            self.tokenizer(
+                negative_prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            .input_ids.type(torch.int32)
+            .to(self.torch_device)
+        )
+        uncond_input_ids_inp = device_view(uncond_input_ids)
+        uncond_embeddings = runEngine(self.engine["clip"], {"input_ids": uncond_input_ids_inp}, self.stream)[
+            "text_embeddings"
+        ]
+
+        # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance
+        text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16)
+
+        return text_embeddings
+
+    def __denoise_latent(
+        self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None
+    ):
+        if not isinstance(timesteps, torch.Tensor):
+            timesteps = self.scheduler.timesteps
+        for step_index, timestep in enumerate(timesteps):
+            # Expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)
+            if isinstance(mask, torch.Tensor):
+                latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+            # Predict the noise residual
+            timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep
+
+            sample_inp = device_view(latent_model_input)
+            timestep_inp = device_view(timestep_float)
+            embeddings_inp = device_view(text_embeddings)
+            noise_pred = runEngine(
+                self.engine["unet"],
+                {"sample": sample_inp, "timestep": timestep_inp, "encoder_hidden_states": embeddings_inp},
+                self.stream,
+            )["latent"]
+
+            # Perform guidance
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample
+
+        latents = 1.0 / 0.18215 * latents
+        return latents
+
+    def __decode_latent(self, latents):
+        images = runEngine(self.engine["vae"], {"latent": device_view(latents)}, self.stream)["images"]
+        images = (images / 2 + 0.5).clamp(0, 1)
+        return images.cpu().permute(0, 2, 3, 1).float().numpy()
+
+    def __loadResources(self, image_height, image_width, batch_size):
+        self.stream = cuda.Stream()
+
+        # Allocate buffers for TensorRT engine bindings
+        for model_name, obj in self.models.items():
+            self.engine[model_name].allocate_buffers(
+                shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device
+            )
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        strength: float = 0.8,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
+                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
+                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
+                be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+
+        """
+        self.generator = generator
+        self.denoising_steps = num_inference_steps
+        self.guidance_scale = guidance_scale
+
+        # Pre-compute latent input scales and linear multistep coefficients
+        self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)
+
+        # Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+            prompt = [prompt]
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"Expected prompt to be of type list or str but got {type(prompt)}")
+
+        if negative_prompt is None:
+            negative_prompt = [""] * batch_size
+
+        if negative_prompt is not None and isinstance(negative_prompt, str):
+            negative_prompt = [negative_prompt]
+
+        assert len(prompt) == len(negative_prompt)
+
+        if batch_size > self.max_batch_size:
+            raise ValueError(
+                f"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4"
+            )
+
+        # load resources
+        self.__loadResources(self.image_height, self.image_width, batch_size)
+
+        with torch.inference_mode(), torch.autocast("cuda"), trt.Runtime(TRT_LOGGER):
+            # Initialize timesteps
+            timesteps, t_start = self.__initialize_timesteps(self.denoising_steps, strength)
+            latent_timestep = timesteps[:1].repeat(batch_size)
+
+            # Pre-process input image
+            if isinstance(image, PIL.Image.Image):
+                image = preprocess_image(image)
+            init_image = self.__preprocess_images(batch_size, (image,))[0]
+
+            # VAE encode init image
+            init_latents = self.__encode_image(init_image)
+
+            # Add noise to latents using timesteps
+            noise = torch.randn(
+                init_latents.shape, generator=self.generator, device=self.torch_device, dtype=torch.float32
+            )
+            latents = self.scheduler.add_noise(init_latents, noise, latent_timestep)
+
+            # CLIP text encoder
+            text_embeddings = self.__encode_prompt(prompt, negative_prompt)
+
+            # UNet denoiser
+            latents = self.__denoise_latent(latents, text_embeddings, timesteps=timesteps, step_offset=t_start)
+
+            # VAE decode latent
+            images = self.__decode_latent(latents)
+
+        images = self.numpy_to_pil(images)
+        return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=None)

v0.19.2/stable_diffusion_tensorrt_inpaint.py

@@ -0,0 +1,1088 @@
+#
+# Copyright 2023 The HuggingFace Inc. team.
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import os
+from collections import OrderedDict
+from copy import copy
+from typing import List, Optional, Union
+
+import numpy as np
+import onnx
+import onnx_graphsurgeon as gs
+import PIL
+import tensorrt as trt
+import torch
+from huggingface_hub import snapshot_download
+from onnx import shape_inference
+from polygraphy import cuda
+from polygraphy.backend.common import bytes_from_path
+from polygraphy.backend.onnx.loader import fold_constants
+from polygraphy.backend.trt import (
+    CreateConfig,
+    Profile,
+    engine_from_bytes,
+    engine_from_network,
+    network_from_onnx_path,
+    save_engine,
+)
+from polygraphy.backend.trt import util as trt_util
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import (
+    StableDiffusionInpaintPipeline,
+    StableDiffusionPipelineOutput,
+    StableDiffusionSafetyChecker,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image
+from diffusers.schedulers import DDIMScheduler
+from diffusers.utils import DIFFUSERS_CACHE, logging
+
+
+"""
+Installation instructions
+python3 -m pip install --upgrade transformers diffusers>=0.16.0
+python3 -m pip install --upgrade tensorrt>=8.6.1
+python3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com
+python3 -m pip install onnxruntime
+"""
+
+TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+# Map of numpy dtype -> torch dtype
+numpy_to_torch_dtype_dict = {
+    np.uint8: torch.uint8,
+    np.int8: torch.int8,
+    np.int16: torch.int16,
+    np.int32: torch.int32,
+    np.int64: torch.int64,
+    np.float16: torch.float16,
+    np.float32: torch.float32,
+    np.float64: torch.float64,
+    np.complex64: torch.complex64,
+    np.complex128: torch.complex128,
+}
+if np.version.full_version >= "1.24.0":
+    numpy_to_torch_dtype_dict[np.bool_] = torch.bool
+else:
+    numpy_to_torch_dtype_dict[np.bool] = torch.bool
+
+# Map of torch dtype -> numpy dtype
+torch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}
+
+
+def device_view(t):
+    return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])
+
+
+def preprocess_image(image):
+    """
+    image: torch.Tensor
+    """
+    w, h = image.size
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h))
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image).contiguous()
+    return 2.0 * image - 1.0
+
+
+class Engine:
+    def __init__(self, engine_path):
+        self.engine_path = engine_path
+        self.engine = None
+        self.context = None
+        self.buffers = OrderedDict()
+        self.tensors = OrderedDict()
+
+    def __del__(self):
+        [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)]
+        del self.engine
+        del self.context
+        del self.buffers
+        del self.tensors
+
+    def build(
+        self,
+        onnx_path,
+        fp16,
+        input_profile=None,
+        enable_preview=False,
+        enable_all_tactics=False,
+        timing_cache=None,
+        workspace_size=0,
+    ):
+        logger.warning(f"Building TensorRT engine for {onnx_path}: {self.engine_path}")
+        p = Profile()
+        if input_profile:
+            for name, dims in input_profile.items():
+                assert len(dims) == 3
+                p.add(name, min=dims[0], opt=dims[1], max=dims[2])
+
+        config_kwargs = {}
+
+        config_kwargs["preview_features"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
+        if enable_preview:
+            # Faster dynamic shapes made optional since it increases engine build time.
+            config_kwargs["preview_features"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)
+        if workspace_size > 0:
+            config_kwargs["memory_pool_limits"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}
+        if not enable_all_tactics:
+            config_kwargs["tactic_sources"] = []
+
+        engine = engine_from_network(
+            network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),
+            config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),
+            save_timing_cache=timing_cache,
+        )
+        save_engine(engine, path=self.engine_path)
+
+    def load(self):
+        logger.warning(f"Loading TensorRT engine: {self.engine_path}")
+        self.engine = engine_from_bytes(bytes_from_path(self.engine_path))
+
+    def activate(self):
+        self.context = self.engine.create_execution_context()
+
+    def allocate_buffers(self, shape_dict=None, device="cuda"):
+        for idx in range(trt_util.get_bindings_per_profile(self.engine)):
+            binding = self.engine[idx]
+            if shape_dict and binding in shape_dict:
+                shape = shape_dict[binding]
+            else:
+                shape = self.engine.get_binding_shape(binding)
+            dtype = trt.nptype(self.engine.get_binding_dtype(binding))
+            if self.engine.binding_is_input(binding):
+                self.context.set_binding_shape(idx, shape)
+            tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)
+            self.tensors[binding] = tensor
+            self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)
+
+    def infer(self, feed_dict, stream):
+        start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)
+        # shallow copy of ordered dict
+        device_buffers = copy(self.buffers)
+        for name, buf in feed_dict.items():
+            assert isinstance(buf, cuda.DeviceView)
+            device_buffers[name] = buf
+        bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]
+        noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)
+        if not noerror:
+            raise ValueError("ERROR: inference failed.")
+
+        return self.tensors
+
+
+class Optimizer:
+    def __init__(self, onnx_graph):
+        self.graph = gs.import_onnx(onnx_graph)
+
+    def cleanup(self, return_onnx=False):
+        self.graph.cleanup().toposort()
+        if return_onnx:
+            return gs.export_onnx(self.graph)
+
+    def select_outputs(self, keep, names=None):
+        self.graph.outputs = [self.graph.outputs[o] for o in keep]
+        if names:
+            for i, name in enumerate(names):
+                self.graph.outputs[i].name = name
+
+    def fold_constants(self, return_onnx=False):
+        onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)
+        self.graph = gs.import_onnx(onnx_graph)
+        if return_onnx:
+            return onnx_graph
+
+    def infer_shapes(self, return_onnx=False):
+        onnx_graph = gs.export_onnx(self.graph)
+        if onnx_graph.ByteSize() > 2147483648:
+            raise TypeError("ERROR: model size exceeds supported 2GB limit")
+        else:
+            onnx_graph = shape_inference.infer_shapes(onnx_graph)
+
+        self.graph = gs.import_onnx(onnx_graph)
+        if return_onnx:
+            return onnx_graph
+
+
+class BaseModel:
+    def __init__(self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77):
+        self.model = model
+        self.name = "SD Model"
+        self.fp16 = fp16
+        self.device = device
+
+        self.min_batch = 1
+        self.max_batch = max_batch_size
+        self.min_image_shape = 256  # min image resolution: 256x256
+        self.max_image_shape = 1024  # max image resolution: 1024x1024
+        self.min_latent_shape = self.min_image_shape // 8
+        self.max_latent_shape = self.max_image_shape // 8
+
+        self.embedding_dim = embedding_dim
+        self.text_maxlen = text_maxlen
+
+    def get_model(self):
+        return self.model
+
+    def get_input_names(self):
+        pass
+
+    def get_output_names(self):
+        pass
+
+    def get_dynamic_axes(self):
+        return None
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        pass
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        return None
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        return None
+
+    def optimize(self, onnx_graph):
+        opt = Optimizer(onnx_graph)
+        opt.cleanup()
+        opt.fold_constants()
+        opt.infer_shapes()
+        onnx_opt_graph = opt.cleanup(return_onnx=True)
+        return onnx_opt_graph
+
+    def check_dims(self, batch_size, image_height, image_width):
+        assert batch_size >= self.min_batch and batch_size <= self.max_batch
+        assert image_height % 8 == 0 or image_width % 8 == 0
+        latent_height = image_height // 8
+        latent_width = image_width // 8
+        assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape
+        assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape
+        return (latent_height, latent_width)
+
+    def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):
+        min_batch = batch_size if static_batch else self.min_batch
+        max_batch = batch_size if static_batch else self.max_batch
+        latent_height = image_height // 8
+        latent_width = image_width // 8
+        min_image_height = image_height if static_shape else self.min_image_shape
+        max_image_height = image_height if static_shape else self.max_image_shape
+        min_image_width = image_width if static_shape else self.min_image_shape
+        max_image_width = image_width if static_shape else self.max_image_shape
+        min_latent_height = latent_height if static_shape else self.min_latent_shape
+        max_latent_height = latent_height if static_shape else self.max_latent_shape
+        min_latent_width = latent_width if static_shape else self.min_latent_shape
+        max_latent_width = latent_width if static_shape else self.max_latent_shape
+        return (
+            min_batch,
+            max_batch,
+            min_image_height,
+            max_image_height,
+            min_image_width,
+            max_image_width,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        )
+
+
+def getOnnxPath(model_name, onnx_dir, opt=True):
+    return os.path.join(onnx_dir, model_name + (".opt" if opt else "") + ".onnx")
+
+
+def getEnginePath(model_name, engine_dir):
+    return os.path.join(engine_dir, model_name + ".plan")
+
+
+def build_engines(
+    models: dict,
+    engine_dir,
+    onnx_dir,
+    onnx_opset,
+    opt_image_height,
+    opt_image_width,
+    opt_batch_size=1,
+    force_engine_rebuild=False,
+    static_batch=False,
+    static_shape=True,
+    enable_preview=False,
+    enable_all_tactics=False,
+    timing_cache=None,
+    max_workspace_size=0,
+):
+    built_engines = {}
+    if not os.path.isdir(onnx_dir):
+        os.makedirs(onnx_dir)
+    if not os.path.isdir(engine_dir):
+        os.makedirs(engine_dir)
+
+    # Export models to ONNX
+    for model_name, model_obj in models.items():
+        engine_path = getEnginePath(model_name, engine_dir)
+        if force_engine_rebuild or not os.path.exists(engine_path):
+            logger.warning("Building Engines...")
+            logger.warning("Engine build can take a while to complete")
+            onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
+            onnx_opt_path = getOnnxPath(model_name, onnx_dir)
+            if force_engine_rebuild or not os.path.exists(onnx_opt_path):
+                if force_engine_rebuild or not os.path.exists(onnx_path):
+                    logger.warning(f"Exporting model: {onnx_path}")
+                    model = model_obj.get_model()
+                    with torch.inference_mode(), torch.autocast("cuda"):
+                        inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width)
+                        torch.onnx.export(
+                            model,
+                            inputs,
+                            onnx_path,
+                            export_params=True,
+                            opset_version=onnx_opset,
+                            do_constant_folding=True,
+                            input_names=model_obj.get_input_names(),
+                            output_names=model_obj.get_output_names(),
+                            dynamic_axes=model_obj.get_dynamic_axes(),
+                        )
+                    del model
+                    torch.cuda.empty_cache()
+                    gc.collect()
+                else:
+                    logger.warning(f"Found cached model: {onnx_path}")
+
+                # Optimize onnx
+                if force_engine_rebuild or not os.path.exists(onnx_opt_path):
+                    logger.warning(f"Generating optimizing model: {onnx_opt_path}")
+                    onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path))
+                    onnx.save(onnx_opt_graph, onnx_opt_path)
+                else:
+                    logger.warning(f"Found cached optimized model: {onnx_opt_path} ")
+
+    # Build TensorRT engines
+    for model_name, model_obj in models.items():
+        engine_path = getEnginePath(model_name, engine_dir)
+        engine = Engine(engine_path)
+        onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
+        onnx_opt_path = getOnnxPath(model_name, onnx_dir)
+
+        if force_engine_rebuild or not os.path.exists(engine.engine_path):
+            engine.build(
+                onnx_opt_path,
+                fp16=True,
+                input_profile=model_obj.get_input_profile(
+                    opt_batch_size,
+                    opt_image_height,
+                    opt_image_width,
+                    static_batch=static_batch,
+                    static_shape=static_shape,
+                ),
+                enable_preview=enable_preview,
+                timing_cache=timing_cache,
+                workspace_size=max_workspace_size,
+            )
+        built_engines[model_name] = engine
+
+    # Load and activate TensorRT engines
+    for model_name, model_obj in models.items():
+        engine = built_engines[model_name]
+        engine.load()
+        engine.activate()
+
+    return built_engines
+
+
+def runEngine(engine, feed_dict, stream):
+    return engine.infer(feed_dict, stream)
+
+
+class CLIP(BaseModel):
+    def __init__(self, model, device, max_batch_size, embedding_dim):
+        super(CLIP, self).__init__(
+            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
+        )
+        self.name = "CLIP"
+
+    def get_input_names(self):
+        return ["input_ids"]
+
+    def get_output_names(self):
+        return ["text_embeddings", "pooler_output"]
+
+    def get_dynamic_axes(self):
+        return {"input_ids": {0: "B"}, "text_embeddings": {0: "B"}}
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        self.check_dims(batch_size, image_height, image_width)
+        min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(
+            batch_size, image_height, image_width, static_batch, static_shape
+        )
+        return {
+            "input_ids": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        self.check_dims(batch_size, image_height, image_width)
+        return {
+            "input_ids": (batch_size, self.text_maxlen),
+            "text_embeddings": (batch_size, self.text_maxlen, self.embedding_dim),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        self.check_dims(batch_size, image_height, image_width)
+        return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)
+
+    def optimize(self, onnx_graph):
+        opt = Optimizer(onnx_graph)
+        opt.select_outputs([0])  # delete graph output#1
+        opt.cleanup()
+        opt.fold_constants()
+        opt.infer_shapes()
+        opt.select_outputs([0], names=["text_embeddings"])  # rename network output
+        opt_onnx_graph = opt.cleanup(return_onnx=True)
+        return opt_onnx_graph
+
+
+def make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
+
+
+class UNet(BaseModel):
+    def __init__(
+        self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4
+    ):
+        super(UNet, self).__init__(
+            model=model,
+            fp16=fp16,
+            device=device,
+            max_batch_size=max_batch_size,
+            embedding_dim=embedding_dim,
+            text_maxlen=text_maxlen,
+        )
+        self.unet_dim = unet_dim
+        self.name = "UNet"
+
+    def get_input_names(self):
+        return ["sample", "timestep", "encoder_hidden_states"]
+
+    def get_output_names(self):
+        return ["latent"]
+
+    def get_dynamic_axes(self):
+        return {
+            "sample": {0: "2B", 2: "H", 3: "W"},
+            "encoder_hidden_states": {0: "2B"},
+            "latent": {0: "2B", 2: "H", 3: "W"},
+        }
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        (
+            min_batch,
+            max_batch,
+            _,
+            _,
+            _,
+            _,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
+        return {
+            "sample": [
+                (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width),
+                (2 * batch_size, self.unet_dim, latent_height, latent_width),
+                (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width),
+            ],
+            "encoder_hidden_states": [
+                (2 * min_batch, self.text_maxlen, self.embedding_dim),
+                (2 * batch_size, self.text_maxlen, self.embedding_dim),
+                (2 * max_batch, self.text_maxlen, self.embedding_dim),
+            ],
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return {
+            "sample": (2 * batch_size, self.unet_dim, latent_height, latent_width),
+            "encoder_hidden_states": (2 * batch_size, self.text_maxlen, self.embedding_dim),
+            "latent": (2 * batch_size, 4, latent_height, latent_width),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        dtype = torch.float16 if self.fp16 else torch.float32
+        return (
+            torch.randn(
+                2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device
+            ),
+            torch.tensor([1.0], dtype=torch.float32, device=self.device),
+            torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
+        )
+
+
+def make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False, unet_dim=4):
+    return UNet(
+        model,
+        fp16=True,
+        device=device,
+        max_batch_size=max_batch_size,
+        embedding_dim=embedding_dim,
+        unet_dim=unet_dim,
+    )
+
+
+class VAE(BaseModel):
+    def __init__(self, model, device, max_batch_size, embedding_dim):
+        super(VAE, self).__init__(
+            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
+        )
+        self.name = "VAE decoder"
+
+    def get_input_names(self):
+        return ["latent"]
+
+    def get_output_names(self):
+        return ["images"]
+
+    def get_dynamic_axes(self):
+        return {"latent": {0: "B", 2: "H", 3: "W"}, "images": {0: "B", 2: "8H", 3: "8W"}}
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        (
+            min_batch,
+            max_batch,
+            _,
+            _,
+            _,
+            _,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
+        return {
+            "latent": [
+                (min_batch, 4, min_latent_height, min_latent_width),
+                (batch_size, 4, latent_height, latent_width),
+                (max_batch, 4, max_latent_height, max_latent_width),
+            ]
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return {
+            "latent": (batch_size, 4, latent_height, latent_width),
+            "images": (batch_size, 3, image_height, image_width),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device)
+
+
+def make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
+
+
+class TorchVAEEncoder(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.vae_encoder = model
+
+    def forward(self, x):
+        return self.vae_encoder.encode(x).latent_dist.sample()
+
+
+class VAEEncoder(BaseModel):
+    def __init__(self, model, device, max_batch_size, embedding_dim):
+        super(VAEEncoder, self).__init__(
+            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
+        )
+        self.name = "VAE encoder"
+
+    def get_model(self):
+        vae_encoder = TorchVAEEncoder(self.model)
+        return vae_encoder
+
+    def get_input_names(self):
+        return ["images"]
+
+    def get_output_names(self):
+        return ["latent"]
+
+    def get_dynamic_axes(self):
+        return {"images": {0: "B", 2: "8H", 3: "8W"}, "latent": {0: "B", 2: "H", 3: "W"}}
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        assert batch_size >= self.min_batch and batch_size <= self.max_batch
+        min_batch = batch_size if static_batch else self.min_batch
+        max_batch = batch_size if static_batch else self.max_batch
+        self.check_dims(batch_size, image_height, image_width)
+        (
+            min_batch,
+            max_batch,
+            min_image_height,
+            max_image_height,
+            min_image_width,
+            max_image_width,
+            _,
+            _,
+            _,
+            _,
+        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
+
+        return {
+            "images": [
+                (min_batch, 3, min_image_height, min_image_width),
+                (batch_size, 3, image_height, image_width),
+                (max_batch, 3, max_image_height, max_image_width),
+            ]
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return {
+            "images": (batch_size, 3, image_height, image_width),
+            "latent": (batch_size, 4, latent_height, latent_width),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        self.check_dims(batch_size, image_height, image_width)
+        return torch.randn(batch_size, 3, image_height, image_width, dtype=torch.float32, device=self.device)
+
+
+def make_VAEEncoder(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return VAEEncoder(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
+
+
+class TensorRTStableDiffusionInpaintPipeline(StableDiffusionInpaintPipeline):
+    r"""
+    Pipeline for inpainting using TensorRT accelerated Stable Diffusion.
+
+    This model inherits from [`StableDiffusionInpaintPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: DDIMScheduler,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+        stages=["clip", "unet", "vae", "vae_encoder"],
+        image_height: int = 512,
+        image_width: int = 512,
+        max_batch_size: int = 16,
+        # ONNX export parameters
+        onnx_opset: int = 17,
+        onnx_dir: str = "onnx",
+        # TensorRT engine build parameters
+        engine_dir: str = "engine",
+        build_preview_features: bool = True,
+        force_engine_rebuild: bool = False,
+        timing_cache: str = "timing_cache",
+    ):
+        super().__init__(
+            vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker
+        )
+
+        self.vae.forward = self.vae.decode
+
+        self.stages = stages
+        self.image_height, self.image_width = image_height, image_width
+        self.inpaint = True
+        self.onnx_opset = onnx_opset
+        self.onnx_dir = onnx_dir
+        self.engine_dir = engine_dir
+        self.force_engine_rebuild = force_engine_rebuild
+        self.timing_cache = timing_cache
+        self.build_static_batch = False
+        self.build_dynamic_shape = False
+        self.build_preview_features = build_preview_features
+
+        self.max_batch_size = max_batch_size
+        # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.
+        if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512:
+            self.max_batch_size = 4
+
+        self.stream = None  # loaded in loadResources()
+        self.models = {}  # loaded in __loadModels()
+        self.engine = {}  # loaded in build_engines()
+
+    def __loadModels(self):
+        # Load pipeline models
+        self.embedding_dim = self.text_encoder.config.hidden_size
+        models_args = {
+            "device": self.torch_device,
+            "max_batch_size": self.max_batch_size,
+            "embedding_dim": self.embedding_dim,
+            "inpaint": self.inpaint,
+        }
+        if "clip" in self.stages:
+            self.models["clip"] = make_CLIP(self.text_encoder, **models_args)
+        if "unet" in self.stages:
+            self.models["unet"] = make_UNet(self.unet, **models_args, unet_dim=self.unet.config.in_channels)
+        if "vae" in self.stages:
+            self.models["vae"] = make_VAE(self.vae, **models_args)
+        if "vae_encoder" in self.stages:
+            self.models["vae_encoder"] = make_VAEEncoder(self.vae, **models_args)
+
+    @classmethod
+    def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
+        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+
+        cls.cached_folder = (
+            pretrained_model_name_or_path
+            if os.path.isdir(pretrained_model_name_or_path)
+            else snapshot_download(
+                pretrained_model_name_or_path,
+                cache_dir=cache_dir,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                use_auth_token=use_auth_token,
+                revision=revision,
+            )
+        )
+
+    def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False):
+        super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings)
+
+        self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir)
+        self.engine_dir = os.path.join(self.cached_folder, self.engine_dir)
+        self.timing_cache = os.path.join(self.cached_folder, self.timing_cache)
+
+        # set device
+        self.torch_device = self._execution_device
+        logger.warning(f"Running inference on device: {self.torch_device}")
+
+        # load models
+        self.__loadModels()
+
+        # build engines
+        self.engine = build_engines(
+            self.models,
+            self.engine_dir,
+            self.onnx_dir,
+            self.onnx_opset,
+            opt_image_height=self.image_height,
+            opt_image_width=self.image_width,
+            force_engine_rebuild=self.force_engine_rebuild,
+            static_batch=self.build_static_batch,
+            static_shape=not self.build_dynamic_shape,
+            enable_preview=self.build_preview_features,
+            timing_cache=self.timing_cache,
+        )
+
+        return self
+
+    def __initialize_timesteps(self, timesteps, strength):
+        self.scheduler.set_timesteps(timesteps)
+        offset = self.scheduler.steps_offset if hasattr(self.scheduler, "steps_offset") else 0
+        init_timestep = int(timesteps * strength) + offset
+        init_timestep = min(init_timestep, timesteps)
+        t_start = max(timesteps - init_timestep + offset, 0)
+        timesteps = self.scheduler.timesteps[t_start:].to(self.torch_device)
+        return timesteps, t_start
+
+    def __preprocess_images(self, batch_size, images=()):
+        init_images = []
+        for image in images:
+            image = image.to(self.torch_device).float()
+            image = image.repeat(batch_size, 1, 1, 1)
+            init_images.append(image)
+        return tuple(init_images)
+
+    def __encode_image(self, init_image):
+        init_latents = runEngine(self.engine["vae_encoder"], {"images": device_view(init_image)}, self.stream)[
+            "latent"
+        ]
+        init_latents = 0.18215 * init_latents
+        return init_latents
+
+    def __encode_prompt(self, prompt, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+        """
+        # Tokenize prompt
+        text_input_ids = (
+            self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            .input_ids.type(torch.int32)
+            .to(self.torch_device)
+        )
+
+        text_input_ids_inp = device_view(text_input_ids)
+        # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt
+        text_embeddings = runEngine(self.engine["clip"], {"input_ids": text_input_ids_inp}, self.stream)[
+            "text_embeddings"
+        ].clone()
+
+        # Tokenize negative prompt
+        uncond_input_ids = (
+            self.tokenizer(
+                negative_prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            .input_ids.type(torch.int32)
+            .to(self.torch_device)
+        )
+        uncond_input_ids_inp = device_view(uncond_input_ids)
+        uncond_embeddings = runEngine(self.engine["clip"], {"input_ids": uncond_input_ids_inp}, self.stream)[
+            "text_embeddings"
+        ]
+
+        # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance
+        text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16)
+
+        return text_embeddings
+
+    def __denoise_latent(
+        self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None
+    ):
+        if not isinstance(timesteps, torch.Tensor):
+            timesteps = self.scheduler.timesteps
+        for step_index, timestep in enumerate(timesteps):
+            # Expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)
+            if isinstance(mask, torch.Tensor):
+                latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+            # Predict the noise residual
+            timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep
+
+            sample_inp = device_view(latent_model_input)
+            timestep_inp = device_view(timestep_float)
+            embeddings_inp = device_view(text_embeddings)
+            noise_pred = runEngine(
+                self.engine["unet"],
+                {"sample": sample_inp, "timestep": timestep_inp, "encoder_hidden_states": embeddings_inp},
+                self.stream,
+            )["latent"]
+
+            # Perform guidance
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample
+
+        latents = 1.0 / 0.18215 * latents
+        return latents
+
+    def __decode_latent(self, latents):
+        images = runEngine(self.engine["vae"], {"latent": device_view(latents)}, self.stream)["images"]
+        images = (images / 2 + 0.5).clamp(0, 1)
+        return images.cpu().permute(0, 2, 3, 1).float().numpy()
+
+    def __loadResources(self, image_height, image_width, batch_size):
+        self.stream = cuda.Stream()
+
+        # Allocate buffers for TensorRT engine bindings
+        for model_name, obj in self.models.items():
+            self.engine[model_name].allocate_buffers(
+                shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device
+            )
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        strength: float = 0.75,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            mask_image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
+                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
+                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
+                be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+
+        """
+        self.generator = generator
+        self.denoising_steps = num_inference_steps
+        self.guidance_scale = guidance_scale
+
+        # Pre-compute latent input scales and linear multistep coefficients
+        self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)
+
+        # Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+            prompt = [prompt]
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"Expected prompt to be of type list or str but got {type(prompt)}")
+
+        if negative_prompt is None:
+            negative_prompt = [""] * batch_size
+
+        if negative_prompt is not None and isinstance(negative_prompt, str):
+            negative_prompt = [negative_prompt]
+
+        assert len(prompt) == len(negative_prompt)
+
+        if batch_size > self.max_batch_size:
+            raise ValueError(
+                f"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4"
+            )
+
+        # Validate image dimensions
+        mask_width, mask_height = mask_image.size
+        if mask_height != self.image_height or mask_width != self.image_width:
+            raise ValueError(
+                f"Input image height and width {self.image_height} and {self.image_width} are not equal to "
+                f"the respective dimensions of the mask image {mask_height} and {mask_width}"
+            )
+
+        # load resources
+        self.__loadResources(self.image_height, self.image_width, batch_size)
+
+        with torch.inference_mode(), torch.autocast("cuda"), trt.Runtime(TRT_LOGGER):
+            # Spatial dimensions of latent tensor
+            latent_height = self.image_height // 8
+            latent_width = self.image_width // 8
+
+            # Pre-initialize latents
+            num_channels_latents = self.vae.config.latent_channels
+            latents = self.prepare_latents(
+                batch_size,
+                num_channels_latents,
+                self.image_height,
+                self.image_width,
+                torch.float32,
+                self.torch_device,
+                generator,
+            )
+
+            # Pre-process input images
+            mask, masked_image = self.__preprocess_images(batch_size, prepare_mask_and_masked_image(image, mask_image))
+            # print(mask)
+            mask = torch.nn.functional.interpolate(mask, size=(latent_height, latent_width))
+            mask = torch.cat([mask] * 2)
+
+            # Initialize timesteps
+            timesteps, t_start = self.__initialize_timesteps(self.denoising_steps, strength)
+
+            # VAE encode masked image
+            masked_latents = self.__encode_image(masked_image)
+            masked_latents = torch.cat([masked_latents] * 2)
+
+            # CLIP text encoder
+            text_embeddings = self.__encode_prompt(prompt, negative_prompt)
+
+            # UNet denoiser
+            latents = self.__denoise_latent(
+                latents,
+                text_embeddings,
+                timesteps=timesteps,
+                step_offset=t_start,
+                mask=mask,
+                masked_image_latents=masked_latents,
+            )
+
+            # VAE decode latent
+            images = self.__decode_latent(latents)
+
+        images = self.numpy_to_pil(images)
+        return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=None)

v0.19.2/stable_diffusion_tensorrt_txt2img.py

@@ -0,0 +1,928 @@
+#
+# Copyright 2023 The HuggingFace Inc. team.
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import os
+from collections import OrderedDict
+from copy import copy
+from typing import List, Optional, Union
+
+import numpy as np
+import onnx
+import onnx_graphsurgeon as gs
+import tensorrt as trt
+import torch
+from huggingface_hub import snapshot_download
+from onnx import shape_inference
+from polygraphy import cuda
+from polygraphy.backend.common import bytes_from_path
+from polygraphy.backend.onnx.loader import fold_constants
+from polygraphy.backend.trt import (
+    CreateConfig,
+    Profile,
+    engine_from_bytes,
+    engine_from_network,
+    network_from_onnx_path,
+    save_engine,
+)
+from polygraphy.backend.trt import util as trt_util
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import (
+    StableDiffusionPipeline,
+    StableDiffusionPipelineOutput,
+    StableDiffusionSafetyChecker,
+)
+from diffusers.schedulers import DDIMScheduler
+from diffusers.utils import DIFFUSERS_CACHE, logging
+
+
+"""
+Installation instructions
+python3 -m pip install --upgrade transformers diffusers>=0.16.0
+python3 -m pip install --upgrade tensorrt>=8.6.1
+python3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com
+python3 -m pip install onnxruntime
+"""
+
+TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+# Map of numpy dtype -> torch dtype
+numpy_to_torch_dtype_dict = {
+    np.uint8: torch.uint8,
+    np.int8: torch.int8,
+    np.int16: torch.int16,
+    np.int32: torch.int32,
+    np.int64: torch.int64,
+    np.float16: torch.float16,
+    np.float32: torch.float32,
+    np.float64: torch.float64,
+    np.complex64: torch.complex64,
+    np.complex128: torch.complex128,
+}
+if np.version.full_version >= "1.24.0":
+    numpy_to_torch_dtype_dict[np.bool_] = torch.bool
+else:
+    numpy_to_torch_dtype_dict[np.bool] = torch.bool
+
+# Map of torch dtype -> numpy dtype
+torch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}
+
+
+def device_view(t):
+    return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])
+
+
+class Engine:
+    def __init__(self, engine_path):
+        self.engine_path = engine_path
+        self.engine = None
+        self.context = None
+        self.buffers = OrderedDict()
+        self.tensors = OrderedDict()
+
+    def __del__(self):
+        [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)]
+        del self.engine
+        del self.context
+        del self.buffers
+        del self.tensors
+
+    def build(
+        self,
+        onnx_path,
+        fp16,
+        input_profile=None,
+        enable_preview=False,
+        enable_all_tactics=False,
+        timing_cache=None,
+        workspace_size=0,
+    ):
+        logger.warning(f"Building TensorRT engine for {onnx_path}: {self.engine_path}")
+        p = Profile()
+        if input_profile:
+            for name, dims in input_profile.items():
+                assert len(dims) == 3
+                p.add(name, min=dims[0], opt=dims[1], max=dims[2])
+
+        config_kwargs = {}
+
+        config_kwargs["preview_features"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
+        if enable_preview:
+            # Faster dynamic shapes made optional since it increases engine build time.
+            config_kwargs["preview_features"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)
+        if workspace_size > 0:
+            config_kwargs["memory_pool_limits"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}
+        if not enable_all_tactics:
+            config_kwargs["tactic_sources"] = []
+
+        engine = engine_from_network(
+            network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),
+            config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),
+            save_timing_cache=timing_cache,
+        )
+        save_engine(engine, path=self.engine_path)
+
+    def load(self):
+        logger.warning(f"Loading TensorRT engine: {self.engine_path}")
+        self.engine = engine_from_bytes(bytes_from_path(self.engine_path))
+
+    def activate(self):
+        self.context = self.engine.create_execution_context()
+
+    def allocate_buffers(self, shape_dict=None, device="cuda"):
+        for idx in range(trt_util.get_bindings_per_profile(self.engine)):
+            binding = self.engine[idx]
+            if shape_dict and binding in shape_dict:
+                shape = shape_dict[binding]
+            else:
+                shape = self.engine.get_binding_shape(binding)
+            dtype = trt.nptype(self.engine.get_binding_dtype(binding))
+            if self.engine.binding_is_input(binding):
+                self.context.set_binding_shape(idx, shape)
+            tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)
+            self.tensors[binding] = tensor
+            self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)
+
+    def infer(self, feed_dict, stream):
+        start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)
+        # shallow copy of ordered dict
+        device_buffers = copy(self.buffers)
+        for name, buf in feed_dict.items():
+            assert isinstance(buf, cuda.DeviceView)
+            device_buffers[name] = buf
+        bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]
+        noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)
+        if not noerror:
+            raise ValueError("ERROR: inference failed.")
+
+        return self.tensors
+
+
+class Optimizer:
+    def __init__(self, onnx_graph):
+        self.graph = gs.import_onnx(onnx_graph)
+
+    def cleanup(self, return_onnx=False):
+        self.graph.cleanup().toposort()
+        if return_onnx:
+            return gs.export_onnx(self.graph)
+
+    def select_outputs(self, keep, names=None):
+        self.graph.outputs = [self.graph.outputs[o] for o in keep]
+        if names:
+            for i, name in enumerate(names):
+                self.graph.outputs[i].name = name
+
+    def fold_constants(self, return_onnx=False):
+        onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)
+        self.graph = gs.import_onnx(onnx_graph)
+        if return_onnx:
+            return onnx_graph
+
+    def infer_shapes(self, return_onnx=False):
+        onnx_graph = gs.export_onnx(self.graph)
+        if onnx_graph.ByteSize() > 2147483648:
+            raise TypeError("ERROR: model size exceeds supported 2GB limit")
+        else:
+            onnx_graph = shape_inference.infer_shapes(onnx_graph)
+
+        self.graph = gs.import_onnx(onnx_graph)
+        if return_onnx:
+            return onnx_graph
+
+
+class BaseModel:
+    def __init__(self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77):
+        self.model = model
+        self.name = "SD Model"
+        self.fp16 = fp16
+        self.device = device
+
+        self.min_batch = 1
+        self.max_batch = max_batch_size
+        self.min_image_shape = 256  # min image resolution: 256x256
+        self.max_image_shape = 1024  # max image resolution: 1024x1024
+        self.min_latent_shape = self.min_image_shape // 8
+        self.max_latent_shape = self.max_image_shape // 8
+
+        self.embedding_dim = embedding_dim
+        self.text_maxlen = text_maxlen
+
+    def get_model(self):
+        return self.model
+
+    def get_input_names(self):
+        pass
+
+    def get_output_names(self):
+        pass
+
+    def get_dynamic_axes(self):
+        return None
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        pass
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        return None
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        return None
+
+    def optimize(self, onnx_graph):
+        opt = Optimizer(onnx_graph)
+        opt.cleanup()
+        opt.fold_constants()
+        opt.infer_shapes()
+        onnx_opt_graph = opt.cleanup(return_onnx=True)
+        return onnx_opt_graph
+
+    def check_dims(self, batch_size, image_height, image_width):
+        assert batch_size >= self.min_batch and batch_size <= self.max_batch
+        assert image_height % 8 == 0 or image_width % 8 == 0
+        latent_height = image_height // 8
+        latent_width = image_width // 8
+        assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape
+        assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape
+        return (latent_height, latent_width)
+
+    def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):
+        min_batch = batch_size if static_batch else self.min_batch
+        max_batch = batch_size if static_batch else self.max_batch
+        latent_height = image_height // 8
+        latent_width = image_width // 8
+        min_image_height = image_height if static_shape else self.min_image_shape
+        max_image_height = image_height if static_shape else self.max_image_shape
+        min_image_width = image_width if static_shape else self.min_image_shape
+        max_image_width = image_width if static_shape else self.max_image_shape
+        min_latent_height = latent_height if static_shape else self.min_latent_shape
+        max_latent_height = latent_height if static_shape else self.max_latent_shape
+        min_latent_width = latent_width if static_shape else self.min_latent_shape
+        max_latent_width = latent_width if static_shape else self.max_latent_shape
+        return (
+            min_batch,
+            max_batch,
+            min_image_height,
+            max_image_height,
+            min_image_width,
+            max_image_width,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        )
+
+
+def getOnnxPath(model_name, onnx_dir, opt=True):
+    return os.path.join(onnx_dir, model_name + (".opt" if opt else "") + ".onnx")
+
+
+def getEnginePath(model_name, engine_dir):
+    return os.path.join(engine_dir, model_name + ".plan")
+
+
+def build_engines(
+    models: dict,
+    engine_dir,
+    onnx_dir,
+    onnx_opset,
+    opt_image_height,
+    opt_image_width,
+    opt_batch_size=1,
+    force_engine_rebuild=False,
+    static_batch=False,
+    static_shape=True,
+    enable_preview=False,
+    enable_all_tactics=False,
+    timing_cache=None,
+    max_workspace_size=0,
+):
+    built_engines = {}
+    if not os.path.isdir(onnx_dir):
+        os.makedirs(onnx_dir)
+    if not os.path.isdir(engine_dir):
+        os.makedirs(engine_dir)
+
+    # Export models to ONNX
+    for model_name, model_obj in models.items():
+        engine_path = getEnginePath(model_name, engine_dir)
+        if force_engine_rebuild or not os.path.exists(engine_path):
+            logger.warning("Building Engines...")
+            logger.warning("Engine build can take a while to complete")
+            onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
+            onnx_opt_path = getOnnxPath(model_name, onnx_dir)
+            if force_engine_rebuild or not os.path.exists(onnx_opt_path):
+                if force_engine_rebuild or not os.path.exists(onnx_path):
+                    logger.warning(f"Exporting model: {onnx_path}")
+                    model = model_obj.get_model()
+                    with torch.inference_mode(), torch.autocast("cuda"):
+                        inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width)
+                        torch.onnx.export(
+                            model,
+                            inputs,
+                            onnx_path,
+                            export_params=True,
+                            opset_version=onnx_opset,
+                            do_constant_folding=True,
+                            input_names=model_obj.get_input_names(),
+                            output_names=model_obj.get_output_names(),
+                            dynamic_axes=model_obj.get_dynamic_axes(),
+                        )
+                    del model
+                    torch.cuda.empty_cache()
+                    gc.collect()
+                else:
+                    logger.warning(f"Found cached model: {onnx_path}")
+
+                # Optimize onnx
+                if force_engine_rebuild or not os.path.exists(onnx_opt_path):
+                    logger.warning(f"Generating optimizing model: {onnx_opt_path}")
+                    onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path))
+                    onnx.save(onnx_opt_graph, onnx_opt_path)
+                else:
+                    logger.warning(f"Found cached optimized model: {onnx_opt_path} ")
+
+    # Build TensorRT engines
+    for model_name, model_obj in models.items():
+        engine_path = getEnginePath(model_name, engine_dir)
+        engine = Engine(engine_path)
+        onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
+        onnx_opt_path = getOnnxPath(model_name, onnx_dir)
+
+        if force_engine_rebuild or not os.path.exists(engine.engine_path):
+            engine.build(
+                onnx_opt_path,
+                fp16=True,
+                input_profile=model_obj.get_input_profile(
+                    opt_batch_size,
+                    opt_image_height,
+                    opt_image_width,
+                    static_batch=static_batch,
+                    static_shape=static_shape,
+                ),
+                enable_preview=enable_preview,
+                timing_cache=timing_cache,
+                workspace_size=max_workspace_size,
+            )
+        built_engines[model_name] = engine
+
+    # Load and activate TensorRT engines
+    for model_name, model_obj in models.items():
+        engine = built_engines[model_name]
+        engine.load()
+        engine.activate()
+
+    return built_engines
+
+
+def runEngine(engine, feed_dict, stream):
+    return engine.infer(feed_dict, stream)
+
+
+class CLIP(BaseModel):
+    def __init__(self, model, device, max_batch_size, embedding_dim):
+        super(CLIP, self).__init__(
+            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
+        )
+        self.name = "CLIP"
+
+    def get_input_names(self):
+        return ["input_ids"]
+
+    def get_output_names(self):
+        return ["text_embeddings", "pooler_output"]
+
+    def get_dynamic_axes(self):
+        return {"input_ids": {0: "B"}, "text_embeddings": {0: "B"}}
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        self.check_dims(batch_size, image_height, image_width)
+        min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(
+            batch_size, image_height, image_width, static_batch, static_shape
+        )
+        return {
+            "input_ids": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        self.check_dims(batch_size, image_height, image_width)
+        return {
+            "input_ids": (batch_size, self.text_maxlen),
+            "text_embeddings": (batch_size, self.text_maxlen, self.embedding_dim),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        self.check_dims(batch_size, image_height, image_width)
+        return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)
+
+    def optimize(self, onnx_graph):
+        opt = Optimizer(onnx_graph)
+        opt.select_outputs([0])  # delete graph output#1
+        opt.cleanup()
+        opt.fold_constants()
+        opt.infer_shapes()
+        opt.select_outputs([0], names=["text_embeddings"])  # rename network output
+        opt_onnx_graph = opt.cleanup(return_onnx=True)
+        return opt_onnx_graph
+
+
+def make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
+
+
+class UNet(BaseModel):
+    def __init__(
+        self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4
+    ):
+        super(UNet, self).__init__(
+            model=model,
+            fp16=fp16,
+            device=device,
+            max_batch_size=max_batch_size,
+            embedding_dim=embedding_dim,
+            text_maxlen=text_maxlen,
+        )
+        self.unet_dim = unet_dim
+        self.name = "UNet"
+
+    def get_input_names(self):
+        return ["sample", "timestep", "encoder_hidden_states"]
+
+    def get_output_names(self):
+        return ["latent"]
+
+    def get_dynamic_axes(self):
+        return {
+            "sample": {0: "2B", 2: "H", 3: "W"},
+            "encoder_hidden_states": {0: "2B"},
+            "latent": {0: "2B", 2: "H", 3: "W"},
+        }
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        (
+            min_batch,
+            max_batch,
+            _,
+            _,
+            _,
+            _,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
+        return {
+            "sample": [
+                (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width),
+                (2 * batch_size, self.unet_dim, latent_height, latent_width),
+                (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width),
+            ],
+            "encoder_hidden_states": [
+                (2 * min_batch, self.text_maxlen, self.embedding_dim),
+                (2 * batch_size, self.text_maxlen, self.embedding_dim),
+                (2 * max_batch, self.text_maxlen, self.embedding_dim),
+            ],
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return {
+            "sample": (2 * batch_size, self.unet_dim, latent_height, latent_width),
+            "encoder_hidden_states": (2 * batch_size, self.text_maxlen, self.embedding_dim),
+            "latent": (2 * batch_size, 4, latent_height, latent_width),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        dtype = torch.float16 if self.fp16 else torch.float32
+        return (
+            torch.randn(
+                2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device
+            ),
+            torch.tensor([1.0], dtype=torch.float32, device=self.device),
+            torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
+        )
+
+
+def make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return UNet(
+        model,
+        fp16=True,
+        device=device,
+        max_batch_size=max_batch_size,
+        embedding_dim=embedding_dim,
+        unet_dim=(9 if inpaint else 4),
+    )
+
+
+class VAE(BaseModel):
+    def __init__(self, model, device, max_batch_size, embedding_dim):
+        super(VAE, self).__init__(
+            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
+        )
+        self.name = "VAE decoder"
+
+    def get_input_names(self):
+        return ["latent"]
+
+    def get_output_names(self):
+        return ["images"]
+
+    def get_dynamic_axes(self):
+        return {"latent": {0: "B", 2: "H", 3: "W"}, "images": {0: "B", 2: "8H", 3: "8W"}}
+
+    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        (
+            min_batch,
+            max_batch,
+            _,
+            _,
+            _,
+            _,
+            min_latent_height,
+            max_latent_height,
+            min_latent_width,
+            max_latent_width,
+        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
+        return {
+            "latent": [
+                (min_batch, 4, min_latent_height, min_latent_width),
+                (batch_size, 4, latent_height, latent_width),
+                (max_batch, 4, max_latent_height, max_latent_width),
+            ]
+        }
+
+    def get_shape_dict(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return {
+            "latent": (batch_size, 4, latent_height, latent_width),
+            "images": (batch_size, 3, image_height, image_width),
+        }
+
+    def get_sample_input(self, batch_size, image_height, image_width):
+        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
+        return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device)
+
+
+def make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False):
+    return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
+
+
+class TensorRTStableDiffusionPipeline(StableDiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using TensorRT accelerated Stable Diffusion.
+
+    This model inherits from [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: DDIMScheduler,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+        stages=["clip", "unet", "vae"],
+        image_height: int = 768,
+        image_width: int = 768,
+        max_batch_size: int = 16,
+        # ONNX export parameters
+        onnx_opset: int = 17,
+        onnx_dir: str = "onnx",
+        # TensorRT engine build parameters
+        engine_dir: str = "engine",
+        build_preview_features: bool = True,
+        force_engine_rebuild: bool = False,
+        timing_cache: str = "timing_cache",
+    ):
+        super().__init__(
+            vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker
+        )
+
+        self.vae.forward = self.vae.decode
+
+        self.stages = stages
+        self.image_height, self.image_width = image_height, image_width
+        self.inpaint = False
+        self.onnx_opset = onnx_opset
+        self.onnx_dir = onnx_dir
+        self.engine_dir = engine_dir
+        self.force_engine_rebuild = force_engine_rebuild
+        self.timing_cache = timing_cache
+        self.build_static_batch = False
+        self.build_dynamic_shape = False
+        self.build_preview_features = build_preview_features
+
+        self.max_batch_size = max_batch_size
+        # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.
+        if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512:
+            self.max_batch_size = 4
+
+        self.stream = None  # loaded in loadResources()
+        self.models = {}  # loaded in __loadModels()
+        self.engine = {}  # loaded in build_engines()
+
+    def __loadModels(self):
+        # Load pipeline models
+        self.embedding_dim = self.text_encoder.config.hidden_size
+        models_args = {
+            "device": self.torch_device,
+            "max_batch_size": self.max_batch_size,
+            "embedding_dim": self.embedding_dim,
+            "inpaint": self.inpaint,
+        }
+        if "clip" in self.stages:
+            self.models["clip"] = make_CLIP(self.text_encoder, **models_args)
+        if "unet" in self.stages:
+            self.models["unet"] = make_UNet(self.unet, **models_args)
+        if "vae" in self.stages:
+            self.models["vae"] = make_VAE(self.vae, **models_args)
+
+    @classmethod
+    def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
+        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+
+        cls.cached_folder = (
+            pretrained_model_name_or_path
+            if os.path.isdir(pretrained_model_name_or_path)
+            else snapshot_download(
+                pretrained_model_name_or_path,
+                cache_dir=cache_dir,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                use_auth_token=use_auth_token,
+                revision=revision,
+            )
+        )
+
+    def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False):
+        super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings)
+
+        self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir)
+        self.engine_dir = os.path.join(self.cached_folder, self.engine_dir)
+        self.timing_cache = os.path.join(self.cached_folder, self.timing_cache)
+
+        # set device
+        self.torch_device = self._execution_device
+        logger.warning(f"Running inference on device: {self.torch_device}")
+
+        # load models
+        self.__loadModels()
+
+        # build engines
+        self.engine = build_engines(
+            self.models,
+            self.engine_dir,
+            self.onnx_dir,
+            self.onnx_opset,
+            opt_image_height=self.image_height,
+            opt_image_width=self.image_width,
+            force_engine_rebuild=self.force_engine_rebuild,
+            static_batch=self.build_static_batch,
+            static_shape=not self.build_dynamic_shape,
+            enable_preview=self.build_preview_features,
+            timing_cache=self.timing_cache,
+        )
+
+        return self
+
+    def __encode_prompt(self, prompt, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+             prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+        """
+        # Tokenize prompt
+        text_input_ids = (
+            self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            .input_ids.type(torch.int32)
+            .to(self.torch_device)
+        )
+
+        text_input_ids_inp = device_view(text_input_ids)
+        # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt
+        text_embeddings = runEngine(self.engine["clip"], {"input_ids": text_input_ids_inp}, self.stream)[
+            "text_embeddings"
+        ].clone()
+
+        # Tokenize negative prompt
+        uncond_input_ids = (
+            self.tokenizer(
+                negative_prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            .input_ids.type(torch.int32)
+            .to(self.torch_device)
+        )
+        uncond_input_ids_inp = device_view(uncond_input_ids)
+        uncond_embeddings = runEngine(self.engine["clip"], {"input_ids": uncond_input_ids_inp}, self.stream)[
+            "text_embeddings"
+        ]
+
+        # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance
+        text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16)
+
+        return text_embeddings
+
+    def __denoise_latent(
+        self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None
+    ):
+        if not isinstance(timesteps, torch.Tensor):
+            timesteps = self.scheduler.timesteps
+        for step_index, timestep in enumerate(timesteps):
+            # Expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)
+            if isinstance(mask, torch.Tensor):
+                latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+            # Predict the noise residual
+            timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep
+
+            sample_inp = device_view(latent_model_input)
+            timestep_inp = device_view(timestep_float)
+            embeddings_inp = device_view(text_embeddings)
+            noise_pred = runEngine(
+                self.engine["unet"],
+                {"sample": sample_inp, "timestep": timestep_inp, "encoder_hidden_states": embeddings_inp},
+                self.stream,
+            )["latent"]
+
+            # Perform guidance
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample
+
+        latents = 1.0 / 0.18215 * latents
+        return latents
+
+    def __decode_latent(self, latents):
+        images = runEngine(self.engine["vae"], {"latent": device_view(latents)}, self.stream)["images"]
+        images = (images / 2 + 0.5).clamp(0, 1)
+        return images.cpu().permute(0, 2, 3, 1).float().numpy()
+
+    def __loadResources(self, image_height, image_width, batch_size):
+        self.stream = cuda.Stream()
+
+        # Allocate buffers for TensorRT engine bindings
+        for model_name, obj in self.models.items():
+            self.engine[model_name].allocate_buffers(
+                shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device
+            )
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+
+        """
+        self.generator = generator
+        self.denoising_steps = num_inference_steps
+        self.guidance_scale = guidance_scale
+
+        # Pre-compute latent input scales and linear multistep coefficients
+        self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)
+
+        # Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+            prompt = [prompt]
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"Expected prompt to be of type list or str but got {type(prompt)}")
+
+        if negative_prompt is None:
+            negative_prompt = [""] * batch_size
+
+        if negative_prompt is not None and isinstance(negative_prompt, str):
+            negative_prompt = [negative_prompt]
+
+        assert len(prompt) == len(negative_prompt)
+
+        if batch_size > self.max_batch_size:
+            raise ValueError(
+                f"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4"
+            )
+
+        # load resources
+        self.__loadResources(self.image_height, self.image_width, batch_size)
+
+        with torch.inference_mode(), torch.autocast("cuda"), trt.Runtime(TRT_LOGGER):
+            # CLIP text encoder
+            text_embeddings = self.__encode_prompt(prompt, negative_prompt)
+
+            # Pre-initialize latents
+            num_channels_latents = self.unet.in_channels
+            latents = self.prepare_latents(
+                batch_size,
+                num_channels_latents,
+                self.image_height,
+                self.image_width,
+                torch.float32,
+                self.torch_device,
+                generator,
+            )
+
+            # UNet denoiser
+            latents = self.__denoise_latent(latents, text_embeddings)
+
+            # VAE decode latent
+            images = self.__decode_latent(latents)
+
+        images, has_nsfw_concept = self.run_safety_checker(images, self.torch_device, text_embeddings.dtype)
+        images = self.numpy_to_pil(images)
+        return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)

v0.19.2/stable_unclip.py

@@ -0,0 +1,287 @@
+import types
+from typing import List, Optional, Tuple, Union
+
+import torch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+from transformers.models.clip.modeling_clip import CLIPTextModelOutput
+
+from diffusers.models import PriorTransformer
+from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
+from diffusers.schedulers import UnCLIPScheduler
+from diffusers.utils import logging, randn_tensor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
+    image = image.to(device=device)
+    image_embeddings = image  # take image as image_embeddings
+    image_embeddings = image_embeddings.unsqueeze(1)
+
+    # duplicate image embeddings for each generation per prompt, using mps friendly method
+    bs_embed, seq_len, _ = image_embeddings.shape
+    image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
+    image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+    if do_classifier_free_guidance:
+        uncond_embeddings = torch.zeros_like(image_embeddings)
+
+        # For classifier free guidance, we need to do two forward passes.
+        # Here we concatenate the unconditional and text embeddings into a single batch
+        # to avoid doing two forward passes
+        image_embeddings = torch.cat([uncond_embeddings, image_embeddings])
+
+    return image_embeddings
+
+
+class StableUnCLIPPipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        prior: PriorTransformer,
+        tokenizer: CLIPTokenizer,
+        text_encoder: CLIPTextModelWithProjection,
+        prior_scheduler: UnCLIPScheduler,
+        decoder_pipe_kwargs: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        decoder_pipe_kwargs = {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
+
+        decoder_pipe_kwargs["torch_dtype"] = decoder_pipe_kwargs.get("torch_dtype", None) or prior.dtype
+
+        self.decoder_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
+            "lambdalabs/sd-image-variations-diffusers", **decoder_pipe_kwargs
+        )
+
+        # replace `_encode_image` method
+        self.decoder_pipe._encode_image = types.MethodType(_encode_image, self.decoder_pipe)
+
+        self.register_modules(
+            prior=prior,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            prior_scheduler=prior_scheduler,
+        )
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
+        text_attention_mask: Optional[torch.Tensor] = None,
+    ):
+        if text_model_output is None:
+            batch_size = len(prompt) if isinstance(prompt, list) else 1
+            # get prompt text embeddings
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            text_mask = text_inputs.attention_mask.bool().to(device)
+
+            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+                removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+
+            text_encoder_output = self.text_encoder(text_input_ids.to(device))
+
+            text_embeddings = text_encoder_output.text_embeds
+            text_encoder_hidden_states = text_encoder_output.last_hidden_state
+
+        else:
+            batch_size = text_model_output[0].shape[0]
+            text_embeddings, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
+            text_mask = text_attention_mask
+
+        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+        if do_classifier_free_guidance:
+            uncond_tokens = [""] * batch_size
+
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
+            uncond_embeddings_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
+
+            uncond_embeddings = uncond_embeddings_text_encoder_output.text_embeds
+            uncond_text_encoder_hidden_states = uncond_embeddings_text_encoder_output.last_hidden_state
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len)
+
+            seq_len = uncond_text_encoder_hidden_states.shape[1]
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+            # done duplicates
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
+
+            text_mask = torch.cat([uncond_text_mask, text_mask])
+
+        return text_embeddings, text_encoder_hidden_states, text_mask
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.prior, "_hf_hook"):
+            return self.device
+        for module in self.prior.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        latents = latents * scheduler.init_noise_sigma
+        return latents
+
+    def to(self, torch_device: Optional[Union[str, torch.device]] = None):
+        self.decoder_pipe.to(torch_device)
+        super().to(torch_device)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_images_per_prompt: int = 1,
+        prior_num_inference_steps: int = 25,
+        generator: Optional[torch.Generator] = None,
+        prior_latents: Optional[torch.FloatTensor] = None,
+        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
+        text_attention_mask: Optional[torch.Tensor] = None,
+        prior_guidance_scale: float = 4.0,
+        decoder_guidance_scale: float = 8.0,
+        decoder_num_inference_steps: int = 50,
+        decoder_num_images_per_prompt: Optional[int] = 1,
+        decoder_eta: float = 0.0,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        if prompt is not None:
+            if isinstance(prompt, str):
+                batch_size = 1
+            elif isinstance(prompt, list):
+                batch_size = len(prompt)
+            else:
+                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        else:
+            batch_size = text_model_output[0].shape[0]
+
+        device = self._execution_device
+
+        batch_size = batch_size * num_images_per_prompt
+
+        do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
+
+        text_embeddings, text_encoder_hidden_states, text_mask = self._encode_prompt(
+            prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask
+        )
+
+        # prior
+
+        self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
+        prior_timesteps_tensor = self.prior_scheduler.timesteps
+
+        embedding_dim = self.prior.config.embedding_dim
+
+        prior_latents = self.prepare_latents(
+            (batch_size, embedding_dim),
+            text_embeddings.dtype,
+            device,
+            generator,
+            prior_latents,
+            self.prior_scheduler,
+        )
+
+        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
+
+            predicted_image_embedding = self.prior(
+                latent_model_input,
+                timestep=t,
+                proj_embedding=text_embeddings,
+                encoder_hidden_states=text_encoder_hidden_states,
+                attention_mask=text_mask,
+            ).predicted_image_embedding
+
+            if do_classifier_free_guidance:
+                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
+                predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
+                    predicted_image_embedding_text - predicted_image_embedding_uncond
+                )
+
+            if i + 1 == prior_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = prior_timesteps_tensor[i + 1]
+
+            prior_latents = self.prior_scheduler.step(
+                predicted_image_embedding,
+                timestep=t,
+                sample=prior_latents,
+                generator=generator,
+                prev_timestep=prev_timestep,
+            ).prev_sample
+
+        prior_latents = self.prior.post_process_latents(prior_latents)
+
+        image_embeddings = prior_latents
+
+        output = self.decoder_pipe(
+            image=image_embeddings,
+            height=height,
+            width=width,
+            num_inference_steps=decoder_num_inference_steps,
+            guidance_scale=decoder_guidance_scale,
+            generator=generator,
+            output_type=output_type,
+            return_dict=return_dict,
+            num_images_per_prompt=decoder_num_images_per_prompt,
+            eta=decoder_eta,
+        )
+        return output

v0.19.2/text_inpainting.py

@@ -0,0 +1,302 @@
+from typing import Callable, List, Optional, Union
+
+import PIL
+import torch
+from transformers import (
+    CLIPImageProcessor,
+    CLIPSegForImageSegmentation,
+    CLIPSegProcessor,
+    CLIPTextModel,
+    CLIPTokenizer,
+)
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, is_accelerate_available, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class TextInpainting(DiffusionPipeline):
+    r"""
+    Pipeline for text based inpainting using Stable Diffusion.
+    Uses CLIPSeg to get a mask from the given text, then calls the Inpainting pipeline with the generated mask
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        segmentation_model ([`CLIPSegForImageSegmentation`]):
+            CLIPSeg Model to generate mask from the given text. Please refer to the [model card]() for details.
+        segmentation_processor ([`CLIPSegProcessor`]):
+            CLIPSeg processor to get image, text features to translate prompt to English, if necessary. Please refer to the
+            [model card](https://huggingface.co/docs/transformers/model_doc/clipseg) for details.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        segmentation_model: CLIPSegForImageSegmentation,
+        segmentation_processor: CLIPSegProcessor,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
+                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
+                " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
+                " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
+                " Hub, it would be very nice if you could open a Pull request for the"
+                " `scheduler/scheduler_config.json` file"
+            )
+            deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["skip_prk_steps"] = True
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            segmentation_model=segmentation_model,
+            segmentation_processor=segmentation_processor,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    def enable_sequential_cpu_offload(self):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device("cuda")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        text: str,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            text (`str``):
+                The text to use to generate the mask.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        # We use the input text to generate the mask
+        inputs = self.segmentation_processor(
+            text=[text], images=[image], padding="max_length", return_tensors="pt"
+        ).to(self.device)
+        outputs = self.segmentation_model(**inputs)
+        mask = torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
+        mask_pil = self.numpy_to_pil(mask)[0].resize(image.size)
+
+        # Run inpainting pipeline with the generated mask
+        inpainting_pipeline = StableDiffusionInpaintPipeline(
+            vae=self.vae,
+            text_encoder=self.text_encoder,
+            tokenizer=self.tokenizer,
+            unet=self.unet,
+            scheduler=self.scheduler,
+            safety_checker=self.safety_checker,
+            feature_extractor=self.feature_extractor,
+        )
+        return inpainting_pipeline(
+            prompt=prompt,
+            image=image,
+            mask_image=mask_pil,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+        )

v0.19.2/tiled_upscaling.py

@@ -0,0 +1,298 @@
+# Copyright 2023 Peter Willemsen <peter@codebuffet.co>. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from PIL import Image
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
+from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
+
+
+def make_transparency_mask(size, overlap_pixels, remove_borders=[]):
+    size_x = size[0] - overlap_pixels * 2
+    size_y = size[1] - overlap_pixels * 2
+    for letter in ["l", "r"]:
+        if letter in remove_borders:
+            size_x += overlap_pixels
+    for letter in ["t", "b"]:
+        if letter in remove_borders:
+            size_y += overlap_pixels
+    mask = np.ones((size_y, size_x), dtype=np.uint8) * 255
+    mask = np.pad(mask, mode="linear_ramp", pad_width=overlap_pixels, end_values=0)
+
+    if "l" in remove_borders:
+        mask = mask[:, overlap_pixels : mask.shape[1]]
+    if "r" in remove_borders:
+        mask = mask[:, 0 : mask.shape[1] - overlap_pixels]
+    if "t" in remove_borders:
+        mask = mask[overlap_pixels : mask.shape[0], :]
+    if "b" in remove_borders:
+        mask = mask[0 : mask.shape[0] - overlap_pixels, :]
+    return mask
+
+
+def clamp(n, smallest, largest):
+    return max(smallest, min(n, largest))
+
+
+def clamp_rect(rect: [int], min: [int], max: [int]):
+    return (
+        clamp(rect[0], min[0], max[0]),
+        clamp(rect[1], min[1], max[1]),
+        clamp(rect[2], min[0], max[0]),
+        clamp(rect[3], min[1], max[1]),
+    )
+
+
+def add_overlap_rect(rect: [int], overlap: int, image_size: [int]):
+    rect = list(rect)
+    rect[0] -= overlap
+    rect[1] -= overlap
+    rect[2] += overlap
+    rect[3] += overlap
+    rect = clamp_rect(rect, [0, 0], [image_size[0], image_size[1]])
+    return rect
+
+
+def squeeze_tile(tile, original_image, original_slice, slice_x):
+    result = Image.new("RGB", (tile.size[0] + original_slice, tile.size[1]))
+    result.paste(
+        original_image.resize((tile.size[0], tile.size[1]), Image.BICUBIC).crop(
+            (slice_x, 0, slice_x + original_slice, tile.size[1])
+        ),
+        (0, 0),
+    )
+    result.paste(tile, (original_slice, 0))
+    return result
+
+
+def unsqueeze_tile(tile, original_image_slice):
+    crop_rect = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
+    tile = tile.crop(crop_rect)
+    return tile
+
+
+def next_divisible(n, d):
+    divisor = n % d
+    return n - divisor
+
+
+class StableDiffusionTiledUpscalePipeline(StableDiffusionUpscalePipeline):
+    r"""
+    Pipeline for tile-based text-guided image super-resolution using Stable Diffusion 2, trading memory for compute
+    to create gigantic images.
+
+    This model inherits from [`StableDiffusionUpscalePipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        low_res_scheduler ([`SchedulerMixin`]):
+            A scheduler used to add initial noise to the low res conditioning image. It must be an instance of
+            [`DDPMScheduler`].
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        low_res_scheduler: DDPMScheduler,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        max_noise_level: int = 350,
+    ):
+        super().__init__(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            low_res_scheduler=low_res_scheduler,
+            scheduler=scheduler,
+            max_noise_level=max_noise_level,
+        )
+
+    def _process_tile(self, original_image_slice, x, y, tile_size, tile_border, image, final_image, **kwargs):
+        torch.manual_seed(0)
+        crop_rect = (
+            min(image.size[0] - (tile_size + original_image_slice), x * tile_size),
+            min(image.size[1] - (tile_size + original_image_slice), y * tile_size),
+            min(image.size[0], (x + 1) * tile_size),
+            min(image.size[1], (y + 1) * tile_size),
+        )
+        crop_rect_with_overlap = add_overlap_rect(crop_rect, tile_border, image.size)
+        tile = image.crop(crop_rect_with_overlap)
+        translated_slice_x = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
+        translated_slice_x = translated_slice_x - (original_image_slice / 2)
+        translated_slice_x = max(0, translated_slice_x)
+        to_input = squeeze_tile(tile, image, original_image_slice, translated_slice_x)
+        orig_input_size = to_input.size
+        to_input = to_input.resize((tile_size, tile_size), Image.BICUBIC)
+        upscaled_tile = super(StableDiffusionTiledUpscalePipeline, self).__call__(image=to_input, **kwargs).images[0]
+        upscaled_tile = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4), Image.BICUBIC)
+        upscaled_tile = unsqueeze_tile(upscaled_tile, original_image_slice)
+        upscaled_tile = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4), Image.BICUBIC)
+        remove_borders = []
+        if x == 0:
+            remove_borders.append("l")
+        elif crop_rect[2] == image.size[0]:
+            remove_borders.append("r")
+        if y == 0:
+            remove_borders.append("t")
+        elif crop_rect[3] == image.size[1]:
+            remove_borders.append("b")
+        transparency_mask = Image.fromarray(
+            make_transparency_mask(
+                (upscaled_tile.size[0], upscaled_tile.size[1]), tile_border * 4, remove_borders=remove_borders
+            ),
+            mode="L",
+        )
+        final_image.paste(
+            upscaled_tile, (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4), transparency_mask
+        )
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[PIL.Image.Image, List[PIL.Image.Image]],
+        num_inference_steps: int = 75,
+        guidance_scale: float = 9.0,
+        noise_level: int = 50,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        tile_size: int = 128,
+        tile_border: int = 32,
+        original_image_slice: int = 32,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`):
+                `Image`, or tensor representing an image batch which will be upscaled. *
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            tile_size (`int`, *optional*):
+                The size of the tiles. Too big can result in an OOM-error.
+            tile_border (`int`, *optional*):
+                The number of pixels around a tile to consider (bigger means less seams, too big can lead to an OOM-error).
+            original_image_slice (`int`, *optional*):
+                The amount of pixels of the original image to calculate with the current tile (bigger means more depth
+                is preserved, less blur occurs in the final image, too big can lead to an OOM-error or loss in detail).
+            callback (`Callable`, *optional*):
+                A function that take a callback function with a single argument, a dict,
+                that contains the (partially) processed image under "image",
+                as well as the progress (0 to 1, where 1 is completed) under "progress".
+
+        Returns: A PIL.Image that is 4 times larger than the original input image.
+
+        """
+
+        final_image = Image.new("RGB", (image.size[0] * 4, image.size[1] * 4))
+        tcx = math.ceil(image.size[0] / tile_size)
+        tcy = math.ceil(image.size[1] / tile_size)
+        total_tile_count = tcx * tcy
+        current_count = 0
+        for y in range(tcy):
+            for x in range(tcx):
+                self._process_tile(
+                    original_image_slice,
+                    x,
+                    y,
+                    tile_size,
+                    tile_border,
+                    image,
+                    final_image,
+                    prompt=prompt,
+                    num_inference_steps=num_inference_steps,
+                    guidance_scale=guidance_scale,
+                    noise_level=noise_level,
+                    negative_prompt=negative_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    eta=eta,
+                    generator=generator,
+                    latents=latents,
+                )
+                current_count += 1
+                if callback is not None:
+                    callback({"progress": current_count / total_tile_count, "image": final_image})
+        return final_image
+
+
+def main():
+    # Run a demo
+    model_id = "stabilityai/stable-diffusion-x4-upscaler"
+    pipe = StableDiffusionTiledUpscalePipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16)
+    pipe = pipe.to("cuda")
+    image = Image.open("../../docs/source/imgs/diffusers_library.jpg")
+
+    def callback(obj):
+        print(f"progress: {obj['progress']:.4f}")
+        obj["image"].save("diffusers_library_progress.jpg")
+
+    final_image = pipe(image=image, prompt="Black font, white background, vector", noise_level=40, callback=callback)
+    final_image.save("diffusers_library.jpg")
+
+
+if __name__ == "__main__":
+    main()

v0.19.2/unclip_image_interpolation.py

@@ -0,0 +1,495 @@
+import inspect
+from typing import List, Optional, Union
+
+import PIL
+import torch
+from torch.nn import functional as F
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers import (
+    DiffusionPipeline,
+    ImagePipelineOutput,
+    UnCLIPScheduler,
+    UNet2DConditionModel,
+    UNet2DModel,
+)
+from diffusers.pipelines.unclip import UnCLIPTextProjModel
+from diffusers.utils import is_accelerate_available, logging, randn_tensor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def slerp(val, low, high):
+    """
+    Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic.
+    """
+    low_norm = low / torch.norm(low)
+    high_norm = high / torch.norm(high)
+    omega = torch.acos((low_norm * high_norm))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high
+    return res
+
+
+class UnCLIPImageInterpolationPipeline(DiffusionPipeline):
+    """
+    Pipeline to generate variations from an input image using unCLIP
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        text_encoder ([`CLIPTextModelWithProjection`]):
+            Frozen text-encoder.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `image_encoder`.
+        image_encoder ([`CLIPVisionModelWithProjection`]):
+            Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),
+            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        text_proj ([`UnCLIPTextProjModel`]):
+            Utility class to prepare and combine the embeddings before they are passed to the decoder.
+        decoder ([`UNet2DConditionModel`]):
+            The decoder to invert the image embedding into an image.
+        super_res_first ([`UNet2DModel`]):
+            Super resolution unet. Used in all but the last step of the super resolution diffusion process.
+        super_res_last ([`UNet2DModel`]):
+            Super resolution unet. Used in the last step of the super resolution diffusion process.
+        decoder_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
+        super_res_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
+
+    """
+
+    decoder: UNet2DConditionModel
+    text_proj: UnCLIPTextProjModel
+    text_encoder: CLIPTextModelWithProjection
+    tokenizer: CLIPTokenizer
+    feature_extractor: CLIPImageProcessor
+    image_encoder: CLIPVisionModelWithProjection
+    super_res_first: UNet2DModel
+    super_res_last: UNet2DModel
+
+    decoder_scheduler: UnCLIPScheduler
+    super_res_scheduler: UnCLIPScheduler
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.__init__
+    def __init__(
+        self,
+        decoder: UNet2DConditionModel,
+        text_encoder: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        text_proj: UnCLIPTextProjModel,
+        feature_extractor: CLIPImageProcessor,
+        image_encoder: CLIPVisionModelWithProjection,
+        super_res_first: UNet2DModel,
+        super_res_last: UNet2DModel,
+        decoder_scheduler: UnCLIPScheduler,
+        super_res_scheduler: UnCLIPScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            decoder=decoder,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_proj=text_proj,
+            feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
+            super_res_first=super_res_first,
+            super_res_last=super_res_last,
+            decoder_scheduler=decoder_scheduler,
+            super_res_scheduler=super_res_scheduler,
+        )
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
+    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        latents = latents * scheduler.init_noise_sigma
+        return latents
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_prompt
+    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        text_mask = text_inputs.attention_mask.bool().to(device)
+        text_encoder_output = self.text_encoder(text_input_ids.to(device))
+
+        prompt_embeds = text_encoder_output.text_embeds
+        text_encoder_hidden_states = text_encoder_output.last_hidden_state
+
+        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+        if do_classifier_free_guidance:
+            uncond_tokens = [""] * batch_size
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
+            negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
+
+            negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
+            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
+
+            seq_len = uncond_text_encoder_hidden_states.shape[1]
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+            # done duplicates
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
+
+            text_mask = torch.cat([uncond_text_mask, text_mask])
+
+        return prompt_embeds, text_encoder_hidden_states, text_mask
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_image
+    def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if image_embeddings is None:
+            if not isinstance(image, torch.Tensor):
+                image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
+
+            image = image.to(device=device, dtype=dtype)
+            image_embeddings = self.image_encoder(image).image_embeds
+
+        image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+        return image_embeddings
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.enable_sequential_cpu_offload
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
+        models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
+        when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        models = [
+            self.decoder,
+            self.text_proj,
+            self.text_encoder,
+            self.super_res_first,
+            self.super_res_last,
+        ]
+        for cpu_offloaded_model in models:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._execution_device
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"):
+            return self.device
+        for module in self.decoder.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Optional[Union[List[PIL.Image.Image], torch.FloatTensor]] = None,
+        steps: int = 5,
+        decoder_num_inference_steps: int = 25,
+        super_res_num_inference_steps: int = 7,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        image_embeddings: Optional[torch.Tensor] = None,
+        decoder_latents: Optional[torch.FloatTensor] = None,
+        super_res_latents: Optional[torch.FloatTensor] = None,
+        decoder_guidance_scale: float = 8.0,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            image (`List[PIL.Image.Image]` or `torch.FloatTensor`):
+                The images to use for the image interpolation. Only accepts a list of two PIL Images or If you provide a tensor, it needs to comply with the
+                configuration of
+                [this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
+                `CLIPImageProcessor` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed.
+            steps (`int`, *optional*, defaults to 5):
+                The number of interpolation images to generate.
+            decoder_num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
+                image at the expense of slower inference.
+            super_res_num_inference_steps (`int`, *optional*, defaults to 7):
+                The number of denoising steps for super resolution. More denoising steps usually lead to a higher
+                quality image at the expense of slower inference.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            image_embeddings (`torch.Tensor`, *optional*):
+                Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings
+                can be passed for tasks like image interpolations. `image` can the be left to `None`.
+            decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
+                Pre-generated noisy latents to be used as inputs for the decoder.
+            super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
+                Pre-generated noisy latents to be used as inputs for the decoder.
+            decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
+        """
+
+        batch_size = steps
+
+        device = self._execution_device
+
+        if isinstance(image, List):
+            if len(image) != 2:
+                raise AssertionError(
+                    f"Expected 'image' List to be of size 2, but passed 'image' length is {len(image)}"
+                )
+            elif not (isinstance(image[0], PIL.Image.Image) and isinstance(image[0], PIL.Image.Image)):
+                raise AssertionError(
+                    f"Expected 'image' List to contain PIL.Image.Image, but passed 'image' contents are {type(image[0])} and {type(image[1])}"
+                )
+        elif isinstance(image, torch.FloatTensor):
+            if image.shape[0] != 2:
+                raise AssertionError(
+                    f"Expected 'image' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image' size is {image.shape[0]}"
+                )
+        elif isinstance(image_embeddings, torch.Tensor):
+            if image_embeddings.shape[0] != 2:
+                raise AssertionError(
+                    f"Expected 'image_embeddings' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image_embeddings' shape is {image_embeddings.shape[0]}"
+                )
+        else:
+            raise AssertionError(
+                f"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or Torch.FloatTensor respectively. Received {type(image)} and {type(image_embeddings)} repsectively"
+            )
+
+        original_image_embeddings = self._encode_image(
+            image=image, device=device, num_images_per_prompt=1, image_embeddings=image_embeddings
+        )
+
+        image_embeddings = []
+
+        for interp_step in torch.linspace(0, 1, steps):
+            temp_image_embeddings = slerp(
+                interp_step, original_image_embeddings[0], original_image_embeddings[1]
+            ).unsqueeze(0)
+            image_embeddings.append(temp_image_embeddings)
+
+        image_embeddings = torch.cat(image_embeddings).to(device)
+
+        do_classifier_free_guidance = decoder_guidance_scale > 1.0
+
+        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
+            prompt=["" for i in range(steps)],
+            device=device,
+            num_images_per_prompt=1,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+        )
+
+        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
+            image_embeddings=image_embeddings,
+            prompt_embeds=prompt_embeds,
+            text_encoder_hidden_states=text_encoder_hidden_states,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+        )
+
+        if device.type == "mps":
+            # HACK: MPS: There is a panic when padding bool tensors,
+            # so cast to int tensor for the pad and back to bool afterwards
+            text_mask = text_mask.type(torch.int)
+            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
+            decoder_text_mask = decoder_text_mask.type(torch.bool)
+        else:
+            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
+
+        self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
+        decoder_timesteps_tensor = self.decoder_scheduler.timesteps
+
+        num_channels_latents = self.decoder.config.in_channels
+        height = self.decoder.config.sample_size
+        width = self.decoder.config.sample_size
+
+        # Get the decoder latents for 1 step and then repeat the same tensor for the entire batch to keep same noise across all interpolation steps.
+        decoder_latents = self.prepare_latents(
+            (1, num_channels_latents, height, width),
+            text_encoder_hidden_states.dtype,
+            device,
+            generator,
+            decoder_latents,
+            self.decoder_scheduler,
+        )
+        decoder_latents = decoder_latents.repeat((batch_size, 1, 1, 1))
+
+        for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
+
+            noise_pred = self.decoder(
+                sample=latent_model_input,
+                timestep=t,
+                encoder_hidden_states=text_encoder_hidden_states,
+                class_labels=additive_clip_time_embeddings,
+                attention_mask=decoder_text_mask,
+            ).sample
+
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
+                noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
+                noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
+                noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
+
+            if i + 1 == decoder_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = decoder_timesteps_tensor[i + 1]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            decoder_latents = self.decoder_scheduler.step(
+                noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+        decoder_latents = decoder_latents.clamp(-1, 1)
+
+        image_small = decoder_latents
+
+        # done decoder
+
+        # super res
+
+        self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
+        super_res_timesteps_tensor = self.super_res_scheduler.timesteps
+
+        channels = self.super_res_first.config.in_channels // 2
+        height = self.super_res_first.config.sample_size
+        width = self.super_res_first.config.sample_size
+
+        super_res_latents = self.prepare_latents(
+            (batch_size, channels, height, width),
+            image_small.dtype,
+            device,
+            generator,
+            super_res_latents,
+            self.super_res_scheduler,
+        )
+
+        if device.type == "mps":
+            # MPS does not support many interpolations
+            image_upscaled = F.interpolate(image_small, size=[height, width])
+        else:
+            interpolate_antialias = {}
+            if "antialias" in inspect.signature(F.interpolate).parameters:
+                interpolate_antialias["antialias"] = True
+
+            image_upscaled = F.interpolate(
+                image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
+            )
+
+        for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
+            # no classifier free guidance
+
+            if i == super_res_timesteps_tensor.shape[0] - 1:
+                unet = self.super_res_last
+            else:
+                unet = self.super_res_first
+
+            latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
+
+            noise_pred = unet(
+                sample=latent_model_input,
+                timestep=t,
+            ).sample
+
+            if i + 1 == super_res_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = super_res_timesteps_tensor[i + 1]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            super_res_latents = self.super_res_scheduler.step(
+                noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+        image = super_res_latents
+        # done super res
+
+        # post processing
+
+        image = image * 0.5 + 0.5
+        image = image.clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

v0.19.2/unclip_text_interpolation.py

@@ -0,0 +1,573 @@
+import inspect
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch.nn import functional as F
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+from transformers.models.clip.modeling_clip import CLIPTextModelOutput
+
+from diffusers import (
+    DiffusionPipeline,
+    ImagePipelineOutput,
+    PriorTransformer,
+    UnCLIPScheduler,
+    UNet2DConditionModel,
+    UNet2DModel,
+)
+from diffusers.pipelines.unclip import UnCLIPTextProjModel
+from diffusers.utils import is_accelerate_available, logging, randn_tensor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def slerp(val, low, high):
+    """
+    Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic.
+    """
+    low_norm = low / torch.norm(low)
+    high_norm = high / torch.norm(high)
+    omega = torch.acos((low_norm * high_norm))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high
+    return res
+
+
+class UnCLIPTextInterpolationPipeline(DiffusionPipeline):
+
+    """
+    Pipeline for prompt-to-prompt interpolation on CLIP text embeddings and using the UnCLIP / Dall-E to decode them to images.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        text_encoder ([`CLIPTextModelWithProjection`]):
+            Frozen text-encoder.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        prior ([`PriorTransformer`]):
+            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
+        text_proj ([`UnCLIPTextProjModel`]):
+            Utility class to prepare and combine the embeddings before they are passed to the decoder.
+        decoder ([`UNet2DConditionModel`]):
+            The decoder to invert the image embedding into an image.
+        super_res_first ([`UNet2DModel`]):
+            Super resolution unet. Used in all but the last step of the super resolution diffusion process.
+        super_res_last ([`UNet2DModel`]):
+            Super resolution unet. Used in the last step of the super resolution diffusion process.
+        prior_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the prior denoising process. Just a modified DDPMScheduler.
+        decoder_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
+        super_res_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
+
+    """
+
+    prior: PriorTransformer
+    decoder: UNet2DConditionModel
+    text_proj: UnCLIPTextProjModel
+    text_encoder: CLIPTextModelWithProjection
+    tokenizer: CLIPTokenizer
+    super_res_first: UNet2DModel
+    super_res_last: UNet2DModel
+
+    prior_scheduler: UnCLIPScheduler
+    decoder_scheduler: UnCLIPScheduler
+    super_res_scheduler: UnCLIPScheduler
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.__init__
+    def __init__(
+        self,
+        prior: PriorTransformer,
+        decoder: UNet2DConditionModel,
+        text_encoder: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        text_proj: UnCLIPTextProjModel,
+        super_res_first: UNet2DModel,
+        super_res_last: UNet2DModel,
+        prior_scheduler: UnCLIPScheduler,
+        decoder_scheduler: UnCLIPScheduler,
+        super_res_scheduler: UnCLIPScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            prior=prior,
+            decoder=decoder,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_proj=text_proj,
+            super_res_first=super_res_first,
+            super_res_last=super_res_last,
+            prior_scheduler=prior_scheduler,
+            decoder_scheduler=decoder_scheduler,
+            super_res_scheduler=super_res_scheduler,
+        )
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
+    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        latents = latents * scheduler.init_noise_sigma
+        return latents
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._encode_prompt
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
+        text_attention_mask: Optional[torch.Tensor] = None,
+    ):
+        if text_model_output is None:
+            batch_size = len(prompt) if isinstance(prompt, list) else 1
+            # get prompt text embeddings
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            text_mask = text_inputs.attention_mask.bool().to(device)
+
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+
+            text_encoder_output = self.text_encoder(text_input_ids.to(device))
+
+            prompt_embeds = text_encoder_output.text_embeds
+            text_encoder_hidden_states = text_encoder_output.last_hidden_state
+
+        else:
+            batch_size = text_model_output[0].shape[0]
+            prompt_embeds, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
+            text_mask = text_attention_mask
+
+        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+        if do_classifier_free_guidance:
+            uncond_tokens = [""] * batch_size
+
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
+            negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
+
+            negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
+            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
+
+            seq_len = uncond_text_encoder_hidden_states.shape[1]
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+            # done duplicates
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
+
+            text_mask = torch.cat([uncond_text_mask, text_mask])
+
+        return prompt_embeds, text_encoder_hidden_states, text_mask
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.enable_sequential_cpu_offload
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
+        models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
+        when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        # TODO: self.prior.post_process_latents is not covered by the offload hooks, so it fails if added to the list
+        models = [
+            self.decoder,
+            self.text_proj,
+            self.text_encoder,
+            self.super_res_first,
+            self.super_res_last,
+        ]
+        for cpu_offloaded_model in models:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._execution_device
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"):
+            return self.device
+        for module in self.decoder.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        start_prompt: str,
+        end_prompt: str,
+        steps: int = 5,
+        prior_num_inference_steps: int = 25,
+        decoder_num_inference_steps: int = 25,
+        super_res_num_inference_steps: int = 7,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        prior_guidance_scale: float = 4.0,
+        decoder_guidance_scale: float = 8.0,
+        enable_sequential_cpu_offload=True,
+        gpu_id=0,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            start_prompt (`str`):
+                The prompt to start the image generation interpolation from.
+            end_prompt (`str`):
+                The prompt to end the image generation interpolation at.
+            steps (`int`, *optional*, defaults to 5):
+                The number of steps over which to interpolate from start_prompt to end_prompt. The pipeline returns
+                the same number of images as this value.
+            prior_num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
+                image at the expense of slower inference.
+            decoder_num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
+                image at the expense of slower inference.
+            super_res_num_inference_steps (`int`, *optional*, defaults to 7):
+                The number of denoising steps for super resolution. More denoising steps usually lead to a higher
+                quality image at the expense of slower inference.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            prior_guidance_scale (`float`, *optional*, defaults to 4.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            enable_sequential_cpu_offload (`bool`, *optional*, defaults to `True`):
+                If True, offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
+                models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
+                when their specific submodule has its `forward` method called.
+            gpu_id (`int`, *optional*, defaults to `0`):
+                The gpu_id to be passed to enable_sequential_cpu_offload. Only works when enable_sequential_cpu_offload is set to True.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
+        """
+
+        if not isinstance(start_prompt, str) or not isinstance(end_prompt, str):
+            raise ValueError(
+                f"`start_prompt` and `end_prompt` should be of type `str` but got {type(start_prompt)} and"
+                f" {type(end_prompt)} instead"
+            )
+
+        if enable_sequential_cpu_offload:
+            self.enable_sequential_cpu_offload(gpu_id=gpu_id)
+
+        device = self._execution_device
+
+        # Turn the prompts into embeddings.
+        inputs = self.tokenizer(
+            [start_prompt, end_prompt],
+            padding="max_length",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        inputs.to(device)
+        text_model_output = self.text_encoder(**inputs)
+
+        text_attention_mask = torch.max(inputs.attention_mask[0], inputs.attention_mask[1])
+        text_attention_mask = torch.cat([text_attention_mask.unsqueeze(0)] * steps).to(device)
+
+        # Interpolate from the start to end prompt using slerp and add the generated images to an image output pipeline
+        batch_text_embeds = []
+        batch_last_hidden_state = []
+
+        for interp_val in torch.linspace(0, 1, steps):
+            text_embeds = slerp(interp_val, text_model_output.text_embeds[0], text_model_output.text_embeds[1])
+            last_hidden_state = slerp(
+                interp_val, text_model_output.last_hidden_state[0], text_model_output.last_hidden_state[1]
+            )
+            batch_text_embeds.append(text_embeds.unsqueeze(0))
+            batch_last_hidden_state.append(last_hidden_state.unsqueeze(0))
+
+        batch_text_embeds = torch.cat(batch_text_embeds)
+        batch_last_hidden_state = torch.cat(batch_last_hidden_state)
+
+        text_model_output = CLIPTextModelOutput(
+            text_embeds=batch_text_embeds, last_hidden_state=batch_last_hidden_state
+        )
+
+        batch_size = text_model_output[0].shape[0]
+
+        do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
+
+        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
+            prompt=None,
+            device=device,
+            num_images_per_prompt=1,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            text_model_output=text_model_output,
+            text_attention_mask=text_attention_mask,
+        )
+
+        # prior
+
+        self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
+        prior_timesteps_tensor = self.prior_scheduler.timesteps
+
+        embedding_dim = self.prior.config.embedding_dim
+
+        prior_latents = self.prepare_latents(
+            (batch_size, embedding_dim),
+            prompt_embeds.dtype,
+            device,
+            generator,
+            None,
+            self.prior_scheduler,
+        )
+
+        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
+
+            predicted_image_embedding = self.prior(
+                latent_model_input,
+                timestep=t,
+                proj_embedding=prompt_embeds,
+                encoder_hidden_states=text_encoder_hidden_states,
+                attention_mask=text_mask,
+            ).predicted_image_embedding
+
+            if do_classifier_free_guidance:
+                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
+                predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
+                    predicted_image_embedding_text - predicted_image_embedding_uncond
+                )
+
+            if i + 1 == prior_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = prior_timesteps_tensor[i + 1]
+
+            prior_latents = self.prior_scheduler.step(
+                predicted_image_embedding,
+                timestep=t,
+                sample=prior_latents,
+                generator=generator,
+                prev_timestep=prev_timestep,
+            ).prev_sample
+
+        prior_latents = self.prior.post_process_latents(prior_latents)
+
+        image_embeddings = prior_latents
+
+        # done prior
+
+        # decoder
+
+        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
+            image_embeddings=image_embeddings,
+            prompt_embeds=prompt_embeds,
+            text_encoder_hidden_states=text_encoder_hidden_states,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+        )
+
+        if device.type == "mps":
+            # HACK: MPS: There is a panic when padding bool tensors,
+            # so cast to int tensor for the pad and back to bool afterwards
+            text_mask = text_mask.type(torch.int)
+            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
+            decoder_text_mask = decoder_text_mask.type(torch.bool)
+        else:
+            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
+
+        self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
+        decoder_timesteps_tensor = self.decoder_scheduler.timesteps
+
+        num_channels_latents = self.decoder.config.in_channels
+        height = self.decoder.config.sample_size
+        width = self.decoder.config.sample_size
+
+        decoder_latents = self.prepare_latents(
+            (batch_size, num_channels_latents, height, width),
+            text_encoder_hidden_states.dtype,
+            device,
+            generator,
+            None,
+            self.decoder_scheduler,
+        )
+
+        for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
+
+            noise_pred = self.decoder(
+                sample=latent_model_input,
+                timestep=t,
+                encoder_hidden_states=text_encoder_hidden_states,
+                class_labels=additive_clip_time_embeddings,
+                attention_mask=decoder_text_mask,
+            ).sample
+
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
+                noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
+                noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
+                noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
+
+            if i + 1 == decoder_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = decoder_timesteps_tensor[i + 1]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            decoder_latents = self.decoder_scheduler.step(
+                noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+        decoder_latents = decoder_latents.clamp(-1, 1)
+
+        image_small = decoder_latents
+
+        # done decoder
+
+        # super res
+
+        self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
+        super_res_timesteps_tensor = self.super_res_scheduler.timesteps
+
+        channels = self.super_res_first.config.in_channels // 2
+        height = self.super_res_first.config.sample_size
+        width = self.super_res_first.config.sample_size
+
+        super_res_latents = self.prepare_latents(
+            (batch_size, channels, height, width),
+            image_small.dtype,
+            device,
+            generator,
+            None,
+            self.super_res_scheduler,
+        )
+
+        if device.type == "mps":
+            # MPS does not support many interpolations
+            image_upscaled = F.interpolate(image_small, size=[height, width])
+        else:
+            interpolate_antialias = {}
+            if "antialias" in inspect.signature(F.interpolate).parameters:
+                interpolate_antialias["antialias"] = True
+
+            image_upscaled = F.interpolate(
+                image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
+            )
+
+        for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
+            # no classifier free guidance
+
+            if i == super_res_timesteps_tensor.shape[0] - 1:
+                unet = self.super_res_last
+            else:
+                unet = self.super_res_first
+
+            latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
+
+            noise_pred = unet(
+                sample=latent_model_input,
+                timestep=t,
+            ).sample
+
+            if i + 1 == super_res_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = super_res_timesteps_tensor[i + 1]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            super_res_latents = self.super_res_scheduler.step(
+                noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+        image = super_res_latents
+        # done super res
+
+        # post processing
+
+        image = image * 0.5 + 0.5
+        image = image.clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

v0.19.2/wildcard_stable_diffusion.py

@@ -0,0 +1,418 @@
+import inspect
+import os
+import random
+import re
+from dataclasses import dataclass
+from typing import Callable, Dict, List, Optional, Union
+
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+global_re_wildcard = re.compile(r"__([^_]*)__")
+
+
+def get_filename(path: str):
+    # this doesn't work on Windows
+    return os.path.basename(path).split(".txt")[0]
+
+
+def read_wildcard_values(path: str):
+    with open(path, encoding="utf8") as f:
+        return f.read().splitlines()
+
+
+def grab_wildcard_values(wildcard_option_dict: Dict[str, List[str]] = {}, wildcard_files: List[str] = []):
+    for wildcard_file in wildcard_files:
+        filename = get_filename(wildcard_file)
+        read_values = read_wildcard_values(wildcard_file)
+        if filename not in wildcard_option_dict:
+            wildcard_option_dict[filename] = []
+        wildcard_option_dict[filename].extend(read_values)
+    return wildcard_option_dict
+
+
+def replace_prompt_with_wildcards(
+    prompt: str, wildcard_option_dict: Dict[str, List[str]] = {}, wildcard_files: List[str] = []
+):
+    new_prompt = prompt
+
+    # get wildcard options
+    wildcard_option_dict = grab_wildcard_values(wildcard_option_dict, wildcard_files)
+
+    for m in global_re_wildcard.finditer(new_prompt):
+        wildcard_value = m.group()
+        replace_value = random.choice(wildcard_option_dict[wildcard_value.strip("__")])
+        new_prompt = new_prompt.replace(wildcard_value, replace_value, 1)
+
+    return new_prompt
+
+
+@dataclass
+class WildcardStableDiffusionOutput(StableDiffusionPipelineOutput):
+    prompts: List[str]
+
+
+class WildcardStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Example Usage:
+        pipe = WildcardStableDiffusionPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4",
+
+            torch_dtype=torch.float16,
+        )
+        prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
+        out = pipe(
+            prompt,
+            wildcard_option_dict={
+                "clothing":["hat", "shirt", "scarf", "beret"]
+            },
+            wildcard_files=["object.txt", "animal.txt"],
+            num_prompt_samples=1
+        )
+
+
+    Pipeline for text-to-image generation with wild cards using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        wildcard_option_dict: Dict[str, List[str]] = {},
+        wildcard_files: List[str] = [],
+        num_prompt_samples: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            wildcard_option_dict (Dict[str, List[str]]):
+                dict with key as `wildcard` and values as a list of possible replacements. For example if a prompt, "A __animal__ sitting on a chair". A wildcard_option_dict can provide possible values for "animal" like this: {"animal":["dog", "cat", "fox"]}
+            wildcard_files: (List[str])
+               List of filenames of txt files for wildcard replacements. For example if a prompt, "A __animal__ sitting on a chair". A file can be provided ["animal.txt"]
+            num_prompt_samples: int
+                Number of times to sample wildcards for each prompt provided
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        if isinstance(prompt, str):
+            prompt = [
+                replace_prompt_with_wildcards(prompt, wildcard_option_dict, wildcard_files)
+                for i in range(num_prompt_samples)
+            ]
+            batch_size = len(prompt)
+        elif isinstance(prompt, list):
+            prompt_list = []
+            for p in prompt:
+                for i in range(num_prompt_samples):
+                    prompt_list.append(replace_prompt_with_wildcards(p, wildcard_option_dict, wildcard_files))
+            prompt = prompt_list
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not exist on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return WildcardStableDiffusionOutput(images=image, nsfw_content_detected=has_nsfw_concept, prompts=prompt)