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# 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 | |
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 | |
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) | |