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# 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. | |
import inspect | |
from typing import Callable, List, Optional, Union | |
import numpy as np | |
import torch | |
import PIL | |
from transformers import CLIPFeatureExtractor, CLIPTokenizer | |
from ...configuration_utils import FrozenDict | |
from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel | |
from ...pipeline_utils import DiffusionPipeline | |
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler | |
from ...utils import PIL_INTERPOLATION, deprecate, logging | |
from . import StableDiffusionPipelineOutput | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
def preprocess(image): | |
w, h = image.size | |
w, h = map(lambda x: x - x % 32, (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 | |
class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline): | |
r""" | |
Pipeline for text-guided image 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 ([`CLIPFeatureExtractor`]): | |
Model that extracts features from generated images to be used as inputs for the `safety_checker`. | |
""" | |
vae_encoder: OnnxRuntimeModel | |
vae_decoder: OnnxRuntimeModel | |
text_encoder: OnnxRuntimeModel | |
tokenizer: CLIPTokenizer | |
unet: OnnxRuntimeModel | |
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] | |
safety_checker: OnnxRuntimeModel | |
feature_extractor: CLIPFeatureExtractor | |
_optional_components = ["safety_checker", "feature_extractor"] | |
def __init__( | |
self, | |
vae_encoder: OnnxRuntimeModel, | |
vae_decoder: OnnxRuntimeModel, | |
text_encoder: OnnxRuntimeModel, | |
tokenizer: CLIPTokenizer, | |
unet: OnnxRuntimeModel, | |
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], | |
safety_checker: OnnxRuntimeModel, | |
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." | |
) | |
self.register_modules( | |
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.register_to_config(requires_safety_checker=requires_safety_checker) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt | |
def _encode_prompt(self, prompt, 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 | |
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 | |
# get prompt text embeddings | |
text_inputs = self.tokenizer( | |
prompt, | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="np", | |
) | |
text_input_ids = text_inputs.input_ids | |
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids | |
if not np.array_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_embeddings = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0] | |
text_embeddings = np.repeat(text_embeddings, num_images_per_prompt, axis=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] * batch_size | |
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="np", | |
) | |
uncond_embeddings = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0] | |
uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=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 = np.concatenate([uncond_embeddings, text_embeddings]) | |
return text_embeddings | |
def __call__( | |
self, | |
prompt: Union[str, List[str]], | |
image: Union[np.ndarray, 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[np.random.RandomState] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
callback: Optional[Callable[[int, int, np.ndarray], None]] = None, | |
callback_steps: 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. | |
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. | |
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. | |
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 (`np.random.RandomState`, *optional*): | |
A np.random.RandomState to make generation deterministic. | |
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`. | |
""" | |
message = "Please use `image` instead of `init_image`." | |
init_image = deprecate("init_image", "0.12.0", message, take_from=kwargs) | |
image = init_image or image | |
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 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 generator is None: | |
generator = np.random | |
# set timesteps | |
self.scheduler.set_timesteps(num_inference_steps) | |
if isinstance(image, PIL.Image.Image): | |
image = preprocess(image) | |
# 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 | |
text_embeddings = self._encode_prompt( | |
prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt | |
) | |
latents_dtype = text_embeddings.dtype | |
image = image.astype(latents_dtype) | |
# encode the init image into latents and scale the latents | |
init_latents = self.vae_encoder(sample=image)[0] | |
init_latents = 0.18215 * init_latents | |
if isinstance(prompt, str): | |
prompt = [prompt] | |
if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0: | |
# expand init_latents for batch_size | |
deprecation_message = ( | |
f"You have passed {len(prompt)} 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 = len(prompt) // init_latents.shape[0] | |
init_latents = np.concatenate([init_latents] * additional_image_per_prompt * num_images_per_prompt, axis=0) | |
elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0: | |
raise ValueError( | |
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts." | |
) | |
else: | |
init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0) | |
# 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) | |
timesteps = self.scheduler.timesteps.numpy()[-init_timestep] | |
timesteps = np.array([timesteps] * batch_size * num_images_per_prompt) | |
# add noise to latents using the timesteps | |
noise = generator.randn(*init_latents.shape).astype(latents_dtype) | |
init_latents = self.scheduler.add_noise( | |
torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps) | |
) | |
init_latents = init_latents.numpy() | |
# 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 | |
latents = init_latents | |
t_start = max(num_inference_steps - init_timestep + offset, 0) | |
timesteps = self.scheduler.timesteps[t_start:].numpy() | |
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] | |
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.cpu().numpy() | |
# predict the noise residual | |
timestep = np.array([t], dtype=timestep_dtype) | |
noise_pred = self.unet( | |
sample=latent_model_input, timestep=timestep, encoder_hidden_states=text_embeddings | |
)[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() | |
# 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_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)) | |
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) | |
# safety_checker does not support batched inputs yet | |
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 | |
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) | |