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# Copyright 2023 Stanford University Team and 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. | |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion | |
# and https://github.com/hojonathanho/diffusion | |
import inspect | |
from typing import Any, Callable, Dict, List, Optional, Union | |
import PIL.Image | |
import torch | |
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection | |
from ...image_processor import PipelineImageInput, VaeImageProcessor | |
from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin | |
from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel | |
from ...models.lora import adjust_lora_scale_text_encoder | |
from ...schedulers import LCMScheduler | |
from ...utils import ( | |
USE_PEFT_BACKEND, | |
deprecate, | |
logging, | |
replace_example_docstring, | |
scale_lora_layers, | |
unscale_lora_layers, | |
) | |
from ...utils.torch_utils import randn_tensor | |
from ..pipeline_utils import DiffusionPipeline | |
from ..stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents | |
def retrieve_latents( | |
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" | |
): | |
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": | |
return encoder_output.latent_dist.sample(generator) | |
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": | |
return encoder_output.latent_dist.mode() | |
elif hasattr(encoder_output, "latents"): | |
return encoder_output.latents | |
else: | |
raise AttributeError("Could not access latents of provided encoder_output") | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps | |
def retrieve_timesteps( | |
scheduler, | |
num_inference_steps: Optional[int] = None, | |
device: Optional[Union[str, torch.device]] = None, | |
timesteps: Optional[List[int]] = None, | |
**kwargs, | |
): | |
""" | |
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles | |
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. | |
Args: | |
scheduler (`SchedulerMixin`): | |
The scheduler to get timesteps from. | |
num_inference_steps (`int`): | |
The number of diffusion steps used when generating samples with a pre-trained model. If used, | |
`timesteps` must be `None`. | |
device (`str` or `torch.device`, *optional*): | |
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. | |
timesteps (`List[int]`, *optional*): | |
Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default | |
timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` | |
must be `None`. | |
Returns: | |
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the | |
second element is the number of inference steps. | |
""" | |
if timesteps is not None: | |
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) | |
if not accepts_timesteps: | |
raise ValueError( | |
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" | |
f" timestep schedules. Please check whether you are using the correct scheduler." | |
) | |
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) | |
timesteps = scheduler.timesteps | |
num_inference_steps = len(timesteps) | |
else: | |
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) | |
timesteps = scheduler.timesteps | |
return timesteps, num_inference_steps | |
EXAMPLE_DOC_STRING = """ | |
Examples: | |
```py | |
>>> from diffusers import AutoPipelineForImage2Image | |
>>> import torch | |
>>> import PIL | |
>>> pipe = AutoPipelineForImage2Image.from_pretrained("SimianLuo/LCM_Dreamshaper_v7") | |
>>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality. | |
>>> pipe.to(torch_device="cuda", torch_dtype=torch.float32) | |
>>> prompt = "High altitude snowy mountains" | |
>>> image = PIL.Image.open("./snowy_mountains.png") | |
>>> # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps. | |
>>> num_inference_steps = 4 | |
>>> images = pipe( | |
... prompt=prompt, image=image, num_inference_steps=num_inference_steps, guidance_scale=8.0 | |
... ).images | |
>>> images[0].save("image.png") | |
``` | |
""" | |
class LatentConsistencyModelImg2ImgPipeline( | |
DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FromSingleFileMixin | |
): | |
r""" | |
Pipeline for image-to-image generation using a latent consistency model. | |
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods | |
implemented for all pipelines (downloading, saving, running on a particular device, etc.). | |
The pipeline also inherits the following loading methods: | |
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings | |
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights | |
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights | |
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files | |
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters | |
Args: | |
vae ([`AutoencoderKL`]): | |
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. | |
text_encoder ([`~transformers.CLIPTextModel`]): | |
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). | |
tokenizer ([`~transformers.CLIPTokenizer`]): | |
A `CLIPTokenizer` to tokenize text. | |
unet ([`UNet2DConditionModel`]): | |
A `UNet2DConditionModel` to denoise the encoded image latents. | |
scheduler ([`SchedulerMixin`]): | |
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only | |
supports [`LCMScheduler`]. | |
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 more details | |
about a model's potential harms. | |
feature_extractor ([`~transformers.CLIPImageProcessor`]): | |
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. | |
requires_safety_checker (`bool`, *optional*, defaults to `True`): | |
Whether the pipeline requires a safety checker component. | |
""" | |
model_cpu_offload_seq = "text_encoder->unet->vae" | |
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"] | |
_exclude_from_cpu_offload = ["safety_checker"] | |
_callback_tensor_inputs = ["latents", "denoised", "prompt_embeds", "w_embedding"] | |
def __init__( | |
self, | |
vae: AutoencoderKL, | |
text_encoder: CLIPTextModel, | |
tokenizer: CLIPTokenizer, | |
unet: UNet2DConditionModel, | |
scheduler: LCMScheduler, | |
safety_checker: StableDiffusionSafetyChecker, | |
feature_extractor: CLIPImageProcessor, | |
image_encoder: Optional[CLIPVisionModelWithProjection] = None, | |
requires_safety_checker: bool = True, | |
): | |
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, | |
image_encoder=image_encoder, | |
) | |
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 ." | |
) | |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing | |
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() | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing | |
def disable_vae_slicing(self): | |
r""" | |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to | |
computing decoding in one step. | |
""" | |
self.vae.disable_slicing() | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling | |
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 for saving a large amount of memory and to allow | |
processing larger images. | |
""" | |
self.vae.enable_tiling() | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling | |
def disable_vae_tiling(self): | |
r""" | |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, 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_freeu | |
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): | |
r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497. | |
The suffixes after the scaling factors represent the stages where they are being applied. | |
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values | |
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. | |
Args: | |
s1 (`float`): | |
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to | |
mitigate "oversmoothing effect" in the enhanced denoising process. | |
s2 (`float`): | |
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to | |
mitigate "oversmoothing effect" in the enhanced denoising process. | |
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. | |
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. | |
""" | |
if not hasattr(self, "unet"): | |
raise ValueError("The pipeline must have `unet` for using FreeU.") | |
self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu | |
def disable_freeu(self): | |
"""Disables the FreeU mechanism if enabled.""" | |
self.unet.disable_freeu() | |
# 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, | |
lora_scale: Optional[float] = None, | |
clip_skip: Optional[int] = 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. | |
lora_scale (`float`, *optional*): | |
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. | |
clip_skip (`int`, *optional*): | |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that | |
the output of the pre-final layer will be used for computing the prompt embeddings. | |
""" | |
# set lora scale so that monkey patched LoRA | |
# function of text encoder can correctly access it | |
if lora_scale is not None and isinstance(self, LoraLoaderMixin): | |
self._lora_scale = lora_scale | |
# dynamically adjust the LoRA scale | |
if not USE_PEFT_BACKEND: | |
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) | |
else: | |
scale_lora_layers(self.text_encoder, lora_scale) | |
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 | |
if clip_skip is None: | |
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask) | |
prompt_embeds = prompt_embeds[0] | |
else: | |
prompt_embeds = self.text_encoder( | |
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True | |
) | |
# Access the `hidden_states` first, that contains a tuple of | |
# all the hidden states from the encoder layers. Then index into | |
# the tuple to access the hidden states from the desired layer. | |
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)] | |
# We also need to apply the final LayerNorm here to not mess with the | |
# representations. The `last_hidden_states` that we typically use for | |
# obtaining the final prompt representations passes through the LayerNorm | |
# layer. | |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | |
if self.text_encoder is not None: | |
prompt_embeds_dtype = self.text_encoder.dtype | |
elif self.unet is not None: | |
prompt_embeds_dtype = self.unet.dtype | |
else: | |
prompt_embeds_dtype = prompt_embeds.dtype | |
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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 prompt is not None and 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=prompt_embeds_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) | |
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: | |
# Retrieve the original scale by scaling back the LoRA layers | |
unscale_lora_layers(self.text_encoder, lora_scale) | |
return prompt_embeds, negative_prompt_embeds | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image | |
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None): | |
dtype = next(self.image_encoder.parameters()).dtype | |
if not isinstance(image, torch.Tensor): | |
image = self.feature_extractor(image, return_tensors="pt").pixel_values | |
image = image.to(device=device, dtype=dtype) | |
if output_hidden_states: | |
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] | |
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) | |
uncond_image_enc_hidden_states = self.image_encoder( | |
torch.zeros_like(image), output_hidden_states=True | |
).hidden_states[-2] | |
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave( | |
num_images_per_prompt, dim=0 | |
) | |
return image_enc_hidden_states, uncond_image_enc_hidden_states | |
else: | |
image_embeds = self.image_encoder(image).image_embeds | |
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) | |
uncond_image_embeds = torch.zeros_like(image_embeds) | |
return image_embeds, uncond_image_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 None: | |
has_nsfw_concept = None | |
else: | |
if torch.is_tensor(image): | |
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") | |
else: | |
feature_extractor_input = self.image_processor.numpy_to_pil(image) | |
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) | |
image, has_nsfw_concept = self.safety_checker( | |
images=image, clip_input=safety_checker_input.pixel_values.to(dtype) | |
) | |
return image, has_nsfw_concept | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents | |
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 image.shape[1] == 4: | |
init_latents = image | |
else: | |
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." | |
) | |
elif isinstance(generator, list): | |
init_latents = [ | |
retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i]) | |
for i in range(batch_size) | |
] | |
init_latents = torch.cat(init_latents, dim=0) | |
else: | |
init_latents = retrieve_latents(self.vae.encode(image), generator=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 | |
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding | |
def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32): | |
""" | |
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 | |
Args: | |
timesteps (`torch.Tensor`): | |
generate embedding vectors at these timesteps | |
embedding_dim (`int`, *optional*, defaults to 512): | |
dimension of the embeddings to generate | |
dtype: | |
data type of the generated embeddings | |
Returns: | |
`torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` | |
""" | |
assert len(w.shape) == 1 | |
w = w * 1000.0 | |
half_dim = embedding_dim // 2 | |
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) | |
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) | |
emb = w.to(dtype)[:, None] * emb[None, :] | |
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) | |
if embedding_dim % 2 == 1: # zero pad | |
emb = torch.nn.functional.pad(emb, (0, 1)) | |
assert emb.shape == (w.shape[0], embedding_dim) | |
return emb | |
# 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_img2img.StableDiffusionImg2ImgPipeline.get_timesteps | |
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 * self.scheduler.order :] | |
return timesteps, num_inference_steps - t_start | |
def check_inputs( | |
self, | |
prompt: Union[str, List[str]], | |
strength: float, | |
callback_steps: int, | |
prompt_embeds: Optional[torch.FloatTensor] = None, | |
callback_on_step_end_tensor_inputs=None, | |
): | |
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 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 callback_on_step_end_tensor_inputs is not None and not all( | |
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs | |
): | |
raise ValueError( | |
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" | |
) | |
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)}") | |
def guidance_scale(self): | |
return self._guidance_scale | |
def cross_attention_kwargs(self): | |
return self._cross_attention_kwargs | |
def clip_skip(self): | |
return self._clip_skip | |
def num_timesteps(self): | |
return self._num_timesteps | |
def __call__( | |
self, | |
prompt: Union[str, List[str]] = None, | |
image: PipelineImageInput = None, | |
num_inference_steps: int = 4, | |
strength: float = 0.8, | |
original_inference_steps: int = None, | |
timesteps: List[int] = None, | |
guidance_scale: float = 8.5, | |
num_images_per_prompt: Optional[int] = 1, | |
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
latents: Optional[torch.FloatTensor] = None, | |
prompt_embeds: Optional[torch.FloatTensor] = None, | |
ip_adapter_image: Optional[PipelineImageInput] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
clip_skip: Optional[int] = None, | |
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, | |
callback_on_step_end_tensor_inputs: List[str] = ["latents"], | |
**kwargs, | |
): | |
r""" | |
The call function to the pipeline for generation. | |
Args: | |
prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. | |
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. | |
original_inference_steps (`int`, *optional*): | |
The original number of inference steps use to generate a linearly-spaced timestep schedule, from which | |
we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule, | |
following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the | |
scheduler's `original_inference_steps` attribute. | |
timesteps (`List[int]`, *optional*): | |
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` | |
timesteps on the original LCM training/distillation timestep schedule are used. Must be in descending | |
order. | |
guidance_scale (`float`, *optional*, defaults to 7.5): | |
A higher guidance scale value encourages the model to generate images closely linked to the text | |
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. | |
Note that the original latent consistency models paper uses a different CFG formulation where the | |
guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale > | |
0`). | |
num_images_per_prompt (`int`, *optional*, defaults to 1): | |
The number of images to generate per prompt. | |
generator (`torch.Generator` or `List[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 is 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 (prompt weighting). If not | |
provided, text embeddings are generated from the `prompt` input argument. | |
ip_adapter_image: (`PipelineImageInput`, *optional*): | |
Optional image input to work with IP Adapters. | |
output_type (`str`, *optional*, defaults to `"pil"`): | |
The output format of the generated image. Choose between `PIL.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. | |
cross_attention_kwargs (`dict`, *optional*): | |
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in | |
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | |
clip_skip (`int`, *optional*): | |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that | |
the output of the pre-final layer will be used for computing the prompt embeddings. | |
callback_on_step_end (`Callable`, *optional*): | |
A function that calls at the end of each denoising steps during the inference. The function is called | |
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, | |
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by | |
`callback_on_step_end_tensor_inputs`. | |
callback_on_step_end_tensor_inputs (`List`, *optional*): | |
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list | |
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the | |
`._callback_tensor_inputs` attribute of your pipeline class. | |
Examples: | |
Returns: | |
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: | |
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, | |
otherwise a `tuple` is returned where the first element is a list with the generated images and the | |
second element is a list of `bool`s indicating whether the corresponding generated image contains | |
"not-safe-for-work" (nsfw) content. | |
""" | |
callback = kwargs.pop("callback", None) | |
callback_steps = kwargs.pop("callback_steps", None) | |
if callback is not None: | |
deprecate( | |
"callback", | |
"1.0.0", | |
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", | |
) | |
if callback_steps is not None: | |
deprecate( | |
"callback_steps", | |
"1.0.0", | |
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", | |
) | |
# 1. Check inputs. Raise error if not correct | |
self.check_inputs(prompt, strength, callback_steps, prompt_embeds, callback_on_step_end_tensor_inputs) | |
self._guidance_scale = guidance_scale | |
self._clip_skip = clip_skip | |
self._cross_attention_kwargs = cross_attention_kwargs | |
# 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 | |
# do_classifier_free_guidance = guidance_scale > 1.0 | |
if ip_adapter_image is not None: | |
output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True | |
image_embeds, negative_image_embeds = self.encode_image( | |
ip_adapter_image, device, num_images_per_prompt, output_hidden_state | |
) | |
# 3. Encode input prompt | |
lora_scale = ( | |
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None | |
) | |
# NOTE: when a LCM is distilled from an LDM via latent consistency distillation (Algorithm 1) with guided | |
# distillation, the forward pass of the LCM learns to approximate sampling from the LDM using CFG with the | |
# unconditional prompt "" (the empty string). Due to this, LCMs currently do not support negative prompts. | |
prompt_embeds, _ = self.encode_prompt( | |
prompt, | |
device, | |
num_images_per_prompt, | |
False, | |
negative_prompt=None, | |
prompt_embeds=prompt_embeds, | |
negative_prompt_embeds=None, | |
lora_scale=lora_scale, | |
clip_skip=self.clip_skip, | |
) | |
# 4. Encode image | |
image = self.image_processor.preprocess(image) | |
# 5. Prepare timesteps | |
timesteps, num_inference_steps = retrieve_timesteps( | |
self.scheduler, | |
num_inference_steps, | |
device, | |
timesteps, | |
original_inference_steps=original_inference_steps, | |
strength=strength, | |
) | |
# 6. Prepare latent variables | |
original_inference_steps = ( | |
original_inference_steps | |
if original_inference_steps is not None | |
else self.scheduler.config.original_inference_steps | |
) | |
latent_timestep = timesteps[:1] | |
latents = self.prepare_latents( | |
image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator | |
) | |
bs = batch_size * num_images_per_prompt | |
# 6. Get Guidance Scale Embedding | |
# NOTE: We use the Imagen CFG formulation that StableDiffusionPipeline uses rather than the original LCM paper | |
# CFG formulation, so we need to subtract 1 from the input guidance_scale. | |
# LCM CFG formulation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond), (cfg_scale > 0.0 using CFG) | |
w = torch.tensor(self.guidance_scale - 1).repeat(bs) | |
w_embedding = self.get_guidance_scale_embedding(w, embedding_dim=self.unet.config.time_cond_proj_dim).to( | |
device=device, dtype=latents.dtype | |
) | |
# 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, None) | |
# 7.1 Add image embeds for IP-Adapter | |
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None | |
# 8. LCM Multistep Sampling Loop | |
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order | |
self._num_timesteps = len(timesteps) | |
with self.progress_bar(total=num_inference_steps) as progress_bar: | |
for i, t in enumerate(timesteps): | |
latents = latents.to(prompt_embeds.dtype) | |
# model prediction (v-prediction, eps, x) | |
model_pred = self.unet( | |
latents, | |
t, | |
timestep_cond=w_embedding, | |
encoder_hidden_states=prompt_embeds, | |
cross_attention_kwargs=self.cross_attention_kwargs, | |
added_cond_kwargs=added_cond_kwargs, | |
return_dict=False, | |
)[0] | |
# compute the previous noisy sample x_t -> x_t-1 | |
latents, denoised = self.scheduler.step(model_pred, t, latents, **extra_step_kwargs, return_dict=False) | |
if callback_on_step_end is not None: | |
callback_kwargs = {} | |
for k in callback_on_step_end_tensor_inputs: | |
callback_kwargs[k] = locals()[k] | |
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) | |
latents = callback_outputs.pop("latents", latents) | |
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) | |
w_embedding = callback_outputs.pop("w_embedding", w_embedding) | |
denoised = callback_outputs.pop("denoised", denoised) | |
# 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: | |
step_idx = i // getattr(self.scheduler, "order", 1) | |
callback(step_idx, t, latents) | |
denoised = denoised.to(prompt_embeds.dtype) | |
if not output_type == "latent": | |
image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0] | |
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) | |
else: | |
image = denoised | |
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 all models | |
self.maybe_free_model_hooks() | |
if not return_dict: | |
return (image, has_nsfw_concept) | |
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | |