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Unverified Commit 738c9869 authored by M. Tolga Cangöz's avatar M. Tolga Cangöz Committed by GitHub
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[`Refactor`] `StableDiffusionReferencePipeline` inheriting from `DiffusionPipeline` (#7071) 

Refactor StableDiffusionReferencePipeline to inherit from DiffusionPipeline rather than StableDiffusionPipeline
parent c09bb588
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examples/community/stable_diffusion_reference.py
View file @ 738c9869
# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280
import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import StableDiffusionPipeline
from diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel
from diffusers.configuration_utils import FrozenDict, deprecate
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models.attention import BasicTransformerBlock
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg
from diffusers.utils import PIL_INTERPOLATION, logging
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
PIL_INTERPOLATION,
USE_PEFT_BACKEND,
logging,
scale_lora_layers,
unscale_lora_layers,
)
from diffusers.utils.torch_utils import randn_tensor
......@@ -45,14 +60,182 @@ EXAMPLE_DOC_STRING = """
def torch_dfs(model: torch.nn.Module):
r"""
Performs a depth-first search on the given PyTorch model and returns a list of all its child modules.
Args:
model (torch.nn.Module): The PyTorch model to perform the depth-first search on.
Returns:
list: A list of all child modules of the given model.
"""
result = [model]
for child in model.children():
result += torch_dfs(child)
return result
class StableDiffusionReferencePipeline(StableDiffusionPipeline):
def _default_height_width(self, height, width, image):
class StableDiffusionReferencePipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
):
r""" "
Pipeline for Stable Diffusion Reference.
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 ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate(
"skip_prk_steps not set",
"1.0.0",
deprecation_message,
standard_warn=False,
)
new_config = dict(scheduler.config)
new_config["skip_prk_steps"] = True
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
# Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
if unet.config.in_channels != 4:
logger.warning(
f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
f" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,"
". If you did not intend to modify"
" this behavior, please check whether you have loaded the right checkpoint."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def _default_height_width(
self,
height: Optional[int],
width: Optional[int],
image: Union[PIL.Image.Image, torch.Tensor, List[PIL.Image.Image]],
) -> Tuple[int, int]:
r"""
Calculate the default height and width for the given image.
Args:
height (int or None): The desired height of the image. If None, the height will be determined based on the input image.
width (int or None): The desired width of the image. If None, the width will be determined based on the input image.
image (PIL.Image.Image or torch.Tensor or list[PIL.Image.Image]): The input image or a list of images.
Returns:
Tuple[int, int]: A tuple containing the calculated height and width.
"""
# NOTE: It is possible that a list of images have different
# dimensions for each image, so just checking the first image
# is not _exactly_ correct, but it is simple.
......@@ -77,18 +260,430 @@ class StableDiffusionReferencePipeline(StableDiffusionPipeline):
return height, width
def prepare_image(
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
guess_mode=False,
prompt: Optional[Union[str, List[str]]],
height: int,
width: int,
callback_steps: Optional[int],
negative_prompt: Optional[str] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[torch.Tensor] = None,
ip_adapter_image_embeds: Optional[torch.FloatTensor] = None,
callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
) -> None:
"""
Check the validity of the input arguments for the diffusion model.
Args:
prompt (Optional[Union[str, List[str]]]): The prompt text or list of prompt texts.
height (int): The height of the input image.
width (int): The width of the input image.
callback_steps (Optional[int]): The number of steps to perform the callback on.
negative_prompt (Optional[str]): The negative prompt text.
prompt_embeds (Optional[torch.FloatTensor]): The prompt embeddings.
negative_prompt_embeds (Optional[torch.FloatTensor]): The negative prompt embeddings.
ip_adapter_image (Optional[torch.Tensor]): The input adapter image.
ip_adapter_image_embeds (Optional[torch.FloatTensor]): The input adapter image embeddings.
callback_on_step_end_tensor_inputs (Optional[List[str]]): The list of tensor inputs to perform the callback on.
Raises:
ValueError: If `height` or `width` is not divisible by 8.
ValueError: If `callback_steps` is not a positive integer.
ValueError: If `callback_on_step_end_tensor_inputs` contains invalid tensor inputs.
ValueError: If both `prompt` and `prompt_embeds` are provided.
ValueError: If neither `prompt` nor `prompt_embeds` are provided.
ValueError: If `prompt` is not of type `str` or `list`.
ValueError: If both `negative_prompt` and `negative_prompt_embeds` are provided.
ValueError: If both `prompt_embeds` and `negative_prompt_embeds` are provided and have different shapes.
ValueError: If both `ip_adapter_image` and `ip_adapter_image_embeds` are provided.
Returns:
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 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)}")
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}."
)
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
raise ValueError(
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt: Union[str, List[str]],
device: torch.device,
num_images_per_prompt: int,
do_classifier_free_guidance: bool,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
**kwargs,
) -> torch.FloatTensor:
r"""
Encodes the prompt into embeddings.
Args:
prompt (Union[str, List[str]]): The prompt text or a list of prompt texts.
device (torch.device): The device to use for encoding.
num_images_per_prompt (int): The number of images per prompt.
do_classifier_free_guidance (bool): Whether to use classifier-free guidance.
negative_prompt (Optional[Union[str, List[str]]], optional): The negative prompt text or a list of negative prompt texts. Defaults to None.
prompt_embeds (Optional[torch.FloatTensor], optional): The prompt embeddings. Defaults to None.
negative_prompt_embeds (Optional[torch.FloatTensor], optional): The negative prompt embeddings. Defaults to None.
lora_scale (Optional[float], optional): The LoRA scale. Defaults to None.
**kwargs: Additional keyword arguments.
Returns:
torch.FloatTensor: The encoded prompt embeddings.
"""
deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=lora_scale,
**kwargs,
)
# concatenate for backwards comp
prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt: Optional[str],
device: torch.device,
num_images_per_prompt: int,
do_classifier_free_guidance: bool,
negative_prompt: Optional[str] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
) -> torch.FloatTensor:
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: process 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: process 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.prepare_latents
def prepare_latents(
self,
batch_size: int,
num_channels_latents: int,
height: int,
width: int,
dtype: torch.dtype,
device: torch.device,
generator: Union[torch.Generator, List[torch.Generator]],
latents: Optional[torch.Tensor] = None,
) -> torch.Tensor:
r"""
Prepare the latent vectors for diffusion.
Args:
batch_size (int): The number of samples in the batch.
num_channels_latents (int): The number of channels in the latent vectors.
height (int): The height of the latent vectors.
width (int): The width of the latent vectors.
dtype (torch.dtype): The data type of the latent vectors.
device (torch.device): The device to place the latent vectors on.
generator (Union[torch.Generator, List[torch.Generator]]): The generator(s) to use for random number generation.
latents (Optional[torch.Tensor]): The pre-existing latent vectors. If None, new latent vectors will be generated.
Returns:
torch.Tensor: The prepared latent vectors.
"""
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(
self, generator: Union[torch.Generator, List[torch.Generator]], eta: float
) -> Dict[str, Any]:
r"""
Prepare extra keyword arguments for the scheduler step.
Args:
generator (Union[torch.Generator, List[torch.Generator]]): The generator used for sampling.
eta (float): The value of eta (η) used with the DDIMScheduler. Should be between 0 and 1.
Returns:
Dict[str, Any]: A dictionary containing the extra keyword arguments for the scheduler step.
"""
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def prepare_image(
self,
image: Union[torch.Tensor, PIL.Image.Image, List[Union[torch.Tensor, PIL.Image.Image]]],
width: int,
height: int,
batch_size: int,
num_images_per_prompt: int,
device: torch.device,
dtype: torch.dtype,
do_classifier_free_guidance: bool = False,
guess_mode: bool = False,
) -> torch.Tensor:
r"""
Prepares the input image for processing.
Args:
image (torch.Tensor or PIL.Image.Image or list): The input image(s).
width (int): The desired width of the image.
height (int): The desired height of the image.
batch_size (int): The batch size for processing.
num_images_per_prompt (int): The number of images per prompt.
device (torch.device): The device to use for processing.
dtype (torch.dtype): The data type of the image.
do_classifier_free_guidance (bool, optional): Whether to perform classifier-free guidance. Defaults to False.
guess_mode (bool, optional): Whether to use guess mode. Defaults to False.
Returns:
torch.Tensor: The prepared image for processing.
"""
if not isinstance(image, torch.Tensor):
if isinstance(image, PIL.Image.Image):
image = [image]
......@@ -130,7 +725,29 @@ class StableDiffusionReferencePipeline(StableDiffusionPipeline):
return image
def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
def prepare_ref_latents(
self,
refimage: torch.Tensor,
batch_size: int,
dtype: torch.dtype,
device: torch.device,
generator: Union[int, List[int]],
do_classifier_free_guidance: bool,
) -> torch.Tensor:
r"""
Prepares reference latents for generating images.
Args:
refimage (torch.Tensor): The reference image.
batch_size (int): The desired batch size.
dtype (torch.dtype): The data type of the tensors.
device (torch.device): The device to perform computations on.
generator (int or list): The generator index or a list of generator indices.
do_classifier_free_guidance (bool): Whether to use classifier-free guidance.
Returns:
torch.Tensor: The prepared reference latents.
"""
refimage = refimage.to(device=device, dtype=dtype)
# encode the mask image into latents space so we can concatenate it to the latents
......@@ -158,6 +775,35 @@ class StableDiffusionReferencePipeline(StableDiffusionPipeline):
ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
return ref_image_latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(
self, image: Union[torch.Tensor, PIL.Image.Image], device: torch.device, dtype: torch.dtype
) -> Tuple[Union[torch.Tensor, PIL.Image.Image], Optional[bool]]:
r"""
Runs the safety checker on the given image.
Args:
image (Union[torch.Tensor, PIL.Image.Image]): The input image to be checked.
device (torch.device): The device to run the safety checker on.
dtype (torch.dtype): The data type of the input image.
Returns:
(image, has_nsfw_concept) Tuple[Union[torch.Tensor, PIL.Image.Image], Optional[bool]]: A tuple containing the processed image and
a boolean indicating whether the image has a NSFW (Not Safe for Work) concept.
"""
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
@torch.no_grad()
def __call__(
self,
......@@ -538,7 +1184,12 @@ class StableDiffusionReferencePipeline(StableDiffusionPipeline):
return hidden_states, output_states
def hacked_DownBlock2D_forward(self, hidden_states, temb=None, **kwargs):
def hacked_DownBlock2D_forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
**kwargs: Any,
) -> Tuple[torch.FloatTensor, ...]:
eps = 1e-6
output_states = ()
......@@ -588,7 +1239,7 @@ class StableDiffusionReferencePipeline(StableDiffusionPipeline):
upsample_size: Optional[int] = None,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
):
) -> torch.FloatTensor:
eps = 1e-6
# TODO(Patrick, William) - attention mask is not used
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
......@@ -635,8 +1286,13 @@ class StableDiffusionReferencePipeline(StableDiffusionPipeline):
return hidden_states
def hacked_UpBlock2D_forward(
self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, **kwargs
):
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
temb: Optional[torch.FloatTensor] = None,
upsample_size: Optional[int] = None,
**kwargs: Any,
) -> torch.FloatTensor:
eps = 1e-6
for i, resnet in enumerate(self.resnets):
# pop res hidden states
......
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