long-prompt-weighting-pipeline / pipeline.py

Update pipeline.py

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	import inspect
	import re
	from typing import Any, Callable, Dict, List, Optional, Union

	import numpy as np
	import PIL
	import torch
	from packaging import version
	from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

	from diffusers import DiffusionPipeline
	from diffusers.configuration_utils import FrozenDict
	from diffusers.image_processor import VaeImageProcessor
	from diffusers.loaders import FromCkptMixin, LoraLoaderMixin, TextualInversionLoaderMixin
	from diffusers.models import AutoencoderKL, UNet2DConditionModel
	from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
	from diffusers.schedulers import KarrasDiffusionSchedulers
	from diffusers.utils import (
	PIL_INTERPOLATION,
	deprecate,
	is_accelerate_available,
	is_accelerate_version,
	logging,
	randn_tensor,
	)


	# ------------------------------------------------------------------------------

	logger = logging.get_logger(__name__) # pylint: disable=invalid-name

	re_attention = re.compile(
	r"""
	\\\(\|
	\\\)\|
	\\\[\|
	\\]\|
	\\\\\|
	\\\|
	\(\|
	\[\|
	:([+-]?[.\d]+)\)\|
	\)\|
	]\|
	[^\\()\[\]:]+\|
	:
	""",
	re.X,
	)


	def parse_prompt_attention(text):
	"""
	Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
	Accepted tokens are:
	(abc) - increases attention to abc by a multiplier of 1.1
	(abc:3.12) - increases attention to abc by a multiplier of 3.12
	[abc] - decreases attention to abc by a multiplier of 1.1
	\( - literal character '('
	\[ - literal character '['
	\) - literal character ')'
	\] - literal character ']'
	\\ - literal character '\'
	anything else - just text
	>>> parse_prompt_attention('normal text')
	[['normal text', 1.0]]
	>>> parse_prompt_attention('an (important) word')
	[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
	>>> parse_prompt_attention('(unbalanced')
	[['unbalanced', 1.1]]
	>>> parse_prompt_attention('\(literal\]')
	[['(literal]', 1.0]]
	>>> parse_prompt_attention('(unnecessary)(parens)')
	[['unnecessaryparens', 1.1]]
	>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
	[['a ', 1.0],
	['house', 1.5730000000000004],
	[' ', 1.1],
	['on', 1.0],
	[' a ', 1.1],
	['hill', 0.55],
	[', sun, ', 1.1],
	['sky', 1.4641000000000006],
	['.', 1.1]]
	"""

	res = []
	round_brackets = []
	square_brackets = []

	round_bracket_multiplier = 1.1
	square_bracket_multiplier = 1 / 1.1

	def multiply_range(start_position, multiplier):
	for p in range(start_position, len(res)):
	res[p][1] *= multiplier

	for m in re_attention.finditer(text):
	text = m.group(0)
	weight = m.group(1)

	if text.startswith("\\"):
	res.append([text[1:], 1.0])
	elif text == "(":
	round_brackets.append(len(res))
	elif text == "[":
	square_brackets.append(len(res))
	elif weight is not None and len(round_brackets) > 0:
	multiply_range(round_brackets.pop(), float(weight))
	elif text == ")" and len(round_brackets) > 0:
	multiply_range(round_brackets.pop(), round_bracket_multiplier)
	elif text == "]" and len(square_brackets) > 0:
	multiply_range(square_brackets.pop(), square_bracket_multiplier)
	else:
	res.append([text, 1.0])

	for pos in round_brackets:
	multiply_range(pos, round_bracket_multiplier)

	for pos in square_brackets:
	multiply_range(pos, square_bracket_multiplier)

	if len(res) == 0:
	res = [["", 1.0]]

	# merge runs of identical weights
	i = 0
	while i + 1 < len(res):
	if res[i][1] == res[i + 1][1]:
	res[i][0] += res[i + 1][0]
	res.pop(i + 1)
	else:
	i += 1

	return res


	def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int):
	r"""
	Tokenize a list of prompts and return its tokens with weights of each token.

	No padding, starting or ending token is included.
	"""
	tokens = []
	weights = []
	truncated = False
	for text in prompt:
	texts_and_weights = parse_prompt_attention(text)
	text_token = []
	text_weight = []
	for word, weight in texts_and_weights:
	# tokenize and discard the starting and the ending token
	token = pipe.tokenizer(word).input_ids[1:-1]
	text_token += token
	# copy the weight by length of token
	text_weight += [weight] * len(token)
	# stop if the text is too long (longer than truncation limit)
	if len(text_token) > max_length:
	truncated = True
	break
	# truncate
	if len(text_token) > max_length:
	truncated = True
	text_token = text_token[:max_length]
	text_weight = text_weight[:max_length]
	tokens.append(text_token)
	weights.append(text_weight)
	if truncated:
	logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
	return tokens, weights


	def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
	r"""
	Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
	"""
	max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
	weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
	for i in range(len(tokens)):
	tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
	if no_boseos_middle:
	weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
	else:
	w = []
	if len(weights[i]) == 0:
	w = [1.0] * weights_length
	else:
	for j in range(max_embeddings_multiples):
	w.append(1.0) # weight for starting token in this chunk
	w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
	w.append(1.0) # weight for ending token in this chunk
	w += [1.0] * (weights_length - len(w))
	weights[i] = w[:]

	return tokens, weights


	def get_unweighted_text_embeddings(
	pipe: DiffusionPipeline,
	text_input: torch.Tensor,
	chunk_length: int,
	no_boseos_middle: Optional[bool] = True,
	):
	"""
	When the length of tokens is a multiple of the capacity of the text encoder,
	it should be split into chunks and sent to the text encoder individually.
	"""
	max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
	if max_embeddings_multiples > 1:
	text_embeddings = []
	for i in range(max_embeddings_multiples):
	# extract the i-th chunk
	text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()

	# cover the head and the tail by the starting and the ending tokens
	text_input_chunk[:, 0] = text_input[0, 0]
	text_input_chunk[:, -1] = text_input[0, -1]
	text_embedding = pipe.text_encoder(text_input_chunk)[0]

	if no_boseos_middle:
	if i == 0:
	# discard the ending token
	text_embedding = text_embedding[:, :-1]
	elif i == max_embeddings_multiples - 1:
	# discard the starting token
	text_embedding = text_embedding[:, 1:]
	else:
	# discard both starting and ending tokens
	text_embedding = text_embedding[:, 1:-1]

	text_embeddings.append(text_embedding)
	text_embeddings = torch.concat(text_embeddings, axis=1)
	else:
	text_embeddings = pipe.text_encoder(text_input)[0]
	return text_embeddings


	def get_weighted_text_embeddings(
	pipe: DiffusionPipeline,
	prompt: Union[str, List[str]],
	uncond_prompt: Optional[Union[str, List[str]]] = None,
	max_embeddings_multiples: Optional[int] = 3,
	no_boseos_middle: Optional[bool] = False,
	skip_parsing: Optional[bool] = False,
	skip_weighting: Optional[bool] = False,
	):
	r"""
	Prompts can be assigned with local weights using brackets. For example,
	prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
	and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.

	Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.

	Args:
	pipe (`DiffusionPipeline`):
	Pipe to provide access to the tokenizer and the text encoder.
	prompt (`str` or `List[str]`):
	The prompt or prompts to guide the image generation.
	uncond_prompt (`str` or `List[str]`):
	The unconditional prompt or prompts for guide the image generation. If unconditional prompt
	is provided, the embeddings of prompt and uncond_prompt are concatenated.
	max_embeddings_multiples (`int`, optional, defaults to `3`):
	The max multiple length of prompt embeddings compared to the max output length of text encoder.
	no_boseos_middle (`bool`, optional, defaults to `False`):
	If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
	ending token in each of the chunk in the middle.
	skip_parsing (`bool`, optional, defaults to `False`):
	Skip the parsing of brackets.
	skip_weighting (`bool`, optional, defaults to `False`):
	Skip the weighting. When the parsing is skipped, it is forced True.
	"""
	max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
	if isinstance(prompt, str):
	prompt = [prompt]

	if not skip_parsing:
	prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
	if uncond_prompt is not None:
	if isinstance(uncond_prompt, str):
	uncond_prompt = [uncond_prompt]
	uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
	else:
	prompt_tokens = [
	token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids
	]
	prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
	if uncond_prompt is not None:
	if isinstance(uncond_prompt, str):
	uncond_prompt = [uncond_prompt]
	uncond_tokens = [
	token[1:-1]
	for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
	]
	uncond_weights = [[1.0] * len(token) for token in uncond_tokens]

	# round up the longest length of tokens to a multiple of (model_max_length - 2)
	max_length = max([len(token) for token in prompt_tokens])
	if uncond_prompt is not None:
	max_length = max(max_length, max([len(token) for token in uncond_tokens]))

	max_embeddings_multiples = min(
	max_embeddings_multiples,
	(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
	)
	max_embeddings_multiples = max(1, max_embeddings_multiples)
	max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2

	# pad the length of tokens and weights
	bos = pipe.tokenizer.bos_token_id
	eos = pipe.tokenizer.eos_token_id
	pad = getattr(pipe.tokenizer, "pad_token_id", eos)
	prompt_tokens, prompt_weights = pad_tokens_and_weights(
	prompt_tokens,
	prompt_weights,
	max_length,
	bos,
	eos,
	pad,
	no_boseos_middle=no_boseos_middle,
	chunk_length=pipe.tokenizer.model_max_length,
	)
	prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
	if uncond_prompt is not None:
	uncond_tokens, uncond_weights = pad_tokens_and_weights(
	uncond_tokens,
	uncond_weights,
	max_length,
	bos,
	eos,
	pad,
	no_boseos_middle=no_boseos_middle,
	chunk_length=pipe.tokenizer.model_max_length,
	)
	uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)

	# get the embeddings
	text_embeddings = get_unweighted_text_embeddings(
	pipe,
	prompt_tokens,
	pipe.tokenizer.model_max_length,
	no_boseos_middle=no_boseos_middle,
	)
	prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=text_embeddings.device)
	if uncond_prompt is not None:
	uncond_embeddings = get_unweighted_text_embeddings(
	pipe,
	uncond_tokens,
	pipe.tokenizer.model_max_length,
	no_boseos_middle=no_boseos_middle,
	)
	uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=uncond_embeddings.device)

	# assign weights to the prompts and normalize in the sense of mean
	# TODO: should we normalize by chunk or in a whole (current implementation)?
	if (not skip_parsing) and (not skip_weighting):
	previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
	text_embeddings *= prompt_weights.unsqueeze(-1)
	current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
	text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
	if uncond_prompt is not None:
	previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
	uncond_embeddings *= uncond_weights.unsqueeze(-1)
	current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
	uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)

	if uncond_prompt is not None:
	return text_embeddings, uncond_embeddings
	return text_embeddings, None


	def preprocess_image(image, batch_size):
	w, h = image.size
	w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
	image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
	image = np.array(image).astype(np.float32) / 255.0
	image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)
	image = torch.from_numpy(image)
	return 2.0 * image - 1.0


	def preprocess_mask(mask, batch_size, scale_factor=8):
	if not isinstance(mask, torch.FloatTensor):
	mask = mask.convert("L")
	w, h = mask.size
	w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
	mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
	mask = np.array(mask).astype(np.float32) / 255.0
	mask = np.tile(mask, (4, 1, 1))
	mask = np.vstack([mask[None]] * batch_size)
	mask = 1 - mask # repaint white, keep black
	mask = torch.from_numpy(mask)
	return mask

	else:
	valid_mask_channel_sizes = [1, 3]
	# if mask channel is fourth tensor dimension, permute dimensions to pytorch standard (B, C, H, W)
	if mask.shape[3] in valid_mask_channel_sizes:
	mask = mask.permute(0, 3, 1, 2)
	elif mask.shape[1] not in valid_mask_channel_sizes:
	raise ValueError(
	f"Mask channel dimension of size in {valid_mask_channel_sizes} should be second or fourth dimension,"
	f" but received mask of shape {tuple(mask.shape)}"
	)
	# (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape
	mask = mask.mean(dim=1, keepdim=True)
	h, w = mask.shape[-2:]
	h, w = (x - x % 8 for x in (h, w)) # resize to integer multiple of 8
	mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor))
	return mask


	class StableDiffusionLongPromptWeightingPipeline(
	DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromCkptMixin
	):
	r"""
	Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
	weighting in prompt.

	This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
	library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

	Args:
	vae ([`AutoencoderKL`]):
	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
	text_encoder ([`CLIPTextModel`]):
	Frozen text-encoder. Stable Diffusion uses the text portion of
	[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
	the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
	tokenizer (`CLIPTokenizer`):
	Tokenizer of class
	[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
	unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
	scheduler ([`SchedulerMixin`]):
	A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
	[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
	safety_checker ([`StableDiffusionSafetyChecker`]):
	Classification module that estimates whether generated images could be considered offensive or harmful.
	Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
	feature_extractor ([`CLIPImageProcessor`]):
	Model that extracts features from generated images to be used as inputs for the `safety_checker`.
	"""

	_optional_components = ["safety_checker", "feature_extractor"]

	def __init__(
	self,
	vae: AutoencoderKL,
	text_encoder: CLIPTextModel,
	tokenizer: CLIPTokenizer,
	unet: UNet2DConditionModel,
	scheduler: KarrasDiffusionSchedulers,
	safety_checker: StableDiffusionSafetyChecker,
	feature_extractor: CLIPImageProcessor,
	requires_safety_checker: bool = True,
	):
	super().__init__()

	if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
	deprecation_message = (
	f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
	f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
	"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
	" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
	" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
	" file"
	)
	deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
	new_config = dict(scheduler.config)
	new_config["steps_offset"] = 1
	scheduler._internal_dict = FrozenDict(new_config)

	if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
	deprecation_message = (
	f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
	" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
	" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
	" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
	" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
	)
	deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
	new_config = dict(scheduler.config)
	new_config["clip_sample"] = False
	scheduler._internal_dict = FrozenDict(new_config)

	if safety_checker is None and requires_safety_checker:
	logger.warning(
	f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
	" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
	" results in services or applications open to the public. Both the diffusers team and Hugging Face"
	" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
	" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
	" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
	)

	if safety_checker is not None and feature_extractor is None:
	raise ValueError(
	"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
	" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
	)

	is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
	version.parse(unet.config._diffusers_version).base_version
	) < version.parse("0.9.0.dev0")
	is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
	if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
	deprecation_message = (
	"The configuration file of the unet has set the default `sample_size` to smaller than"
	" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
	" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
	" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
	" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
	" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
	" in the config might lead to incorrect results in future versions. If you have downloaded this"
	" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
	" the `unet/config.json` file"
	)
	deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
	new_config = dict(unet.config)
	new_config["sample_size"] = 64
	unet._internal_dict = FrozenDict(new_config)
	self.register_modules(
	vae=vae,
	text_encoder=text_encoder,
	tokenizer=tokenizer,
	unet=unet,
	scheduler=scheduler,
	safety_checker=safety_checker,
	feature_extractor=feature_extractor,
	)
	self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)

	self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
	self.register_to_config(
	requires_safety_checker=requires_safety_checker,
	)

	def enable_vae_slicing(self):
	r"""
	Enable sliced VAE decoding.

	When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
	steps. This is useful to save some memory and allow larger batch sizes.
	"""
	self.vae.enable_slicing()

	def disable_vae_slicing(self):
	r"""
	Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
	computing decoding in one step.
	"""
	self.vae.disable_slicing()

	def enable_vae_tiling(self):
	r"""
	Enable tiled VAE decoding.

	When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
	several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
	"""
	self.vae.enable_tiling()

	def disable_vae_tiling(self):
	r"""
	Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
	computing decoding in one step.
	"""
	self.vae.disable_tiling()

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
	def enable_sequential_cpu_offload(self, gpu_id=0):
	r"""
	Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
	text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
	`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
	Note that offloading happens on a submodule basis. Memory savings are higher than with
	`enable_model_cpu_offload`, but performance is lower.
	"""
	if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
	from accelerate import cpu_offload
	else:
	raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")

	device = torch.device(f"cuda:{gpu_id}")

	if self.device.type != "cpu":
	self.to("cpu", silence_dtype_warnings=True)
	torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)

	for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
	cpu_offload(cpu_offloaded_model, device)

	if self.safety_checker is not None:
	cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
	def enable_model_cpu_offload(self, gpu_id=0):
	r"""
	Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
	to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
	method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
	`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
	"""
	if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
	from accelerate import cpu_offload_with_hook
	else:
	raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")

	device = torch.device(f"cuda:{gpu_id}")

	if self.device.type != "cpu":
	self.to("cpu", silence_dtype_warnings=True)
	torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)

	hook = None
	for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
	_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)

	if self.safety_checker is not None:
	_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)

	# We'll offload the last model manually.
	self.final_offload_hook = hook

	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def _execution_device(self):
	r"""
	Returns the device on which the pipeline's models will be executed. After calling
	`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
	hooks.
	"""
	if not hasattr(self.unet, "_hf_hook"):
	return self.device
	for module in self.unet.modules():
	if (
	hasattr(module, "_hf_hook")
	and hasattr(module._hf_hook, "execution_device")
	and module._hf_hook.execution_device is not None
	):
	return torch.device(module._hf_hook.execution_device)
	return self.device

	def _encode_prompt(
	self,
	prompt,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance,
	negative_prompt=None,
	max_embeddings_multiples=3,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	):
	r"""
	Encodes the prompt into text encoder hidden states.

	Args:
	prompt (`str` or `list(int)`):
	prompt to be encoded
	device: (`torch.device`):
	torch device
	num_images_per_prompt (`int`):
	number of images that should be generated per prompt
	do_classifier_free_guidance (`bool`):
	whether to use classifier free guidance or not
	negative_prompt (`str` or `List[str]`):
	The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
	if `guidance_scale` is less than `1`).
	max_embeddings_multiples (`int`, optional, defaults to `3`):
	The max multiple length of prompt embeddings compared to the max output length of text encoder.
	"""
	if prompt is not None and isinstance(prompt, str):
	batch_size = 1
	elif prompt is not None and isinstance(prompt, list):
	batch_size = len(prompt)
	else:
	batch_size = prompt_embeds.shape[0]

	if negative_prompt_embeds is None:
	if negative_prompt is None:
	negative_prompt = [""] * batch_size
	elif isinstance(negative_prompt, str):
	negative_prompt = [negative_prompt] * batch_size
	if batch_size != len(negative_prompt):
	raise ValueError(
	f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
	f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
	" the batch size of `prompt`."
	)
	if prompt_embeds is None or negative_prompt_embeds is None:
	if isinstance(self, TextualInversionLoaderMixin):
	prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
	if do_classifier_free_guidance and negative_prompt_embeds is None:
	negative_prompt = self.maybe_convert_prompt(negative_prompt, self.tokenizer)

	prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(
	pipe=self,
	prompt=prompt,
	uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
	max_embeddings_multiples=max_embeddings_multiples,
	)
	if prompt_embeds is None:
	prompt_embeds = prompt_embeds1
	if negative_prompt_embeds is None:
	negative_prompt_embeds = negative_prompt_embeds1

	bs_embed, seq_len, _ = prompt_embeds.shape
	# duplicate text embeddings for each generation per prompt, using mps friendly method
	prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
	prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

	if do_classifier_free_guidance:
	bs_embed, seq_len, _ = negative_prompt_embeds.shape
	negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
	negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
	prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

	return prompt_embeds

	def check_inputs(
	self,
	prompt,
	height,
	width,
	strength,
	callback_steps,
	negative_prompt=None,
	prompt_embeds=None,
	negative_prompt_embeds=None,
	):
	if height % 8 != 0 or width % 8 != 0:
	raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

	if strength < 0 or strength > 1:
	raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")

	if (callback_steps is None) or (
	callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
	):
	raise ValueError(
	f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
	f" {type(callback_steps)}."
	)

	if prompt is not None and prompt_embeds is not None:
	raise ValueError(
	f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
	" only forward one of the two."
	)
	elif prompt is None and prompt_embeds is None:
	raise ValueError(
	"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
	)
	elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
	raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

	if negative_prompt is not None and negative_prompt_embeds is not None:
	raise ValueError(
	f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
	f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
	)

	if prompt_embeds is not None and negative_prompt_embeds is not None:
	if prompt_embeds.shape != negative_prompt_embeds.shape:
	raise ValueError(
	"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
	f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
	f" {negative_prompt_embeds.shape}."
	)

	def get_timesteps(self, num_inference_steps, strength, device, is_text2img):
	if is_text2img:
	return self.scheduler.timesteps.to(device), num_inference_steps
	else:
	# get the original timestep using init_timestep
	init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

	t_start = max(num_inference_steps - init_timestep, 0)
	timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]

	return timesteps, num_inference_steps - t_start

	def run_safety_checker(self, image, device, dtype):
	if self.safety_checker is not None:
	safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
	image, has_nsfw_concept = self.safety_checker(
	images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
	)
	else:
	has_nsfw_concept = None
	return image, has_nsfw_concept

	def decode_latents(self, latents):
	latents = 1 / self.vae.config.scaling_factor * latents
	image = self.vae.decode(latents).sample
	image = (image / 2 + 0.5).clamp(0, 1)
	# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
	image = image.cpu().permute(0, 2, 3, 1).float().numpy()
	return image

	def prepare_extra_step_kwargs(self, generator, eta):
	# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
	# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
	# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
	# and should be between [0, 1]

	accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
	extra_step_kwargs = {}
	if accepts_eta:
	extra_step_kwargs["eta"] = eta

	# check if the scheduler accepts generator
	accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
	if accepts_generator:
	extra_step_kwargs["generator"] = generator
	return extra_step_kwargs

	def prepare_latents(
	self,
	image,
	timestep,
	num_images_per_prompt,
	batch_size,
	num_channels_latents,
	height,
	width,
	dtype,
	device,
	generator,
	latents=None,
	):
	if image is None:
	batch_size = batch_size * num_images_per_prompt
	shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
	if isinstance(generator, list) and len(generator) != batch_size:
	raise ValueError(
	f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
	f" size of {batch_size}. Make sure the batch size matches the length of the generators."
	)

	if latents is None:
	latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
	else:
	latents = latents.to(device)

	# scale the initial noise by the standard deviation required by the scheduler
	latents = latents * self.scheduler.init_noise_sigma
	return latents, None, None
	else:
	image = image.to(device=self.device, dtype=dtype)
	init_latent_dist = self.vae.encode(image).latent_dist
	init_latents = init_latent_dist.sample(generator=generator)
	init_latents = self.vae.config.scaling_factor * init_latents

	# Expand init_latents for batch_size and num_images_per_prompt
	init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
	init_latents_orig = init_latents

	# add noise to latents using the timesteps
	noise = randn_tensor(init_latents.shape, generator=generator, device=self.device, dtype=dtype)
	init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
	latents = init_latents
	return latents, init_latents_orig, noise

	@torch.no_grad()
	def __call__(
	self,
	prompt: Union[str, List[str]],
	negative_prompt: Optional[Union[str, List[str]]] = None,
	image: Union[torch.FloatTensor, PIL.Image.Image] = None,
	mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
	height: int = 512,
	width: int = 512,
	num_inference_steps: int = 50,
	guidance_scale: float = 7.5,
	strength: float = 0.8,
	num_images_per_prompt: Optional[int] = 1,
	add_predicted_noise: Optional[bool] = False,
	eta: float = 0.0,
	generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	latents: Optional[torch.FloatTensor] = None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	max_embeddings_multiples: Optional[int] = 3,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	is_cancelled_callback: Optional[Callable[[], bool]] = None,
	callback_steps: int = 1,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	):
	r"""
	Function invoked when calling the pipeline for generation.

	Args:
	prompt (`str` or `List[str]`):
	The prompt or prompts to guide the image generation.
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
	if `guidance_scale` is less than `1`).
	image (`torch.FloatTensor` or `PIL.Image.Image`):
	`Image`, or tensor representing an image batch, that will be used as the starting point for the
	process.
	mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
	`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
	replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
	PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
	contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
	height (`int`, optional, defaults to 512):
	The height in pixels of the generated image.
	width (`int`, optional, defaults to 512):
	The width in pixels of the generated image.
	num_inference_steps (`int`, optional, defaults to 50):
	The number of denoising steps. More denoising steps usually lead to a higher quality image at the
	expense of slower inference.
	guidance_scale (`float`, optional, defaults to 7.5):
	Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
	`guidance_scale` is defined as `w` of equation 2. of [Imagen
	Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
	1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
	usually at the expense of lower image quality.
	strength (`float`, optional, defaults to 0.8):
	Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
	`image` will be used as a starting point, adding more noise to it the larger the `strength`. The
	number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
	noise will be maximum and the denoising process will run for the full number of iterations specified in
	`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
	num_images_per_prompt (`int`, optional, defaults to 1):
	The number of images to generate per prompt.
	add_predicted_noise (`bool`, optional, defaults to True):
	Use predicted noise instead of random noise when constructing noisy versions of the original image in
	the reverse diffusion process
	eta (`float`, optional, defaults to 0.0):
	Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
	[`schedulers.DDIMScheduler`], will be ignored for others.
	generator (`torch.Generator` or `List[torch.Generator]`, optional):
	One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
	to make generation deterministic.
	latents (`torch.FloatTensor`, optional):
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will ge generated by sampling using the supplied random `generator`.
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	max_embeddings_multiples (`int`, optional, defaults to `3`):
	The max multiple length of prompt embeddings compared to the max output length of text encoder.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generate image. Choose between
	[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.
	callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. The function will be
	called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
	is_cancelled_callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. If the function returns
	`True`, the inference will be cancelled.
	callback_steps (`int`, optional, defaults to 1):
	The frequency at which the `callback` function will be called. If not specified, the callback will be
	called at every step.
	cross_attention_kwargs (`dict`, optional):
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
	`self.processor` in
	[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).

	Returns:
	`None` if cancelled by `is_cancelled_callback`,
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
	When returning a tuple, the first element is a list with the generated images, and the second element is a
	list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
	(nsfw) content, according to the `safety_checker`.
	"""
	# 0. Default height and width to unet
	height = height or self.unet.config.sample_size * self.vae_scale_factor
	width = width or self.unet.config.sample_size * self.vae_scale_factor

	# 1. Check inputs. Raise error if not correct
	self.check_inputs(
	prompt, height, width, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
	)

	# 2. Define call parameters
	if prompt is not None and isinstance(prompt, str):
	batch_size = 1
	elif prompt is not None and isinstance(prompt, list):
	batch_size = len(prompt)
	else:
	batch_size = prompt_embeds.shape[0]

	device = self._execution_device
	# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
	# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
	# corresponds to doing no classifier free guidance.
	do_classifier_free_guidance = guidance_scale > 1.0

	# 3. Encode input prompt
	prompt_embeds = self._encode_prompt(
	prompt,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance,
	negative_prompt,
	max_embeddings_multiples,
	prompt_embeds=prompt_embeds,
	negative_prompt_embeds=negative_prompt_embeds,
	)
	dtype = prompt_embeds.dtype

	# 4. Preprocess image and mask
	if isinstance(image, PIL.Image.Image):
	image = preprocess_image(image, batch_size)
	if image is not None:
	image = image.to(device=self.device, dtype=dtype)
	if isinstance(mask_image, PIL.Image.Image):
	mask_image = preprocess_mask(mask_image, batch_size, self.vae_scale_factor)
	if mask_image is not None:
	mask = mask_image.to(device=self.device, dtype=dtype)
	mask = torch.cat([mask] * num_images_per_prompt)
	else:
	mask = None

	# 5. set timesteps
	self.scheduler.set_timesteps(num_inference_steps, device=device)
	timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None)
	latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)

	# 6. Prepare latent variables
	latents, init_latents_orig, noise = self.prepare_latents(
	image,
	latent_timestep,
	num_images_per_prompt,
	batch_size,
	self.unet.config.in_channels,
	height,
	width,
	dtype,
	device,
	generator,
	latents,
	)

	# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
	extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

	# 8. Denoising loop
	num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
	with self.progress_bar(total=num_inference_steps) as progress_bar:
	for i, t in enumerate(timesteps):
	# expand the latents if we are doing classifier free guidance
	latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
	latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

	# predict the noise residual
	noise_pred = self.unet(
	latent_model_input,
	t,
	encoder_hidden_states=prompt_embeds,
	cross_attention_kwargs=cross_attention_kwargs,
	).sample

	# perform guidance
	if do_classifier_free_guidance:
	noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
	noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

	# compute the previous noisy sample x_t -> x_t-1
	latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

	if mask is not None:
	# masking
	if add_predicted_noise:
	init_latents_proper = self.scheduler.add_noise(
	init_latents_orig, noise_pred_uncond, torch.tensor([t])
	)
	else:
	init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
	latents = (init_latents_proper * mask) + (latents * (1 - mask))

	# call the callback, if provided
	if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
	progress_bar.update()
	if i % callback_steps == 0:
	if callback is not None:
	callback(i, t, latents)
	if is_cancelled_callback is not None and is_cancelled_callback():
	return None

	if output_type == "latent":
	image = latents
	has_nsfw_concept = None
	elif output_type == "pil":
	# 9. Post-processing
	image = self.decode_latents(latents)

	# 10. Run safety checker
	image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

	# 11. Convert to PIL
	image = self.numpy_to_pil(image)
	else:
	# 9. Post-processing
	image = self.decode_latents(latents)

	# 10. Run safety checker
	image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

	# Offload last model to CPU
	if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
	self.final_offload_hook.offload()

	if not return_dict:
	return image, has_nsfw_concept

	return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

	def text2img(
	self,
	prompt: Union[str, List[str]],
	negative_prompt: Optional[Union[str, List[str]]] = None,
	height: int = 512,
	width: int = 512,
	num_inference_steps: int = 50,
	guidance_scale: float = 7.5,
	num_images_per_prompt: Optional[int] = 1,
	eta: float = 0.0,
	generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	latents: Optional[torch.FloatTensor] = None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	max_embeddings_multiples: Optional[int] = 3,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	is_cancelled_callback: Optional[Callable[[], bool]] = None,
	callback_steps: int = 1,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	):
	r"""
	Function for text-to-image generation.
	Args:
	prompt (`str` or `List[str]`):
	The prompt or prompts to guide the image generation.
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
	if `guidance_scale` is less than `1`).
	height (`int`, optional, defaults to 512):
	The height in pixels of the generated image.
	width (`int`, optional, defaults to 512):
	The width in pixels of the generated image.
	num_inference_steps (`int`, optional, defaults to 50):
	The number of denoising steps. More denoising steps usually lead to a higher quality image at the
	expense of slower inference.
	guidance_scale (`float`, optional, defaults to 7.5):
	Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
	`guidance_scale` is defined as `w` of equation 2. of [Imagen
	Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
	1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
	usually at the expense of lower image quality.
	num_images_per_prompt (`int`, optional, defaults to 1):
	The number of images to generate per prompt.
	eta (`float`, optional, defaults to 0.0):
	Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
	[`schedulers.DDIMScheduler`], will be ignored for others.
	generator (`torch.Generator` or `List[torch.Generator]`, optional):
	One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
	to make generation deterministic.
	latents (`torch.FloatTensor`, optional):
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will ge generated by sampling using the supplied random `generator`.
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	max_embeddings_multiples (`int`, optional, defaults to `3`):
	The max multiple length of prompt embeddings compared to the max output length of text encoder.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generate image. Choose between
	[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.
	callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. The function will be
	called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
	is_cancelled_callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. If the function returns
	`True`, the inference will be cancelled.
	callback_steps (`int`, optional, defaults to 1):
	The frequency at which the `callback` function will be called. If not specified, the callback will be
	called at every step.
	cross_attention_kwargs (`dict`, optional):
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
	`self.processor` in
	[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).

	Returns:
	`None` if cancelled by `is_cancelled_callback`,
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
	When returning a tuple, the first element is a list with the generated images, and the second element is a
	list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
	(nsfw) content, according to the `safety_checker`.
	"""
	return self.__call__(
	prompt=prompt,
	negative_prompt=negative_prompt,
	height=height,
	width=width,
	num_inference_steps=num_inference_steps,
	guidance_scale=guidance_scale,
	num_images_per_prompt=num_images_per_prompt,
	eta=eta,
	generator=generator,
	latents=latents,
	prompt_embeds=prompt_embeds,
	negative_prompt_embeds=negative_prompt_embeds,
	max_embeddings_multiples=max_embeddings_multiples,
	output_type=output_type,
	return_dict=return_dict,
	callback=callback,
	is_cancelled_callback=is_cancelled_callback,
	callback_steps=callback_steps,
	cross_attention_kwargs=cross_attention_kwargs,
	)

	def img2img(
	self,
	image: Union[torch.FloatTensor, PIL.Image.Image],
	prompt: Union[str, List[str]],
	negative_prompt: Optional[Union[str, List[str]]] = None,
	strength: float = 0.8,
	num_inference_steps: Optional[int] = 50,
	guidance_scale: Optional[float] = 7.5,
	num_images_per_prompt: Optional[int] = 1,
	eta: Optional[float] = 0.0,
	generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	max_embeddings_multiples: Optional[int] = 3,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	is_cancelled_callback: Optional[Callable[[], bool]] = None,
	callback_steps: int = 1,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	):
	r"""
	Function for image-to-image generation.
	Args:
	image (`torch.FloatTensor` or `PIL.Image.Image`):
	`Image`, or tensor representing an image batch, that will be used as the starting point for the
	process.
	prompt (`str` or `List[str]`):
	The prompt or prompts to guide the image generation.
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
	if `guidance_scale` is less than `1`).
	strength (`float`, optional, defaults to 0.8):
	Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
	`image` will be used as a starting point, adding more noise to it the larger the `strength`. The
	number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
	noise will be maximum and the denoising process will run for the full number of iterations specified in
	`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
	num_inference_steps (`int`, optional, defaults to 50):
	The number of denoising steps. More denoising steps usually lead to a higher quality image at the
	expense of slower inference. This parameter will be modulated by `strength`.
	guidance_scale (`float`, optional, defaults to 7.5):
	Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
	`guidance_scale` is defined as `w` of equation 2. of [Imagen
	Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
	1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
	usually at the expense of lower image quality.
	num_images_per_prompt (`int`, optional, defaults to 1):
	The number of images to generate per prompt.
	eta (`float`, optional, defaults to 0.0):
	Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
	[`schedulers.DDIMScheduler`], will be ignored for others.
	generator (`torch.Generator` or `List[torch.Generator]`, optional):
	One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
	to make generation deterministic.
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	max_embeddings_multiples (`int`, optional, defaults to `3`):
	The max multiple length of prompt embeddings compared to the max output length of text encoder.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generate image. Choose between
	[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.
	callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. The function will be
	called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
	is_cancelled_callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. If the function returns
	`True`, the inference will be cancelled.
	callback_steps (`int`, optional, defaults to 1):
	The frequency at which the `callback` function will be called. If not specified, the callback will be
	called at every step.
	cross_attention_kwargs (`dict`, optional):
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
	`self.processor` in
	[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).

	Returns:
	`None` if cancelled by `is_cancelled_callback`,
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
	When returning a tuple, the first element is a list with the generated images, and the second element is a
	list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
	(nsfw) content, according to the `safety_checker`.
	"""
	return self.__call__(
	prompt=prompt,
	negative_prompt=negative_prompt,
	image=image,
	num_inference_steps=num_inference_steps,
	guidance_scale=guidance_scale,
	strength=strength,
	num_images_per_prompt=num_images_per_prompt,
	eta=eta,
	generator=generator,
	prompt_embeds=prompt_embeds,
	negative_prompt_embeds=negative_prompt_embeds,
	max_embeddings_multiples=max_embeddings_multiples,
	output_type=output_type,
	return_dict=return_dict,
	callback=callback,
	is_cancelled_callback=is_cancelled_callback,
	callback_steps=callback_steps,
	cross_attention_kwargs=cross_attention_kwargs,
	)

	def inpaint(
	self,
	image: Union[torch.FloatTensor, PIL.Image.Image],
	mask_image: Union[torch.FloatTensor, PIL.Image.Image],
	prompt: Union[str, List[str]],
	negative_prompt: Optional[Union[str, List[str]]] = None,
	strength: float = 0.8,
	num_inference_steps: Optional[int] = 50,
	guidance_scale: Optional[float] = 7.5,
	num_images_per_prompt: Optional[int] = 1,
	add_predicted_noise: Optional[bool] = False,
	eta: Optional[float] = 0.0,
	generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	max_embeddings_multiples: Optional[int] = 3,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	is_cancelled_callback: Optional[Callable[[], bool]] = None,
	callback_steps: int = 1,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	):
	r"""
	Function for inpaint.
	Args:
	image (`torch.FloatTensor` or `PIL.Image.Image`):
	`Image`, or tensor representing an image batch, that will be used as the starting point for the
	process. This is the image whose masked region will be inpainted.
	mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
	`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
	replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
	PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
	contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
	prompt (`str` or `List[str]`):
	The prompt or prompts to guide the image generation.
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
	if `guidance_scale` is less than `1`).
	strength (`float`, optional, defaults to 0.8):
	Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
	is 1, the denoising process will be run on the masked area for the full number of iterations specified
	in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
	noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
	num_inference_steps (`int`, optional, defaults to 50):
	The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
	the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
	guidance_scale (`float`, optional, defaults to 7.5):
	Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
	`guidance_scale` is defined as `w` of equation 2. of [Imagen
	Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
	1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
	usually at the expense of lower image quality.
	num_images_per_prompt (`int`, optional, defaults to 1):
	The number of images to generate per prompt.
	add_predicted_noise (`bool`, optional, defaults to True):
	Use predicted noise instead of random noise when constructing noisy versions of the original image in
	the reverse diffusion process
	eta (`float`, optional, defaults to 0.0):
	Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
	[`schedulers.DDIMScheduler`], will be ignored for others.
	generator (`torch.Generator` or `List[torch.Generator]`, optional):
	One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
	to make generation deterministic.
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	max_embeddings_multiples (`int`, optional, defaults to `3`):
	The max multiple length of prompt embeddings compared to the max output length of text encoder.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generate image. Choose between
	[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.
	callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. The function will be
	called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
	is_cancelled_callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. If the function returns
	`True`, the inference will be cancelled.
	callback_steps (`int`, optional, defaults to 1):
	The frequency at which the `callback` function will be called. If not specified, the callback will be
	called at every step.
	cross_attention_kwargs (`dict`, optional):
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
	`self.processor` in
	[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).

	Returns:
	`None` if cancelled by `is_cancelled_callback`,
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
	When returning a tuple, the first element is a list with the generated images, and the second element is a
	list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
	(nsfw) content, according to the `safety_checker`.
	"""
	return self.__call__(
	prompt=prompt,
	negative_prompt=negative_prompt,
	image=image,
	mask_image=mask_image,
	num_inference_steps=num_inference_steps,
	guidance_scale=guidance_scale,
	strength=strength,
	num_images_per_prompt=num_images_per_prompt,
	add_predicted_noise=add_predicted_noise,
	eta=eta,
	generator=generator,
	prompt_embeds=prompt_embeds,
	negative_prompt_embeds=negative_prompt_embeds,
	max_embeddings_multiples=max_embeddings_multiples,
	output_type=output_type,
	return_dict=return_dict,
	callback=callback,
	is_cancelled_callback=is_cancelled_callback,
	callback_steps=callback_steps,
	cross_attention_kwargs=cross_attention_kwargs,
	)