sd_perturbed_attention_guidance / pipeline.py

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	# Implementation of StableDiffusionPAGPipeline

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

	import torch
	import torch.nn.functional as F
	from packaging import version
	from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection

	from diffusers.configuration_utils import FrozenDict
	from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
	from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
	from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
	from diffusers.models.attention_processor import Attention, AttnProcessor2_0, FusedAttnProcessor2_0
	from diffusers.models.lora import adjust_lora_scale_text_encoder
	from diffusers.pipelines.pipeline_utils import DiffusionPipeline
	from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
	from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
	from diffusers.schedulers import KarrasDiffusionSchedulers
	from diffusers.utils import (
	USE_PEFT_BACKEND,
	deprecate,
	logging,
	replace_example_docstring,
	scale_lora_layers,
	unscale_lora_layers,
	)
	from diffusers.utils.torch_utils import randn_tensor


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

	EXAMPLE_DOC_STRING = """
	Examples:
	```py
	>>> import torch
	>>> from diffusers import StableDiffusionPipeline
	>>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
	>>> pipe = pipe.to("cuda")
	>>> prompt = "a photo of an astronaut riding a horse on mars"
	>>> image = pipe(prompt).images[0]
	```
	"""


	class PAGIdentitySelfAttnProcessor:
	r"""
	Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
	"""

	def __init__(self):
	if not hasattr(F, "scaled_dot_product_attention"):
	raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

	def __call__(
	self,
	attn: Attention,
	hidden_states: torch.FloatTensor,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	temb: Optional[torch.FloatTensor] = None,
	*args,
	**kwargs,
	) -> torch.FloatTensor:
	if len(args) > 0 or kwargs.get("scale", None) is not None:
	deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
	deprecate("scale", "1.0.0", deprecation_message)

	residual = hidden_states
	if attn.spatial_norm is not None:
	hidden_states = attn.spatial_norm(hidden_states, temb)

	input_ndim = hidden_states.ndim
	if input_ndim == 4:
	batch_size, channel, height, width = hidden_states.shape
	hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

	# chunk
	hidden_states_org, hidden_states_ptb = hidden_states.chunk(2)

	# original path
	batch_size, sequence_length, _ = hidden_states_org.shape

	if attention_mask is not None:
	attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
	# scaled_dot_product_attention expects attention_mask shape to be
	# (batch, heads, source_length, target_length)
	attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

	if attn.group_norm is not None:
	hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2)

	query = attn.to_q(hidden_states_org)
	key = attn.to_k(hidden_states_org)
	value = attn.to_v(hidden_states_org)

	inner_dim = key.shape[-1]
	head_dim = inner_dim // attn.heads

	query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

	key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
	value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

	# the output of sdp = (batch, num_heads, seq_len, head_dim)
	# TODO: add support for attn.scale when we move to Torch 2.1
	hidden_states_org = F.scaled_dot_product_attention(
	query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
	)

	hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
	hidden_states_org = hidden_states_org.to(query.dtype)

	# linear proj
	hidden_states_org = attn.to_out[0](hidden_states_org)
	# dropout
	hidden_states_org = attn.to_out[1](hidden_states_org)

	if input_ndim == 4:
	hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)

	# perturbed path (identity attention)
	batch_size, sequence_length, _ = hidden_states_ptb.shape

	if attention_mask is not None:
	attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
	# scaled_dot_product_attention expects attention_mask shape to be
	# (batch, heads, source_length, target_length)
	attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

	if attn.group_norm is not None:
	hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2)

	value = attn.to_v(hidden_states_ptb)

	# hidden_states_ptb = torch.zeros(value.shape).to(value.get_device())
	hidden_states_ptb = value

	hidden_states_ptb = hidden_states_ptb.to(query.dtype)

	# linear proj
	hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
	# dropout
	hidden_states_ptb = attn.to_out[1](hidden_states_ptb)

	if input_ndim == 4:
	hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)

	# cat
	hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])

	if attn.residual_connection:
	hidden_states = hidden_states + residual

	hidden_states = hidden_states / attn.rescale_output_factor

	return hidden_states


	class PAGCFGIdentitySelfAttnProcessor:
	r"""
	Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
	"""

	def __init__(self):
	if not hasattr(F, "scaled_dot_product_attention"):
	raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

	def __call__(
	self,
	attn: Attention,
	hidden_states: torch.FloatTensor,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	temb: Optional[torch.FloatTensor] = None,
	*args,
	**kwargs,
	) -> torch.FloatTensor:
	if len(args) > 0 or kwargs.get("scale", None) is not None:
	deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
	deprecate("scale", "1.0.0", deprecation_message)

	residual = hidden_states
	if attn.spatial_norm is not None:
	hidden_states = attn.spatial_norm(hidden_states, temb)

	input_ndim = hidden_states.ndim
	if input_ndim == 4:
	batch_size, channel, height, width = hidden_states.shape
	hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

	# chunk
	hidden_states_uncond, hidden_states_org, hidden_states_ptb = hidden_states.chunk(3)
	hidden_states_org = torch.cat([hidden_states_uncond, hidden_states_org])

	# original path
	batch_size, sequence_length, _ = hidden_states_org.shape

	if attention_mask is not None:
	attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
	# scaled_dot_product_attention expects attention_mask shape to be
	# (batch, heads, source_length, target_length)
	attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

	if attn.group_norm is not None:
	hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2)

	query = attn.to_q(hidden_states_org)
	key = attn.to_k(hidden_states_org)
	value = attn.to_v(hidden_states_org)

	inner_dim = key.shape[-1]
	head_dim = inner_dim // attn.heads

	query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

	key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
	value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

	# the output of sdp = (batch, num_heads, seq_len, head_dim)
	# TODO: add support for attn.scale when we move to Torch 2.1
	hidden_states_org = F.scaled_dot_product_attention(
	query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
	)

	hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
	hidden_states_org = hidden_states_org.to(query.dtype)

	# linear proj
	hidden_states_org = attn.to_out[0](hidden_states_org)
	# dropout
	hidden_states_org = attn.to_out[1](hidden_states_org)

	if input_ndim == 4:
	hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)

	# perturbed path (identity attention)
	batch_size, sequence_length, _ = hidden_states_ptb.shape

	if attention_mask is not None:
	attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
	# scaled_dot_product_attention expects attention_mask shape to be
	# (batch, heads, source_length, target_length)
	attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

	if attn.group_norm is not None:
	hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2)

	value = attn.to_v(hidden_states_ptb)
	hidden_states_ptb = value
	hidden_states_ptb = hidden_states_ptb.to(query.dtype)

	# linear proj
	hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
	# dropout
	hidden_states_ptb = attn.to_out[1](hidden_states_ptb)

	if input_ndim == 4:
	hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)

	# cat
	hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])

	if attn.residual_connection:
	hidden_states = hidden_states + residual

	hidden_states = hidden_states / attn.rescale_output_factor

	return hidden_states


	def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
	"""
	Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
	Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
	"""
	std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
	std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
	# rescale the results from guidance (fixes overexposure)
	noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
	# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
	noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
	return noise_cfg


	def retrieve_timesteps(
	scheduler,
	num_inference_steps: Optional[int] = None,
	device: Optional[Union[str, torch.device]] = None,
	timesteps: Optional[List[int]] = None,
	**kwargs,
	):
	"""
	Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
	custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
	Args:
	scheduler (`SchedulerMixin`):
	The scheduler to get timesteps from.
	num_inference_steps (`int`):
	The number of diffusion steps used when generating samples with a pre-trained model. If used,
	`timesteps` must be `None`.
	device (`str` or `torch.device`, optional):
	The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
	timesteps (`List[int]`, optional):
	Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
	timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
	must be `None`.
	Returns:
	`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
	second element is the number of inference steps.
	"""
	if timesteps is not None:
	accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
	if not accepts_timesteps:
	raise ValueError(
	f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
	f" timestep schedules. Please check whether you are using the correct scheduler."
	)
	scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
	timesteps = scheduler.timesteps
	num_inference_steps = len(timesteps)
	else:
	scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
	timesteps = scheduler.timesteps
	return timesteps, num_inference_steps


	class StableDiffusionPAGPipeline(
	DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
	):
	r"""
	Pipeline for text-to-image generation using Stable Diffusion.
	This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
	implemented for all pipelines (downloading, saving, running on a particular device, etc.).
	The pipeline also inherits the following loading methods:
	- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
	- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
	- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
	- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
	- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
	Args:
	vae ([`AutoencoderKL`]):
	Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
	text_encoder ([`~transformers.CLIPTextModel`]):
	Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
	tokenizer ([`~transformers.CLIPTokenizer`]):
	A `CLIPTokenizer` to tokenize text.
	unet ([`UNet2DConditionModel`]):
	A `UNet2DConditionModel` to denoise the encoded image latents.
	scheduler ([`SchedulerMixin`]):
	A scheduler to be used in combination with `unet` to denoise the encoded image latents. 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 more details
	about a model's potential harms.
	feature_extractor ([`~transformers.CLIPImageProcessor`]):
	A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
	"""

	model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
	_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
	_exclude_from_cpu_offload = ["safety_checker"]
	_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

	def __init__(
	self,
	vae: AutoencoderKL,
	text_encoder: CLIPTextModel,
	tokenizer: CLIPTokenizer,
	unet: UNet2DConditionModel,
	scheduler: KarrasDiffusionSchedulers,
	safety_checker: StableDiffusionSafetyChecker,
	feature_extractor: CLIPImageProcessor,
	image_encoder: CLIPVisionModelWithProjection = None,
	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,
	image_encoder=image_encoder,
	)
	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 enabled, 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 for saving a large amount of memory and to allow
	processing larger images.
	"""
	self.vae.enable_tiling()

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

	def _encode_prompt(
	self,
	prompt,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance,
	negative_prompt=None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	lora_scale: Optional[float] = None,
	**kwargs,
	):
	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

	def encode_prompt(
	self,
	prompt,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance,
	negative_prompt=None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	lora_scale: Optional[float] = None,
	clip_skip: Optional[int] = None,
	):
	r"""
	Encodes the prompt into text encoder hidden states.
	Args:
	prompt (`str` or `List[str]`, optional):
	prompt to be encoded
	device: (`torch.device`):
	torch device
	num_images_per_prompt (`int`):
	number of images that should be generated per prompt
	do_classifier_free_guidance (`bool`):
	whether to use classifier free guidance or not
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. If not defined, one has to pass
	`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
	less than `1`).
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	lora_scale (`float`, optional):
	A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
	clip_skip (`int`, optional):
	Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
	the output of the pre-final layer will be used for computing the prompt embeddings.
	"""
	# set lora scale so that monkey patched LoRA
	# function of text encoder can correctly access it
	if lora_scale is not None and isinstance(self, LoraLoaderMixin):
	self._lora_scale = lora_scale

	# dynamically adjust the LoRA scale
	if not USE_PEFT_BACKEND:
	adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
	else:
	scale_lora_layers(self.text_encoder, lora_scale)

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

	if prompt_embeds is None:
	# textual inversion: 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

	def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
	dtype = next(self.image_encoder.parameters()).dtype

	if not isinstance(image, torch.Tensor):
	image = self.feature_extractor(image, return_tensors="pt").pixel_values

	image = image.to(device=device, dtype=dtype)
	if output_hidden_states:
	image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
	image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
	uncond_image_enc_hidden_states = self.image_encoder(
	torch.zeros_like(image), output_hidden_states=True
	).hidden_states[-2]
	uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
	num_images_per_prompt, dim=0
	)
	return image_enc_hidden_states, uncond_image_enc_hidden_states
	else:
	image_embeds = self.image_encoder(image).image_embeds
	image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
	uncond_image_embeds = torch.zeros_like(image_embeds)

	return image_embeds, uncond_image_embeds

	def prepare_ip_adapter_image_embeds(
	self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
	):
	if ip_adapter_image_embeds is None:
	if not isinstance(ip_adapter_image, list):
	ip_adapter_image = [ip_adapter_image]

	if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
	raise ValueError(
	f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
	)

	image_embeds = []
	for single_ip_adapter_image, image_proj_layer in zip(
	ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
	):
	output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
	single_image_embeds, single_negative_image_embeds = self.encode_image(
	single_ip_adapter_image, device, 1, output_hidden_state
	)
	single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
	single_negative_image_embeds = torch.stack(
	[single_negative_image_embeds] * num_images_per_prompt, dim=0
	)

	if self.do_classifier_free_guidance:
	single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
	single_image_embeds = single_image_embeds.to(device)

	image_embeds.append(single_image_embeds)
	else:
	image_embeds = ip_adapter_image_embeds
	return image_embeds

	def run_safety_checker(self, image, device, dtype):
	if self.safety_checker is None:
	has_nsfw_concept = None
	else:
	if torch.is_tensor(image):
	feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
	else:
	feature_extractor_input = self.image_processor.numpy_to_pil(image)
	safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
	image, has_nsfw_concept = self.safety_checker(
	images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
	)
	return image, has_nsfw_concept

	def decode_latents(self, latents):
	deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
	deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)

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

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

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

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

	def check_inputs(
	self,
	prompt,
	height,
	width,
	callback_steps,
	negative_prompt=None,
	prompt_embeds=None,
	negative_prompt_embeds=None,
	ip_adapter_image=None,
	ip_adapter_image_embeds=None,
	callback_on_step_end_tensor_inputs=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."
	)

	def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
	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

	def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
	r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
	The suffixes after the scaling factors represent the stages where they are being applied.
	Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
	that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
	Args:
	s1 (`float`):
	Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
	mitigate "oversmoothing effect" in the enhanced denoising process.
	s2 (`float`):
	Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
	mitigate "oversmoothing effect" in the enhanced denoising process.
	b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
	b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
	"""
	if not hasattr(self, "unet"):
	raise ValueError("The pipeline must have `unet` for using FreeU.")
	self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)

	def disable_freeu(self):
	"""Disables the FreeU mechanism if enabled."""
	self.unet.disable_freeu()

	# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.fuse_qkv_projections
	def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):
	"""
	Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
	key, value) are fused. For cross-attention modules, key and value projection matrices are fused.
	<Tip warning={true}>
	This API is 🧪 experimental.
	</Tip>
	Args:
	unet (`bool`, defaults to `True`): To apply fusion on the UNet.
	vae (`bool`, defaults to `True`): To apply fusion on the VAE.
	"""
	self.fusing_unet = False
	self.fusing_vae = False

	if unet:
	self.fusing_unet = True
	self.unet.fuse_qkv_projections()
	self.unet.set_attn_processor(FusedAttnProcessor2_0())

	if vae:
	if not isinstance(self.vae, AutoencoderKL):
	raise ValueError("`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.")

	self.fusing_vae = True
	self.vae.fuse_qkv_projections()
	self.vae.set_attn_processor(FusedAttnProcessor2_0())

	# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.unfuse_qkv_projections
	def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):
	"""Disable QKV projection fusion if enabled.
	<Tip warning={true}>
	This API is 🧪 experimental.
	</Tip>
	Args:
	unet (`bool`, defaults to `True`): To apply fusion on the UNet.
	vae (`bool`, defaults to `True`): To apply fusion on the VAE.
	"""
	if unet:
	if not self.fusing_unet:
	logger.warning("The UNet was not initially fused for QKV projections. Doing nothing.")
	else:
	self.unet.unfuse_qkv_projections()
	self.fusing_unet = False

	if vae:
	if not self.fusing_vae:
	logger.warning("The VAE was not initially fused for QKV projections. Doing nothing.")
	else:
	self.vae.unfuse_qkv_projections()
	self.fusing_vae = False

	# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
	def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
	"""
	See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
	Args:
	timesteps (`torch.Tensor`):
	generate embedding vectors at these timesteps
	embedding_dim (`int`, optional, defaults to 512):
	dimension of the embeddings to generate
	dtype:
	data type of the generated embeddings
	Returns:
	`torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
	"""
	assert len(w.shape) == 1
	w = w * 1000.0

	half_dim = embedding_dim // 2
	emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
	emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
	emb = w.to(dtype)[:, None] * emb[None, :]
	emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
	if embedding_dim % 2 == 1: # zero pad
	emb = torch.nn.functional.pad(emb, (0, 1))
	assert emb.shape == (w.shape[0], embedding_dim)
	return emb

	def pred_z0(self, sample, model_output, timestep):
	alpha_prod_t = self.scheduler.alphas_cumprod[timestep].to(sample.device)

	beta_prod_t = 1 - alpha_prod_t
	if self.scheduler.config.prediction_type == "epsilon":
	pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
	elif self.scheduler.config.prediction_type == "sample":
	pred_original_sample = model_output
	elif self.scheduler.config.prediction_type == "v_prediction":
	pred_original_sample = (alpha_prod_t*0.5) sample - (beta_prod_t*0.5) model_output
	# predict V
	model_output = (alpha_prod_t*0.5) model_output + (beta_prod_t*0.5) sample
	else:
	raise ValueError(
	f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
	" or `v_prediction`"
	)

	return pred_original_sample

	def pred_x0(self, latents, noise_pred, t, generator, device, prompt_embeds, output_type):
	pred_z0 = self.pred_z0(latents, noise_pred, t)
	pred_x0 = self.vae.decode(pred_z0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]
	pred_x0, ____ = self.run_safety_checker(pred_x0, device, prompt_embeds.dtype)
	do_denormalize = [True] * pred_x0.shape[0]
	pred_x0 = self.image_processor.postprocess(pred_x0, output_type=output_type, do_denormalize=do_denormalize)

	return pred_x0

	@property
	def guidance_scale(self):
	return self._guidance_scale

	@property
	def guidance_rescale(self):
	return self._guidance_rescale

	@property
	def clip_skip(self):
	return self._clip_skip

	# 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.
	@property
	def do_classifier_free_guidance(self):
	return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None

	@property
	def cross_attention_kwargs(self):
	return self._cross_attention_kwargs

	@property
	def num_timesteps(self):
	return self._num_timesteps

	@property
	def interrupt(self):
	return self._interrupt

	@property
	def pag_scale(self):
	return self._pag_scale

	@property
	def do_perturbed_attention_guidance(self):
	return self._pag_scale > 0

	@property
	def pag_adaptive_scaling(self):
	return self._pag_adaptive_scaling

	@property
	def do_pag_adaptive_scaling(self):
	return self._pag_adaptive_scaling > 0

	@property
	def pag_applied_layers_index(self):
	return self._pag_applied_layers_index

	@torch.no_grad()
	@replace_example_docstring(EXAMPLE_DOC_STRING)
	def __call__(
	self,
	prompt: Union[str, List[str]] = None,
	height: Optional[int] = None,
	width: Optional[int] = None,
	num_inference_steps: int = 50,
	timesteps: List[int] = None,
	guidance_scale: float = 7.5,
	pag_scale: float = 0.0,
	pag_adaptive_scaling: float = 0.0,
	pag_applied_layers_index: List[str] = ["d4"], # ['d4', 'd5', 'm0']
	negative_prompt: Optional[Union[str, List[str]]] = None,
	num_images_per_prompt: Optional[int] = 1,
	eta: float = 0.0,
	generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	latents: Optional[torch.FloatTensor] = None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	ip_adapter_image: Optional[PipelineImageInput] = None,
	ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	guidance_rescale: float = 0.0,
	clip_skip: Optional[int] = None,
	callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
	callback_on_step_end_tensor_inputs: List[str] = ["latents"],
	**kwargs,
	):
	r"""
	The call function to the pipeline for generation.
	Args:
	prompt (`str` or `List[str]`, optional):
	The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
	height (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
	The height in pixels of the generated image.
	width (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
	The width in pixels of the generated image.
	num_inference_steps (`int`, optional, defaults to 50):
	The number of denoising steps. More denoising steps usually lead to a higher quality image at the
	expense of slower inference.
	timesteps (`List[int]`, optional):
	Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
	in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
	passed will be used. Must be in descending order.
	guidance_scale (`float`, optional, defaults to 7.5):
	A higher guidance scale value encourages the model to generate images closely linked to the text
	`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts to guide what to not include in image generation. If not defined, you need to
	pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
	num_images_per_prompt (`int`, optional, defaults to 1):
	The number of images to generate per prompt.
	eta (`float`, optional, defaults to 0.0):
	Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
	to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
	generator (`torch.Generator` or `List[torch.Generator]`, optional):
	A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
	generation deterministic.
	latents (`torch.FloatTensor`, optional):
	Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor is generated by sampling using the supplied random `generator`.
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
	provided, text embeddings are generated from the `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
	not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
	ip_adapter_image: (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
	ip_adapter_image_embeds (`List[torch.FloatTensor]`, optional):
	Pre-generated image embeddings for IP-Adapter. If not
	provided, embeddings are computed from the `ip_adapter_image` input argument.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generated image. Choose between `PIL.Image` or `np.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.
	cross_attention_kwargs (`dict`, optional):
	A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
	[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
	guidance_rescale (`float`, optional, defaults to 0.0):
	Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
	Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
	using zero terminal SNR.
	clip_skip (`int`, optional):
	Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
	the output of the pre-final layer will be used for computing the prompt embeddings.
	callback_on_step_end (`Callable`, optional):
	A function that calls at the end of each denoising steps during the inference. The function is called
	with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
	callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
	`callback_on_step_end_tensor_inputs`.
	callback_on_step_end_tensor_inputs (`List`, optional):
	The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
	will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
	`._callback_tensor_inputs` attribute of your pipeline class.
	Examples:
	Returns:
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
	If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
	otherwise a `tuple` is returned where the first element is a list with the generated images and the
	second element is a list of `bool`s indicating whether the corresponding generated image contains
	"not-safe-for-work" (nsfw) content.
	"""

	callback = kwargs.pop("callback", None)
	callback_steps = kwargs.pop("callback_steps", None)

	if callback is not None:
	deprecate(
	"callback",
	"1.0.0",
	"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
	)
	if callback_steps is not None:
	deprecate(
	"callback_steps",
	"1.0.0",
	"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
	)

	# 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
	# to deal with lora scaling and other possible forward hooks

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

	self._guidance_scale = guidance_scale
	self._guidance_rescale = guidance_rescale
	self._clip_skip = clip_skip
	self._cross_attention_kwargs = cross_attention_kwargs
	self._interrupt = False

	self._pag_scale = pag_scale
	self._pag_adaptive_scaling = pag_adaptive_scaling
	self._pag_applied_layers_index = pag_applied_layers_index

	# 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

	# 3. Encode input prompt
	lora_scale = (
	self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
	)

	prompt_embeds, negative_prompt_embeds = self.encode_prompt(
	prompt,
	device,
	num_images_per_prompt,
	self.do_classifier_free_guidance,
	negative_prompt,
	prompt_embeds=prompt_embeds,
	negative_prompt_embeds=negative_prompt_embeds,
	lora_scale=lora_scale,
	clip_skip=self.clip_skip,
	)

	# For classifier free guidance, we need to do two forward passes.
	# Here we concatenate the unconditional and text embeddings into a single batch
	# to avoid doing two forward passes

	# cfg
	if self.do_classifier_free_guidance and not self.do_perturbed_attention_guidance:
	prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
	# pag
	elif not self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
	prompt_embeds = torch.cat([prompt_embeds, prompt_embeds])
	# both
	elif self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
	prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds, prompt_embeds])

	if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
	image_embeds = self.prepare_ip_adapter_image_embeds(
	ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt
	)

	# 4. Prepare timesteps
	timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)

	# 5. Prepare latent variables
	num_channels_latents = self.unet.config.in_channels
	latents = self.prepare_latents(
	batch_size * num_images_per_prompt,
	num_channels_latents,
	height,
	width,
	prompt_embeds.dtype,
	device,
	generator,
	latents,
	)

	# 6. 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)

	# 6.1 Add image embeds for IP-Adapter
	added_cond_kwargs = (
	{"image_embeds": image_embeds}
	if (ip_adapter_image is not None or ip_adapter_image_embeds is not None)
	else None
	)

	# 6.2 Optionally get Guidance Scale Embedding
	timestep_cond = None
	if self.unet.config.time_cond_proj_dim is not None:
	guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
	timestep_cond = self.get_guidance_scale_embedding(
	guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
	).to(device=device, dtype=latents.dtype)

	# 7. Denoising loop
	if self.do_perturbed_attention_guidance:
	down_layers = []
	mid_layers = []
	up_layers = []
	for name, module in self.unet.named_modules():
	if "attn1" in name and "to" not in name:
	layer_type = name.split(".")[0].split("_")[0]
	if layer_type == "down":
	down_layers.append(module)
	elif layer_type == "mid":
	mid_layers.append(module)
	elif layer_type == "up":
	up_layers.append(module)
	else:
	raise ValueError(f"Invalid layer type: {layer_type}")

	# change attention layer in UNet if use PAG
	if self.do_perturbed_attention_guidance:
	if self.do_classifier_free_guidance:
	replace_processor = PAGCFGIdentitySelfAttnProcessor()
	else:
	replace_processor = PAGIdentitySelfAttnProcessor()

	drop_layers = self.pag_applied_layers_index
	for drop_layer in drop_layers:
	try:
	if drop_layer[0] == "d":
	down_layers[int(drop_layer[1])].processor = replace_processor
	elif drop_layer[0] == "m":
	mid_layers[int(drop_layer[1])].processor = replace_processor
	elif drop_layer[0] == "u":
	up_layers[int(drop_layer[1])].processor = replace_processor
	else:
	raise ValueError(f"Invalid layer type: {drop_layer[0]}")
	except IndexError:
	raise ValueError(
	f"Invalid layer index: {drop_layer}. Available layers: {len(down_layers)} down layers, {len(mid_layers)} mid layers, {len(up_layers)} up layers."
	)

	num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
	self._num_timesteps = len(timesteps)
	with self.progress_bar(total=num_inference_steps) as progress_bar:
	for i, t in enumerate(timesteps):
	if self.interrupt:
	continue

	# cfg
	if self.do_classifier_free_guidance and not self.do_perturbed_attention_guidance:
	latent_model_input = torch.cat([latents] * 2)
	# pag
	elif not self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
	latent_model_input = torch.cat([latents] * 2)
	# both
	elif self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
	latent_model_input = torch.cat([latents] * 3)
	# no
	else:
	latent_model_input = 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,
	timestep_cond=timestep_cond,
	cross_attention_kwargs=self.cross_attention_kwargs,
	added_cond_kwargs=added_cond_kwargs,
	return_dict=False,
	)[0]

	# perform guidance

	# cfg
	if self.do_classifier_free_guidance and not self.do_perturbed_attention_guidance:
	noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)

	delta = noise_pred_text - noise_pred_uncond
	noise_pred = noise_pred_uncond + self.guidance_scale * delta

	# pag
	elif not self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
	noise_pred_original, noise_pred_perturb = noise_pred.chunk(2)

	signal_scale = self.pag_scale
	if self.do_pag_adaptive_scaling:
	signal_scale = self.pag_scale - self.pag_adaptive_scaling * (1000 - t)
	if signal_scale < 0:
	signal_scale = 0

	noise_pred = noise_pred_original + signal_scale * (noise_pred_original - noise_pred_perturb)

	# both
	elif self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
	noise_pred_uncond, noise_pred_text, noise_pred_text_perturb = noise_pred.chunk(3)

	signal_scale = self.pag_scale
	if self.do_pag_adaptive_scaling:
	signal_scale = self.pag_scale - self.pag_adaptive_scaling * (1000 - t)
	if signal_scale < 0:
	signal_scale = 0

	noise_pred = (
	noise_pred_text
	+ (self.guidance_scale - 1.0) * (noise_pred_text - noise_pred_uncond)
	+ signal_scale * (noise_pred_text - noise_pred_text_perturb)
	)

	if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
	# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
	noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)

	# compute the previous noisy sample x_t -> x_t-1
	latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

	if callback_on_step_end is not None:
	callback_kwargs = {}
	for k in callback_on_step_end_tensor_inputs:
	callback_kwargs[k] = locals()[k]
	callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

	latents = callback_outputs.pop("latents", latents)
	prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
	negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)

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

	if not output_type == "latent":
	image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
	0
	]
	image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
	else:
	image = latents
	has_nsfw_concept = None

	if has_nsfw_concept is None:
	do_denormalize = [True] * image.shape[0]
	else:
	do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

	image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

	# Offload all models
	self.maybe_free_model_hooks()

	# change attention layer in UNet if use PAG
	if self.do_perturbed_attention_guidance:
	drop_layers = self.pag_applied_layers_index
	for drop_layer in drop_layers:
	try:
	if drop_layer[0] == "d":
	down_layers[int(drop_layer[1])].processor = AttnProcessor2_0()
	elif drop_layer[0] == "m":
	mid_layers[int(drop_layer[1])].processor = AttnProcessor2_0()
	elif drop_layer[0] == "u":
	up_layers[int(drop_layer[1])].processor = AttnProcessor2_0()
	else:
	raise ValueError(f"Invalid layer type: {drop_layer[0]}")
	except IndexError:
	raise ValueError(
	f"Invalid layer index: {drop_layer}. Available layers: {len(down_layers)} down layers, {len(mid_layers)} mid layers, {len(up_layers)} up layers."
	)

	if not return_dict:
	return (image, has_nsfw_concept)

	return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)