Duplicate from hf-internal-testing/tiny-sdxl-custom-components

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	# Copyright 2023 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

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

	import torch
	from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

	from diffusers.image_processor import VaeImageProcessor
	from diffusers.loaders import (
	FromSingleFileMixin,
	StableDiffusionXLLoraLoaderMixin,
	TextualInversionLoaderMixin,
	)
	from diffusers.models import AutoencoderKL, UNet2DConditionModel
	from diffusers.models.attention_processor import (
	AttnProcessor2_0,
	LoRAAttnProcessor2_0,
	LoRAXFormersAttnProcessor,
	XFormersAttnProcessor,
	)
	from diffusers.models.lora import adjust_lora_scale_text_encoder
	from diffusers.schedulers import KarrasDiffusionSchedulers
	from diffusers.utils import (
	USE_PEFT_BACKEND,
	is_invisible_watermark_available,
	is_torch_xla_available,
	logging,
	replace_example_docstring,
	scale_lora_layers,
	unscale_lora_layers,
	)
	from diffusers.utils.torch_utils import randn_tensor
	from diffusers import DiffusionPipeline


	if is_torch_xla_available():
	import torch_xla.core.xla_model as xm

	XLA_AVAILABLE = True
	else:
	XLA_AVAILABLE = False


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

	EXAMPLE_DOC_STRING = """
	Examples:
	```py
	>>> import torch
	>>> from diffusers import MyPipeline

	>>> pipe = MyPipeline.from_pretrained(
	... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
	... )
	>>> pipe = pipe.to("cuda")

	>>> prompt = "a photo of an astronaut riding a horse on mars"
	>>> image = pipe(prompt).images[0]
	```
	"""


	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
	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


	class MyPipeline(
	DiffusionPipeline, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
	):
	r"""
	Pipeline for text-to-image generation using Stable Diffusion XL.

	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.)

	In addition the pipeline inherits the following loading methods:
	- LoRA: [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`]
	- Ckpt: [`loaders.FromSingleFileMixin.from_single_file`]

	as well as the following saving methods:
	- LoRA: [`loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`]

	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 XL 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.
	text_encoder_2 ([` CLIPTextModelWithProjection`]):
	Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
	[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
	specifically the
	[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
	variant.
	tokenizer (`CLIPTokenizer`):
	Tokenizer of class
	[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
	tokenizer_2 (`CLIPTokenizer`):
	Second 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`].
	force_zeros_for_empty_prompt (`bool`, optional, defaults to `"True"`):
	Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
	`stabilityai/stable-diffusion-xl-base-1-0`.
	add_watermarker (`bool`, optional):
	Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
	watermark output images. If not defined, it will default to True if the package is installed, otherwise no
	watermarker will be used.
	"""
	model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
	_optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]

	def __init__(
	self,
	vae: AutoencoderKL,
	text_encoder: CLIPTextModel,
	text_encoder_2: CLIPTextModelWithProjection,
	tokenizer: CLIPTokenizer,
	tokenizer_2: CLIPTokenizer,
	unet: UNet2DConditionModel,
	scheduler: KarrasDiffusionSchedulers,
	force_zeros_for_empty_prompt: bool = True,
	add_watermarker: Optional[bool] = None,
	):
	super().__init__()

	self.register_modules(
	vae=vae,
	text_encoder=text_encoder,
	text_encoder_2=text_encoder_2,
	tokenizer=tokenizer,
	tokenizer_2=tokenizer_2,
	unet=unet,
	scheduler=scheduler,
	)
	self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
	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.default_sample_size = self.unet.config.sample_size

	add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()

	self.watermark = None

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
	def enable_vae_slicing(self):
	r"""
	Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
	compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
	"""
	self.vae.enable_slicing()

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
	def disable_vae_slicing(self):
	r"""
	Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
	computing decoding in one step.
	"""
	self.vae.disable_slicing()

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
	def enable_vae_tiling(self):
	r"""
	Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
	compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
	processing larger images.
	"""
	self.vae.enable_tiling()

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

	def encode_prompt(
	self,
	prompt: str,
	prompt_2: Optional[str] = None,
	device: Optional[torch.device] = None,
	num_images_per_prompt: int = 1,
	do_classifier_free_guidance: bool = True,
	negative_prompt: Optional[str] = None,
	negative_prompt_2: Optional[str] = None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_pooled_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
	prompt_2 (`str` or `List[str]`, optional):
	The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
	used in both text-encoders
	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`).
	negative_prompt_2 (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
	`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
	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.
	pooled_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
	If not provided, pooled text embeddings will be generated from `prompt` input argument.
	negative_pooled_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, pooled 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.
	"""
	device = device or self._execution_device

	# 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, StableDiffusionXLLoraLoaderMixin):
	self._lora_scale = lora_scale

	# dynamically adjust the LoRA scale
	if self.text_encoder is not None:
	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 self.text_encoder_2 is not None:
	if not USE_PEFT_BACKEND:
	adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
	else:
	scale_lora_layers(self.text_encoder_2, lora_scale)

	prompt = [prompt] if isinstance(prompt, str) else prompt

	if prompt is not None:
	batch_size = len(prompt)
	else:
	batch_size = prompt_embeds.shape[0]

	# Define tokenizers and text encoders
	tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
	text_encoders = (
	[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
	)

	if prompt_embeds is None:
	prompt_2 = prompt_2 or prompt
	prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2

	# textual inversion: procecss multi-vector tokens if necessary
	prompt_embeds_list = []
	prompts = [prompt, prompt_2]
	for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
	if isinstance(self, TextualInversionLoaderMixin):
	prompt = self.maybe_convert_prompt(prompt, tokenizer)

	text_inputs = tokenizer(
	prompt,
	padding="max_length",
	max_length=tokenizer.model_max_length,
	truncation=True,
	return_tensors="pt",
	)

	text_input_ids = text_inputs.input_ids
	untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, 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" {tokenizer.model_max_length} tokens: {removed_text}"
	)

	prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)

	# We are only ALWAYS interested in the pooled output of the final text encoder
	pooled_prompt_embeds = prompt_embeds[0]
	if clip_skip is None:
	prompt_embeds = prompt_embeds.hidden_states[-2]
	else:
	# "2" because SDXL always indexes from the penultimate layer.
	prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]

	prompt_embeds_list.append(prompt_embeds)

	prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)

	# get unconditional embeddings for classifier free guidance
	zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
	if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
	negative_prompt_embeds = torch.zeros_like(prompt_embeds)
	negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
	elif do_classifier_free_guidance and negative_prompt_embeds is None:
	negative_prompt = negative_prompt or ""
	negative_prompt_2 = negative_prompt_2 or negative_prompt

	# normalize str to list
	negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
	negative_prompt_2 = (
	batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
	)

	uncond_tokens: List[str]
	if 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 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, negative_prompt_2]

	negative_prompt_embeds_list = []
	for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
	if isinstance(self, TextualInversionLoaderMixin):
	negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)

	max_length = prompt_embeds.shape[1]
	uncond_input = tokenizer(
	negative_prompt,
	padding="max_length",
	max_length=max_length,
	truncation=True,
	return_tensors="pt",
	)

	negative_prompt_embeds = text_encoder(
	uncond_input.input_ids.to(device),
	output_hidden_states=True,
	)
	# We are only ALWAYS interested in the pooled output of the final text encoder
	negative_pooled_prompt_embeds = negative_prompt_embeds[0]
	negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]

	negative_prompt_embeds_list.append(negative_prompt_embeds)

	negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)

	if self.text_encoder_2 is not None:
	prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
	else:
	prompt_embeds = prompt_embeds.to(dtype=self.unet.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)

	if do_classifier_free_guidance:
	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
	seq_len = negative_prompt_embeds.shape[1]

	if self.text_encoder_2 is not None:
	negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
	else:
	negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.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)

	pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
	bs_embed * num_images_per_prompt, -1
	)
	if do_classifier_free_guidance:
	negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
	bs_embed * num_images_per_prompt, -1
	)

	if self.text_encoder is not None:
	if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
	# Retrieve the original scale by scaling back the LoRA layers
	unscale_lora_layers(self.text_encoder)

	if self.text_encoder_2 is not None:
	if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
	# Retrieve the original scale by scaling back the LoRA layers
	unscale_lora_layers(self.text_encoder_2)

	return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds

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

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

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

	def check_inputs(
	self,
	prompt,
	prompt_2,
	height,
	width,
	callback_steps,
	negative_prompt=None,
	negative_prompt_2=None,
	prompt_embeds=None,
	negative_prompt_embeds=None,
	pooled_prompt_embeds=None,
	negative_pooled_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 (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_2 is not None and prompt_embeds is not None:
	raise ValueError(
	f"Cannot forward both `prompt_2`: {prompt_2} 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)}")
	elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
	raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")

	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."
	)
	elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
	raise ValueError(
	f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} 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 prompt_embeds is not None and pooled_prompt_embeds is None:
	raise ValueError(
	"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
	)

	if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
	raise ValueError(
	"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
	)

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
	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 _get_add_time_ids(
	self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
	):
	add_time_ids = list(original_size + crops_coords_top_left + target_size)

	passed_add_embed_dim = (
	self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
	)
	expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features

	if expected_add_embed_dim != passed_add_embed_dim:
	raise ValueError(
	f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
	)

	add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
	return add_time_ids

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
	def upcast_vae(self):
	dtype = self.vae.dtype
	self.vae.to(dtype=torch.float32)
	use_torch_2_0_or_xformers = isinstance(
	self.vae.decoder.mid_block.attentions[0].processor,
	(
	AttnProcessor2_0,
	XFormersAttnProcessor,
	LoRAXFormersAttnProcessor,
	LoRAAttnProcessor2_0,
	),
	)
	# if xformers or torch_2_0 is used attention block does not need
	# to be in float32 which can save lots of memory
	if use_torch_2_0_or_xformers:
	self.vae.post_quant_conv.to(dtype)
	self.vae.decoder.conv_in.to(dtype)
	self.vae.decoder.mid_block.to(dtype)

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
	def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
	r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.

	The suffixes after the scaling factors represent the stages where they are being applied.

	Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
	that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.

	Args:
	s1 (`float`):
	Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
	mitigate "oversmoothing effect" in the enhanced denoising process.
	s2 (`float`):
	Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
	mitigate "oversmoothing effect" in the enhanced denoising process.
	b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
	b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
	"""
	if not hasattr(self, "unet"):
	raise ValueError("The pipeline must have `unet` for using FreeU.")
	self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
	def disable_freeu(self):
	"""Disables the FreeU mechanism if enabled."""
	self.unet.disable_freeu()

	@torch.no_grad()
	@replace_example_docstring(EXAMPLE_DOC_STRING)
	def __call__(
	self,
	prompt: Union[str, List[str]] = None,
	prompt_2: Optional[Union[str, List[str]]] = None,
	height: Optional[int] = None,
	width: Optional[int] = None,
	num_inference_steps: int = 50,
	denoising_end: Optional[float] = None,
	guidance_scale: float = 5.0,
	negative_prompt: Optional[Union[str, List[str]]] = None,
	negative_prompt_2: 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,
	pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	callback_steps: int = 1,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	guidance_rescale: float = 0.0,
	original_size: Optional[Tuple[int, int]] = None,
	crops_coords_top_left: Tuple[int, int] = (0, 0),
	target_size: Optional[Tuple[int, int]] = None,
	negative_original_size: Optional[Tuple[int, int]] = None,
	negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
	negative_target_size: Optional[Tuple[int, int]] = None,
	clip_skip: Optional[int] = None,
	):
	r"""
	Function invoked when calling the pipeline for generation.

	Args:
	prompt (`str` or `List[str]`, optional):
	The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
	instead.
	prompt_2 (`str` or `List[str]`, optional):
	The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
	used in both text-encoders
	height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor):
	The height in pixels of the generated image. This is set to 1024 by default for the best results.
	Anything below 512 pixels won't work well for
	[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
	and checkpoints that are not specifically fine-tuned on low resolutions.
	width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor):
	The width in pixels of the generated image. This is set to 1024 by default for the best results.
	Anything below 512 pixels won't work well for
	[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
	and checkpoints that are not specifically fine-tuned on low resolutions.
	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.
	denoising_end (`float`, optional):
	When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
	completed before it is intentionally prematurely terminated. As a result, the returned sample will
	still retain a substantial amount of noise as determined by the discrete timesteps selected by the
	scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
	"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
	Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
	guidance_scale (`float`, optional, defaults to 5.0):
	Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
	`guidance_scale` is defined as `w` of equation 2. of [Imagen
	Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
	1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
	usually at the expense of lower image quality.
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. 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`).
	negative_prompt_2 (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
	`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
	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.
	pooled_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
	If not provided, pooled text embeddings will be generated from `prompt` input argument.
	negative_pooled_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
	input argument.
	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_xl.MyPipelineOutput`] 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)`.
	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.models.attention_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 proposed by [Common Diffusion Noise Schedules and Sample Steps are
	Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
	[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.
	original_size (`Tuple[int]`, optional, defaults to (1024, 1024)):
	If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
	`original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
	explained in section 2.2 of
	[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
	crops_coords_top_left (`Tuple[int]`, optional, defaults to (0, 0)):
	`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
	`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
	`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
	[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
	target_size (`Tuple[int]`, optional, defaults to (1024, 1024)):
	For most cases, `target_size` should be set to the desired height and width of the generated image. If
	not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
	section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
	negative_original_size (`Tuple[int]`, optional, defaults to (1024, 1024)):
	To negatively condition the generation process based on a specific image resolution. Part of SDXL's
	micro-conditioning as explained in section 2.2 of
	[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
	information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
	negative_crops_coords_top_left (`Tuple[int]`, optional, defaults to (0, 0)):
	To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
	micro-conditioning as explained in section 2.2 of
	[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
	information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
	negative_target_size (`Tuple[int]`, optional, defaults to (1024, 1024)):
	To negatively condition the generation process based on a target image resolution. It should be as same
	as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
	[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
	information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.

	Examples:

	Returns:
	[`~pipelines.stable_diffusion_xl.MyPipelineOutput`] or `tuple`:
	[`~pipelines.stable_diffusion_xl.MyPipelineOutput`] if `return_dict` is True, otherwise a
	`tuple`. When returning a tuple, the first element is a list with the generated images.
	"""
	# 0. Default height and width to unet
	height = height or self.default_sample_size * self.vae_scale_factor
	width = width or self.default_sample_size * self.vae_scale_factor

	original_size = original_size or (height, width)
	target_size = target_size or (height, width)

	# 1. Check inputs. Raise error if not correct
	self.check_inputs(
	prompt,
	prompt_2,
	height,
	width,
	callback_steps,
	negative_prompt,
	negative_prompt_2,
	prompt_embeds,
	negative_prompt_embeds,
	pooled_prompt_embeds,
	negative_pooled_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
	lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None

	(
	prompt_embeds,
	negative_prompt_embeds,
	pooled_prompt_embeds,
	negative_pooled_prompt_embeds,
	) = self.encode_prompt(
	prompt=prompt,
	prompt_2=prompt_2,
	device=device,
	num_images_per_prompt=num_images_per_prompt,
	do_classifier_free_guidance=do_classifier_free_guidance,
	negative_prompt=negative_prompt,
	negative_prompt_2=negative_prompt_2,
	prompt_embeds=prompt_embeds,
	negative_prompt_embeds=negative_prompt_embeds,
	pooled_prompt_embeds=pooled_prompt_embeds,
	negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
	lora_scale=lora_scale,
	clip_skip=clip_skip,
	)

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

	timesteps = self.scheduler.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)

	# 7. Prepare added time ids & embeddings
	add_text_embeds = pooled_prompt_embeds
	if self.text_encoder_2 is None:
	text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
	else:
	text_encoder_projection_dim = self.text_encoder_2.config.projection_dim

	add_time_ids = self._get_add_time_ids(
	original_size,
	crops_coords_top_left,
	target_size,
	dtype=prompt_embeds.dtype,
	text_encoder_projection_dim=text_encoder_projection_dim,
	)
	if negative_original_size is not None and negative_target_size is not None:
	negative_add_time_ids = self._get_add_time_ids(
	negative_original_size,
	negative_crops_coords_top_left,
	negative_target_size,
	dtype=prompt_embeds.dtype,
	text_encoder_projection_dim=text_encoder_projection_dim,
	)
	else:
	negative_add_time_ids = add_time_ids

	if do_classifier_free_guidance:
	prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
	add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
	add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)

	prompt_embeds = prompt_embeds.to(device)
	add_text_embeds = add_text_embeds.to(device)
	add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)

	# 8. Denoising loop
	num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

	# 8.1 Apply denoising_end
	if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
	discrete_timestep_cutoff = int(
	round(
	self.scheduler.config.num_train_timesteps
	- (denoising_end * self.scheduler.config.num_train_timesteps)
	)
	)
	num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
	timesteps = timesteps[:num_inference_steps]

	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
	added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
	noise_pred = self.unet(
	latent_model_input,
	t,
	encoder_hidden_states=prompt_embeds,
	cross_attention_kwargs=cross_attention_kwargs,
	added_cond_kwargs=added_cond_kwargs,
	return_dict=False,
	)[0]

	# 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)

	if do_classifier_free_guidance and 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=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]

	# 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 XLA_AVAILABLE:
	xm.mark_step()

	if not output_type == "latent":
	# make sure the VAE is in float32 mode, as it overflows in float16
	needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

	if needs_upcasting:
	self.upcast_vae()
	latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)

	image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]

	# cast back to fp16 if needed
	if needs_upcasting:
	self.vae.to(dtype=torch.float16)
	else:
	image = latents

	if not output_type == "latent":
	# apply watermark if available
	if self.watermark is not None:
	image = self.watermark.apply_watermark(image)

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

	# Offload all models
	self.maybe_free_model_hooks()

	return (image,)