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Animagine-XL / lpw_stable_diffusion_xl.py

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	## ----------------------------------------------------------
	# A SDXL pipeline can take unlimited weighted prompt
	#
	# Author: Andrew Zhu
	# Github: https://github.com/xhinker
	# Medium: https://medium.com/@xhinker
	## -----------------------------------------------------------

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

	import torch
	from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

	from diffusers import DiffusionPipeline, StableDiffusionXLPipeline
	from diffusers.image_processor import VaeImageProcessor
	from diffusers.loaders import (
	FromSingleFileMixin,
	LoraLoaderMixin,
	TextualInversionLoaderMixin,
	)
	from diffusers.models import AutoencoderKL, UNet2DConditionModel
	from diffusers.models.attention_processor import (
	AttnProcessor2_0,
	LoRAAttnProcessor2_0,
	LoRAXFormersAttnProcessor,
	XFormersAttnProcessor,
	)
	from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
	from diffusers.schedulers import KarrasDiffusionSchedulers
	from diffusers.utils import (
	is_accelerate_available,
	is_accelerate_version,
	is_invisible_watermark_available,
	logging,
	randn_tensor,
	replace_example_docstring,
	)


	if is_invisible_watermark_available():
	from diffusers.pipelines.stable_diffusion_xl.watermark import (
	StableDiffusionXLWatermarker,
	)


	def parse_prompt_attention(text):
	"""
	Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
	Accepted tokens are:
	(abc) - increases attention to abc by a multiplier of 1.1
	(abc:3.12) - increases attention to abc by a multiplier of 3.12
	[abc] - decreases attention to abc by a multiplier of 1.1
	\( - literal character '('
	\[ - literal character '['
	\) - literal character ')'
	\] - literal character ']'
	\\ - literal character '\'
	anything else - just text

	>>> parse_prompt_attention('normal text')
	[['normal text', 1.0]]
	>>> parse_prompt_attention('an (important) word')
	[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
	>>> parse_prompt_attention('(unbalanced')
	[['unbalanced', 1.1]]
	>>> parse_prompt_attention('\(literal\]')
	[['(literal]', 1.0]]
	>>> parse_prompt_attention('(unnecessary)(parens)')
	[['unnecessaryparens', 1.1]]
	>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
	[['a ', 1.0],
	['house', 1.5730000000000004],
	[' ', 1.1],
	['on', 1.0],
	[' a ', 1.1],
	['hill', 0.55],
	[', sun, ', 1.1],
	['sky', 1.4641000000000006],
	['.', 1.1]]
	"""
	import re

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

	re_break = re.compile(r"\s\bBREAK\b\s", re.S)

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

	round_bracket_multiplier = 1.1
	square_bracket_multiplier = 1 / 1.1

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

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

	if text.startswith("\\"):
	res.append([text[1:], 1.0])
	elif text == "(":
	round_brackets.append(len(res))
	elif text == "[":
	square_brackets.append(len(res))
	elif weight is not None and len(round_brackets) > 0:
	multiply_range(round_brackets.pop(), float(weight))
	elif text == ")" and len(round_brackets) > 0:
	multiply_range(round_brackets.pop(), round_bracket_multiplier)
	elif text == "]" and len(square_brackets) > 0:
	multiply_range(square_brackets.pop(), square_bracket_multiplier)
	else:
	parts = re.split(re_break, text)
	for i, part in enumerate(parts):
	if i > 0:
	res.append(["BREAK", -1])
	res.append([part, 1.0])

	for pos in round_brackets:
	multiply_range(pos, round_bracket_multiplier)

	for pos in square_brackets:
	multiply_range(pos, square_bracket_multiplier)

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

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

	return res


	def get_prompts_tokens_with_weights(clip_tokenizer: CLIPTokenizer, prompt: str):
	"""
	Get prompt token ids and weights, this function works for both prompt and negative prompt

	Args:
	pipe (CLIPTokenizer)
	A CLIPTokenizer
	prompt (str)
	A prompt string with weights

	Returns:
	text_tokens (list)
	A list contains token ids
	text_weight (list)
	A list contains the correspodent weight of token ids

	Example:
	import torch
	from transformers import CLIPTokenizer

	clip_tokenizer = CLIPTokenizer.from_pretrained(
	"stablediffusionapi/deliberate-v2"
	, subfolder = "tokenizer"
	, dtype = torch.float16
	)

	token_id_list, token_weight_list = get_prompts_tokens_with_weights(
	clip_tokenizer = clip_tokenizer
	,prompt = "a (red:1.5) cat"*70
	)
	"""
	texts_and_weights = parse_prompt_attention(prompt)
	text_tokens, text_weights = [], []
	for word, weight in texts_and_weights:
	# tokenize and discard the starting and the ending token
	token = clip_tokenizer(word, truncation=False).input_ids[
	1:-1
	] # so that tokenize whatever length prompt
	# the returned token is a 1d list: [320, 1125, 539, 320]

	# merge the new tokens to the all tokens holder: text_tokens
	text_tokens = [text_tokens, token]

	# each token chunk will come with one weight, like ['red cat', 2.0]
	# need to expand weight for each token.
	chunk_weights = [weight] * len(token)

	# append the weight back to the weight holder: text_weights
	text_weights = [text_weights, chunk_weights]
	return text_tokens, text_weights


	def group_tokens_and_weights(token_ids: list, weights: list, pad_last_block=False):
	"""
	Produce tokens and weights in groups and pad the missing tokens

	Args:
	token_ids (list)
	The token ids from tokenizer
	weights (list)
	The weights list from function get_prompts_tokens_with_weights
	pad_last_block (bool)
	Control if fill the last token list to 75 tokens with eos
	Returns:
	new_token_ids (2d list)
	new_weights (2d list)

	Example:
	token_groups,weight_groups = group_tokens_and_weights(
	token_ids = token_id_list
	, weights = token_weight_list
	)
	"""
	bos, eos = 49406, 49407

	# this will be a 2d list
	new_token_ids = []
	new_weights = []
	while len(token_ids) >= 75:
	# get the first 75 tokens
	head_75_tokens = [token_ids.pop(0) for _ in range(75)]
	head_75_weights = [weights.pop(0) for _ in range(75)]

	# extract token ids and weights
	temp_77_token_ids = [bos] + head_75_tokens + [eos]
	temp_77_weights = [1.0] + head_75_weights + [1.0]

	# add 77 token and weights chunk to the holder list
	new_token_ids.append(temp_77_token_ids)
	new_weights.append(temp_77_weights)

	# padding the left
	if len(token_ids) > 0:
	padding_len = 75 - len(token_ids) if pad_last_block else 0

	temp_77_token_ids = [bos] + token_ids + [eos] * padding_len + [eos]
	new_token_ids.append(temp_77_token_ids)

	temp_77_weights = [1.0] + weights + [1.0] * padding_len + [1.0]
	new_weights.append(temp_77_weights)

	return new_token_ids, new_weights


	def get_weighted_text_embeddings_sdxl(
	pipe: StableDiffusionXLPipeline,
	prompt: str = "",
	prompt_2: str = None,
	neg_prompt: str = "",
	neg_prompt_2: str = None,
	):
	"""
	This function can process long prompt with weights, no length limitation
	for Stable Diffusion XL

	Args:
	pipe (StableDiffusionPipeline)
	prompt (str)
	prompt_2 (str)
	neg_prompt (str)
	neg_prompt_2 (str)
	Returns:
	prompt_embeds (torch.Tensor)
	neg_prompt_embeds (torch.Tensor)
	"""
	if prompt_2:
	prompt = f"{prompt} {prompt_2}"

	if neg_prompt_2:
	neg_prompt = f"{neg_prompt} {neg_prompt_2}"

	eos = pipe.tokenizer.eos_token_id

	# tokenizer 1
	prompt_tokens, prompt_weights = get_prompts_tokens_with_weights(
	pipe.tokenizer, prompt
	)

	neg_prompt_tokens, neg_prompt_weights = get_prompts_tokens_with_weights(
	pipe.tokenizer, neg_prompt
	)

	# tokenizer 2
	prompt_tokens_2, prompt_weights_2 = get_prompts_tokens_with_weights(
	pipe.tokenizer_2, prompt
	)

	neg_prompt_tokens_2, neg_prompt_weights_2 = get_prompts_tokens_with_weights(
	pipe.tokenizer_2, neg_prompt
	)

	# padding the shorter one for prompt set 1
	prompt_token_len = len(prompt_tokens)
	neg_prompt_token_len = len(neg_prompt_tokens)

	if prompt_token_len > neg_prompt_token_len:
	# padding the neg_prompt with eos token
	neg_prompt_tokens = neg_prompt_tokens + [eos] * abs(
	prompt_token_len - neg_prompt_token_len
	)
	neg_prompt_weights = neg_prompt_weights + [1.0] * abs(
	prompt_token_len - neg_prompt_token_len
	)
	else:
	# padding the prompt
	prompt_tokens = prompt_tokens + [eos] * abs(
	prompt_token_len - neg_prompt_token_len
	)
	prompt_weights = prompt_weights + [1.0] * abs(
	prompt_token_len - neg_prompt_token_len
	)

	# padding the shorter one for token set 2
	prompt_token_len_2 = len(prompt_tokens_2)
	neg_prompt_token_len_2 = len(neg_prompt_tokens_2)

	if prompt_token_len_2 > neg_prompt_token_len_2:
	# padding the neg_prompt with eos token
	neg_prompt_tokens_2 = neg_prompt_tokens_2 + [eos] * abs(
	prompt_token_len_2 - neg_prompt_token_len_2
	)
	neg_prompt_weights_2 = neg_prompt_weights_2 + [1.0] * abs(
	prompt_token_len_2 - neg_prompt_token_len_2
	)
	else:
	# padding the prompt
	prompt_tokens_2 = prompt_tokens_2 + [eos] * abs(
	prompt_token_len_2 - neg_prompt_token_len_2
	)
	prompt_weights_2 = prompt_weights + [1.0] * abs(
	prompt_token_len_2 - neg_prompt_token_len_2
	)

	embeds = []
	neg_embeds = []

	prompt_token_groups, prompt_weight_groups = group_tokens_and_weights(
	prompt_tokens.copy(), prompt_weights.copy()
	)

	neg_prompt_token_groups, neg_prompt_weight_groups = group_tokens_and_weights(
	neg_prompt_tokens.copy(), neg_prompt_weights.copy()
	)

	prompt_token_groups_2, prompt_weight_groups_2 = group_tokens_and_weights(
	prompt_tokens_2.copy(), prompt_weights_2.copy()
	)

	neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = group_tokens_and_weights(
	neg_prompt_tokens_2.copy(), neg_prompt_weights_2.copy()
	)

	# get prompt embeddings one by one is not working.
	for i in range(len(prompt_token_groups)):
	# get positive prompt embeddings with weights
	token_tensor = torch.tensor(
	[prompt_token_groups[i]], dtype=torch.long, device=pipe.device
	)
	weight_tensor = torch.tensor(
	prompt_weight_groups[i], dtype=torch.float16, device=pipe.device
	)

	token_tensor_2 = torch.tensor(
	[prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device
	)

	# use first text encoder
	prompt_embeds_1 = pipe.text_encoder(
	token_tensor.to(pipe.device), output_hidden_states=True
	)
	prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]

	# use second text encoder
	prompt_embeds_2 = pipe.text_encoder_2(
	token_tensor_2.to(pipe.device), output_hidden_states=True
	)
	prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]
	pooled_prompt_embeds = prompt_embeds_2[0]

	prompt_embeds_list = [
	prompt_embeds_1_hidden_states,
	prompt_embeds_2_hidden_states,
	]
	token_embedding = torch.concat(prompt_embeds_list, dim=-1).squeeze(0)

	for j in range(len(weight_tensor)):
	if weight_tensor[j] != 1.0:
	token_embedding[j] = (
	token_embedding[-1]
	+ (token_embedding[j] - token_embedding[-1]) * weight_tensor[j]
	)

	token_embedding = token_embedding.unsqueeze(0)
	embeds.append(token_embedding)

	# get negative prompt embeddings with weights
	neg_token_tensor = torch.tensor(
	[neg_prompt_token_groups[i]], dtype=torch.long, device=pipe.device
	)
	neg_token_tensor_2 = torch.tensor(
	[neg_prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device
	)
	neg_weight_tensor = torch.tensor(
	neg_prompt_weight_groups[i], dtype=torch.float16, device=pipe.device
	)

	# use first text encoder
	neg_prompt_embeds_1 = pipe.text_encoder(
	neg_token_tensor.to(pipe.device), output_hidden_states=True
	)
	neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]

	# use second text encoder
	neg_prompt_embeds_2 = pipe.text_encoder_2(
	neg_token_tensor_2.to(pipe.device), output_hidden_states=True
	)
	neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]
	negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]

	neg_prompt_embeds_list = [
	neg_prompt_embeds_1_hidden_states,
	neg_prompt_embeds_2_hidden_states,
	]
	neg_token_embedding = torch.concat(neg_prompt_embeds_list, dim=-1).squeeze(0)

	for z in range(len(neg_weight_tensor)):
	if neg_weight_tensor[z] != 1.0:
	neg_token_embedding[z] = (
	neg_token_embedding[-1]
	+ (neg_token_embedding[z] - neg_token_embedding[-1])
	* neg_weight_tensor[z]
	)

	neg_token_embedding = neg_token_embedding.unsqueeze(0)
	neg_embeds.append(neg_token_embedding)

	prompt_embeds = torch.cat(embeds, dim=1)
	negative_prompt_embeds = torch.cat(neg_embeds, dim=1)

	return (
	prompt_embeds,
	negative_prompt_embeds,
	pooled_prompt_embeds,
	negative_pooled_prompt_embeds,
	)


	# -------------------------------------------------------------------------------------------------------------------------------
	# reuse the backbone code from StableDiffusionXLPipeline
	# -------------------------------------------------------------------------------------------------------------------------------

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

	EXAMPLE_DOC_STRING = """
	Examples:
	```py
	from diffusers import DiffusionPipeline
	import torch

	pipe = DiffusionPipeline.from_pretrained(
	"stabilityai/stable-diffusion-xl-base-1.0"
	, torch_dtype = torch.float16
	, use_safetensors = True
	, variant = "fp16"
	, custom_pipeline = "lpw_stable_diffusion_xl",
	)

	prompt = "a white cat running on the grass"*20
	prompt2 = "play a football"*20
	prompt = f"{prompt},{prompt2}"
	neg_prompt = "blur, low quality"

	pipe.to("cuda")
	images = pipe(
	prompt = prompt
	, negative_prompt = neg_prompt
	).images[0]

	pipe.to("cpu")
	torch.cuda.empty_cache()
	images
	```
	"""


	# 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 SDXLLongPromptWeightingPipeline(
	DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin
	):
	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: [`StableDiffusionXLPipeline.load_lora_weights`]
	- Ckpt: [`loaders.FromSingleFileMixin.from_single_file`]

	as well as the following saving methods:
	- LoRA: [`loaders.StableDiffusionXLPipeline.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`].
	"""

	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()
	)

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

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

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

	model_sequence = (
	[self.text_encoder, self.text_encoder_2]
	if self.text_encoder is not None
	else [self.text_encoder_2]
	)
	model_sequence.extend([self.unet, self.vae])

	hook = None
	for cpu_offloaded_model in model_sequence:
	_, hook = cpu_offload_with_hook(
	cpu_offloaded_model, device, prev_module_hook=hook
	)

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

	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,
	):
	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.
	"""
	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, LoraLoaderMixin):
	self._lora_scale = 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]

	# 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
	# 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]
	prompt_embeds = prompt_embeds.hidden_states[-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

	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 isinstance(negative_prompt, str):
	uncond_tokens = [negative_prompt, negative_prompt_2]
	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)

	prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.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]
	negative_prompt_embeds = negative_prompt_embeds.to(
	dtype=self.text_encoder_2.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)

	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
	):
	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)
	+ self.text_encoder_2.config.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)

	@torch.no_grad()
	@replace_example_docstring(EXAMPLE_DOC_STRING)
	def __call__(
	self,
	prompt: str = None,
	prompt_2: Optional[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[str] = None,
	negative_prompt_2: Optional[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,
	):
	r"""
	Function invoked when calling the pipeline for generation.

	Args:
	prompt (`str`):
	The prompt to guide the image generation. If not defined, one has to pass `prompt_embeds`.
	instead.
	prompt_2 (`str`):
	The prompt 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.
	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.
	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`):
	The prompt 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`):
	The prompt 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.StableDiffusionXLPipelineOutput`] 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 0.7):
	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 `(width, height)` 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 `(width, height)`. 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).

	Examples:

	Returns:
	[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
	[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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
	(
	cross_attention_kwargs.get("scale", None)
	if cross_attention_kwargs is not None
	else None
	)

	negative_prompt = negative_prompt if negative_prompt is not None else ""

	(
	prompt_embeds,
	negative_prompt_embeds,
	pooled_prompt_embeds,
	negative_pooled_prompt_embeds,
	) = get_weighted_text_embeddings_sdxl(
	pipe=self, prompt=prompt, neg_prompt=negative_prompt
	)

	# 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
	add_time_ids = self._get_add_time_ids(
	original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
	)

	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([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
	)

	# 7.1 Apply denoising_end
	if (
	denoising_end is not None
	and type(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:
	callback(i, t, latents)

	# make sure the VAE is in float32 mode, as it overflows in float16
	if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
	self.upcast_vae()
	latents = latents.to(
	next(iter(self.vae.post_quant_conv.parameters())).dtype
	)

	if not output_type == "latent":
	image = self.vae.decode(
	latents / self.vae.config.scaling_factor, return_dict=False
	)[0]
	else:
	image = latents
	return StableDiffusionXLPipelineOutput(images=image)

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

	if not return_dict:
	return (image,)

	return StableDiffusionXLPipelineOutput(images=image)

	# Overrride to properly handle the loading and unloading of the additional text encoder.
	def load_lora_weights(
	self,
	pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
	**kwargs,
	):
	# We could have accessed the unet config from `lora_state_dict()` too. We pass
	# it here explicitly to be able to tell that it's coming from an SDXL
	# pipeline.
	state_dict, network_alphas = self.lora_state_dict(
	pretrained_model_name_or_path_or_dict,
	unet_config=self.unet.config,
	**kwargs,
	)
	self.load_lora_into_unet(
	state_dict, network_alphas=network_alphas, unet=self.unet
	)

	text_encoder_state_dict = {
	k: v for k, v in state_dict.items() if "text_encoder." in k
	}
	if len(text_encoder_state_dict) > 0:
	self.load_lora_into_text_encoder(
	text_encoder_state_dict,
	network_alphas=network_alphas,
	text_encoder=self.text_encoder,
	prefix="text_encoder",
	lora_scale=self.lora_scale,
	)

	text_encoder_2_state_dict = {
	k: v for k, v in state_dict.items() if "text_encoder_2." in k
	}
	if len(text_encoder_2_state_dict) > 0:
	self.load_lora_into_text_encoder(
	text_encoder_2_state_dict,
	network_alphas=network_alphas,
	text_encoder=self.text_encoder_2,
	prefix="text_encoder_2",
	lora_scale=self.lora_scale,
	)

	@classmethod
	def save_lora_weights(
	self,
	save_directory: Union[str, os.PathLike],
	unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
	text_encoder_lora_layers: Dict[
	str, Union[torch.nn.Module, torch.Tensor]
	] = None,
	text_encoder_2_lora_layers: Dict[
	str, Union[torch.nn.Module, torch.Tensor]
	] = None,
	is_main_process: bool = True,
	weight_name: str = None,
	save_function: Callable = None,
	safe_serialization: bool = False,
	):
	state_dict = {}

	def pack_weights(layers, prefix):
	layers_weights = (
	layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
	)
	layers_state_dict = {
	f"{prefix}.{module_name}": param
	for module_name, param in layers_weights.items()
	}
	return layers_state_dict

	state_dict.update(pack_weights(unet_lora_layers, "unet"))

	if text_encoder_lora_layers and text_encoder_2_lora_layers:
	state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
	state_dict.update(
	pack_weights(text_encoder_2_lora_layers, "text_encoder_2")
	)

	self.write_lora_layers(
	state_dict=state_dict,
	save_directory=save_directory,
	is_main_process=is_main_process,
	weight_name=weight_name,
	save_function=save_function,
	safe_serialization=safe_serialization,
	)

	def _remove_text_encoder_monkey_patch(self):
	self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
	self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)