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leditsplusplus / pipeline_semantic_stable_diffusion_img2img_solver.py

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	import inspect
	import warnings
	from itertools import repeat
	from typing import Callable, List, Optional, Union

	import torch
	from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

	from diffusers.image_processor import VaeImageProcessor
	from diffusers.models import AutoencoderKL, UNet2DConditionModel
	from diffusers.models.attention_processor import AttnProcessor, Attention
	from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
	from diffusers.schedulers import DDIMScheduler
	from scheduling_dpmsolver_multistep_inject import DPMSolverMultistepSchedulerInject
	# from diffusers.utils import logging, randn_tensor
	from diffusers.utils import logging
	from diffusers.utils.torch_utils import randn_tensor
	from diffusers.pipelines.pipeline_utils import DiffusionPipeline
	from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipelineOutput

	import numpy as np
	from PIL import Image
	from tqdm import tqdm
	import torch.nn.functional as F
	import math
	from collections.abc import Iterable

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


	class AttentionStore():
	@staticmethod
	def get_empty_store():
	return {"down_cross": [], "mid_cross": [], "up_cross": [],
	"down_self": [], "mid_self": [], "up_self": []}

	def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False):
	# attn.shape = batch_size * head_size, seq_len query, seq_len_key
	bs = 2 + int(PnP) + editing_prompts
	skip = 2 if PnP else 1 # skip PnP & unconditional

	head_size = int(attn.shape[0] / self.batch_size)
	attn = torch.stack(attn.split(self.batch_size)).permute(1, 0, 2, 3)
	source_batch_size = int(attn.shape[1] // bs)
	self.forward(
	attn[:, skip * source_batch_size:],
	is_cross,
	place_in_unet)

	def forward(self, attn, is_cross: bool, place_in_unet: str):
	key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
	if attn.shape[1] <= 32 ** 2: # avoid memory overhead
	self.step_store[key].append(attn)

	def between_steps(self, store_step=True):
	if store_step:
	if self.average:
	if len(self.attention_store) == 0:
	self.attention_store = self.step_store
	else:
	for key in self.attention_store:
	for i in range(len(self.attention_store[key])):
	self.attention_store[key][i] += self.step_store[key][i]
	else:
	if len(self.attention_store) == 0:
	self.attention_store = [self.step_store]
	else:
	self.attention_store.append(self.step_store)

	self.cur_step += 1
	self.step_store = self.get_empty_store()

	def get_attention(self, step: int):
	if self.average:
	attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in
	self.attention_store}
	else:
	assert (step is not None)
	attention = self.attention_store[step]
	return attention

	def aggregate_attention(self, attention_maps, prompts, res: int,
	from_where: List[str], is_cross: bool, select: int
	):
	out = [[] for x in range(self.batch_size)]
	num_pixels = res ** 2
	for location in from_where:
	for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
	for batch, item in enumerate(bs_item):
	if item.shape[1] == num_pixels:
	cross_maps = item.reshape(len(prompts), -1, res, res, item.shape[-1])[select]
	out[batch].append(cross_maps)

	out = torch.stack([torch.cat(x, dim=0) for x in out])
	# average over heads
	out = out.sum(1) / out.shape[1]
	return out

	def __init__(self, average: bool, batch_size=1):
	self.step_store = self.get_empty_store()
	self.attention_store = []
	self.cur_step = 0
	self.average = average
	self.batch_size = batch_size


	class CrossAttnProcessor:

	def __init__(self, attention_store, place_in_unet, PnP, editing_prompts):
	self.attnstore = attention_store
	self.place_in_unet = place_in_unet
	self.editing_prompts = editing_prompts
	self.PnP = PnP

	def __call__(
	self,
	attn: Attention,
	hidden_states,
	encoder_hidden_states=None,
	attention_mask=None,
	temb=None,
	):
	assert (not attn.residual_connection)
	assert (attn.spatial_norm is None)
	assert (attn.group_norm is None)
	assert (hidden_states.ndim != 4)
	assert (encoder_hidden_states is not None) # is cross

	batch_size, sequence_length, _ = (
	hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
	)
	attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

	query = attn.to_q(hidden_states)

	if encoder_hidden_states is None:
	encoder_hidden_states = hidden_states
	elif attn.norm_cross:
	encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

	key = attn.to_k(encoder_hidden_states)
	value = attn.to_v(encoder_hidden_states)

	query = attn.head_to_batch_dim(query)
	key = attn.head_to_batch_dim(key)
	value = attn.head_to_batch_dim(value)

	attention_probs = attn.get_attention_scores(query, key, attention_mask)
	self.attnstore(attention_probs,
	is_cross=True,
	place_in_unet=self.place_in_unet,
	editing_prompts=self.editing_prompts,
	PnP=self.PnP)

	hidden_states = torch.bmm(attention_probs, value)
	hidden_states = attn.batch_to_head_dim(hidden_states)

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

	hidden_states = hidden_states / attn.rescale_output_factor
	return hidden_states


	# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.GaussianSmoothing
	class GaussianSmoothing():

	def __init__(self, device):
	kernel_size = [3, 3]
	sigma = [0.5, 0.5]

	# The gaussian kernel is the product of the gaussian function of each dimension.
	kernel = 1
	meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])
	for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
	mean = (size - 1) / 2
	kernel = 1 / (std math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2))

	# Make sure sum of values in gaussian kernel equals 1.
	kernel = kernel / torch.sum(kernel)

	# Reshape to depthwise convolutional weight
	kernel = kernel.view(1, 1, *kernel.size())
	kernel = kernel.repeat(1, [1] (kernel.dim() - 1))

	self.weight = kernel.to(device)

	def __call__(self, input):
	"""
	Arguments:
	Apply gaussian filter to input.
	input (torch.Tensor): Input to apply gaussian filter on.
	Returns:
	filtered (torch.Tensor): Filtered output.
	"""
	return F.conv2d(input, weight=self.weight.to(input.dtype))


	def load_512(image_path, size, left=0, right=0, top=0, bottom=0, device=None, dtype=None):
	def pre_process(im, size, left=0, right=0, top=0, bottom=0):
	if type(im) is str:
	image = np.array(Image.open(im).convert('RGB'))[:, :, :3]
	elif isinstance(im, Image.Image):
	image = np.array((im).convert('RGB'))[:, :, :3]
	else:
	image = im
	h, w, c = image.shape
	left = min(left, w - 1)
	right = min(right, w - left - 1)
	top = min(top, h - left - 1)
	bottom = min(bottom, h - top - 1)
	image = image[top:h - bottom, left:w - right]
	h, w, c = image.shape
	if h < w:
	offset = (w - h) // 2
	image = image[:, offset:offset + h]
	elif w < h:
	offset = (h - w) // 2
	image = image[offset:offset + w]
	image = np.array(Image.fromarray(image).resize((size, size)))
	image = torch.from_numpy(image).float().permute(2, 0, 1)
	return image

	tmps = []
	if isinstance(image_path, list):
	for item in image_path:
	tmps.append(pre_process(item, size, left, right, top, bottom))
	else:
	tmps.append(pre_process(image_path, size, left, right, top, bottom))
	image = torch.stack(tmps) / 127.5 - 1

	image = image.to(device=device, dtype=dtype)
	return image


	# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.GaussianSmoothing

	def reset_dpm(scheduler):
	if isinstance(scheduler, DPMSolverMultistepSchedulerInject):
	scheduler.model_outputs = [
	None,
	] * scheduler.config.solver_order
	scheduler.lower_order_nums = 0


	class SemanticStableDiffusionImg2ImgPipeline_DPMSolver(DiffusionPipeline):
	r"""
	Pipeline for text-to-image generation with latent editing.

	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.)

	This model builds on the implementation of ['StableDiffusionPipeline']

	Args:
	vae ([`AutoencoderKL`]):
	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
	text_encoder ([`CLIPTextModel`]):
	Frozen text-encoder. Stable Diffusion uses the text portion of
	[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
	the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
	tokenizer (`CLIPTokenizer`):
	Tokenizer of class
	[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
	unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
	scheduler ([`SchedulerMixin`]):
	A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
	[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
	safety_checker ([`Q16SafetyChecker`]):
	Classification module that estimates whether generated images could be considered offensive or harmful.
	Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
	feature_extractor ([`CLIPImageProcessor`]):
	Model that extracts features from generated images to be used as inputs for the `safety_checker`.
	"""

	_optional_components = ["safety_checker", "feature_extractor"]

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

	if not isinstance(scheduler, DDIMScheduler) or not isinstance(scheduler, DPMSolverMultistepSchedulerInject):
	scheduler = DPMSolverMultistepSchedulerInject.from_config(scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2)
	logger.warning("This pipeline only supports DDIMScheduler and DPMSolverMultistepSchedulerInject. "
	"The scheduler has been changed to DPMSolverMultistepSchedulerInject.")

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

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

	self.register_modules(
	vae=vae,
	text_encoder=text_encoder,
	tokenizer=tokenizer,
	unet=unet,
	scheduler=scheduler,
	safety_checker=safety_checker,
	feature_extractor=feature_extractor,
	)
	self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
	self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
	self.register_to_config(requires_safety_checker=requires_safety_checker)

	def progress_bar(self, iterable=None, total=None, verbose=True):
	if not hasattr(self, "_progress_bar_config"):
	self._progress_bar_config = {}
	elif not isinstance(self._progress_bar_config, dict):
	raise ValueError(
	f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
	)
	if not verbose:
	return iterable
	elif iterable is not None:
	return tqdm(iterable, **self._progress_bar_config)
	elif total is not None:
	return tqdm(total=total, **self._progress_bar_config)
	else:
	raise ValueError("Either `total` or `iterable` has to be defined.")

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

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
	def decode_latents(self, latents):
	warnings.warn(
	"The decode_latents method is deprecated and will be removed in a future version. Please"
	" use VaeImageProcessor instead",
	FutureWarning,
	)
	latents = 1 / self.vae.config.scaling_factor * latents
	image = self.vae.decode(latents, return_dict=False)[0]
	image = (image / 2 + 0.5).clamp(0, 1)
	# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
	image = image.cpu().permute(0, 2, 3, 1).float().numpy()
	return image

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
	def prepare_extra_step_kwargs(self, 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

	return extra_step_kwargs

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

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

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

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

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

	# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
	def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents):
	shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)

	if latents.shape != shape:
	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")

	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 prepare_unet(self, attention_store, PnP: bool = False):
	attn_procs = {}
	for name in self.unet.attn_processors.keys():
	if name.startswith("mid_block"):
	place_in_unet = "mid"
	elif name.startswith("up_blocks"):
	place_in_unet = "up"
	elif name.startswith("down_blocks"):
	place_in_unet = "down"
	else:
	continue

	if "attn2" in name:
	attn_procs[name] = CrossAttnProcessor(
	attention_store=attention_store,
	place_in_unet=place_in_unet,
	PnP=PnP,
	editing_prompts=self.enabled_editing_prompts)
	else:
	attn_procs[name] = AttnProcessor()

	self.unet.set_attn_processor(attn_procs)

	@torch.no_grad()
	def __call__(
	self,
	prompt: Union[str, List[str]] = "",
	height: Optional[int] = None,
	width: Optional[int] = None,
	# num_inference_steps: int = 50,
	guidance_scale: float = 7.5,
	negative_prompt: Optional[Union[str, List[str]]] = None,
	# num_images_per_prompt: int = 1,
	eta: float = 1.0,
	# generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	# latents: 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,
	editing_prompt: Optional[Union[str, List[str]]] = None,
	editing_prompt_embeddings: Optional[torch.Tensor] = None,
	reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,
	edit_guidance_scale: Optional[Union[float, List[float]]] = 5,
	edit_warmup_steps: Optional[Union[int, List[int]]] = 10,
	edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
	edit_threshold: Optional[Union[float, List[float]]] = 0.9,
	user_mask: Optional[torch.FloatTensor] = None,
	edit_momentum_scale: Optional[float] = 0.1,
	edit_mom_beta: Optional[float] = 0.4,
	edit_weights: Optional[List[float]] = None,
	sem_guidance: Optional[List[torch.Tensor]] = None,
	verbose=True,
	use_cross_attn_mask: bool = False,
	# Attention store (just for visualization purposes)
	attention_store = None,
	text_cross_attention_maps = None,
	attn_store_steps: Optional[List[int]] = [],
	store_averaged_over_steps: bool = True,
	use_intersect_mask: bool = False,
	init_latents = None,
	zs = None,

	):
	r"""
	Function invoked when calling the pipeline for generation.

	Args:
	prompt (`str` or `List[str]`):
	The prompt or prompts to guide the image generation.
	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.
	guidance_scale (`float`, optional, defaults to 7.5):
	Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
	`guidance_scale` is defined as `w` of equation 2. of [Imagen
	Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
	1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
	usually at the expense of lower image quality.
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
	if `guidance_scale` is less than `1`).
	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`, 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`.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generate image. Choose between
	[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.
	callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. The function will be
	called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
	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.
	editing_prompt (`str` or `List[str]`, optional):
	The prompt or prompts to use for Semantic guidance. Semantic guidance is disabled by setting
	`editing_prompt = None`. Guidance direction of prompt should be specified via
	`reverse_editing_direction`.
	editing_prompt_embeddings (`torch.Tensor>`, optional):
	Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
	specified via `reverse_editing_direction`.
	reverse_editing_direction (`bool` or `List[bool]`, optional, defaults to `False`):
	Whether the corresponding prompt in `editing_prompt` should be increased or decreased.
	edit_guidance_scale (`float` or `List[float]`, optional, defaults to 5):
	Guidance scale for semantic guidance. If provided as list values should correspond to `editing_prompt`.
	`edit_guidance_scale` is defined as `s_e` of equation 6 of [SEGA
	Paper](https://arxiv.org/pdf/2301.12247.pdf).
	edit_warmup_steps (`float` or `List[float]`, optional, defaults to 10):
	Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum
	will still be calculated for those steps and applied once all warmup periods are over.
	`edit_warmup_steps` is defined as `delta` (δ) of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf).
	edit_cooldown_steps (`float` or `List[float]`, optional, defaults to `None`):
	Number of diffusion steps (for each prompt) after which semantic guidance will no longer be applied.
	edit_threshold (`float` or `List[float]`, optional, defaults to 0.9):
	Threshold of semantic guidance.
	edit_momentum_scale (`float`, optional, defaults to 0.1):
	Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0
	momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
	than `sld_warmup_steps`. Momentum will only be added to latent guidance once all warmup periods are
	finished. `edit_momentum_scale` is defined as `s_m` of equation 7 of [SEGA
	Paper](https://arxiv.org/pdf/2301.12247.pdf).
	edit_mom_beta (`float`, optional, defaults to 0.4):
	Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous
	momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
	than `edit_warmup_steps`. `edit_mom_beta` is defined as `beta_m` (β) of equation 8 of [SEGA
	Paper](https://arxiv.org/pdf/2301.12247.pdf).
	edit_weights (`List[float]`, optional, defaults to `None`):
	Indicates how much each individual concept should influence the overall guidance. If no weights are
	provided all concepts are applied equally. `edit_mom_beta` is defined as `g_i` of equation 9 of [SEGA
	Paper](https://arxiv.org/pdf/2301.12247.pdf).
	sem_guidance (`List[torch.Tensor]`, optional):
	List of pre-generated guidance vectors to be applied at generation. Length of the list has to
	correspond to `num_inference_steps`.

	Returns:
	[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:
	[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] if `return_dict` is True,
	otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the
	second element is a list of `bool`s denoting whether the corresponding generated image likely represents
	"not-safe-for-work" (nsfw) content, according to the `safety_checker`.
	"""
	self.unet.set_attn_processor(AttnProcessor())

	# eta = self.eta
	num_images_per_prompt = 1
	# latents = self.init_latents
	latents = init_latents

	use_ddpm = True
	# zs = self.zs
	reset_dpm(self.scheduler)

	if use_intersect_mask:
	use_cross_attn_mask = True

	if use_cross_attn_mask:
	self.smoothing = GaussianSmoothing(self.device)

	# 0. Default height and width to unet
	height = height or self.unet.config.sample_size * self.vae_scale_factor
	width = width or self.unet.config.sample_size * self.vae_scale_factor

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

	org_prompt = prompt
	if isinstance(prompt, list):
	assert len(prompt) == self.batch_size
	elif isinstance(prompt, str):
	prompt = list(repeat(prompt, self.batch_size))

	# 2. Define call parameters
	batch_size = self.batch_size

	if editing_prompt:
	enable_edit_guidance = True
	if isinstance(editing_prompt, str):
	editing_prompt = [editing_prompt]
	self.enabled_editing_prompts = len(editing_prompt)
	elif editing_prompt_embeddings is not None:
	enable_edit_guidance = True
	self.enabled_editing_prompts = editing_prompt_embeddings.shape[0]
	else:
	self.enabled_editing_prompts = 0
	enable_edit_guidance = False

	# get prompt text embeddings
	text_inputs = self.tokenizer(
	prompt,
	padding="max_length",
	max_length=self.tokenizer.model_max_length,
	truncation=True,
	return_tensors="pt",
	)
	text_input_ids = text_inputs.input_ids
	untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

	if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
	text_input_ids, untruncated_ids
	):
	removed_text = self.tokenizer.batch_decode(
	untruncated_ids[:, self.tokenizer.model_max_length - 1: -1]
	)
	logger.warning(
	"The following part of your input was truncated because CLIP can only handle sequences up to"
	f" {self.tokenizer.model_max_length} tokens: {removed_text}"
	)

	text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]

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

	if enable_edit_guidance:
	# get safety text embeddings
	if editing_prompt_embeddings is None:
	edit_concepts_input = self.tokenizer(
	[x for item in editing_prompt for x in repeat(item, batch_size)],
	padding="max_length",
	max_length=self.tokenizer.model_max_length,
	truncation=True,
	return_tensors="pt",
	return_length=True
	)

	num_edit_tokens = edit_concepts_input.length - 2 # not counting startoftext and endoftext
	edit_concepts_input_ids = edit_concepts_input.input_ids
	untruncated_ids = self.tokenizer(
	[x for item in editing_prompt for x in repeat(item, batch_size)],
	padding="longest",
	return_tensors="pt").input_ids

	if untruncated_ids.shape[-1] >= edit_concepts_input_ids.shape[-1] and not torch.equal(
	edit_concepts_input_ids, untruncated_ids
	):
	removed_text = self.tokenizer.batch_decode(
	untruncated_ids[:, self.tokenizer.model_max_length - 1: -1]
	)
	logger.warning(
	"The following part of your input was truncated because CLIP can only handle sequences up to"
	f" {self.tokenizer.model_max_length} tokens: {removed_text}"
	)

	edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0]
	else:
	edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1)

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

	# 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
	# get unconditional embeddings for classifier free guidance

	if do_classifier_free_guidance:
	uncond_tokens: List[str]
	if negative_prompt is None:
	uncond_tokens = [""]
	elif type(prompt) is not type(negative_prompt):
	raise TypeError(
	f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
	f" {type(prompt)}."
	)
	elif isinstance(negative_prompt, str):
	uncond_tokens = [negative_prompt]
	elif batch_size != len(negative_prompt):
	raise ValueError(
	f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
	f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
	" the batch size of `prompt`."
	)
	else:
	uncond_tokens = negative_prompt

	max_length = text_input_ids.shape[-1]
	uncond_input = self.tokenizer(
	uncond_tokens,
	padding="max_length",
	max_length=max_length,
	truncation=True,
	return_tensors="pt",
	)
	uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]

	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
	seq_len = uncond_embeddings.shape[1]
	uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
	uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)

	# For classifier free guidance, we need to do two forward passes.
	# Here we concatenate the unconditional and text embeddings into a single batch
	# to avoid doing two forward passes
	text_cross_attention_maps = [org_prompt] if isinstance(org_prompt, str) else org_prompt
	if enable_edit_guidance:
	text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts])
	text_cross_attention_maps += \
	([editing_prompt] if isinstance(editing_prompt, str) else editing_prompt)
	else:
	text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

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

	if use_ddpm:
	t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs.shape[0]:])}
	timesteps = timesteps[-zs.shape[0]:]

	if use_cross_attn_mask:
	attention_store = AttentionStore(average=store_averaged_over_steps, batch_size=batch_size)
	self.prepare_unet(attention_store, PnP=False)
	# 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,
	text_embeddings.dtype,
	self.device,
	latents,
	)

	# 6. Prepare extra step kwargs.
	extra_step_kwargs = self.prepare_extra_step_kwargs(eta)

	# Initialize edit_momentum to None
	edit_momentum = None

	self.uncond_estimates = None
	self.text_estimates = None
	self.edit_estimates = None
	self.sem_guidance = None
	self.activation_mask = None

	for i, t in enumerate(self.progress_bar(timesteps, verbose=verbose)):
	idx = t_to_idx[int(t)]


	# expand the latents if we are doing classifier free guidance

	if do_classifier_free_guidance:
	latent_model_input = torch.cat([latents] * (2 + self.enabled_editing_prompts))
	else:
	latent_model_input = latents

	latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

	text_embed_input = text_embeddings

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

	# perform guidance
	if do_classifier_free_guidance:

	noise_pred_out = noise_pred.chunk(2 + self.enabled_editing_prompts) # [b,4, 64, 64]
	noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
	noise_pred_edit_concepts = noise_pred_out[2:]

	# default text guidance
	noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)

	if self.uncond_estimates is None:
	self.uncond_estimates = torch.zeros((len(timesteps), *noise_pred_uncond.shape))
	self.uncond_estimates[i] = noise_pred_uncond.detach().cpu()

	if self.text_estimates is None:
	self.text_estimates = torch.zeros((len(timesteps), *noise_pred_text.shape))
	self.text_estimates[i] = noise_pred_text.detach().cpu()

	if edit_momentum is None:
	edit_momentum = torch.zeros_like(noise_guidance)

	if sem_guidance is not None and len(sem_guidance) > i:
	edit_guidance = sem_guidance[i].to(self.device)
	edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * edit_guidance
	noise_guidance = noise_guidance + edit_guidance

	elif enable_edit_guidance:
	if self.activation_mask is None:
	self.activation_mask = torch.zeros(
	(len(timesteps), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
	)
	if self.edit_estimates is None and enable_edit_guidance:
	self.edit_estimates = torch.zeros(
	(len(timesteps), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
	)

	if self.sem_guidance is None:
	self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_text.shape))

	concept_weights = torch.zeros(
	(len(noise_pred_edit_concepts), noise_guidance.shape[0]),
	device=self.device,
	dtype=noise_guidance.dtype,
	)
	noise_guidance_edit = torch.zeros(
	(len(noise_pred_edit_concepts), *noise_guidance.shape),
	device=self.device,
	dtype=noise_guidance.dtype,
	)
	# noise_guidance_edit = torch.zeros_like(noise_guidance)
	warmup_inds = []
	for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
	self.edit_estimates[i, c] = noise_pred_edit_concept
	if isinstance(edit_guidance_scale, list):
	edit_guidance_scale_c = edit_guidance_scale[c]
	else:
	edit_guidance_scale_c = edit_guidance_scale

	if isinstance(edit_threshold, list):
	edit_threshold_c = edit_threshold[c]
	else:
	edit_threshold_c = edit_threshold
	if isinstance(reverse_editing_direction, list):
	reverse_editing_direction_c = reverse_editing_direction[c]
	else:
	reverse_editing_direction_c = reverse_editing_direction
	if edit_weights:
	edit_weight_c = edit_weights[c]
	else:
	edit_weight_c = 1.0
	if isinstance(edit_warmup_steps, list):
	edit_warmup_steps_c = edit_warmup_steps[c]
	else:
	edit_warmup_steps_c = edit_warmup_steps

	if isinstance(edit_cooldown_steps, list):
	edit_cooldown_steps_c = edit_cooldown_steps[c]
	elif edit_cooldown_steps is None:
	edit_cooldown_steps_c = i + 1
	else:
	edit_cooldown_steps_c = edit_cooldown_steps
	if i >= edit_warmup_steps_c:
	warmup_inds.append(c)
	if i >= edit_cooldown_steps_c:
	noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)
	continue

	noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
	# tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))
	tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3))

	tmp_weights = torch.full_like(tmp_weights, edit_weight_c) # * (1 / enabled_editing_prompts)
	if reverse_editing_direction_c:
	noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
	concept_weights[c, :] = tmp_weights

	noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c

	if user_mask is not None:
	noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask

	if use_cross_attn_mask:
	out = attention_store.aggregate_attention(
	attention_maps=attention_store.step_store,
	prompts=text_cross_attention_maps,
	res=16,
	from_where=["up", "down"],
	is_cross=True,
	select=text_cross_attention_maps.index(editing_prompt[c]),
	)
	attn_map = out[:, :, :, 1:1 + num_edit_tokens[c]] # 0 -> startoftext

	# average over all tokens
	assert (attn_map.shape[3] == num_edit_tokens[c])
	attn_map = torch.sum(attn_map, dim=3)

	# gaussian_smoothing
	attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode="reflect")
	attn_map = self.smoothing(attn_map).squeeze(1)

	# create binary mask
	if attn_map.dtype == torch.float32:
	tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1)
	else:
	tmp = torch.quantile(attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1).to(attn_map.dtype)
	attn_mask = torch.where(attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1,16,16), 1.0, 0.0)

	# resolution must match latent space dimension
	attn_mask = F.interpolate(
	attn_mask.unsqueeze(1),
	noise_guidance_edit_tmp.shape[-2:] # 64,64
	).repeat(1, 4, 1, 1)
	self.activation_mask[i, c] = attn_mask.detach().cpu()
	if not use_intersect_mask:
	noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask

	if use_intersect_mask:
	noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp)
	noise_guidance_edit_tmp_quantile = torch.sum(noise_guidance_edit_tmp_quantile, dim=1,
	keepdim=True)
	noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1)

	# torch.quantile function expects float32
	if noise_guidance_edit_tmp_quantile.dtype == torch.float32:
	tmp = torch.quantile(
	noise_guidance_edit_tmp_quantile.flatten(start_dim=2),
	edit_threshold_c,
	dim=2,
	keepdim=False,
	)
	else:
	tmp = torch.quantile(
	noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32),
	edit_threshold_c,
	dim=2,
	keepdim=False,
	).to(noise_guidance_edit_tmp_quantile.dtype)

	intersect_mask = torch.where(
	noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],
	torch.ones_like(noise_guidance_edit_tmp),
	torch.zeros_like(noise_guidance_edit_tmp),
	) * attn_mask

	self.activation_mask[i, c] = intersect_mask.detach().cpu()

	noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask

	elif not use_cross_attn_mask:
	# calculate quantile
	noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp)
	noise_guidance_edit_tmp_quantile = torch.sum(noise_guidance_edit_tmp_quantile, dim=1,
	keepdim=True)
	noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1)

	# torch.quantile function expects float32
	if noise_guidance_edit_tmp_quantile.dtype == torch.float32:
	tmp = torch.quantile(
	noise_guidance_edit_tmp_quantile.flatten(start_dim=2),
	edit_threshold_c,
	dim=2,
	keepdim=False,
	)
	else:
	tmp = torch.quantile(
	noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32),
	edit_threshold_c,
	dim=2,
	keepdim=False,
	).to(noise_guidance_edit_tmp_quantile.dtype)

	self.activation_mask[i, c] = torch.where(
	noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],
	torch.ones_like(noise_guidance_edit_tmp),
	torch.zeros_like(noise_guidance_edit_tmp),
	).detach().cpu()

	noise_guidance_edit_tmp = torch.where(
	noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],
	noise_guidance_edit_tmp,
	torch.zeros_like(noise_guidance_edit_tmp),
	)

	noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp

	warmup_inds = torch.tensor(warmup_inds).to(self.device)
	if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
	concept_weights = concept_weights.to("cpu") # Offload to cpu
	noise_guidance_edit = noise_guidance_edit.to("cpu")

	concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds)
	concept_weights_tmp = torch.where(
	concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
	)
	concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
	# concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)

	noise_guidance_edit_tmp = torch.index_select(
	noise_guidance_edit.to(self.device), 0, warmup_inds
	)
	noise_guidance_edit_tmp = torch.einsum(
	"cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp
	)
	noise_guidance_edit_tmp = noise_guidance_edit_tmp
	noise_guidance = noise_guidance + noise_guidance_edit_tmp

	self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu()

	del noise_guidance_edit_tmp
	del concept_weights_tmp
	concept_weights = concept_weights.to(self.device)
	noise_guidance_edit = noise_guidance_edit.to(self.device)

	concept_weights = torch.where(
	concept_weights < 0, torch.zeros_like(concept_weights), concept_weights
	)

	concept_weights = torch.nan_to_num(concept_weights)

	noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit)

	noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum

	edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit

	if warmup_inds.shape[0] == len(noise_pred_edit_concepts):
	noise_guidance = noise_guidance + noise_guidance_edit
	self.sem_guidance[i] = noise_guidance_edit.detach().cpu()

	noise_pred = noise_pred_uncond + noise_guidance

	# compute the previous noisy sample x_t -> x_t-1
	if use_ddpm:
	idx = t_to_idx[int(t)]
	latents = self.scheduler.step(noise_pred, t, latents, variance_noise=zs[idx],
	**extra_step_kwargs).prev_sample

	else: #if not use_ddpm:
	latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

	# step callback
	if use_cross_attn_mask:
	store_step = i in attn_store_steps
	if store_step:
	print(f"storing attention for step {i}")
	attention_store.between_steps(store_step)

	# call the callback, if provided
	if callback is not None and i % callback_steps == 0:
	callback(i, t, latents)

	# 8. Post-processing
	if not output_type == "latent":
	image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
	image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
	else:
	image = latents
	has_nsfw_concept = None

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

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

	if not return_dict:
	return (image, has_nsfw_concept), attention_store

	return SemanticStableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept), attention_store, text_cross_attention_maps

	def encode_text(self, prompts):
	text_inputs = self.tokenizer(
	prompts,
	padding="max_length",
	max_length=self.tokenizer.model_max_length,
	return_tensors="pt",
	)
	text_input_ids = text_inputs.input_ids

	if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
	removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length:])
	logger.warning(
	"The following part of your input was truncated because CLIP can only handle sequences up to"
	f" {self.tokenizer.model_max_length} tokens: {removed_text}"
	)
	text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
	text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]

	return text_embeddings

	@torch.no_grad()
	def invert(self,
	image_path: str,
	source_prompt: str = "",
	source_guidance_scale=3.5,
	num_inversion_steps: int = 30,
	skip: float = 0.15,
	eta: float = 1.0,
	generator: Optional[torch.Generator] = None,
	verbose=True,
	):
	"""
	Inverts a real image according to Algorihm 1 in https://arxiv.org/pdf/2304.06140.pdf,
	based on the code in https://github.com/inbarhub/DDPM_inversion

	returns:
	zs - noise maps
	xts - intermediate inverted latents
	"""

	# self.eta = eta
	# assert (self.eta > 0)
	skip = skip/100
	print("YOOOOOOOOOOOOOOOOO ", skip, num_inversion_steps)
	train_steps = self.scheduler.config.num_train_timesteps
	timesteps = torch.from_numpy(
	np.linspace(train_steps - skip * train_steps - 1, 1, num_inversion_steps).astype(np.int64)).to(self.device)


	self.num_inversion_steps = timesteps.shape[0]
	self.scheduler.num_inference_steps = timesteps.shape[0]
	self.scheduler.timesteps = timesteps

	# Reset attn processor, we do not want to store attn maps during inversion
	# self.unet.set_default_attn_processor()
	self.unet.set_attn_processor(AttnProcessor())

	# 1. get embeddings

	uncond_embedding = self.encode_text("")

	# 2. encode image
	x0 = self.encode_image(image_path, dtype=uncond_embedding.dtype)
	self.batch_size = x0.shape[0]

	if not source_prompt == "":
	text_embeddings = self.encode_text(source_prompt).repeat((self.batch_size, 1, 1))
	uncond_embedding = uncond_embedding.repeat((self.batch_size, 1, 1))
	# autoencoder reconstruction
	# image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False)[0]
	# image_rec = self.image_processor.postprocess(image_rec, output_type="pil")
	# 3. find zs and xts
	variance_noise_shape = (
	self.num_inversion_steps,
	self.batch_size,
	self.unet.config.in_channels,
	self.unet.sample_size,
	self.unet.sample_size)

	# intermediate latents
	t_to_idx = {int(v): k for k, v in enumerate(timesteps)}
	xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype)

	for t in reversed(timesteps):
	idx = self.num_inversion_steps-t_to_idx[int(t)] - 1
	noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype)
	xts[idx] = self.scheduler.add_noise(x0, noise, t)
	xts = torch.cat([x0.unsqueeze(0), xts], dim=0)

	reset_dpm(self.scheduler)
	# noise maps
	zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype)

	for t in self.progress_bar(timesteps, verbose=verbose):

	idx = self.num_inversion_steps-t_to_idx[int(t)]-1
	# 1. predict noise residual
	xt = xts[idx+1]

	noise_pred = self.unet(xt, timestep=t, encoder_hidden_states=uncond_embedding).sample

	if not source_prompt == "":
	noise_pred_cond = self.unet(xt, timestep=t, encoder_hidden_states=text_embeddings).sample
	noise_pred = noise_pred + source_guidance_scale * (noise_pred_cond - noise_pred)

	xtm1 = xts[idx]
	z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, eta)
	zs[idx] = z

	# correction to avoid error accumulation
	xts[idx] = xtm1_corrected

	# TODO: I don't think that the noise map for the last step should be discarded ?!
	# if not zs is None:
	# zs[-1] = torch.zeros_like(zs[-1])
	# self.init_latents = xts[-1].expand(self.batch_size, -1, -1, -1)
	zs = zs.flip(0)
	# self.zs = zs


	return zs, xts
	# return zs, xts, image_rec

	@torch.no_grad()
	def encode_image(self, image_path, dtype=None):
	image = load_512(image_path,
	size=self.unet.sample_size * self.vae_scale_factor,
	device=self.device,
	dtype=dtype)
	x0 = self.vae.encode(image).latent_dist.mode()
	x0 = self.vae.config.scaling_factor * x0
	return x0

	def compute_noise_ddim(scheduler, prev_latents, latents, timestep, noise_pred, eta):
	# 1. get previous step value (=t-1)
	prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps

	# 2. compute alphas, betas
	alpha_prod_t = scheduler.alphas_cumprod[timestep]
	alpha_prod_t_prev = (
	scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod
	)

	beta_prod_t = 1 - alpha_prod_t

	# 3. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)

	# 4. Clip "predicted x_0"
	if scheduler.config.clip_sample:
	pred_original_sample = torch.clamp(pred_original_sample, -1, 1)

	# 5. compute variance: "sigma_t(η)" -> see formula (16)
	# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
	variance = scheduler._get_variance(timestep, prev_timestep)
	std_dev_t = eta * variance ** (0.5)

	# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t 2) (0.5) * noise_pred

	# modifed so that updated xtm1 is returned as well (to avoid error accumulation)
	mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
	noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta)

	return noise, mu_xt + (eta * variance ** 0.5) * noise

	# Copied from pipelines.StableDiffusion.CycleDiffusionPipeline.compute_noise
	def compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta):

	def first_order_update(model_output, timestep, prev_timestep, sample):
	lambda_t, lambda_s = scheduler.lambda_t[prev_timestep], scheduler.lambda_t[timestep]
	alpha_t, alpha_s = scheduler.alpha_t[prev_timestep], scheduler.alpha_t[timestep]
	sigma_t, sigma_s = scheduler.sigma_t[prev_timestep], scheduler.sigma_t[timestep]
	h = lambda_t - lambda_s

	mu_xt = (
	(sigma_t / sigma_s * torch.exp(-h)) * sample
	+ (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output
	)
	sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h))

	noise = (prev_latents - mu_xt) / sigma

	prev_sample = mu_xt + sigma * noise

	return noise, prev_sample
	def second_order_update(model_output_list, timestep_list, prev_timestep, sample):
	t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2]
	m0, m1 = model_output_list[-1], model_output_list[-2]
	lambda_t, lambda_s0, lambda_s1 = scheduler.lambda_t[t], scheduler.lambda_t[s0], scheduler.lambda_t[s1]
	alpha_t, alpha_s0 = scheduler.alpha_t[t], scheduler.alpha_t[s0]
	sigma_t, sigma_s0 = scheduler.sigma_t[t], scheduler.sigma_t[s0]
	h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1
	r0 = h_0 / h
	D0, D1 = m0, (1.0 / r0) * (m0 - m1)

	mu_xt = (
	(sigma_t / sigma_s0 * torch.exp(-h)) * sample
	+ (alpha_t * (1 - torch.exp(-2.0 * h))) * D0
	+ 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1
	)
	sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h))

	noise = (prev_latents - mu_xt) / sigma

	prev_sample = mu_xt + sigma * noise

	return noise, prev_sample

	step_index = (scheduler.timesteps == timestep).nonzero()
	if len(step_index) == 0:
	step_index = len(scheduler.timesteps) - 1
	else:
	step_index = step_index.item()

	prev_timestep = 0 if step_index == len(scheduler.timesteps) - 1 else scheduler.timesteps[step_index + 1]

	model_output = scheduler.convert_model_output(noise_pred, timestep, latents)

	for i in range(scheduler.config.solver_order - 1):
	scheduler.model_outputs[i] = scheduler.model_outputs[i + 1]
	scheduler.model_outputs[-1] = model_output

	if scheduler.lower_order_nums < 1:
	noise, prev_sample = first_order_update(model_output, timestep, prev_timestep, latents)
	else:
	timestep_list = [scheduler.timesteps[step_index - 1], timestep]
	noise, prev_sample = second_order_update(scheduler.model_outputs, timestep_list, prev_timestep, latents)

	if scheduler.lower_order_nums < scheduler.config.solver_order:
	scheduler.lower_order_nums += 1

	return noise, prev_sample

	def compute_noise(scheduler, *args):
	if isinstance(scheduler, DDIMScheduler):
	return compute_noise_ddim(scheduler, *args)
	elif isinstance(scheduler, DPMSolverMultistepSchedulerInject) and scheduler.config.algorithm_type == 'sde-dpmsolver++'\
	and scheduler.config.solver_order == 2:
	return compute_noise_sde_dpm_pp_2nd(scheduler, *args)
	else:
	raise NotImplementedError