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	import gradio as gr
	import spaces
	import torch
	from clip_slider_pipeline import CLIPSliderXL, CLIPSliderXL_inv
	from diffusers import StableDiffusionXLPipeline, ControlNetModel, StableDiffusionXLControlNetPipeline, EulerDiscreteScheduler, AutoencoderKL
	import time
	import numpy as np
	import cv2
	from PIL import Image
	#from ledits.pipeline_leditspp_stable_diffusion_xl import LEditsPPPipelineStableDiffusionXL



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

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

	import torch
	import torch.nn.functional as F
	from transformers import (
	CLIPImageProcessor,
	CLIPTextModel,
	CLIPTextModelWithProjection,
	CLIPTokenizer,
	CLIPVisionModelWithProjection,
	)

	from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
	from diffusers.loaders import (
	FromSingleFileMixin,
	IPAdapterMixin,
	StableDiffusionXLLoraLoaderMixin,
	TextualInversionLoaderMixin,
	)
	from diffusers.models import AutoencoderKL, UNet2DConditionModel
	from diffusers.models.attention_processor import (
	Attention,
	AttnProcessor,
	AttnProcessor2_0,
	XFormersAttnProcessor,
	)
	from diffusers.models.lora import adjust_lora_scale_text_encoder
	from diffusers.schedulers import DDIMScheduler, DPMSolverMultistepScheduler
	from diffusers.utils import (
	USE_PEFT_BACKEND,
	is_invisible_watermark_available,
	is_torch_xla_available,
	logging,
	replace_example_docstring,
	scale_lora_layers,
	unscale_lora_layers,
	)
	from diffusers.utils.torch_utils import randn_tensor
	from diffusers.pipelines.pipeline_utils import DiffusionPipeline
	from ledits.pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput


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

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

	XLA_AVAILABLE = True
	else:
	XLA_AVAILABLE = False

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

	EXAMPLE_DOC_STRING = """
	Examples:
	```py
	>>> import torch
	>>> import PIL
	>>> import requests
	>>> from io import BytesIO

	>>> from diffusers import LEditsPPPipelineStableDiffusionXL

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


	>>> def download_image(url):
	... response = requests.get(url)
	... return PIL.Image.open(BytesIO(response.content)).convert("RGB")


	>>> img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/tennis.jpg"
	>>> image = download_image(img_url)

	>>> _ = pipe.invert(image=image, num_inversion_steps=50, skip=0.2)

	>>> edited_image = pipe(
	... editing_prompt=["tennis ball", "tomato"],
	... reverse_editing_direction=[True, False],
	... edit_guidance_scale=[5.0, 10.0],
	... edit_threshold=[0.9, 0.85],
	... ).images[0]
	```
	"""


	# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsAttentionStore
	class LeditsAttentionStore:
	@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
	if attn.shape[1] <= self.max_size:
	bs = 1 + int(PnP) + editing_prompts
	skip = 2 if PnP else 1 # skip PnP & unconditional
	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'}"

	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: Union[int, Tuple[int]], from_where: List[str], is_cross: bool, select: int
	):
	out = [[] for x in range(self.batch_size)]
	if isinstance(res, int):
	num_pixels = res**2
	resolution = (res, res)
	else:
	num_pixels = res[0] * res[1]
	resolution = 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, *resolution, 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, max_resolution=16, max_size: int = None):
	self.step_store = self.get_empty_store()
	self.attention_store = []
	self.cur_step = 0
	self.average = average
	self.batch_size = batch_size
	if max_size is None:
	self.max_size = max_resolution**2
	elif max_size is not None and max_resolution is None:
	self.max_size = max_size
	else:
	raise ValueError("Only allowed to set one of max_resolution or max_size")


	# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsGaussianSmoothing
	class LeditsGaussianSmoothing:
	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))


	# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEDITSCrossAttnProcessor
	class LEDITSCrossAttnProcessor:
	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,
	attention_mask=None,
	temb=None,
	):
	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


	class LEditsPPPipelineStableDiffusionXL(
	DiffusionPipeline,
	FromSingleFileMixin,
	StableDiffusionXLLoraLoaderMixin,
	TextualInversionLoaderMixin,
	IPAdapterMixin,
	):
	"""
	Pipeline for textual image editing using LEDits++ with Stable Diffusion XL.

	This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionXLPipeline`]. Check the
	superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a
	particular device, etc.).

	In addition the pipeline inherits the following loading methods:
	- LoRA: [`LEditsPPPipelineStableDiffusionXL.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 ([`~transformers.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 ([`~transformers.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 ([`~transformers.CLIPTokenizer`]):
	Tokenizer of class
	[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
	tokenizer_2 ([`~transformers.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 ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]):
	A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
	[`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will
	automatically be set to [`DPMSolverMultistepScheduler`].
	force_zeros_for_empty_prompt (`bool`, optional, defaults to `"True"`):
	Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
	`stabilityai/stable-diffusion-xl-base-1-0`.
	add_watermarker (`bool`, optional):
	Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
	watermark output images. If not defined, it will default to True if the package is installed, otherwise no
	watermarker will be used.
	"""

	model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
	_optional_components = [
	"tokenizer",
	"tokenizer_2",
	"text_encoder",
	"text_encoder_2",
	"image_encoder",
	"feature_extractor",
	]
	_callback_tensor_inputs = [
	"latents",
	"prompt_embeds",
	"negative_prompt_embeds",
	"add_text_embeds",
	"add_time_ids",
	"negative_pooled_prompt_embeds",
	"negative_add_time_ids",
	]

	def __init__(
	self,
	vae: AutoencoderKL,
	text_encoder: CLIPTextModel,
	text_encoder_2: CLIPTextModelWithProjection,
	tokenizer: CLIPTokenizer,
	tokenizer_2: CLIPTokenizer,
	unet: UNet2DConditionModel,
	scheduler: Union[DPMSolverMultistepScheduler, DDIMScheduler],
	image_encoder: CLIPVisionModelWithProjection = None,
	feature_extractor: CLIPImageProcessor = None,
	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,
	image_encoder=image_encoder,
	feature_extractor=feature_extractor,
	)
	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)

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

	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
	self.inversion_steps = None

	def encode_prompt(
	self,
	device: Optional[torch.device] = None,
	num_images_per_prompt: int = 1,
	negative_prompt: Optional[str] = None,
	negative_prompt_2: Optional[str] = None,
	negative_prompt_embeds: Optional[torch.Tensor] = None,
	negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
	lora_scale: Optional[float] = None,
	clip_skip: Optional[int] = None,
	enable_edit_guidance: bool = True,
	editing_prompt: Optional[str] = None,
	editing_prompt_embeds: Optional[torch.Tensor] = None,
	editing_pooled_prompt_embeds: Optional[torch.Tensor] = None,
	avg_diff=None, # [0] -> text encoder 1,[1] ->text encoder 2
	avg_diff_2nd=None, # text encoder 1,2
	correlation_weight_factor=0.7,
	scale=2,
	scale_2nd=2,
	) -> object:
	r"""
	Encodes the prompt into text encoder hidden states.

	Args:
	device: (`torch.device`):
	torch device
	num_images_per_prompt (`int`):
	number of images that should be generated per prompt
	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.
	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
	negative_prompt_embeds (`torch.Tensor`, 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.
	negative_pooled_prompt_embeds (`torch.Tensor`, optional):
	Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
	input argument.
	lora_scale (`float`, optional):
	A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
	clip_skip (`int`, optional):
	Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
	the output of the pre-final layer will be used for computing the prompt embeddings.
	enable_edit_guidance (`bool`):
	Whether to guide towards an editing prompt or not.
	editing_prompt (`str` or `List[str]`, optional):
	Editing prompt(s) to be encoded. If not defined and 'enable_edit_guidance' is True, one has to pass
	`editing_prompt_embeds` instead.
	editing_prompt_embeds (`torch.Tensor`, optional):
	Pre-generated edit text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
	If not provided and 'enable_edit_guidance' is True, editing_prompt_embeds will be generated from
	`editing_prompt` input argument.
	editing_pooled_prompt_embeds (`torch.Tensor`, optional):
	Pre-generated edit pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, pooled editing_pooled_prompt_embeds will be generated from `editing_prompt`
	input argument.
	"""
	device = device or self._execution_device

	# set lora scale so that monkey patched LoRA
	# function of text encoder can correctly access it
	if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
	self._lora_scale = lora_scale

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

	if self.text_encoder_2 is not None:
	if not USE_PEFT_BACKEND:
	adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
	else:
	scale_lora_layers(self.text_encoder_2, lora_scale)

	batch_size = self.batch_size

	# 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]
	)
	num_edit_tokens = 0

	# get unconditional embeddings for classifier free guidance
	zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt

	if negative_prompt_embeds is None:
	negative_prompt = negative_prompt or ""
	negative_prompt_2 = negative_prompt_2 or negative_prompt

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

	uncond_tokens: List[str]

	if batch_size != len(negative_prompt):
	raise ValueError(
	f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but image inversion "
	f" has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
	" the batch size of the input images."
	)
	else:
	uncond_tokens = [negative_prompt, negative_prompt_2]

	j=0
	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)


	uncond_input = tokenizer(
	negative_prompt,
	padding="max_length",
	max_length=tokenizer.model_max_length,
	truncation=True,
	return_tensors="pt",
	)
	toks = uncond_input.input_ids

	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]

	if avg_diff is not None:
	# scale=3
	normed_prompt_embeds = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1, keepdim=True)
	sims = normed_prompt_embeds[0] @ normed_prompt_embeds[0].T
	if j == 0:
	weights = sims[toks.argmax(), :][None, :, None].repeat(1, 1, 768)

	standard_weights = torch.ones_like(weights)

	weights = standard_weights + (weights - standard_weights) * correlation_weight_factor
	edit_concepts_embeds = negative_prompt_embeds + (
	weights * avg_diff[0][None, :].repeat(1, tokenizer.model_max_length, 1) * scale)

	if avg_diff_2nd is not None:
	edit_concepts_embeds += (weights * avg_diff_2nd[0][None, :].repeat(1,
	self.pipe.tokenizer.model_max_length,
	1) * scale_2nd)
	else:
	weights = sims[toks.argmax(), :][None, :, None].repeat(1, 1, 1280)

	standard_weights = torch.ones_like(weights)

	weights = standard_weights + (weights - standard_weights) * correlation_weight_factor
	edit_concepts_embeds = negative_prompt_embeds + (
	weights * avg_diff[1][None, :].repeat(1, tokenizer.model_max_length, 1) * scale)

	if avg_diff_2nd is not None:
	edit_concepts_embeds += (weights * avg_diff_2nd[1][None, :].repeat(1,
	self.pipe.tokenizer_2.model_max_length,
	1) * scale_2nd)

	negative_prompt_embeds_list.append(negative_prompt_embeds)
	j+=1

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

	if zero_out_negative_prompt:
	negative_prompt_embeds = torch.zeros_like(negative_prompt_embeds)
	negative_pooled_prompt_embeds = torch.zeros_like(negative_pooled_prompt_embeds)

	if enable_edit_guidance and editing_prompt_embeds is None:
	editing_prompt_2 = editing_prompt

	editing_prompts = [editing_prompt, editing_prompt_2]
	edit_prompt_embeds_list = []

	i = 0
	for editing_prompt, tokenizer, text_encoder in zip(editing_prompts, tokenizers, text_encoders):
	if isinstance(self, TextualInversionLoaderMixin):
	editing_prompt = self.maybe_convert_prompt(editing_prompt, tokenizer)

	max_length = negative_prompt_embeds.shape[1]
	edit_concepts_input = tokenizer(
	# [x for item in editing_prompt for x in repeat(item, batch_size)],
	editing_prompt,
	padding="max_length",
	max_length=max_length,
	truncation=True,
	return_tensors="pt",
	return_length=True,
	)
	num_edit_tokens = edit_concepts_input.length - 2
	toks = edit_concepts_input.input_ids
	edit_concepts_embeds = text_encoder(
	edit_concepts_input.input_ids.to(device),
	output_hidden_states=True,
	)
	# We are only ALWAYS interested in the pooled output of the final text encoder
	editing_pooled_prompt_embeds = edit_concepts_embeds[0]
	if clip_skip is None:
	edit_concepts_embeds = edit_concepts_embeds.hidden_states[-2]
	else:
	# "2" because SDXL always indexes from the penultimate layer.
	edit_concepts_embeds = edit_concepts_embeds.hidden_states[-(clip_skip + 2)]


	if avg_diff is not None:

	normed_prompt_embeds = edit_concepts_embeds / edit_concepts_embeds.norm(dim=-1, keepdim=True)
	sims = normed_prompt_embeds[0] @ normed_prompt_embeds[0].T
	if i == 0:
	weights = sims[toks.argmax(), :][None, :, None].repeat(1, 1, 768)

	standard_weights = torch.ones_like(weights)

	weights = standard_weights + (weights - standard_weights) * correlation_weight_factor
	edit_concepts_embeds = edit_concepts_embeds + (
	weights * avg_diff[0][None, :].repeat(1, tokenizer.model_max_length, 1) * scale)

	if avg_diff_2nd is not None:
	edit_concepts_embeds += (weights * avg_diff_2nd[0][None, :].repeat(1,
	self.pipe.tokenizer.model_max_length,
	1) * scale_2nd)
	else:
	weights = sims[toks.argmax(), :][None, :, None].repeat(1, 1, 1280)

	standard_weights = torch.ones_like(weights)

	weights = standard_weights + (weights - standard_weights) * correlation_weight_factor
	edit_concepts_embeds = edit_concepts_embeds + (
	weights * avg_diff[1][None, :].repeat(1, tokenizer.model_max_length, 1) * scale)
	if avg_diff_2nd is not None:
	edit_concepts_embeds += (weights * avg_diff_2nd[1][None, :].repeat(1,
	self.pipe.tokenizer_2.model_max_length,
	1) * scale_2nd)


	edit_prompt_embeds_list.append(edit_concepts_embeds)
	i+=1

	edit_concepts_embeds = torch.concat(edit_prompt_embeds_list, dim=-1)
	elif not enable_edit_guidance:
	edit_concepts_embeds = None
	editing_pooled_prompt_embeds = None

	negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
	bs_embed, seq_len, _ = negative_prompt_embeds.shape
	# 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)

	if enable_edit_guidance:
	bs_embed_edit, seq_len, _ = edit_concepts_embeds.shape
	edit_concepts_embeds = edit_concepts_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
	edit_concepts_embeds = edit_concepts_embeds.repeat(1, num_images_per_prompt, 1)
	edit_concepts_embeds = edit_concepts_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1)

	negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
	bs_embed * num_images_per_prompt, -1
	)

	if enable_edit_guidance:
	editing_pooled_prompt_embeds = editing_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
	bs_embed_edit * num_images_per_prompt, -1
	)

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

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

	return (
	negative_prompt_embeds,
	edit_concepts_embeds,
	negative_pooled_prompt_embeds,
	editing_pooled_prompt_embeds,
	num_edit_tokens,
	)

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
	def prepare_extra_step_kwargs(self, eta, generator=None):
	# 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,
	negative_prompt=None,
	negative_prompt_2=None,
	negative_prompt_embeds=None,
	negative_pooled_prompt_embeds=None,
	):
	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 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`."
	)

	# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
	def prepare_latents(self, device, latents):
	latents = latents.to(device)

	# scale the initial noise by the standard deviation required by the scheduler
	latents = latents * self.scheduler.init_noise_sigma
	return latents

	def _get_add_time_ids(
	self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
	):
	add_time_ids = list(original_size + crops_coords_top_left + target_size)

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

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

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

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

	# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
	def get_guidance_scale_embedding(
	self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
	) -> torch.Tensor:
	"""
	See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

	Args:
	w (`torch.Tensor`):
	Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
	embedding_dim (`int`, optional, defaults to 512):
	Dimension of the embeddings to generate.
	dtype (`torch.dtype`, optional, defaults to `torch.float32`):
	Data type of the generated embeddings.

	Returns:
	`torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
	"""
	assert len(w.shape) == 1
	w = w * 1000.0

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

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

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

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

	# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
	# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
	# corresponds to doing no classifier free guidance.
	@property
	def do_classifier_free_guidance(self):
	return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None

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

	@property
	def denoising_end(self):
	return self._denoising_end

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

	# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.prepare_unet
	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 and place_in_unet != "mid":
	attn_procs[name] = LEDITSCrossAttnProcessor(
	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()
	@spaces.GPU
	@replace_example_docstring(EXAMPLE_DOC_STRING)
	def __call__(
	self,
	denoising_end: Optional[float] = None,
	negative_prompt: Optional[Union[str, List[str]]] = None,
	negative_prompt_2: Optional[Union[str, List[str]]] = None,
	negative_prompt_embeds: Optional[torch.Tensor] = None,
	negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
	ip_adapter_image: Optional[PipelineImageInput] = None,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	guidance_rescale: float = 0.0,
	crops_coords_top_left: Tuple[int, int] = (0, 0),
	target_size: Optional[Tuple[int, int]] = None,
	editing_prompt: Optional[Union[str, List[str]]] = None,
	editing_prompt_embeddings: Optional[torch.Tensor] = None,
	editing_pooled_prompt_embeds: 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]]] = 0,
	edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
	edit_threshold: Optional[Union[float, List[float]]] = 0.9,
	sem_guidance: Optional[List[torch.Tensor]] = None,
	use_cross_attn_mask: bool = False,
	use_intersect_mask: bool = False,
	user_mask: Optional[torch.Tensor] = None,
	attn_store_steps: Optional[List[int]] = [],
	store_averaged_over_steps: bool = True,
	clip_skip: Optional[int] = None,
	callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
	callback_on_step_end_tensor_inputs: List[str] = ["latents"],
	avg_diff=None, # [0] -> text encoder 1,[1] ->text encoder 2
	avg_diff_2nd=None, # text encoder 1,2
	correlation_weight_factor=0.7,
	scale=2,
	scale_2nd=2,
	init_latents: [torch.Tensor] = None,
	zs: [torch.Tensor] = None,
	**kwargs,
	):
	r"""
	The call function to the pipeline for editing. The
	[`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert`] method has to be called beforehand. Edits
	will always be performed for the last inverted image(s).

	Args:
	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
	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
	negative_prompt_embeds (`torch.Tensor`, 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.
	negative_pooled_prompt_embeds (`torch.Tensor`, 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.
	ip_adapter_image: (`PipelineImageInput`, optional):
	Optional image input to work with IP Adapters.
	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.Tensor)`.
	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.
	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).
	editing_prompt (`str` or `List[str]`, optional):
	The prompt or prompts to guide the image generation. The image is reconstructed by setting
	`editing_prompt = None`. Guidance direction of prompt should be specified via
	`reverse_editing_direction`.
	editing_prompt_embeddings (`torch.Tensor`, optional):
	Pre-generated edit text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
	If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input argument.
	editing_pooled_prompt_embeddings (`torch.Tensor`, optional):
	Pre-generated pooled edit text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input
	argument.
	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 guiding the image generation. If provided as list values should correspond to
	`editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 12 of [LEDITS++
	Paper](https://arxiv.org/abs/2301.12247).
	edit_warmup_steps (`float` or `List[float]`, optional, defaults to 10):
	Number of diffusion steps (for each prompt) for which guidance is not applied.
	edit_cooldown_steps (`float` or `List[float]`, optional, defaults to `None`):
	Number of diffusion steps (for each prompt) after which guidance is no longer applied.
	edit_threshold (`float` or `List[float]`, optional, defaults to 0.9):
	Masking threshold of guidance. Threshold should be proportional to the image region that is modified.
	'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++
	Paper](https://arxiv.org/abs/2301.12247).
	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`.
	use_cross_attn_mask:
	Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask
	is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of [LEDITS++
	paper](https://arxiv.org/pdf/2311.16711.pdf).
	use_intersect_mask:
	Whether the masking term is calculated as intersection of cross-attention masks and masks derived from
	the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate
	are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf).
	user_mask:
	User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s
	implicit masks do not meet user preferences.
	attn_store_steps:
	Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes.
	store_averaged_over_steps:
	Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If
	False, attention maps for each step are stores separately. Just for visualization purposes.
	clip_skip (`int`, optional):
	Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
	the output of the pre-final layer will be used for computing the prompt embeddings.
	callback_on_step_end (`Callable`, optional):
	A function that calls at the end of each denoising steps during the inference. The function is called
	with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
	callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
	`callback_on_step_end_tensor_inputs`.
	callback_on_step_end_tensor_inputs (`List`, optional):
	The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
	will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
	`._callback_tensor_inputs` attribute of your pipeline class.

	Examples:

	Returns:
	[`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`:
	[`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
	returning a tuple, the first element is a list with the generated images.
	"""
	if self.inversion_steps is None:
	raise ValueError(
	"You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s)."
	)

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

	#zs = self.zs
	self.scheduler.set_timesteps(len(self.scheduler.timesteps))

	if use_intersect_mask:
	use_cross_attn_mask = True

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

	if user_mask is not None:
	user_mask = user_mask.to(self.device)

	# TODO: Check inputs
	# 1. Check inputs. Raise error if not correct
	# self.check_inputs(
	# callback_steps,
	# negative_prompt,
	# negative_prompt_2,
	# prompt_embeds,
	# negative_prompt_embeds,
	# pooled_prompt_embeds,
	# negative_pooled_prompt_embeds,
	# )
	self._guidance_rescale = guidance_rescale
	self._clip_skip = clip_skip
	self._cross_attention_kwargs = cross_attention_kwargs
	self._denoising_end = denoising_end

	# 2. Define call parameters
	batch_size = self.batch_size

	device = self._execution_device

	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
	print("negative_prompt", negative_prompt)
	# 3. Encode input prompt
	text_encoder_lora_scale = (
	cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
	)
	(
	prompt_embeds,
	edit_prompt_embeds,
	negative_pooled_prompt_embeds,
	pooled_edit_embeds,
	num_edit_tokens,
	) = self.encode_prompt(
	device=device,
	num_images_per_prompt=num_images_per_prompt,
	negative_prompt=negative_prompt,
	negative_prompt_2=negative_prompt_2,
	negative_prompt_embeds=negative_prompt_embeds,
	negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
	lora_scale=text_encoder_lora_scale,
	clip_skip=self.clip_skip,
	enable_edit_guidance=enable_edit_guidance,
	editing_prompt=editing_prompt,
	editing_prompt_embeds=editing_prompt_embeddings,
	editing_pooled_prompt_embeds=editing_pooled_prompt_embeds,
	avg_diff = avg_diff,
	avg_diff_2nd = avg_diff_2nd,
	correlation_weight_factor = correlation_weight_factor,
	scale=scale,
	scale_2nd=scale_2nd
	)

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

	timesteps = self.inversion_steps
	timesteps = inversion_steps
	t_to_idx = {int(v): k for k, v in enumerate(timesteps)}

	if use_cross_attn_mask:
	self.attention_store = LeditsAttentionStore(
	average=store_averaged_over_steps,
	batch_size=batch_size,
	max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0),
	max_resolution=None,
	)
	self.prepare_unet(self.attention_store)
	resolution = latents.shape[-2:]
	att_res = (int(resolution[0] / 4), int(resolution[1] / 4))

	# 5. Prepare latent variables
	latents = self.prepare_latents(device=device, latents=latents)

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

	if self.text_encoder_2 is None:
	text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1])
	else:
	text_encoder_projection_dim = self.text_encoder_2.config.projection_dim

	# 7. Prepare added time ids & embeddings
	add_text_embeds = negative_pooled_prompt_embeds
	add_time_ids = self._get_add_time_ids(
	self.size,
	crops_coords_top_left,
	self.size,
	dtype=negative_pooled_prompt_embeds.dtype,
	text_encoder_projection_dim=text_encoder_projection_dim,
	)

	if enable_edit_guidance:
	prompt_embeds = torch.cat([prompt_embeds, edit_prompt_embeds], dim=0)
	add_text_embeds = torch.cat([add_text_embeds, pooled_edit_embeds], dim=0)
	edit_concepts_time_ids = add_time_ids.repeat(edit_prompt_embeds.shape[0], 1)
	add_time_ids = torch.cat([add_time_ids, edit_concepts_time_ids], dim=0)
	self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt

	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)

	if ip_adapter_image is not None:
	# TODO: fix image encoding
	image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
	if self.do_classifier_free_guidance:
	image_embeds = torch.cat([negative_image_embeds, image_embeds])
	image_embeds = image_embeds.to(device)

	# 8. Denoising loop
	self.sem_guidance = None
	self.activation_mask = None

	if (
	self.denoising_end is not None
	and isinstance(self.denoising_end, float)
	and self.denoising_end > 0
	and self.denoising_end < 1
	):
	discrete_timestep_cutoff = int(
	round(
	self.scheduler.config.num_train_timesteps
	- (self.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]

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

	self._num_timesteps = len(timesteps)
	with self.progress_bar(total=self._num_timesteps) 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] * (1 + self.enabled_editing_prompts))
	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}
	if ip_adapter_image is not None:
	added_cond_kwargs["image_embeds"] = image_embeds
	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]

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

	noise_guidance_edit = torch.zeros(
	noise_pred_uncond.shape,
	device=self.device,
	dtype=noise_pred_uncond.dtype,
	)

	if sem_guidance is not None and len(sem_guidance) > i:
	noise_guidance_edit += sem_guidance[i].to(self.device)

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

	# noise_guidance_edit = torch.zeros_like(noise_guidance)
	for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
	if isinstance(edit_warmup_steps, list):
	edit_warmup_steps_c = edit_warmup_steps[c]
	else:
	edit_warmup_steps_c = edit_warmup_steps
	if i < edit_warmup_steps_c:
	continue

	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 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_cooldown_steps_c:
	continue

	noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond

	if reverse_editing_direction_c:
	noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1

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

	# average over all tokens
	if attn_map.shape[3] != num_edit_tokens[c]:
	raise ValueError(
	f"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!"
	)
	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)

	# torch.quantile function expects float32
	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, *att_res), 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, self.unet.config.in_channels, 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 += noise_guidance_edit_tmp

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

	noise_pred = noise_pred_uncond + noise_guidance_edit

	# compute the previous noisy sample x_t -> x_t-1
	if enable_edit_guidance and self.guidance_rescale > 0.0:
	# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
	noise_pred = rescale_noise_cfg(
	noise_pred,
	noise_pred_edit_concepts.mean(dim=0, keepdim=False),
	guidance_rescale=self.guidance_rescale,
	)

	idx = t_to_idx[int(t)]
	latents = self.scheduler.step(
	noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs, return_dict=False
	)[0]

	# step callback
	if use_cross_attn_mask:
	store_step = i in attn_store_steps
	self.attention_store.between_steps(store_step)

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

	latents = callback_outputs.pop("latents", latents)
	prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
	negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
	add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
	negative_pooled_prompt_embeds = callback_outputs.pop(
	"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
	)
	add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
	# negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)

	# call the callback, if provided
	if i == len(timesteps) - 1 or ((i + 1) > 0 and (i + 1) % self.scheduler.order == 0):
	progress_bar.update()

	if XLA_AVAILABLE:
	xm.mark_step()

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

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

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

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

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

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

	# Offload all models
	self.maybe_free_model_hooks()

	if not return_dict:
	return (image,)

	return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=None)

	@torch.no_grad()
	# Modified from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.encode_image
	def encode_image(self, image, dtype=None, height=None, width=None, resize_mode="default", crops_coords=None):
	image = self.image_processor.preprocess(
	image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
	)
	resized = self.image_processor.postprocess(image=image, output_type="pil")

	if max(image.shape[-2:]) > self.vae.config["sample_size"] * 1.5:
	logger.warning(
	"Your input images far exceed the default resolution of the underlying diffusion model. "
	"The output images may contain severe artifacts! "
	"Consider down-sampling the input using the `height` and `width` parameters"
	)
	image = image.to(self.device, dtype=dtype)
	needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

	if needs_upcasting:
	image = image.float()
	self.upcast_vae()

	x0 = self.vae.encode(image).latent_dist.mode()
	x0 = x0.to(dtype)
	# cast back to fp16 if needed
	if needs_upcasting:
	self.vae.to(dtype=torch.float16)

	x0 = self.vae.config.scaling_factor * x0
	return x0, resized

	@torch.no_grad()
	@spaces.GPU
	def invert(
	self,
	image: PipelineImageInput,
	source_prompt: str = "",
	source_guidance_scale=3.5,
	negative_prompt: str = None,
	negative_prompt_2: str = None,
	num_inversion_steps: int = 50,
	skip: float = 0.15,
	generator: Optional[torch.Generator] = None,
	crops_coords_top_left: Tuple[int, int] = (0, 0),
	num_zero_noise_steps: int = 3,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	):
	r"""
	The function to the pipeline for image inversion as described by the [LEDITS++
	Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the
	inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead.

	Args:
	image (`PipelineImageInput`):
	Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect
	ratio.
	source_prompt (`str`, defaults to `""`):
	Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled
	if the `source_prompt` is `""`.
	source_guidance_scale (`float`, defaults to `3.5`):
	Strength of guidance during inversion.
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. If not defined, one has to pass
	`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
	less than `1`).
	negative_prompt_2 (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
	`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
	num_inversion_steps (`int`, defaults to `50`):
	Number of total performed inversion steps after discarding the initial `skip` steps.
	skip (`float`, defaults to `0.15`):
	Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values
	will lead to stronger changes to the input image. `skip` has to be between `0` and `1`.
	generator (`torch.Generator`, optional):
	A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make inversion
	deterministic.
	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).
	num_zero_noise_steps (`int`, defaults to `3`):
	Number of final diffusion steps that will not renoise the current image. If no steps are set to zero
	SD-XL in combination with [`DPMSolverMultistepScheduler`] will produce noise artifacts.
	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).

	Returns:
	[`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: Output will contain the resized input image(s)
	and respective VAE reconstruction(s).
	"""

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

	self.eta = 1.0

	self.scheduler.config.timestep_spacing = "leading"
	self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip)))
	self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:]
	timesteps = self.inversion_steps

	num_images_per_prompt = 1

	device = self._execution_device

	# 0. Ensure that only uncond embedding is used if prompt = ""
	if source_prompt == "":
	# noise pred should only be noise_pred_uncond
	source_guidance_scale = 0.0
	do_classifier_free_guidance = False
	else:
	do_classifier_free_guidance = source_guidance_scale > 1.0

	# 1. prepare image
	x0, resized = self.encode_image(image, dtype=self.text_encoder_2.dtype)
	width = x0.shape[2] * self.vae_scale_factor
	height = x0.shape[3] * self.vae_scale_factor
	self.size = (height, width)

	self.batch_size = x0.shape[0]

	# 2. get embeddings
	text_encoder_lora_scale = (
	cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
	)

	if isinstance(source_prompt, str):
	source_prompt = [source_prompt] * self.batch_size

	(
	negative_prompt_embeds,
	prompt_embeds,
	negative_pooled_prompt_embeds,
	edit_pooled_prompt_embeds,
	_,
	) = self.encode_prompt(
	device=device,
	num_images_per_prompt=num_images_per_prompt,
	negative_prompt=negative_prompt,
	negative_prompt_2=negative_prompt_2,
	editing_prompt=source_prompt,
	lora_scale=text_encoder_lora_scale,
	enable_edit_guidance=do_classifier_free_guidance,
	)
	if self.text_encoder_2 is None:
	text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1])
	else:
	text_encoder_projection_dim = self.text_encoder_2.config.projection_dim

	# 3. Prepare added time ids & embeddings
	add_text_embeds = negative_pooled_prompt_embeds
	add_time_ids = self._get_add_time_ids(
	self.size,
	crops_coords_top_left,
	self.size,
	dtype=negative_prompt_embeds.dtype,
	text_encoder_projection_dim=text_encoder_projection_dim,
	)

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

	negative_prompt_embeds = negative_prompt_embeds.to(device)

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

	# autoencoder reconstruction
	if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
	self.upcast_vae()
	x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
	image_rec = self.vae.decode(
	x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator
	)[0]
	elif self.vae.config.force_upcast:
	x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
	image_rec = self.vae.decode(
	x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator
	)[0]
	else:
	image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]

	image_rec = self.image_processor.postprocess(image_rec, output_type="pil")

	# 5. find zs and xts
	variance_noise_shape = (num_inversion_steps, *x0.shape)

	# 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=negative_prompt_embeds.dtype)

	for t in reversed(timesteps):
	idx = 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.unsqueeze(0))
	xts = torch.cat([x0.unsqueeze(0), xts], dim=0)

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

	self.scheduler.set_timesteps(len(self.scheduler.timesteps))

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

	latent_model_input = torch.cat([xt] * 2) if do_classifier_free_guidance else xt
	latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

	added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}

	noise_pred = self.unet(
	latent_model_input,
	t,
	encoder_hidden_states=negative_prompt_embeds,
	cross_attention_kwargs=cross_attention_kwargs,
	added_cond_kwargs=added_cond_kwargs,
	return_dict=False,
	)[0]

	# 2. perform guidance
	if do_classifier_free_guidance:
	noise_pred_out = noise_pred.chunk(2)
	noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
	noise_pred = noise_pred_uncond + source_guidance_scale * (noise_pred_text - noise_pred_uncond)

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

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

	self.init_latents = xts[-1]
	zs = zs.flip(0)

	if num_zero_noise_steps > 0:
	zs[-num_zero_noise_steps:] = torch.zeros_like(zs[-num_zero_noise_steps:])
	self.zs = zs
	#return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec)
	return xts[-1], zs


	# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.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


	# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_ddim
	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_t2) (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
	if variance > 0.0:
	noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta)
	else:
	noise = torch.tensor([0.0]).to(latents.device)

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


	# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_sde_dpm_pp_2nd
	def compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta):
	def first_order_update(model_output, sample): # timestep, prev_timestep, sample):
	sigma_t, sigma_s = scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index]
	alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t)
	alpha_s, sigma_s = scheduler._sigma_to_alpha_sigma_t(sigma_s)
	lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
	lambda_s = torch.log(alpha_s) - torch.log(sigma_s)

	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

	mu_xt = scheduler.dpm_solver_first_order_update(
	model_output=model_output, sample=sample, noise=torch.zeros_like(sample)
	)

	sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h))
	if sigma > 0.0:
	noise = (prev_latents - mu_xt) / sigma
	else:
	noise = torch.tensor([0.0]).to(sample.device)

	prev_sample = mu_xt + sigma * noise
	return noise, prev_sample

	def second_order_update(model_output_list, sample): # timestep_list, prev_timestep, sample):
	sigma_t, sigma_s0, sigma_s1 = (
	scheduler.sigmas[scheduler.step_index + 1],
	scheduler.sigmas[scheduler.step_index],
	scheduler.sigmas[scheduler.step_index - 1],
	)

	alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t)
	alpha_s0, sigma_s0 = scheduler._sigma_to_alpha_sigma_t(sigma_s0)
	alpha_s1, sigma_s1 = scheduler._sigma_to_alpha_sigma_t(sigma_s1)

	lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
	lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
	lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)

	m0, m1 = model_output_list[-1], model_output_list[-2]

	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))
	if sigma > 0.0:
	noise = (prev_latents - mu_xt) / sigma
	else:
	noise = torch.tensor([0.0]).to(sample.device)

	prev_sample = mu_xt + sigma * noise

	return noise, prev_sample

	if scheduler.step_index is None:
	scheduler._init_step_index(timestep)

	model_output = scheduler.convert_model_output(model_output=noise_pred, sample=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, latents)
	else:
	noise, prev_sample = second_order_update(scheduler.model_outputs, latents)

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

	# upon completion increase step index by one
	scheduler._step_index += 1

	return noise, prev_sample


	# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise
	def compute_noise(scheduler, *args):
	if isinstance(scheduler, DDIMScheduler):
	return compute_noise_ddim(scheduler, *args)
	elif (
	isinstance(scheduler, DPMSolverMultistepScheduler)
	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


	def HWC3(x):
	assert x.dtype == np.uint8
	if x.ndim == 2:
	x = x[:, :, None]
	assert x.ndim == 3
	H, W, C = x.shape
	assert C == 1 or C == 3 or C == 4
	if C == 3:
	return x
	if C == 1:
	return np.concatenate([x, x, x], axis=2)
	if C == 4:
	color = x[:, :, 0:3].astype(np.float32)
	alpha = x[:, :, 3:4].astype(np.float32) / 255.0
	y = color * alpha + 255.0 * (1.0 - alpha)
	y = y.clip(0, 255).astype(np.uint8)
	return y

	def process_controlnet_img(image):
	controlnet_img = np.array(image)
	controlnet_img = cv2.Canny(controlnet_img, 100, 200)
	controlnet_img = HWC3(controlnet_img)
	controlnet_img = Image.fromarray(controlnet_img)

	# load pipelines
	vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
	pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", vae=vae).to("cuda", torch.float16)
	pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
	clip_slider = CLIPSliderXL(pipe, device=torch.device("cuda"))

	pipe_adapter = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash").to("cuda", torch.float16)
	pipe_adapter.scheduler = EulerDiscreteScheduler.from_config(pipe_adapter.scheduler.config)
	#pipe_adapter.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
	# scale = 0.8
	# pipe_adapter.set_ip_adapter_scale(scale)
	clip_slider_ip = CLIPSliderXL(sd_pipe=pipe_adapter, device=torch.device("cuda"))

	controlnet = ControlNetModel.from_pretrained(
	"xinsir/controlnet-canny-sdxl-1.0", # insert here your choice of controlnet
	torch_dtype=torch.float16
	)
	vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
	pipe_controlnet = StableDiffusionXLControlNetPipeline.from_pretrained(
	"sd-community/sdxl-flash",
	controlnet=controlnet,
	vae=vae,
	torch_dtype=torch.float16,
	)
	clip_slider_controlnet = CLIPSliderXL(sd_pipe=pipe_controlnet,device=torch.device("cuda"))

	pipe_inv = LEditsPPPipelineStableDiffusionXL.from_pretrained(
	"stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
	torch_dtype=torch.float16
	)
	clip_slider_inv = CLIPSliderXL_inv(sd_pipe=pipe_inv,device=torch.device("cuda"))

	@spaces.GPU(duration=120)
	def generate(slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale,
	x_concept_1, x_concept_2, y_concept_1, y_concept_2,
	avg_diff_x_1, avg_diff_x_2,
	avg_diff_y_1, avg_diff_y_2,
	img2img_type = None, img = None,
	controlnet_scale= None, ip_adapter_scale=None,
	edit_threshold=None, edit_guidance_scale = None,
	init_latents=None, zs=None):

	start_time = time.time()
	# check if avg diff for directions need to be re-calculated
	print("slider_x", slider_x)
	print("x_concept_1", x_concept_1, "x_concept_2", x_concept_2)

	if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]):
	avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations)
	avg_diff_0 = avg_diff[0].to(torch.float16)
	avg_diff_1 = avg_diff[1].to(torch.float16)
	x_concept_1, x_concept_2 = slider_x[0], slider_x[1]

	print("avg_diff_0", avg_diff_0.dtype)

	if not sorted(slider_y) == sorted([y_concept_1, y_concept_2]):
	avg_diff_2nd = clip_slider.find_latent_direction(slider_y[0], slider_y[1], num_iterations=iterations)
	avg_diff_2nd_0 = avg_diff_2nd[0].to(torch.float16)
	avg_diff_2nd_1 = avg_diff_2nd[1].to(torch.float16)
	y_concept_1, y_concept_2 = slider_y[0], slider_y[1]
	end_time = time.time()
	print(f"direction time: {end_time - start_time:.2f} ms")

	start_time = time.time()

	if img2img_type=="controlnet canny" and img is not None:
	control_img = process_controlnet_img(img)
	image = clip_slider.generate(prompt, guidance_scale=guidance_scale, image=control_img, controlnet_conditioning_scale =controlnet_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1))
	elif img2img_type=="ip adapter" and img is not None:
	image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1))
	elif img2img_type=="inversion":
	image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=0, scale_2nd=0, seed=seed, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1), init_latents = init_latents, zs=zs, edit_threshold=[edit_threshold], edit_guidance_scale = [edit_guidance_scale])
	else: # text to image
	image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1))

	end_time = time.time()
	print(f"generation time: {end_time - start_time:.2f} ms")

	comma_concepts_x = ', '.join(slider_x)
	comma_concepts_y = ', '.join(slider_y)

	avg_diff_x_1 = avg_diff_0.cpu()
	avg_diff_x_2 = avg_diff_1.cpu()
	avg_diff_y_1 = avg_diff_2nd_0.cpu()
	avg_diff_y_2 = avg_diff_2nd_1.cpu()

	return gr.update(label=comma_concepts_x, interactive=True),gr.update(label=comma_concepts_y, interactive=True), x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, image

	@spaces.GPU
	def update_scales(x,y,prompt,seed, steps, guidance_scale,
	avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2,
	img2img_type = None, img = None,
	controlnet_scale= None, ip_adapter_scale=None,
	edit_threshold=None, edit_guidance_scale = None,
	init_latents=None, zs=None):
	avg_diff = (avg_diff_x_1.cuda(), avg_diff_x_2.cuda())
	avg_diff_2nd = (avg_diff_y_1.cuda(), avg_diff_y_2.cuda())
	if img2img_type=="controlnet canny" and img is not None:
	control_img = process_controlnet_img(img)
	image = clip_slider.generate(prompt, guidance_scale=guidance_scale, image=control_img, controlnet_conditioning_scale =controlnet_scale, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
	elif img2img_type=="ip adapter" and img is not None:
	image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
	elif img2img_type=="inversion":
	image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=x, scale_2nd=y, seed=seed, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1), edit_threshold=[edit_threshold], edit_guidance_scale = [edit_guidance_scale], init_latents = init_latents, zs=zs)
	else:
	image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
	return image

	@spaces.GPU
	def update_x(x,y,prompt,seed, steps,
	avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2,
	img2img_type = None,
	img = None):
	avg_diff = (avg_diff_x_1.cuda(), avg_diff_x_2.cuda())
	avg_diff_2nd = (avg_diff_y_1.cuda(), avg_diff_y_2.cuda())
	image = clip_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
	return image

	@spaces.GPU
	def update_y(x,y,prompt, seed, steps,
	avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2,
	img2img_type = None,
	img = None):
	avg_diff = (avg_diff_x_1.cuda(), avg_diff_x_2.cuda())
	avg_diff_2nd = (avg_diff_y_1.cuda(), avg_diff_y_2.cuda())
	image = clip_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
	return image

	@spaces.GPU
	def invert_image(image, num_inversion_steps=50, skip=0.3):
	image = image.resize((512,512))
	init_latents,zs = clip_slider_inv.pipe.invert(
	source_prompt = "",
	image = image,
	num_inversion_steps = num_inversion_steps,
	skip = skip
	)
	print("post 5")
	return init_latents,zs

	def reset_do_inversion():
	return True
	css = '''
	#group {
	position: relative;
	width: 420px;
	height: 420px;
	margin-bottom: 20px;
	background-color: white
	}
	#x {
	position: absolute;
	bottom: 0;
	left: 25px;
	width: 400px;
	}
	#y {
	position: absolute;
	bottom: 20px;
	left: 67px;
	width: 400px;
	transform: rotate(-90deg);
	transform-origin: left bottom;
	}
	#image_out{position:absolute; width: 80%; right: 10px; top: 40px}
	'''
	with gr.Blocks(css=css) as demo:

	x_concept_1 = gr.State("")
	x_concept_2 = gr.State("")
	y_concept_1 = gr.State("")
	y_concept_2 = gr.State("")

	avg_diff_x_1 = gr.State()
	avg_diff_x_2 = gr.State()
	avg_diff_y_1 = gr.State()
	avg_diff_y_2 = gr.State()

	do_inversion = gr.State()
	init_latents = gr.State()
	zs = gr.State()

	with gr.Tab("text2image"):
	with gr.Row():
	with gr.Column():
	slider_x = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2)
	slider_y = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2)
	prompt = gr.Textbox(label="Prompt")
	submit = gr.Button("find directions")
	with gr.Column():
	with gr.Group(elem_id="group"):
	x = gr.Slider(minimum=-7, value=0, maximum=7, elem_id="x", interactive=False)
	y = gr.Slider(minimum=-7, value=0, maximum=7, elem_id="y", interactive=False)
	output_image = gr.Image(elem_id="image_out")
	with gr.Row():
	generate_butt = gr.Button("generate")

	with gr.Accordion(label="advanced options", open=False):
	iterations = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=400)
	steps = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
	guidance_scale = gr.Slider(
	label="Guidance scale",
	minimum=0.1,
	maximum=10.0,
	step=0.1,
	value=5,
	)
	seed = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True)


	with gr.Tab(label="image2image"):
	with gr.Row():
	with gr.Column():
	image = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512))
	slider_x_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2)
	slider_y_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2)
	img2img_type = gr.Radio(["controlnet canny", "ip adapter"], label="", info="")
	prompt_a = gr.Textbox(label="Prompt")
	submit_a = gr.Button("Submit")
	with gr.Column():
	with gr.Group(elem_id="group"):
	x_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="x", interactive=False)
	y_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False)
	output_image_a = gr.Image(elem_id="image_out")
	with gr.Row():
	generate_butt_a = gr.Button("generate")

	with gr.Accordion(label="advanced options", open=False):
	iterations_a = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=300)
	steps_a = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
	guidance_scale_a = gr.Slider(
	label="Guidance scale",
	minimum=0.1,
	maximum=10.0,
	step=0.1,
	value=5,
	)
	controlnet_conditioning_scale = gr.Slider(
	label="controlnet conditioning scale",
	minimum=0.5,
	maximum=5.0,
	step=0.1,
	value=0.7,
	)
	ip_adapter_scale = gr.Slider(
	label="ip adapter scale",
	minimum=0.5,
	maximum=5.0,
	step=0.1,
	value=0.8,
	)
	seed_a = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True)

	with gr.Tab(label="inversion"):
	with gr.Row():
	with gr.Column():
	image_inv = gr.Image(type="pil", image_mode = "RGB", height=512, width=512)
	slider_x_inv = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2)
	slider_y_inv = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2)
	prompt_inv = gr.Textbox(label="Prompt")
	img2img_type_inv = gr.Radio(["inversion"], label="",value="inversion", info="", visible=False)
	submit_inv = gr.Button("Submit")
	with gr.Column():
	with gr.Group(elem_id="group"):
	x_inv = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="x", interactive=False)
	y_inv = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False)
	output_image_inv = gr.Image(elem_id="image_out")
	with gr.Row():
	generate_butt_inv = gr.Button("generate")

	with gr.Accordion(label="advanced options", open=False):
	iterations_inv = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=300)
	steps_inv = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
	guidance_scale_inv = gr.Slider(
	label="Guidance scale",
	minimum=0.1,
	maximum=10.0,
	step=0.1,
	value=5,
	)
	# edit_threshold=None, edit_guidance_scale = None,
	# init_latents=None, zs=None
	edit_threshold = gr.Slider(
	label="edit threshold",
	minimum=0.01,
	maximum=0.99,
	step=0.1,
	value=0.3,
	)
	edit_guidance_scale = gr.Slider(
	label="edit guidance scale",
	minimum=0,
	maximum=20,
	step=0.25,
	value=5,
	)
	seed_inv = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True)

	submit.click(fn=generate,
	inputs=[slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2],
	outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image])

	image_inv.change(fn=reset_do_inversion, outputs=[do_inversion]).then(fn=invert_image, inputs=[image_inv], outputs=[init_latents,zs])
	submit_inv.click(fn=generate,
	inputs=[slider_x_inv, slider_y_inv, prompt_inv, seed_inv, iterations_inv, steps_inv, guidance_scale_inv, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, img2img_type_inv, image, controlnet_conditioning_scale, ip_adapter_scale ,edit_threshold, edit_guidance_scale, init_latents, zs],
	outputs=[x_inv, y_inv, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image_inv])

	generate_butt.click(fn=update_scales, inputs=[x,y, prompt, seed, steps, guidance_scale, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image])
	generate_butt_a.click(fn=update_scales, inputs=[x_a,y_a, prompt_a, seed_a, steps_a, guidance_scale_a, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], outputs=[output_image_a])
	generate_butt_inv.click(fn=update_scales, inputs=[x_inv,y_inv, prompt_inv, seed_inv, steps_inv, guidance_scale_inv, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, img2img_type_inv, image, controlnet_conditioning_scale, ip_adapter_scale ,edit_threshold, edit_guidance_scale, init_latents, zs], outputs=[output_image_inv])
	#x.change(fn=update_scales, inputs=[x,y, prompt, seed, steps, guidance_scale, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image])
	#y.change(fn=update_scales, inputs=[x,y, prompt, seed, steps, guidance_scale, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image])
	submit_a.click(fn=generate,
	inputs=[slider_x_a, slider_y_a, prompt_a, seed_a, iterations_a, steps_a, guidance_scale_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale],
	outputs=[x_a, y_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image_a])
	#x_a.change(fn=update_scales, inputs=[x_a,y_a, prompt_a, seed_a, steps_a, guidance_scale_a, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], outputs=[output_image_a])
	#y_a.change(fn=update_scales, inputs=[x_a,y_a, prompt, seed_a, steps_a, guidance_scale_a, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], outputs=[output_image_a])


	if __name__ == "__main__":
	demo.launch()