Update app.py

app.py

@@ -1,3 +1,1866 @@
+# 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,
+        avg_diff_2 = None,
+        correlation_weight_factor = 0.7,
+        scale=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 and avg_diff_2 is not None:
+                    #scale=3
+                    print("SHALOM neg")
+                    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[None, :].repeat(1,tokenizer.model_max_length, 1) * scale)
+                    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_2[None, :].repeat(1, tokenizer.model_max_length, 1) * scale)
+
+
+                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)]
+                
+                print("SHALOM???")
+                if avg_diff is not None and avg_diff_2 is not None:
+                    #scale=3
+                    print("SHALOM")
+                    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[None, :].repeat(1,tokenizer.model_max_length, 1) * scale)
+                    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_2[None, :].repeat(1, tokenizer.model_max_length, 1) * scale)
+
+                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)
+
+    @spaces.GPU
+    @torch.no_grad()
+    @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,
+        avg_diff_2 = None,
+        correlation_weight_factor = 0.7,
+        scale=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_2 = avg_diff_2,
+            correlation_weight_factor = correlation_weight_factor,
+            scale=scale,
+        )
+
+        # 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
+
+    @spaces.GPU
+    @torch.no_grad()
+    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_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
+    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
+
+
 import gradio as gr
 import spaces
 import torch
@@ -7,7 +1870,7 @@ import time
 import numpy as np
 import cv2
 from PIL import Image
-from ledits.pipeline_leditspp_stable_diffusion_xl import LEditsPPPipelineStableDiffusionXL
+#from ledits.pipeline_leditspp_stable_diffusion_xl import LEditsPPPipelineStableDiffusionXL
 
 def HWC3(x):
     assert x.dtype == np.uint8
@@ -162,7 +2025,7 @@ def update_y(x,y,prompt, seed, steps,
     return image
 
 @spaces.GPU
-def invert(image, num_inversion_steps=50, skip=0.3):
+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 = "", 
@@ -334,7 +2197,7 @@ with gr.Blocks(css=css) as demo:
                      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, inputs=[image_inv], outputs=[init_latents,zs])
+    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])