[Pipeline] Add LEDITS++ pipelines (#6074)

* Setup LEdits++ file structure

* Fix import

* LEditsPP Stable Diffusion pipeline

* Include variable image aspect ratios

* Implement LEDITS++ for SDXL

* clean up LEditsPPPipelineStableDiffusion

* Adjust inversion output

* Added docu, more cleanup for LEditsPPPipelineStableDiffusion

* clean up LEditsPPPipelineStableDiffusionXL

* Update documentation

* Fix documentation import

* Add skeleton IF implementation

* Fix documentation typo

* Add LEDTIS docu to toctree

* Add missing title

* Finalize SD documentation

* Finalize SD-XL documentation

* Fix code style and quality

* Fix typo

* Fix return types

* added LEditsPPPipelineIF; minor changes for LEditsPPPipelineStableDiffusion and LEditsPPPipelineStableDiffusionXL

* Fix copy reference

* add documentation for IF

* Add first tests

* Fix batching for SD-XL

* Fix text encoding and perfect reconstruction for SD-XL

* Add tests for SD-XL, minor changes

* move user_mask to correct device, use cross_attention_kwargs also for inversion

* Example docstring

* Fix attention resolution for non-square images

* Refactoring for PR review

* Safely remove ledits_utils.py

* Style fixes

* Replace assertions with ValueError

* Remove LEditsPPPipelineIF

* Remove unecessary input checks

* Refactoring of CrossAttnProcessor

* Revert unecessary changes to scheduler

* Remove first progress-bar in inversion

* Refactor scheduler usage and reset

* Use imageprocessor instead of custom logic

* Fix scheduler init warning

* Fix error when running the pipeline in fp16

* Update documentation wrt perfect inversion

* Update tests

* Fix code quality and copy consistency

* Update LEditsPP import

* Remove enable/disable methods that are now in StableDiffusionMixin

* Change import in docs

* Revert import structure change

* Fix ledits imports

---------
Co-authored-by: Katharina Kornmeier <katharina.kornmeier@stud.tu-darmstadt.de>

docs/source/en/_toctree.yml

@@ -304,6 +304,8 @@
       title: Latent Consistency Models
     - local: api/pipelines/latent_diffusion
       title: Latent Diffusion
+    - local: api/pipelines/ledits_pp
+      title: LEDITS++
     - local: api/pipelines/panorama
       title: MultiDiffusion
     - local: api/pipelines/musicldm

docs/source/en/api/pipelines/ledits_pp.md

+<!--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.
+-->
+
+# LEDITS++
+
+LEDITS++ was proposed in [LEDITS++: Limitless Image Editing using Text-to-Image Models](https://huggingface.co/papers/2311.16711) by Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinário Passos.
+
+The abstract from the paper is:
+
+*Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However, existing image-to-image methods are often inefficient, imprecise, and of limited versatility. They either require time-consuming fine-tuning, deviate unnecessarily strongly from the input image, and/or lack support for multiple, simultaneous edits. To address these issues, we introduce LEDITS++, an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second, our methodology supports multiple simultaneous edits and is architecture-agnostic. Third, we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods. The project page is available at https://leditsplusplus-project.static.hf.space .*
+
+<Tip>
+
+You can find additional information about LEDITS++ on the [project page](https://leditsplusplus-project.static.hf.space/index.html) and try it out in a [demo](https://huggingface.co/spaces/editing-images/leditsplusplus).
+
+</Tip>
+
+<Tip warning={true}>
+Due to some backward compatability issues with the current diffusers implementation of [`~schedulers.DPMSolverMultistepScheduler`] this implementation of LEdits++ can no longer guarantee perfect inversion. 
+This issue is unlikely to have any noticeable effects on applied use-cases. However, we provide an alternative implementation that guarantees perfect inversion in a dedicated [GitHub repo](https://github.com/ml-research/ledits_pp). 
+</Tip>
+
+We provide two distinct pipelines based on different pre-trained models. 
+
+## LEditsPPPipelineStableDiffusion
+[[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusion
+	- all
+	- __call__
+	- invert
+
+## LEditsPPPipelineStableDiffusionXL
+[[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL
+	- all
+	- __call__
+	- invert
+
+
+
+## LEditsPPDiffusionPipelineOutput
+[[autodoc]] pipelines.ledits_pp.pipeline_output.LEditsPPDiffusionPipelineOutput
+	- all
+
+## LEditsPPInversionPipelineOutput
+[[autodoc]] pipelines.ledits_pp.pipeline_output.LEditsPPInversionPipelineOutput
+	- all

docs/source/en/api/pipelines/overview.md

@@ -57,6 +57,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | [Latent Consistency Models](latent_consistency_models) | text2image |
 | [Latent Diffusion](latent_diffusion) | text2image, super-resolution |
 | [LDM3D](stable_diffusion/ldm3d_diffusion) | text2image, text-to-3D, text-to-pano, upscaling |
+| [LEDITS++](ledits_pp) | image editing |
 | [MultiDiffusion](panorama) | text2image |
 | [MusicLDM](musicldm) | text2audio |
 | [Paint by Example](paint_by_example) | inpainting |

docs/source/en/api/pipelines/semantic_stable_diffusion.md

@@ -30,6 +30,6 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 	- all
 	- __call__
 
-## StableDiffusionSafePipelineOutput
+## SemanticStableDiffusionPipelineOutput
 [[autodoc]] pipelines.semantic_stable_diffusion.pipeline_output.SemanticStableDiffusionPipelineOutput
 	- all

src/diffusers/__init__.py

@@ -253,6 +253,8 @@ else:
             "LatentConsistencyModelImg2ImgPipeline",
             "LatentConsistencyModelPipeline",
             "LDMTextToImagePipeline",
+            "LEditsPPPipelineStableDiffusion",
+            "LEditsPPPipelineStableDiffusionXL",
             "MusicLDMPipeline",
             "PaintByExamplePipeline",
             "PIAPipeline",
@@ -623,6 +625,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LatentConsistencyModelImg2ImgPipeline,
             LatentConsistencyModelPipeline,
             LDMTextToImagePipeline,
+            LEditsPPPipelineStableDiffusion,
+            LEditsPPPipelineStableDiffusionXL,
             MusicLDMPipeline,
             PaintByExamplePipeline,
             PIAPipeline,

src/diffusers/pipelines/__init__.py

@@ -23,6 +23,7 @@ _import_structure = {
     "controlnet_xs": [],
     "deprecated": [],
     "latent_diffusion": [],
+    "ledits_pp": [],
     "stable_diffusion": [],
     "stable_diffusion_xl": [],
 }
@@ -171,6 +172,12 @@ else:
         "LatentConsistencyModelPipeline",
     ]
     _import_structure["latent_diffusion"].extend(["LDMTextToImagePipeline"])
+    _import_structure["ledits_pp"].extend(
+        [
+            "LEditsPPPipelineStableDiffusion",
+            "LEditsPPPipelineStableDiffusionXL",
+        ]
+    )
     _import_structure["musicldm"] = ["MusicLDMPipeline"]
     _import_structure["paint_by_example"] = ["PaintByExamplePipeline"]
     _import_structure["pia"] = ["PIAPipeline"]
@@ -424,6 +431,12 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LatentConsistencyModelPipeline,
         )
         from .latent_diffusion import LDMTextToImagePipeline
+        from .ledits_pp import (
+            LEditsPPDiffusionPipelineOutput,
+            LEditsPPInversionPipelineOutput,
+            LEditsPPPipelineStableDiffusion,
+            LEditsPPPipelineStableDiffusionXL,
+        )
         from .musicldm import MusicLDMPipeline
         from .paint_by_example import PaintByExamplePipeline
         from .pia import PIAPipeline

src/diffusers/pipelines/ledits_pp/__init__.py

+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_leditspp_stable_diffusion"] = ["LEditsPPPipelineStableDiffusion"]
+    _import_structure["pipeline_leditspp_stable_diffusion_xl"] = ["LEditsPPPipelineStableDiffusionXL"]
+
+    _import_structure["pipeline_output"] = ["LEditsPPDiffusionPipelineOutput", "LEditsPPDiffusionPipelineOutput"]
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_leditspp_stable_diffusion import (
+            LEditsPPDiffusionPipelineOutput,
+            LEditsPPInversionPipelineOutput,
+            LEditsPPPipelineStableDiffusion,
+        )
+        from .pipeline_leditspp_stable_diffusion_xl import LEditsPPPipelineStableDiffusionXL
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py

+import inspect
+import math
+from itertools import repeat
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from ...configuration_utils import FrozenDict
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from ...models import AutoencoderKL, UNet2DConditionModel
+from ...models.attention_processor import Attention, AttnProcessor
+from ...models.lora import adjust_lora_scale_text_encoder
+from ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler
+from ...utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import PIL
+        >>> import requests
+        >>> import torch
+        >>> from io import BytesIO
+
+        >>> from diffusers import LEditsPPPipelineStableDiffusion
+
+        >>> pipe = LEditsPPPipelineStableDiffusion.from_pretrained(
+        ...     "runwayml/stable-diffusion-v1-5", 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/cherry_blossom.png"
+        >>> image = download_image(img_url)
+
+        >>> _ = pipe.invert(
+        ...     image = image,
+        ...     num_inversion_steps=50,
+        ...     skip=0.1
+        ... )
+
+        >>> edited_image = pipe(
+        ...     editing_prompt=["cherry blossom"],
+        ...     edit_guidance_scale=10.0,
+        ...     edit_threshold=0.75,
+        ).images[0]
+        ```
+"""
+
+
+# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.AttentionStore
+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")
+
+
+# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.GaussianSmoothing
+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))
+
+
+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
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+class LEditsPPPipelineStableDiffusion(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
+):
+    """
+    Pipeline for textual image editing using LEDits++ with Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass
+    documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular
+    device, etc.).
+
+    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 uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer ([`~transformers.CLIPTokenizer`]):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`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`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`~transformers.CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    model_cpu_offload_seq = "text_encoder->unet->vae"
+    _exclude_from_cpu_offload = ["safety_checker"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, DPMSolverMultistepScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler):
+            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."
+            )
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+        self.inversion_steps = None
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
+    def decode_latents(self, latents):
+        deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
+        deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
+
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents, return_dict=False)[0]
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, eta, 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
+
+    # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
+    def check_inputs(
+        self,
+        negative_prompt=None,
+        editing_prompt_embeddings=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if editing_prompt_embeddings is not None and negative_prompt_embeds is not None:
+            if editing_prompt_embeddings.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`editing_prompt_embeddings` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `editing_prompt_embeddings` {editing_prompt_embeddings.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents):
+        # shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+
+        # if latents.shape != shape:
+        #    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+
+        latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_unet(self, attention_store, PnP: bool = False):
+        attn_procs = {}
+        for name in self.unet.attn_processors.keys():
+            if name.startswith("mid_block"):
+                place_in_unet = "mid"
+            elif name.startswith("up_blocks"):
+                place_in_unet = "up"
+            elif name.startswith("down_blocks"):
+                place_in_unet = "down"
+            else:
+                continue
+
+            if "attn2" in name 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)
+
+    def encode_prompt(
+        self,
+        device,
+        num_images_per_prompt,
+        enable_edit_guidance,
+        negative_prompt=None,
+        editing_prompt=None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        editing_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            enable_edit_guidance (`bool`):
+                whether to perform any editing or reconstruct the input image instead
+            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`).
+            editing_prompt (`str` or `List[str]`, *optional*):
+                Editing prompt(s) to be encoded. If not defined, one has to pass
+                `editing_prompt_embeds` instead.
+            editing_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            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.
+        """
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if not USE_PEFT_BACKEND:
+                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+            else:
+                scale_lora_layers(self.text_encoder, lora_scale)
+
+        batch_size = self.batch_size
+        num_edit_tokens = None
+
+        if negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but exoected"
+                    f"{batch_size} based on the input images. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = negative_prompt_embeds.dtype
+
+        negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+        if enable_edit_guidance:
+            if editing_prompt_embeds is None:
+                # textual inversion: procecss multi-vector tokens if necessary
+                # if isinstance(self, TextualInversionLoaderMixin):
+                #    prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+                if isinstance(editing_prompt, str):
+                    editing_prompt = [editing_prompt]
+
+                max_length = negative_prompt_embeds.shape[1]
+                text_inputs = self.tokenizer(
+                    [x for item in editing_prompt for x in repeat(item, batch_size)],
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                    return_length=True,
+                )
+
+                num_edit_tokens = text_inputs.length - 2  # not counting startoftext and endoftext
+                text_input_ids = text_inputs.input_ids
+                untruncated_ids = self.tokenizer(
+                    [x for item in editing_prompt for x in repeat(item, batch_size)],
+                    padding="longest",
+                    return_tensors="pt",
+                ).input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = self.tokenizer.batch_decode(
+                        untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                    )
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                if (
+                    hasattr(self.text_encoder.config, "use_attention_mask")
+                    and self.text_encoder.config.use_attention_mask
+                ):
+                    attention_mask = text_inputs.attention_mask.to(device)
+                else:
+                    attention_mask = None
+
+                if clip_skip is None:
+                    editing_prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
+                    editing_prompt_embeds = editing_prompt_embeds[0]
+                else:
+                    editing_prompt_embeds = self.text_encoder(
+                        text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
+                    )
+                    # Access the `hidden_states` first, that contains a tuple of
+                    # all the hidden states from the encoder layers. Then index into
+                    # the tuple to access the hidden states from the desired layer.
+                    editing_prompt_embeds = editing_prompt_embeds[-1][-(clip_skip + 1)]
+                    # We also need to apply the final LayerNorm here to not mess with the
+                    # representations. The `last_hidden_states` that we typically use for
+                    # obtaining the final prompt representations passes through the LayerNorm
+                    # layer.
+                    editing_prompt_embeds = self.text_encoder.text_model.final_layer_norm(editing_prompt_embeds)
+
+            editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device)
+
+            bs_embed_edit, seq_len, _ = editing_prompt_embeds.shape
+            editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device)
+            editing_prompt_embeds = editing_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            editing_prompt_embeds = editing_prompt_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+
+        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+        seq_len = negative_prompt_embeds.shape[1]
+
+        negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_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 isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(self.text_encoder, lora_scale)
+
+        return editing_prompt_embeds, negative_prompt_embeds, num_edit_tokens
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        editing_prompt: Optional[Union[str, List[str]]] = None,
+        editing_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = 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,
+        user_mask: Optional[torch.FloatTensor] = None,
+        sem_guidance: Optional[List[torch.Tensor]] = None,
+        use_cross_attn_mask: bool = False,
+        use_intersect_mask: bool = True,
+        attn_store_steps: Optional[List[int]] = [],
+        store_averaged_over_steps: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        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"],
+        **kwargs,
+    ):
+        r"""
+        The call function to the pipeline for editing. The [`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.invert`]
+        method has to be called beforehand. Edits will always be performed for the last inverted image(s).
+
+        Args:
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            generator (`torch.Generator`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            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.ledits_pp.LEditsPPDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            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_embeds (`torch.Tensor>`, *optional*):
+                Pre-computed embeddings to use for guiding the image generation. Guidance direction of embedding should be
+                specified via `reverse_editing_direction`.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_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 will not be applied.
+            edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
+                Number of diffusion steps (for each prompt) after which guidance will no longer be 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).
+            user_mask (`torch.FloatTensor`, *optional*):
+                User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s implicit
+                masks do not meet user preferences.
+            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 (`bool`, defaults to `False`):
+                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 (`bool`, defaults to `True`):
+                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).
+            attn_store_steps (`List[int]`, *optional*):
+                Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes.
+            store_averaged_over_steps (`bool`, defaults to `True`):
+                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.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Guidance rescale factor from [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.
+            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, and the
+            second element is a list of `bool`s denoting whether the corresponding generated image likely represents
+            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
+        """
+
+        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
+
+        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)
+
+        org_prompt = ""
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            negative_prompt,
+            editing_prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+
+        # 2. Define call parameters
+        batch_size = self.batch_size
+
+        if editing_prompt:
+            enable_edit_guidance = True
+            if isinstance(editing_prompt, str):
+                editing_prompt = [editing_prompt]
+            self.enabled_editing_prompts = len(editing_prompt)
+        elif editing_prompt_embeds is not None:
+            enable_edit_guidance = True
+            self.enabled_editing_prompts = editing_prompt_embeds.shape[0]
+        else:
+            self.enabled_editing_prompts = 0
+            enable_edit_guidance = False
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+
+        edit_concepts, uncond_embeddings, num_edit_tokens = self.encode_prompt(
+            editing_prompt=editing_prompt,
+            device=self.device,
+            num_images_per_prompt=num_images_per_prompt,
+            enable_edit_guidance=enable_edit_guidance,
+            negative_prompt=negative_prompt,
+            editing_prompt_embeds=editing_prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # For classifier free guidance, we need to do two forward passes.
+        # Here we concatenate the unconditional and text embeddings into a single batch
+        # to avoid doing two forward passes
+        if enable_edit_guidance:
+            text_embeddings = torch.cat([uncond_embeddings, edit_concepts])
+            self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt
+        else:
+            text_embeddings = torch.cat([uncond_embeddings])
+
+        # 4. Prepare timesteps
+        # self.scheduler.set_timesteps(num_inference_steps, device=self.device)
+        timesteps = self.inversion_steps
+        t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs.shape[0] :])}
+
+        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, PnP=False)
+            resolution = latents.shape[-2:]
+            att_res = (int(resolution[0] / 4), int(resolution[1] / 4))
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            None,
+            None,
+            text_embeddings.dtype,
+            self.device,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
+
+        self.sem_guidance = None
+        self.activation_mask = None
+
+        # 7. Denoising loop
+        num_warmup_steps = 0
+        with self.progress_bar(total=len(timesteps)) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+
+                if enable_edit_guidance:
+                    latent_model_input = torch.cat([latents] * (1 + self.enabled_editing_prompts))
+                else:
+                    latent_model_input = latents
+
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                text_embed_input = text_embeddings
+
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embed_input).sample
+
+                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), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
+                        )
+
+                    if self.sem_guidance is None:
+                        self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape))
+
+                    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:
+                            if t <= 800:
+                                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
+
+                            else:
+                                # print(f"only attention mask for step {i}")
+                                noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_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
+
+                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
+                ).prev_sample
+
+                # 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)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        # 8. Post-processing
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
+                0
+            ]
+            image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    @torch.no_grad()
+    def invert(
+        self,
+        image: PipelineImageInput,
+        source_prompt: str = "",
+        source_guidance_scale: float = 3.5,
+        num_inversion_steps: int = 30,
+        skip: float = 0.15,
+        generator: Optional[torch.Generator] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        clip_skip: Optional[int] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        resize_mode: Optional[str] = "default",
+        crops_coords: Optional[Tuple[int, int, int, int]] = 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.
+            num_inversion_steps (`int`, defaults to `30`):
+                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.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            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.
+            height (`int`, *optional*, defaults to `None`):
+                The height in preprocessed image. If `None`, will use the `get_default_height_width()` to get default height.
+            width (`int`, *optional*`, defaults to `None`):
+                The width in preprocessed. If `None`, will use  get_default_height_width()` to get the default width.
+            resize_mode (`str`, *optional*, defaults to `default`):
+                The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit
+                within the specified width and height, and it may not maintaining the original aspect ratio.
+                If `fill`, will resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image
+                within the dimensions, filling empty with data from image.
+                If `crop`, will resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image
+                within the dimensions, cropping the excess.
+                Note that resize_mode `fill` and `crop` are only supported for PIL image input.
+            crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`):
+                The crop coordinates for each image in the batch. If `None`, will not crop the image.
+
+        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
+
+        # 1. encode image
+        x0, resized = self.encode_image(
+            image,
+            dtype=self.text_encoder.dtype,
+            height=height,
+            width=width,
+            resize_mode=resize_mode,
+            crops_coords=crops_coords,
+        )
+        self.batch_size = x0.shape[0]
+
+        # autoencoder reconstruction
+        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")
+
+        # 2. get embeddings
+        do_classifier_free_guidance = source_guidance_scale > 1.0
+
+        lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+
+        uncond_embedding, text_embeddings, _ = self.encode_prompt(
+            num_images_per_prompt=1,
+            device=self.device,
+            negative_prompt=None,
+            enable_edit_guidance=do_classifier_free_guidance,
+            editing_prompt=source_prompt,
+            lora_scale=lora_scale,
+            clip_skip=clip_skip,
+        )
+
+        # 3. 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=uncond_embedding.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, torch.Tensor([t]))
+        xts = torch.cat([x0.unsqueeze(0), xts], dim=0)
+
+        self.scheduler.set_timesteps(len(self.scheduler.timesteps))
+        # noise maps
+        zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype)
+
+        with self.progress_bar(total=len(timesteps)) as progress_bar:
+            for t in timesteps:
+                idx = num_inversion_steps - t_to_idx[int(t)] - 1
+                # 1. predict noise residual
+                xt = xts[idx + 1]
+
+                noise_pred = self.unet(xt, timestep=t, encoder_hidden_states=uncond_embedding).sample
+
+                if not source_prompt == "":
+                    noise_pred_cond = self.unet(xt, timestep=t, encoder_hidden_states=text_embeddings).sample
+                    noise_pred = noise_pred + source_guidance_scale * (noise_pred_cond - noise_pred)
+
+                xtm1 = xts[idx]
+                z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta)
+                zs[idx] = z
+
+                # correction to avoid error accumulation
+                xts[idx] = xtm1_corrected
+
+                progress_bar.update()
+
+        self.init_latents = xts[-1].expand(self.batch_size, -1, -1, -1)
+        zs = zs.flip(0)
+        self.zs = zs
+
+        return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec)
+
+    @torch.no_grad()
+    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(dtype)
+
+        x0 = self.vae.encode(image.to(self.device)).latent_dist.mode()
+        x0 = x0.to(dtype)
+        x0 = self.vae.config.scaling_factor * x0
+        return x0, resized
+
+
+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
+
+
+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
+
+
+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

src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py

+# 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 ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...loaders import (
+    FromSingleFileMixin,
+    IPAdapterMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from ...models import AutoencoderKL, UNet2DConditionModel
+from ...models.attention_processor import (
+    Attention,
+    AttnProcessor,
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from ...models.lora import adjust_lora_scale_text_encoder
+from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler
+from ...utils import (
+    USE_PEFT_BACKEND,
+    is_invisible_watermark_available,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput
+
+
+if is_invisible_watermark_available():
+    from ..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.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = 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.FloatTensor] = None,
+        editing_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ) -> 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.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            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.FloatTensor`, *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.FloatTensor`, *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]
+
+            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",
+                )
+
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+            if 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 = []
+
+            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
+
+                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)]
+
+                edit_prompt_embeds_list.append(edit_concepts_embeds)
+
+            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,
+                LoRAXFormersAttnProcessor,
+                LoRAAttnProcessor2_0,
+            ),
+        )
+        # if xformers or torch_2_0 is used attention block does not need
+        # to be in float32 which can save lots of memory
+        if use_torch_2_0_or_xformers:
+            self.vae.post_quant_conv.to(dtype)
+            self.vae.decoder.conv_in.to(dtype)
+            self.vae.decoder.mid_block.to(dtype)
+
+    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+        """
+        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+        Args:
+            timesteps (`torch.Tensor`):
+                generate embedding vectors at these timesteps
+            embedding_dim (`int`, *optional*, defaults to 512):
+                dimension of the embeddings to generate
+            dtype:
+                data type of the generated embeddings
+
+        Returns:
+            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if embedding_dim % 2 == 1:  # zero pad
+            emb = torch.nn.functional.pad(emb, (0, 1))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def denoising_end(self):
+        return self._denoising_end
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.prepare_unet
+    def prepare_unet(self, attention_store, PnP: bool = False):
+        attn_procs = {}
+        for name in self.unet.attn_processors.keys():
+            if name.startswith("mid_block"):
+                place_in_unet = "mid"
+            elif name.startswith("up_blocks"):
+                place_in_unet = "up"
+            elif name.startswith("down_blocks"):
+                place_in_unet = "down"
+            else:
+                continue
+
+            if "attn2" in name and place_in_unet != "mid":
+                attn_procs[name] = LEDITSCrossAttnProcessor(
+                    attention_store=attention_store,
+                    place_in_unet=place_in_unet,
+                    pnp=PnP,
+                    editing_prompts=self.enabled_editing_prompts,
+                )
+            else:
+                attn_procs[name] = AttnProcessor()
+
+        self.unet.set_attn_processor(attn_procs)
+
+    @torch.no_grad()
+    @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.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = 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.FloatTensor] = 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"],
+        **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.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            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.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.7):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            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
+
+        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
+
+        # 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,
+        )
+
+        # 4. Prepare timesteps
+        # self.scheduler.set_timesteps(num_inference_steps, device=device)
+
+        timesteps = self.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()
+            image = image.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+        x0 = self.vae.encode(image).latent_dist.mode()
+        x0 = x0.to(dtype)
+        # cast back to fp16 if needed
+        if needs_upcasting:
+            self.vae.to(dtype=torch.float16)
+
+        x0 = self.vae.config.scaling_factor * x0
+        return x0, resized
+
+    @torch.no_grad()
+    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)
+
+
+# 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

src/diffusers/pipelines/ledits_pp/pipeline_output.py

+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import numpy as np
+import PIL.Image
+
+from ...utils import BaseOutput
+
+
+@dataclass
+class LEditsPPDiffusionPipelineOutput(BaseOutput):
+    """
+    Output class for LEdits++ Diffusion pipelines.
+
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
+            num_channels)`.
+        nsfw_content_detected (`List[bool]`)
+            List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
+            `None` if safety checking could not be performed.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]
+    nsfw_content_detected: Optional[List[bool]]
+
+
+@dataclass
+class LEditsPPInversionPipelineOutput(BaseOutput):
+    """
+    Output class for LEdits++ Diffusion pipelines.
+
+    Args:
+        input_images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of the cropped and resized input images as PIL images of length `batch_size` or NumPy array of shape `
+            (batch_size, height, width, num_channels)`.
+        vae_reconstruction_images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of VAE reconstruction of all input images as PIL images of length `batch_size` or NumPy array of shape `
+            (batch_size, height, width, num_channels)`.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]
+    vae_reconstruction_images: Union[List[PIL.Image.Image], np.ndarray]

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -899,6 +899,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         timestep: int,
         sample: torch.FloatTensor,
         generator=None,
+        variance_noise: Optional[torch.FloatTensor] = None,
         return_dict: bool = True,
     ) -> Union[SchedulerOutput, Tuple]:
         """
@@ -914,6 +915,9 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
                 A current instance of a sample created by the diffusion process.
             generator (`torch.Generator`, *optional*):
                 A random number generator.
+            variance_noise (`torch.FloatTensor`):
+                Alternative to generating noise with `generator` by directly providing the noise for the variance
+                itself. Useful for methods such as [`LEdits++`].
             return_dict (`bool`):
                 Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
 
@@ -948,11 +952,12 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
 
         # Upcast to avoid precision issues when computing prev_sample
         sample = sample.to(torch.float32)
-
-        if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
+        if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"] and variance_noise is None:
             noise = randn_tensor(
                 model_output.shape, generator=generator, device=model_output.device, dtype=torch.float32
             )
+        elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
+            noise = variance_noise.to(device=model_output.device, dtype=torch.float32)
         else:
             noise = None
 

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -792,6 +792,7 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
         timestep: int,
         sample: torch.FloatTensor,
         generator=None,
+        variance_noise: Optional[torch.FloatTensor] = None,
         return_dict: bool = True,
     ) -> Union[SchedulerOutput, Tuple]:
         """
@@ -807,6 +808,9 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
                 A current instance of a sample created by the diffusion process.
             generator (`torch.Generator`, *optional*):
                 A random number generator.
+            variance_noise (`torch.FloatTensor`):
+                Alternative to generating noise with `generator` by directly providing the noise for the variance
+                itself. Useful for methods such as [`CycleDiffusion`].
             return_dict (`bool`):
                 Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
 
@@ -837,10 +841,12 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
             self.model_outputs[i] = self.model_outputs[i + 1]
         self.model_outputs[-1] = model_output
 
-        if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
+        if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"] and variance_noise is None:
             noise = randn_tensor(
                 model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype
             )
+        elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
+            noise = variance_noise
         else:
             noise = None
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -647,6 +647,36 @@ class LDMTextToImagePipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class LEditsPPPipelineStableDiffusion(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
+class LEditsPPPipelineStableDiffusionXL(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class MusicLDMPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py

+# coding=utf-8
+# Copyright 2023 HuggingFace Inc.
+#
+# 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 gc
+import random
+import unittest
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    DPMSolverMultistepScheduler,
+    LEditsPPPipelineStableDiffusion,
+    UNet2DConditionModel,
+)
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    floats_tensor,
+    load_image,
+    require_torch_gpu,
+    skip_mps,
+    slow,
+    torch_device,
+)
+
+
+enable_full_determinism()
+
+
+@skip_mps
+class LEditsPPPipelineStableDiffusionFastTests(unittest.TestCase):
+    pipeline_class = LEditsPPPipelineStableDiffusion
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(32, 64, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
+            cross_attention_dim=32,
+        )
+        scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++", solver_order=2)
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+        )
+        torch.manual_seed(0)
+        text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+        )
+        text_encoder = CLIPTextModel(text_encoder_config)
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        components = {
+            "unet": unet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "safety_checker": None,
+            "feature_extractor": None,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+        inputs = {
+            "generator": generator,
+            "editing_prompt": ["wearing glasses", "sunshine"],
+            "reverse_editing_direction": [False, True],
+            "edit_guidance_scale": [10.0, 5.0],
+        }
+        return inputs
+
+    def get_dummy_inversion_inputs(self, device, seed=0):
+        images = floats_tensor((2, 3, 32, 32), rng=random.Random(0)).cpu().permute(0, 2, 3, 1)
+        images = 255 * images
+        image_1 = Image.fromarray(np.uint8(images[0])).convert("RGB")
+        image_2 = Image.fromarray(np.uint8(images[1])).convert("RGB")
+
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        inputs = {
+            "image": [image_1, image_2],
+            "source_prompt": "",
+            "source_guidance_scale": 3.5,
+            "num_inversion_steps": 20,
+            "skip": 0.15,
+            "generator": generator,
+        }
+        return inputs
+
+    def test_ledits_pp_inversion(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = LEditsPPPipelineStableDiffusion(**components)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inversion_inputs(device)
+        inputs["image"] = inputs["image"][0]
+        sd_pipe.invert(**inputs)
+        assert sd_pipe.init_latents.shape == (
+            1,
+            4,
+            int(32 / sd_pipe.vae_scale_factor),
+            int(32 / sd_pipe.vae_scale_factor),
+        )
+
+        latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device)
+        print(latent_slice.flatten())
+        expected_slice = np.array([-0.9084, -0.0367, 0.2940, 0.0839, 0.6890, 0.2651, -0.7104, 2.1090, -0.7822])
+        assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3
+
+    def test_ledits_pp_inversion_batch(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = LEditsPPPipelineStableDiffusion(**components)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inversion_inputs(device)
+        sd_pipe.invert(**inputs)
+        assert sd_pipe.init_latents.shape == (
+            2,
+            4,
+            int(32 / sd_pipe.vae_scale_factor),
+            int(32 / sd_pipe.vae_scale_factor),
+        )
+
+        latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device)
+        print(latent_slice.flatten())
+        expected_slice = np.array([0.2528, 0.1458, -0.2166, 0.4565, -0.5657, -1.0286, -0.9961, 0.5933, 1.1173])
+        assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3
+
+        latent_slice = sd_pipe.init_latents[1, -1, -3:, -3:].to(device)
+        print(latent_slice.flatten())
+        expected_slice = np.array([-0.0796, 2.0583, 0.5501, 0.5358, 0.0282, -0.2803, -1.0470, 0.7023, -0.0072])
+        assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3
+
+    def test_ledits_pp_warmup_steps(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        pipe = LEditsPPPipelineStableDiffusion(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inversion_inputs = self.get_dummy_inversion_inputs(device)
+        pipe.invert(**inversion_inputs)
+
+        inputs = self.get_dummy_inputs(device)
+
+        inputs["edit_warmup_steps"] = [0, 5]
+        pipe(**inputs).images
+
+        inputs["edit_warmup_steps"] = [5, 0]
+        pipe(**inputs).images
+
+        inputs["edit_warmup_steps"] = [5, 10]
+        pipe(**inputs).images
+
+        inputs["edit_warmup_steps"] = [10, 5]
+        pipe(**inputs).images
+
+
+@slow
+@require_torch_gpu
+class LEditsPPPipelineStableDiffusionSlowTests(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    @classmethod
+    def setUpClass(cls):
+        raw_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png"
+        )
+        raw_image = raw_image.convert("RGB").resize((512, 512))
+        cls.raw_image = raw_image
+
+    def test_ledits_pp_editing(self):
+        pipe = LEditsPPPipelineStableDiffusion.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", safety_checker=None, torch_dtype=torch.float16
+        )
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        generator = torch.manual_seed(0)
+        _ = pipe.invert(image=self.raw_image, generator=generator)
+        generator = torch.manual_seed(0)
+        inputs = {
+            "generator": generator,
+            "editing_prompt": ["cat", "dog"],
+            "reverse_editing_direction": [True, False],
+            "edit_guidance_scale": [5.0, 5.0],
+            "edit_threshold": [0.8, 0.8],
+        }
+        reconstruction = pipe(**inputs, output_type="np").images[0]
+
+        output_slice = reconstruction[150:153, 140:143, -1]
+        output_slice = output_slice.flatten()
+        expected_slice = np.array(
+            [0.9453125, 0.93310547, 0.84521484, 0.94628906, 0.9111328, 0.80859375, 0.93847656, 0.9042969, 0.8144531]
+        )
+        assert np.abs(output_slice - expected_slice).max() < 1e-2

tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion_xl.py

+# coding=utf-8
+# Copyright 2023 HuggingFace Inc.
+#
+# 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 random
+import unittest
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextConfig,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionConfig,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers import (
+    AutoencoderKL,
+    DPMSolverMultistepScheduler,
+    LEditsPPPipelineStableDiffusionXL,
+    UNet2DConditionModel,
+)
+
+# from diffusers.image_processor import VaeImageProcessor
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    floats_tensor,
+    load_image,
+    require_torch_gpu,
+    skip_mps,
+    slow,
+    torch_device,
+)
+
+
+enable_full_determinism()
+
+
+@skip_mps
+class LEditsPPPipelineStableDiffusionXLFastTests(unittest.TestCase):
+    pipeline_class = LEditsPPPipelineStableDiffusionXL
+
+    def get_dummy_components(self, skip_first_text_encoder=False, time_cond_proj_dim=None):
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            time_cond_proj_dim=time_cond_proj_dim,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            # SD2-specific config below
+            attention_head_dim=(2, 4),
+            use_linear_projection=True,
+            addition_embed_type="text_time",
+            addition_time_embed_dim=8,
+            transformer_layers_per_block=(1, 2),
+            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
+            cross_attention_dim=64 if not skip_first_text_encoder else 32,
+        )
+        scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++", solver_order=2)
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+            sample_size=128,
+        )
+        torch.manual_seed(0)
+        image_encoder_config = CLIPVisionConfig(
+            hidden_size=32,
+            image_size=224,
+            projection_dim=32,
+            intermediate_size=37,
+            num_attention_heads=4,
+            num_channels=3,
+            num_hidden_layers=5,
+            patch_size=14,
+        )
+        image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
+
+        feature_extractor = CLIPImageProcessor(
+            crop_size=224,
+            do_center_crop=True,
+            do_normalize=True,
+            do_resize=True,
+            image_mean=[0.48145466, 0.4578275, 0.40821073],
+            image_std=[0.26862954, 0.26130258, 0.27577711],
+            resample=3,
+            size=224,
+        )
+
+        torch.manual_seed(0)
+        text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+            # SD2-specific config below
+            hidden_act="gelu",
+            projection_dim=32,
+        )
+        text_encoder = CLIPTextModel(text_encoder_config)
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
+        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        components = {
+            "unet": unet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "text_encoder": text_encoder if not skip_first_text_encoder else None,
+            "tokenizer": tokenizer if not skip_first_text_encoder else None,
+            "text_encoder_2": text_encoder_2,
+            "tokenizer_2": tokenizer_2,
+            "image_encoder": image_encoder,
+            "feature_extractor": feature_extractor,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+        inputs = {
+            "generator": generator,
+            "editing_prompt": ["wearing glasses", "sunshine"],
+            "reverse_editing_direction": [False, True],
+            "edit_guidance_scale": [10.0, 5.0],
+        }
+        return inputs
+
+    def get_dummy_inversion_inputs(self, device, seed=0):
+        images = floats_tensor((2, 3, 32, 32), rng=random.Random(0)).cpu().permute(0, 2, 3, 1)
+        images = 255 * images
+        image_1 = Image.fromarray(np.uint8(images[0])).convert("RGB")
+        image_2 = Image.fromarray(np.uint8(images[1])).convert("RGB")
+
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        inputs = {
+            "image": [image_1, image_2],
+            "source_prompt": "",
+            "source_guidance_scale": 3.5,
+            "num_inversion_steps": 20,
+            "skip": 0.15,
+            "generator": generator,
+        }
+        return inputs
+
+    def test_ledits_pp_inversion(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = LEditsPPPipelineStableDiffusionXL(**components)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inversion_inputs(device)
+        inputs["image"] = inputs["image"][0]
+        sd_pipe.invert(**inputs)
+        assert sd_pipe.init_latents.shape == (
+            1,
+            4,
+            int(32 / sd_pipe.vae_scale_factor),
+            int(32 / sd_pipe.vae_scale_factor),
+        )
+
+        latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device)
+        expected_slice = np.array([-0.9084, -0.0367, 0.2940, 0.0839, 0.6890, 0.2651, -0.7103, 2.1090, -0.7821])
+        assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3
+
+    def test_ledits_pp_inversion_batch(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = LEditsPPPipelineStableDiffusionXL(**components)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inversion_inputs(device)
+        sd_pipe.invert(**inputs)
+        assert sd_pipe.init_latents.shape == (
+            2,
+            4,
+            int(32 / sd_pipe.vae_scale_factor),
+            int(32 / sd_pipe.vae_scale_factor),
+        )
+
+        latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device)
+        print(latent_slice.flatten())
+        expected_slice = np.array([0.2528, 0.1458, -0.2166, 0.4565, -0.5656, -1.0286, -0.9961, 0.5933, 1.1172])
+        assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3
+
+        latent_slice = sd_pipe.init_latents[1, -1, -3:, -3:].to(device)
+        print(latent_slice.flatten())
+        expected_slice = np.array([-0.0796, 2.0583, 0.5500, 0.5358, 0.0282, -0.2803, -1.0470, 0.7024, -0.0072])
+        print(latent_slice.flatten())
+        assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3
+
+    def test_ledits_pp_warmup_steps(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        pipe = LEditsPPPipelineStableDiffusionXL(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inversion_inputs = self.get_dummy_inversion_inputs(device)
+        inversion_inputs["image"] = inversion_inputs["image"][0]
+        pipe.invert(**inversion_inputs)
+
+        inputs = self.get_dummy_inputs(device)
+
+        inputs["edit_warmup_steps"] = [0, 5]
+        pipe(**inputs).images
+
+        inputs["edit_warmup_steps"] = [5, 0]
+        pipe(**inputs).images
+
+        inputs["edit_warmup_steps"] = [5, 10]
+        pipe(**inputs).images
+
+        inputs["edit_warmup_steps"] = [10, 5]
+        pipe(**inputs).images
+
+
+@slow
+@require_torch_gpu
+class LEditsPPPipelineStableDiffusionXLSlowTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        raw_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png"
+        )
+        raw_image = raw_image.convert("RGB").resize((512, 512))
+        cls.raw_image = raw_image
+
+    def test_ledits_pp_edit(self):
+        pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", safety_checker=None, add_watermarker=None
+        )
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        generator = torch.manual_seed(0)
+        _ = pipe.invert(image=self.raw_image, generator=generator, num_zero_noise_steps=0)
+        inputs = {
+            "generator": generator,
+            "editing_prompt": ["cat", "dog"],
+            "reverse_editing_direction": [True, False],
+            "edit_guidance_scale": [2.0, 4.0],
+            "edit_threshold": [0.8, 0.8],
+        }
+        reconstruction = pipe(**inputs, output_type="np").images[0]
+
+        output_slice = reconstruction[150:153, 140:143, -1]
+        output_slice = output_slice.flatten()
+        expected_slice = np.array(
+            [0.56419, 0.44121838, 0.2765603, 0.5708484, 0.42763475, 0.30945742, 0.5387106, 0.4735807, 0.3547244]
+        )
+        assert np.abs(output_slice - expected_slice).max() < 1e-3