# Copyright 2023 DiffEdit Authors and Pix2Pix Zero Authors and 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 warnings
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import numpy as np
import PIL
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import DDIMInverseScheduler, KarrasDiffusionSchedulers
from ...utils import (
    PIL_INTERPOLATION,
    BaseOutput,
    deprecate,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker


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


@dataclass
class DiffEditInversionPipelineOutput(BaseOutput):
    """
    Output class for Stable Diffusion pipelines.

    Args:
        latents (`torch.FloatTensor`)
            inverted latents tensor
        images (`List[PIL.Image.Image]` or `np.ndarray`)
            List of denoised PIL images of length `num_timesteps * batch_size` or numpy array of shape `(num_timesteps,
            batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the
            diffusion pipeline.
    """

    latents: torch.FloatTensor
    images: Union[List[PIL.Image.Image], np.ndarray]


EXAMPLE_DOC_STRING = """

        ```py
        >>> import PIL
        >>> import requests
        >>> import torch
        >>> from io import BytesIO

        >>> from diffusers import StableDiffusionDiffEditPipeline


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


        >>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"

        >>> init_image = download_image(img_url).resize((768, 768))

        >>> pipe = StableDiffusionDiffEditPipeline.from_pretrained(
        ...     "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
        >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
        >>> pipeline.enable_model_cpu_offload()

        >>> mask_prompt = "A bowl of fruits"
        >>> prompt = "A bowl of pears"

        >>> mask_image = pipe.generate_mask(image=init_image, source_prompt=prompt, target_prompt=mask_prompt)
        >>> image_latents = pipe.invert(image=init_image, prompt=mask_prompt).latents
        >>> image = pipe(prompt=prompt, mask_image=mask_image, image_latents=image_latents).images[0]
        ```
"""

EXAMPLE_INVERT_DOC_STRING = """
        ```py
        >>> import PIL
        >>> import requests
        >>> import torch
        >>> from io import BytesIO

        >>> from diffusers import StableDiffusionDiffEditPipeline


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


        >>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"

        >>> init_image = download_image(img_url).resize((768, 768))

        >>> pipe = StableDiffusionDiffEditPipeline.from_pretrained(
        ...     "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
        >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
        >>> pipeline.enable_model_cpu_offload()

        >>> prompt = "A bowl of fruits"

        >>> inverted_latents = pipe.invert(image=init_image, prompt=prompt).latents
        ```
"""


def auto_corr_loss(hidden_states, generator=None):
    reg_loss = 0.0
    for i in range(hidden_states.shape[0]):
        for j in range(hidden_states.shape[1]):
            noise = hidden_states[i : i + 1, j : j + 1, :, :]
            while True:
                roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item()
                reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2
                reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2

                if noise.shape[2] <= 8:
                    break
                noise = torch.nn.functional.avg_pool2d(noise, kernel_size=2)
    return reg_loss


def kl_divergence(hidden_states):
    return hidden_states.var() + hidden_states.mean() ** 2 - 1 - torch.log(hidden_states.var() + 1e-7)


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
    warnings.warn(
        "The preprocess method is deprecated and will be removed in a future version. Please"
        " use VaeImageProcessor.preprocess instead",
        FutureWarning,
    )
    if isinstance(image, torch.Tensor):
        return image
    elif isinstance(image, PIL.Image.Image):
        image = [image]

    if isinstance(image[0], PIL.Image.Image):
        w, h = image[0].size
        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
        image = np.concatenate(image, axis=0)
        image = np.array(image).astype(np.float32) / 255.0
        image = image.transpose(0, 3, 1, 2)
        image = 2.0 * image - 1.0
        image = torch.from_numpy(image)
    elif isinstance(image[0], torch.Tensor):
        image = torch.cat(image, dim=0)
    return image


def preprocess_mask(mask, batch_size: int = 1):
    if not isinstance(mask, torch.Tensor):
        # preprocess mask
        if isinstance(mask, PIL.Image.Image) or isinstance(mask, np.ndarray):
            mask = [mask]

        if isinstance(mask, list):
            if isinstance(mask[0], PIL.Image.Image):
                mask = [np.array(m.convert("L")).astype(np.float32) / 255.0 for m in mask]
            if isinstance(mask[0], np.ndarray):
                mask = np.stack(mask, axis=0) if mask[0].ndim < 3 else np.concatenate(mask, axis=0)
                mask = torch.from_numpy(mask)
            elif isinstance(mask[0], torch.Tensor):
                mask = torch.stack(mask, dim=0) if mask[0].ndim < 3 else torch.cat(mask, dim=0)

    # Batch and add channel dim for single mask
    if mask.ndim == 2:
        mask = mask.unsqueeze(0).unsqueeze(0)

    # Batch single mask or add channel dim
    if mask.ndim == 3:
        # Single batched mask, no channel dim or single mask not batched but channel dim
        if mask.shape[0] == 1:
            mask = mask.unsqueeze(0)

        # Batched masks no channel dim
        else:
            mask = mask.unsqueeze(1)

    # Check mask shape
    if batch_size > 1:
        if mask.shape[0] == 1:
            mask = torch.cat([mask] * batch_size)
        elif mask.shape[0] > 1 and mask.shape[0] != batch_size:
            raise ValueError(
                f"`mask_image` with batch size {mask.shape[0]} cannot be broadcasted to batch size {batch_size} "
                f"inferred by prompt inputs"
            )

    if mask.shape[1] != 1:
        raise ValueError(f"`mask_image` must have 1 channel, but has {mask.shape[1]} channels")

    # Check mask is in [0, 1]
    if mask.min() < 0 or mask.max() > 1:
        raise ValueError("`mask_image` should be in [0, 1] range")

    # Binarize mask
    mask[mask < 0.5] = 0
    mask[mask >= 0.5] = 1

    return mask


class StableDiffusionDiffEditPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
    r"""
    <Tip warning={true}>

    This is an experimental feature!

    </Tip>

    Pipeline for text-guided image inpainting using Stable Diffusion and DiffEdit.

    This model inherits from [`DiffusionPipeline`]. 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:
        - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
        - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]

    as well as the following saving methods:
        - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer (`CLIPTokenizer`):
            A [`~transformers.CLIPTokenizer`] to tokenize text.
        unet ([`UNet2DConditionModel`]):
            A [`UNet2DConditionModel`] to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents.
        inverse_scheduler (`[DDIMInverseScheduler]`):
            A scheduler to be used in combination with `unet` to fill in the unmasked part of the input latents.
        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/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`CLIPImageProcessor`]):
            A [`CLIPImageProcessor`] to extract features from generated images; used as inputs to the `safety_checker`.
    """
    _optional_components = ["safety_checker", "feature_extractor", "inverse_scheduler"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPImageProcessor,
        inverse_scheduler: DDIMInverseScheduler,
        requires_safety_checker: bool = True,
    ):
        super().__init__()

        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, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
                " sure to update the config accordingly as not setting `skip_prk_steps` 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("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(scheduler.config)
            new_config["skip_prk_steps"] = True
            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 you're 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,
            inverse_scheduler=inverse_scheduler,
        )
        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)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
    def enable_vae_slicing(self):
        r"""
        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.vae.enable_slicing()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
    def disable_vae_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_slicing()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
    def enable_vae_tiling(self):
        r"""
        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
        processing larger images.
        """
        self.vae.enable_tiling()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
    def disable_vae_tiling(self):
        r"""
        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_tiling()

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

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

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

        hook = None
        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)

        if self.safety_checker is not None:
            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)

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

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
    def _encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        lora_scale: Optional[float] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
             prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            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.
        """
        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale

        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if prompt_embeds is None:
            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

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

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

            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

            prompt_embeds = self.text_encoder(
                text_input_ids.to(device),
                attention_mask=attention_mask,
            )
            prompt_embeds = 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 = prompt_embeds.dtype

        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

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

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

            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=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 do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=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)

            # 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
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        return prompt_embeds

    # 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.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

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

    def check_inputs(
        self,
        prompt,
        strength,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
    ):
        if (strength is None) or (strength is not None and (strength < 0 or strength > 1)):
            raise ValueError(
                f"The value of `strength` should in [0.0, 1.0] but is, but is {strength} of type {type(strength)}."
            )

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

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

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

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

    def check_source_inputs(
        self,
        source_prompt=None,
        source_negative_prompt=None,
        source_prompt_embeds=None,
        source_negative_prompt_embeds=None,
    ):
        if source_prompt is not None and source_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `source_prompt`: {source_prompt} and `source_prompt_embeds`: {source_prompt_embeds}."
                "  Please make sure to only forward one of the two."
            )
        elif source_prompt is None and source_prompt_embeds is None:
            raise ValueError(
                "Provide either `source_image` or `source_prompt_embeds`. Cannot leave all both of the arguments undefined."
            )
        elif source_prompt is not None and (
            not isinstance(source_prompt, str) and not isinstance(source_prompt, list)
        ):
            raise ValueError(f"`source_prompt` has to be of type `str` or `list` but is {type(source_prompt)}")

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

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

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
    def get_timesteps(self, num_inference_steps, strength, device):
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]

        return timesteps, num_inference_steps - t_start

    def get_inverse_timesteps(self, num_inference_steps, strength, device):
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

        t_start = max(num_inference_steps - init_timestep, 0)

        # safety for t_start overflow to prevent empty timsteps slice
        if t_start == 0:
            return self.inverse_scheduler.timesteps, num_inference_steps
        timesteps = self.inverse_scheduler.timesteps[:-t_start]

        return timesteps, num_inference_steps - t_start

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

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

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.StableDiffusionPix2PixZeroPipeline.prepare_image_latents
    def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):
        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
            raise ValueError(
                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
            )

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

        if image.shape[1] == 4:
            latents = image

        else:
            if isinstance(generator, list) and len(generator) != batch_size:
                raise ValueError(
                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
                )

            if isinstance(generator, list):
                latents = [
                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
                ]
                latents = torch.cat(latents, dim=0)
            else:
                latents = self.vae.encode(image).latent_dist.sample(generator)

            latents = self.vae.config.scaling_factor * latents

        if batch_size != latents.shape[0]:
            if batch_size % latents.shape[0] == 0:
                # expand image_latents for batch_size
                deprecation_message = (
                    f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial"
                    " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
                    " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
                    " your script to pass as many initial images as text prompts to suppress this warning."
                )
                deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
                additional_latents_per_image = batch_size // latents.shape[0]
                latents = torch.cat([latents] * additional_latents_per_image, dim=0)
            else:
                raise ValueError(
                    f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts."
                )
        else:
            latents = torch.cat([latents], dim=0)

        return latents

    def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):
        pred_type = self.inverse_scheduler.config.prediction_type
        alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]

        beta_prod_t = 1 - alpha_prod_t

        if pred_type == "epsilon":
            return model_output
        elif pred_type == "sample":
            return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5)
        elif pred_type == "v_prediction":
            return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
            raise ValueError(
                f"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`"
            )

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def generate_mask(
        self,
        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        target_prompt: Optional[Union[str, List[str]]] = None,
        target_negative_prompt: Optional[Union[str, List[str]]] = None,
        target_prompt_embeds: Optional[torch.FloatTensor] = None,
        target_negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        source_prompt: Optional[Union[str, List[str]]] = None,
        source_negative_prompt: Optional[Union[str, List[str]]] = None,
        source_prompt_embeds: Optional[torch.FloatTensor] = None,
        source_negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        num_maps_per_mask: Optional[int] = 10,
        mask_encode_strength: Optional[float] = 0.5,
        mask_thresholding_ratio: Optional[float] = 3.0,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        output_type: Optional[str] = "np",
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    ):
        r"""
        Generate a latent mask given a mask prompt, a target prompt, and an image.

        Args:
            image (`PIL.Image.Image`):
                `Image` or tensor representing an image batch to be used for computing the mask.
            target_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide semantic mask generation. If not defined, you need to pass
                `prompt_embeds`.
            target_negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            target_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            target_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.
            source_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide semantic mask generation using DiffEdit. If not defined, you need to
                pass `source_prompt_embeds` or `source_image` instead.
            source_negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide semantic mask generation away from using DiffEdit. If not defined, you
                need to pass `source_negative_prompt_embeds` or `source_image` instead.
            source_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings to guide the semantic mask generation. Can be used to easily tweak text
                inputs (prompt weighting). If not provided, text embeddings are generated from `source_prompt` input
                argument.
            source_negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings to negatively guide the semantic mask generation. Can be used to easily
                tweak text inputs (prompt weighting). If not provided, text embeddings are generated from
                `source_negative_prompt` input argument.
            num_maps_per_mask (`int`, *optional*, defaults to 10):
                The number of noise maps sampled to generate the semantic mask using DiffEdit.
            mask_encode_strength (`float`, *optional*, defaults to 0.5):
                The strength of the noise maps sampled to generate the semantic mask using DiffEdit. Must be between 0
                and 1.
            mask_thresholding_ratio (`float`, *optional*, defaults to 3.0):
                The maximum multiple of the mean absolute difference used to clamp the semantic guidance map before
                mask binarization.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                A [`torch.Generator`](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 generated image. Choose between `PIL.Image` or `np.array`.
            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).

        Examples:

        Returns:
            `List[PIL.Image.Image]` or `np.array`:
                When returning a `List[PIL.Image.Image]`, the list consists of a batch of single-channel binary images
                with dimensions `(height // self.vae_scale_factor, width // self.vae_scale_factor)`. If it's
                `np.array`, the shape is `(batch_size, height // self.vae_scale_factor, width //
                self.vae_scale_factor)`.
        """

        # 1. Check inputs (Provide dummy argument for callback_steps)
        self.check_inputs(
            target_prompt,
            mask_encode_strength,
            1,
            target_negative_prompt,
            target_prompt_embeds,
            target_negative_prompt_embeds,
        )

        self.check_source_inputs(
            source_prompt,
            source_negative_prompt,
            source_prompt_embeds,
            source_negative_prompt_embeds,
        )

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

        if mask_thresholding_ratio is None or mask_thresholding_ratio <= 0:
            raise ValueError(
                f"`mask_thresholding_ratio` has to be positive but is {mask_thresholding_ratio} of type"
                f" {type(mask_thresholding_ratio)}."
            )

        # 2. Define call parameters
        if target_prompt is not None and isinstance(target_prompt, str):
            batch_size = 1
        elif target_prompt is not None and isinstance(target_prompt, list):
            batch_size = len(target_prompt)
        else:
            batch_size = target_prompt_embeds.shape[0]
        if cross_attention_kwargs is None:
            cross_attention_kwargs = {}

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompts
        (cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None)
        target_prompt_embeds = self._encode_prompt(
            target_prompt,
            device,
            num_maps_per_mask,
            do_classifier_free_guidance,
            target_negative_prompt,
            prompt_embeds=target_prompt_embeds,
            negative_prompt_embeds=target_negative_prompt_embeds,
        )

        source_prompt_embeds = self._encode_prompt(
            source_prompt,
            device,
            num_maps_per_mask,
            do_classifier_free_guidance,
            source_negative_prompt,
            prompt_embeds=source_prompt_embeds,
            negative_prompt_embeds=source_negative_prompt_embeds,
        )

        # 4. Preprocess image
        image = self.image_processor.preprocess(image).repeat_interleave(num_maps_per_mask, dim=0)

        # 5. Set timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, _ = self.get_timesteps(num_inference_steps, mask_encode_strength, device)
        encode_timestep = timesteps[0]

        # 6. Prepare image latents and add noise with specified strength
        image_latents = self.prepare_image_latents(
            image, batch_size * num_maps_per_mask, self.vae.dtype, device, generator
        )
        noise = randn_tensor(image_latents.shape, generator=generator, device=device, dtype=self.vae.dtype)
        image_latents = self.scheduler.add_noise(image_latents, noise, encode_timestep)

        latent_model_input = torch.cat([image_latents] * (4 if do_classifier_free_guidance else 2))
        latent_model_input = self.scheduler.scale_model_input(latent_model_input, encode_timestep)

        # 7. Predict the noise residual
        prompt_embeds = torch.cat([source_prompt_embeds, target_prompt_embeds])
        noise_pred = self.unet(
            latent_model_input,
            encode_timestep,
            encoder_hidden_states=prompt_embeds,
            cross_attention_kwargs=cross_attention_kwargs,
        ).sample

        if do_classifier_free_guidance:
            noise_pred_neg_src, noise_pred_source, noise_pred_uncond, noise_pred_target = noise_pred.chunk(4)
            noise_pred_source = noise_pred_neg_src + guidance_scale * (noise_pred_source - noise_pred_neg_src)
            noise_pred_target = noise_pred_uncond + guidance_scale * (noise_pred_target - noise_pred_uncond)
        else:
            noise_pred_source, noise_pred_target = noise_pred.chunk(2)

        # 8. Compute the mask from the absolute difference of predicted noise residuals
        # TODO: Consider smoothing mask guidance map
        mask_guidance_map = (
            torch.abs(noise_pred_target - noise_pred_source)
            .reshape(batch_size, num_maps_per_mask, *noise_pred_target.shape[-3:])
            .mean([1, 2])
        )
        clamp_magnitude = mask_guidance_map.mean() * mask_thresholding_ratio
        semantic_mask_image = mask_guidance_map.clamp(0, clamp_magnitude) / clamp_magnitude
        semantic_mask_image = torch.where(semantic_mask_image <= 0.5, 0, 1)
        mask_image = semantic_mask_image.cpu().numpy()

        # 9. Convert to Numpy array or PIL.
        if output_type == "pil":
            mask_image = self.image_processor.numpy_to_pil(mask_image)

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

        return mask_image

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)
    def invert(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        num_inference_steps: int = 50,
        inpaint_strength: float = 0.8,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        decode_latents: bool = False,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        lambda_auto_corr: float = 20.0,
        lambda_kl: float = 20.0,
        num_reg_steps: int = 0,
        num_auto_corr_rolls: int = 5,
    ):
        r"""
        Generate inverted latents given a prompt and image.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
            image (`PIL.Image.Image`):
                `Image` or tensor representing an image batch to produce the inverted latents guided by `prompt`.
            inpaint_strength (`float`, *optional*, defaults to 0.8):
                Indicates extent of the noising process to run latent inversion. Must be between 0 and 1. When
                `strength` is 1, the inversion process iss ru for the full number of iterations specified in
                `num_inference_steps`. `image` is used as a reference for the inversion process, adding more noise the
                larger the `strength`. If `strength` is 0, no inpainting occurs.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            generator (`torch.Generator`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            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.
            decode_latents (`bool`, *optional*, defaults to `False`):
                Whether or not to decode the inverted latents into a generated image. Setting this argument to `True`
                decodes all inverted latents for each timestep into a list of generated images.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.DiffEditInversionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
                every step.
            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).
            lambda_auto_corr (`float`, *optional*, defaults to 20.0):
                Lambda parameter to control auto correction.
            lambda_kl (`float`, *optional*, defaults to 20.0):
                Lambda parameter to control Kullback–Leibler divergence output.
            num_reg_steps (`int`, *optional*, defaults to 0):
                Number of regularization loss steps.
            num_auto_corr_rolls (`int`, *optional*, defaults to 5):
                Number of auto correction roll steps.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] or
            `tuple`:
                If `return_dict` is `True`,
                [`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] is
                returned, otherwise a `tuple` is returned where the first element is the inverted latents tensors
                ordered by increasing noise, and the second is the corresponding decoded images if `decode_latents` is
                `True`, otherwise `None`.
        """

        # 1. Check inputs
        self.check_inputs(
            prompt,
            inpaint_strength,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        if image is None:
            raise ValueError("`image` input cannot be undefined.")

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]
        if cross_attention_kwargs is None:
            cross_attention_kwargs = {}

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Preprocess image
        image = self.image_processor.preprocess(image)

        # 4. Prepare latent variables
        num_images_per_prompt = 1
        latents = self.prepare_image_latents(
            image, batch_size * num_images_per_prompt, self.vae.dtype, device, generator
        )

        # 5. Encode input prompt
        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        # 6. Prepare timesteps
        self.inverse_scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, num_inference_steps = self.get_inverse_timesteps(num_inference_steps, inpaint_strength, device)

        # 7. Noising loop where we obtain the intermediate noised latent image for each timestep.
        num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order
        inverted_latents = []
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.inverse_scheduler.scale_model_input(latent_model_input, t)

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

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # regularization of the noise prediction (not in original code or paper but borrowed from Pix2PixZero)
                if num_reg_steps > 0:
                    with torch.enable_grad():
                        for _ in range(num_reg_steps):
                            if lambda_auto_corr > 0:
                                for _ in range(num_auto_corr_rolls):
                                    var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)

                                    # Derive epsilon from model output before regularizing to IID standard normal
                                    var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)

                                    l_ac = auto_corr_loss(var_epsilon, generator=generator)
                                    l_ac.backward()

                                    grad = var.grad.detach() / num_auto_corr_rolls
                                    noise_pred = noise_pred - lambda_auto_corr * grad

                            if lambda_kl > 0:
                                var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)

                                # Derive epsilon from model output before regularizing to IID standard normal
                                var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)

                                l_kld = kl_divergence(var_epsilon)
                                l_kld.backward()

                                grad = var.grad.detach()
                                noise_pred = noise_pred - lambda_kl * grad

                            noise_pred = noise_pred.detach()

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample
                inverted_latents.append(latents.detach().clone())

                # call the callback, if provided
                if i == len(timesteps) - 1 or (
                    (i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0
                ):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        assert len(inverted_latents) == len(timesteps)
        latents = torch.stack(list(reversed(inverted_latents)), 1)

        # 8. Post-processing
        image = None
        if decode_latents:
            image = self.decode_latents(latents.flatten(0, 1))

        # 9. Convert to PIL.
        if decode_latents and output_type == "pil":
            image = self.image_processor.numpy_to_pil(image)

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

        if not return_dict:
            return (latents, image)

        return DiffEditInversionPipelineOutput(latents=latents, images=image)

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        image_latents: Union[torch.FloatTensor, PIL.Image.Image] = None,
        inpaint_strength: Optional[float] = 0.8,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
            mask_image (`PIL.Image.Image`):
                `Image` or tensor representing an image batch to mask the generated image. White pixels in the mask are
                repainted, while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a
                single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
                instead of 3, so the expected shape would be `(B, 1, H, W)`.
            image_latents (`PIL.Image.Image` or `torch.FloatTensor`):
                Partially noised image latents from the inversion process to be used as inputs for image generation.
            inpaint_strength (`float`, *optional*, defaults to 0.8):
                Indicates extent to inpaint the masked area. Must be between 0 and 1. When `strength` is 1, the
                denoising process is run on the masked area for the full number of iterations specified in
                `num_inference_steps`. `image_latents` is used as a reference for the masked area, adding more noise to
                that region the larger the `strength`. If `strength` is 0, no inpainting occurs.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            generator (`torch.Generator`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor is generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            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.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
                every step.
            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).

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list with the generated images and the
                second element is a list of `bool`s indicating whether the corresponding generated image contains
                "not-safe-for-work" (nsfw) content.
        """

        # 1. Check inputs
        self.check_inputs(
            prompt,
            inpaint_strength,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        if mask_image is None:
            raise ValueError(
                "`mask_image` input cannot be undefined. Use `generate_mask()` to compute `mask_image` from text prompts."
            )
        if image_latents is None:
            raise ValueError(
                "`image_latents` input cannot be undefined. Use `invert()` to compute `image_latents` from input images."
            )

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]
        if cross_attention_kwargs is None:
            cross_attention_kwargs = {}

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
        )
        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
        )

        # 4. Preprocess mask
        mask_image = preprocess_mask(mask_image, batch_size)
        latent_height, latent_width = mask_image.shape[-2:]
        mask_image = torch.cat([mask_image] * num_images_per_prompt)
        mask_image = mask_image.to(device=device, dtype=prompt_embeds.dtype)

        # 5. Set timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, inpaint_strength, device)

        # 6. Preprocess image latents
        if isinstance(image_latents, list) and any(isinstance(l, torch.Tensor) and l.ndim == 5 for l in image_latents):
            image_latents = torch.cat(image_latents).detach()
        elif isinstance(image_latents, torch.Tensor) and image_latents.ndim == 5:
            image_latents = image_latents.detach()
        else:
            image_latents = self.image_processor.preprocess(image_latents).detach()

        latent_shape = (self.vae.config.latent_channels, latent_height, latent_width)
        if image_latents.shape[-3:] != latent_shape:
            raise ValueError(
                f"Each latent image in `image_latents` must have shape {latent_shape}, "
                f"but has shape {image_latents.shape[-3:]}"
            )
        if image_latents.ndim == 4:
            image_latents = image_latents.reshape(batch_size, len(timesteps), *latent_shape)
        if image_latents.shape[:2] != (batch_size, len(timesteps)):
            raise ValueError(
                f"`image_latents` must have batch size {batch_size} with latent images from {len(timesteps)}"
                f" timesteps, but has batch size {image_latents.shape[0]} with latent images from"
                f" {image_latents.shape[1]} timesteps."
            )
        image_latents = image_latents.transpose(0, 1).repeat_interleave(num_images_per_prompt, dim=1)
        image_latents = image_latents.to(device=device, dtype=prompt_embeds.dtype)

        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 8. Denoising loop
        latents = image_latents[0].clone()
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

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

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

                # mask with inverted latents from appropriate timestep - use original image latent for last step
                latents = latents * mask_image + image_latents[i] * (1 - mask_image)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.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 last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
