class StableDiffusionDiffEditPipeline(
    DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin
):
    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.).

    The pipeline also inherits the following loading and saving methods:
        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
        - [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
        - [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving 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 ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer ([`~transformers.CLIPTokenizer`]):
            A `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/stable-diffusion-v1-5/stable-diffusion-v1-5) for
            more details about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
    """

    model_cpu_offload_seq = "text_encoder->unet->vae"
    _optional_components = ["safety_checker", "feature_extractor", "inverse_scheduler"]
    _exclude_from_cpu_offload = ["safety_checker"]

    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 scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 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 scheduler is not None and getattr(scheduler.config, "skip_prk_steps", True) 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 = (
            unet is not None
            and 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 = (
            unet is not None and 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- stable-diffusion-v1-5/stable-diffusion-v1-5"
                " \n- stable-diffusion-v1-5/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) if getattr(self, "vae", None) else 8
        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._encode_prompt
    def _encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        lora_scale: Optional[float] = None,
        **kwargs,
    ):
        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)

        prompt_embeds_tuple = self.encode_prompt(
            prompt=prompt,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=lora_scale,
            **kwargs,
        )

        # concatenate for backwards comp
        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])

        return prompt_embeds

    # 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.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        lora_scale: Optional[float] = None,
        clip_skip: Optional[int] = 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.Tensor`, *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.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            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, StableDiffusionLoraLoaderMixin):
            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)

        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: process 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

            if clip_skip is None:
                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                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.
                prompt_embeds = 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.
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)

        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: process 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)

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

        return prompt_embeds, negative_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):
        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

    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}."
                )

    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,
            int(height) // self.vae_scale_factor,
            int(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

    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.Tensor, 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.Tensor] = None,
        target_negative_prompt_embeds: Optional[torch.Tensor] = None,
        source_prompt: Optional[Union[str, List[str]]] = None,
        source_negative_prompt: Optional[Union[str, List[str]]] = None,
        source_prompt_embeds: Optional[torch.Tensor] = None,
        source_negative_prompt_embeds: Optional[torch.Tensor] = 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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor`, *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
                [`~models.attention_processor.AttnProcessor`] 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_negative_prompt_embeds, 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,
        )
        # 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 do_classifier_free_guidance:
            target_prompt_embeds = torch.cat([target_negative_prompt_embeds, target_prompt_embeds])

        source_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,
        )
        if do_classifier_free_guidance:
            source_prompt_embeds = torch.cat([source_negative_prompt_embeds, source_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 all models
        self.maybe_free_model_hooks()

        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.Tensor, 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.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        decode_latents: bool = False,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], 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
                `inpaint_strength` is 1, the inversion process is run for the full number of iterations specified in
                `num_inference_steps`. `image` is used as a reference for the inversion process, and adding more noise
                increases `inpaint_strength`. If `inpaint_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.Tensor`, *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.Tensor`, *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.Tensor)`.
            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
                [`~models.attention_processor.AttnProcessor`] 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, negative_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,
        )
        # 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 do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, 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:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, 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 all models
        self.maybe_free_model_hooks()

        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.Tensor, PIL.Image.Image] = None,
        image_latents: Union[torch.Tensor, 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.Tensor] = None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        clip_skip: int = 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.Tensor`):
                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 `inpaint_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, and adding more
                noise to a region increases `inpaint_strength`. If `inpaint_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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor)`.
            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).
            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.
        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, negative_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,
            clip_skip=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 do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        # 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:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

                if XLA_AVAILABLE:
                    xm.mark_step()

        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 all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py