# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/

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

import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, ControlNetModel, UNet2DConditionModel, logging
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
    PIL_INTERPOLATION,
    replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor


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

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import numpy as np
        >>> import torch
        >>> from PIL import Image
        >>> from stable_diffusion_controlnet_inpaint_img2img import StableDiffusionControlNetInpaintImg2ImgPipeline

        >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
        >>> from diffusers import ControlNetModel, UniPCMultistepScheduler
        >>> from diffusers.utils import load_image

        >>> def ade_palette():
                return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
                        [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
                        [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
                        [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
                        [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
                        [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
                        [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
                        [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
                        [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
                        [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
                        [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
                        [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
                        [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
                        [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
                        [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
                        [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
                        [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
                        [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
                        [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
                        [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
                        [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
                        [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
                        [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
                        [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
                        [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
                        [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
                        [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
                        [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
                        [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
                        [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
                        [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
                        [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
                        [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
                        [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
                        [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
                        [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
                        [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
                        [102, 255, 0], [92, 0, 255]]

        >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
        >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")

        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)

        >>> pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained(
                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
            )

        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
        >>> pipe.enable_xformers_memory_efficient_attention()
        >>> pipe.enable_model_cpu_offload()

        >>> def image_to_seg(image):
                pixel_values = image_processor(image, return_tensors="pt").pixel_values
                with torch.no_grad():
                    outputs = image_segmentor(pixel_values)
                seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
                color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)  # height, width, 3
                palette = np.array(ade_palette())
                for label, color in enumerate(palette):
                    color_seg[seg == label, :] = color
                color_seg = color_seg.astype(np.uint8)
                seg_image = Image.fromarray(color_seg)
                return seg_image

        >>> image = load_image(
                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
            )

        >>> mask_image = load_image(
                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
            )

        >>> controlnet_conditioning_image = image_to_seg(image)

        >>> image = pipe(
                "Face of a yellow cat, high resolution, sitting on a park bench",
                image,
                mask_image,
                controlnet_conditioning_image,
                num_inference_steps=20,
            ).images[0]

        >>> image.save("out.png")
        ```
"""


def prepare_image(image):
    if isinstance(image, torch.Tensor):
        # Batch single image
        if image.ndim == 3:
            image = image.unsqueeze(0)

        image = image.to(dtype=torch.float32)
    else:
        # preprocess image
        if isinstance(image, (PIL.Image.Image, np.ndarray)):
            image = [image]

        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
            image = [np.array(i.convert("RGB"))[None, :] for i in image]
            image = np.concatenate(image, axis=0)
        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
            image = np.concatenate([i[None, :] for i in image], axis=0)

        image = image.transpose(0, 3, 1, 2)
        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0

    return image


def prepare_mask_image(mask_image):
    if isinstance(mask_image, torch.Tensor):
        if mask_image.ndim == 2:
            # Batch and add channel dim for single mask
            mask_image = mask_image.unsqueeze(0).unsqueeze(0)
        elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
            # Single mask, the 0'th dimension is considered to be
            # the existing batch size of 1
            mask_image = mask_image.unsqueeze(0)
        elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
            # Batch of mask, the 0'th dimension is considered to be
            # the batching dimension
            mask_image = mask_image.unsqueeze(1)

        # Binarize mask
        mask_image[mask_image < 0.5] = 0
        mask_image[mask_image >= 0.5] = 1
    else:
        # preprocess mask
        if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):
            mask_image = [mask_image]

        if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):
            mask_image = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0)
            mask_image = mask_image.astype(np.float32) / 255.0
        elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):
            mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)

        mask_image[mask_image < 0.5] = 0
        mask_image[mask_image >= 0.5] = 1
        mask_image = torch.from_numpy(mask_image)

    return mask_image


def prepare_controlnet_conditioning_image(
    controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype
):
    if not isinstance(controlnet_conditioning_image, torch.Tensor):
        if isinstance(controlnet_conditioning_image, PIL.Image.Image):
            controlnet_conditioning_image = [controlnet_conditioning_image]

        if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):
            controlnet_conditioning_image = [
                np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
                for i in controlnet_conditioning_image
            ]
            controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)
            controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0
            controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)
            controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)
        elif isinstance(controlnet_conditioning_image[0], torch.Tensor):
            controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)

    image_batch_size = controlnet_conditioning_image.shape[0]

    if image_batch_size == 1:
        repeat_by = batch_size
    else:
        # image batch size is the same as prompt batch size
        repeat_by = num_images_per_prompt

    controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)

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

    return controlnet_conditioning_image


class StableDiffusionControlNetInpaintImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin):
    """
    Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
    """

    _optional_components = ["safety_checker", "feature_extractor"]

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

        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."
            )

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            controlnet=controlnet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    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,
    ):
        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.
        """
        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:
            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]

        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.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 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

            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=self.text_encoder.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

    def run_safety_checker(self, image, device, dtype):
        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
            image, has_nsfw_concept = self.safety_checker(
                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
            )
        else:
            has_nsfw_concept = None
        return image, has_nsfw_concept

    def decode_latents(self, latents):
        latents = 1 / self.vae.config.scaling_factor * latents
        image = self.vae.decode(latents).sample
        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 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

    def check_inputs(
        self,
        prompt,
        image,
        mask_image,
        controlnet_conditioning_image,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        strength=None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        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}."
                )

        controlnet_cond_image_is_pil = isinstance(controlnet_conditioning_image, PIL.Image.Image)
        controlnet_cond_image_is_tensor = isinstance(controlnet_conditioning_image, torch.Tensor)
        controlnet_cond_image_is_pil_list = isinstance(controlnet_conditioning_image, list) and isinstance(
            controlnet_conditioning_image[0], PIL.Image.Image
        )
        controlnet_cond_image_is_tensor_list = isinstance(controlnet_conditioning_image, list) and isinstance(
            controlnet_conditioning_image[0], torch.Tensor
        )

        if (
            not controlnet_cond_image_is_pil
            and not controlnet_cond_image_is_tensor
            and not controlnet_cond_image_is_pil_list
            and not controlnet_cond_image_is_tensor_list
        ):
            raise TypeError(
                "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
            )

        if controlnet_cond_image_is_pil:
            controlnet_cond_image_batch_size = 1
        elif controlnet_cond_image_is_tensor:
            controlnet_cond_image_batch_size = controlnet_conditioning_image.shape[0]
        elif controlnet_cond_image_is_pil_list:
            controlnet_cond_image_batch_size = len(controlnet_conditioning_image)
        elif controlnet_cond_image_is_tensor_list:
            controlnet_cond_image_batch_size = len(controlnet_conditioning_image)

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

        if controlnet_cond_image_batch_size != 1 and controlnet_cond_image_batch_size != prompt_batch_size:
            raise ValueError(
                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {controlnet_cond_image_batch_size}, prompt batch size: {prompt_batch_size}"
            )

        if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor):
            raise TypeError("if `image` is a tensor, `mask_image` must also be a tensor")

        if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image):
            raise TypeError("if `image` is a PIL image, `mask_image` must also be a PIL image")

        if isinstance(image, torch.Tensor):
            if image.ndim != 3 and image.ndim != 4:
                raise ValueError("`image` must have 3 or 4 dimensions")

            if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4:
                raise ValueError("`mask_image` must have 2, 3, or 4 dimensions")

            if image.ndim == 3:
                image_batch_size = 1
                image_channels, image_height, image_width = image.shape
            elif image.ndim == 4:
                image_batch_size, image_channels, image_height, image_width = image.shape

            if mask_image.ndim == 2:
                mask_image_batch_size = 1
                mask_image_channels = 1
                mask_image_height, mask_image_width = mask_image.shape
            elif mask_image.ndim == 3:
                mask_image_channels = 1
                mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape
            elif mask_image.ndim == 4:
                mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape

            if image_channels != 3:
                raise ValueError("`image` must have 3 channels")

            if mask_image_channels != 1:
                raise ValueError("`mask_image` must have 1 channel")

            if image_batch_size != mask_image_batch_size:
                raise ValueError("`image` and `mask_image` mush have the same batch sizes")

            if image_height != mask_image_height or image_width != mask_image_width:
                raise ValueError("`image` and `mask_image` must have the same height and width dimensions")

            if image.min() < -1 or image.max() > 1:
                raise ValueError("`image` should be in range [-1, 1]")

            if mask_image.min() < 0 or mask_image.max() > 1:
                raise ValueError("`mask_image` should be in range [0, 1]")
        else:
            mask_image_channels = 1
            image_channels = 3

        single_image_latent_channels = self.vae.config.latent_channels

        total_latent_channels = single_image_latent_channels * 2 + mask_image_channels

        if total_latent_channels != self.unet.config.in_channels:
            raise ValueError(
                f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received"
                f" non inpainting latent channels: {single_image_latent_channels},"
                f" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}."
                f" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs."
            )

        if strength < 0 or strength > 1:
            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")

    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:]

        return timesteps, num_inference_steps - t_start

    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, 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)

        batch_size = batch_size * num_images_per_prompt
        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):
            init_latents = [
                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
            ]
            init_latents = torch.cat(init_latents, dim=0)
        else:
            init_latents = self.vae.encode(image).latent_dist.sample(generator)

        init_latents = self.vae.config.scaling_factor * init_latents

        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
            )
        else:
            init_latents = torch.cat([init_latents], dim=0)

        shape = init_latents.shape
        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)

        # get latents
        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
        latents = init_latents

        return latents

    def prepare_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance):
        # resize the mask to latents shape as we concatenate the mask to the latents
        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
        # and half precision
        mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))
        mask_image = mask_image.to(device=device, dtype=dtype)

        # duplicate mask for each generation per prompt, using mps friendly method
        if mask_image.shape[0] < batch_size:
            if not batch_size % mask_image.shape[0] == 0:
                raise ValueError(
                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
                    f" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number"
                    " of masks that you pass is divisible by the total requested batch size."
                )
            mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1)

        mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image

        mask_image_latents = mask_image

        return mask_image_latents

    def prepare_masked_image_latents(
        self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
    ):
        masked_image = masked_image.to(device=device, dtype=dtype)

        # encode the mask image into latents space so we can concatenate it to the latents
        if isinstance(generator, list):
            masked_image_latents = [
                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
                for i in range(batch_size)
            ]
            masked_image_latents = torch.cat(masked_image_latents, dim=0)
        else:
            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents

        # duplicate masked_image_latents for each generation per prompt, using mps friendly method
        if masked_image_latents.shape[0] < batch_size:
            if not batch_size % masked_image_latents.shape[0] == 0:
                raise ValueError(
                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
                    " Make sure the number of images that you pass is divisible by the total requested batch size."
                )
            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)

        masked_image_latents = (
            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
        )

        # aligning device to prevent device errors when concating it with the latent model input
        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
        return masked_image_latents

    def _default_height_width(self, height, width, image):
        if isinstance(image, list):
            image = image[0]

        if height is None:
            if isinstance(image, PIL.Image.Image):
                height = image.height
            elif isinstance(image, torch.Tensor):
                height = image.shape[3]

            height = (height // 8) * 8  # round down to nearest multiple of 8

        if width is None:
            if isinstance(image, PIL.Image.Image):
                width = image.width
            elif isinstance(image, torch.Tensor):
                width = image.shape[2]

            width = (width // 8) * 8  # round down to nearest multiple of 8

        return height, width

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        image: Union[torch.Tensor, PIL.Image.Image] = None,
        mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
        controlnet_conditioning_image: Union[
            torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]
        ] = None,
        strength: float = 0.8,
        height: Optional[int] = None,
        width: Optional[int] = None,
        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,
        controlnet_conditioning_scale: float = 1.0,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            image (`torch.Tensor` or `PIL.Image.Image`):
                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
                be masked out with `mask_image` and repainted according to `prompt`.
            mask_image (`torch.Tensor` or `PIL.Image.Image`):
                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be 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, H, W, 1)`.
            controlnet_conditioning_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]`):
                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
                the type is specified as `torch.Tensor`, it is passed to ControlNet as is. PIL.Image.Image` can
                also be accepted as an image. The control image is automatically resized to fit the output image.
            strength (`float`, *optional*):
                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
                be maximum and the denoising process will run for the full number of iterations specified in
                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image.
            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):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            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`).
            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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](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 will ge 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, *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.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
                to the residual in the original unet.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """
        # 0. Default height and width to unet
        height, width = self._default_height_width(height, width, controlnet_conditioning_image)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            image,
            mask_image,
            controlnet_conditioning_image,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            strength,
        )

        # 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]

        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
        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,
        )

        # 4. Prepare mask, image, and controlnet_conditioning_image
        image = prepare_image(image)

        mask_image = prepare_mask_image(mask_image)

        controlnet_conditioning_image = prepare_controlnet_conditioning_image(
            controlnet_conditioning_image,
            width,
            height,
            batch_size * num_images_per_prompt,
            num_images_per_prompt,
            device,
            self.controlnet.dtype,
        )

        masked_image = image * (mask_image < 0.5)

        # 5. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)

        # 6. Prepare latent variables
        if latents is None:
            latents = self.prepare_latents(
                image,
                latent_timestep,
                batch_size,
                num_images_per_prompt,
                prompt_embeds.dtype,
                device,
                generator,
            )

        mask_image_latents = self.prepare_mask_latents(
            mask_image,
            batch_size * num_images_per_prompt,
            height,
            width,
            prompt_embeds.dtype,
            device,
            do_classifier_free_guidance,
        )

        masked_image_latents = self.prepare_masked_image_latents(
            masked_image,
            batch_size * num_images_per_prompt,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            do_classifier_free_guidance,
        )

        if do_classifier_free_guidance:
            controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)

        # 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
        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
                non_inpainting_latent_model_input = (
                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                )

                non_inpainting_latent_model_input = self.scheduler.scale_model_input(
                    non_inpainting_latent_model_input, t
                )

                inpainting_latent_model_input = torch.cat(
                    [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1
                )

                down_block_res_samples, mid_block_res_sample = self.controlnet(
                    non_inpainting_latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    controlnet_cond=controlnet_conditioning_image,
                    return_dict=False,
                )

                down_block_res_samples = [
                    down_block_res_sample * controlnet_conditioning_scale
                    for down_block_res_sample in down_block_res_samples
                ]
                mid_block_res_sample *= controlnet_conditioning_scale

                # predict the noise residual
                noise_pred = self.unet(
                    inpainting_latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample,
                ).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

                # 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 we do sequential model offloading, let's offload unet and controlnet
        # manually for max memory savings
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.unet.to("cpu")
            self.controlnet.to("cpu")
            torch.cuda.empty_cache()

        if output_type == "latent":
            image = latents
            has_nsfw_concept = None
        elif output_type == "pil":
            # 8. Post-processing
            image = self.decode_latents(latents)

            # 9. Run safety checker
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

            # 10. Convert to PIL
            image = self.numpy_to_pil(image)
        else:
            # 8. Post-processing
            image = self.decode_latents(latents)

            # 9. Run safety checker
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

        # 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)
