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	# All the code in this file has been taken from: https://github.com/huggingface/diffusers/blob/main/examples/community/stable_diffusion_controlnet_inpaint.py
	# 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, DiffusionPipeline, UNet2DConditionModel, logging
	from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
	from diffusers.schedulers import KarrasDiffusionSchedulers
	from diffusers.utils import (
	PIL_INTERPOLATION,
	is_accelerate_available,
	is_accelerate_version,
	randn_tensor,
	replace_example_docstring,
	)


	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 import StableDiffusionControlNetInpaintPipeline

	>>> 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 = StableDiffusionControlNetInpaintPipeline.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 StableDiffusionControlNetInpaintPipeline(DiffusionPipeline):
	"""
	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 enable_vae_slicing(self):
	r"""
	Enable sliced VAE decoding.

	When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
	steps. This is useful to save some memory and allow larger batch sizes.
	"""
	self.vae.enable_slicing()

	def disable_vae_slicing(self):
	r"""
	Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
	computing decoding in one step.
	"""
	self.vae.disable_slicing()

	def enable_sequential_cpu_offload(self, gpu_id=0):
	r"""
	Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
	text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
	`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
	Note that offloading happens on a submodule basis. Memory savings are higher than with
	`enable_model_cpu_offload`, but performance is lower.
	"""
	if is_accelerate_available():
	from accelerate import cpu_offload
	else:
	raise ImportError("Please install accelerate via `pip install accelerate`")

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

	for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:
	cpu_offload(cpu_offloaded_model, device)

	if self.safety_checker is not None:
	cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)

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

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

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

	if self.safety_checker is not None:
	# the safety checker can offload the vae again
	_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)

	# control net hook has be manually offloaded as it alternates with unet
	cpu_offload_with_hook(self.controlnet, device)

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

	@property
	def _execution_device(self):
	r"""
	Returns the device on which the pipeline's models will be executed. After calling
	`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
	hooks.
	"""
	if not hasattr(self.unet, "_hf_hook"):
	return self.device
	for module in self.unet.modules():
	if (
	hasattr(module, "_hf_hook")
	and hasattr(module._hf_hook, "execution_device")
	and module._hf_hook.execution_device is not None
	):
	return torch.device(module._hf_hook.execution_device)
	return self.device

	def _encode_prompt(
	self,
	prompt,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance,
	negative_prompt=None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	):
	r"""
	Encodes the prompt into text encoder hidden states.

	Args:
	prompt (`str` or `List[str]`, optional):
	prompt to be encoded
	device: (`torch.device`):
	torch device
	num_images_per_prompt (`int`):
	number of images that should be generated per prompt
	do_classifier_free_guidance (`bool`):
	whether to use classifier free guidance or not
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.
	Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	"""
	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,
	):
	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."
	)

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

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

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

	return latents

	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.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]
	] = None,
	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.FloatTensor] = None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	callback_steps: int = 1,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	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.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` 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.FloatTensor`, 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.
	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.FloatTensor`, optional):
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will ge generated by sampling using the supplied random `generator`.
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	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.FloatTensor)`.
	callback_steps (`int`, optional, defaults to 1):
	The frequency at which the `callback` function will be called. If not specified, the callback will be
	called at every step.
	cross_attention_kwargs (`dict`, optional):
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
	`self.processor` in
	[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.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,
	)

	# 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 = self.scheduler.timesteps

	# 6. Prepare latent variables
	num_channels_latents = self.vae.config.latent_channels
	latents = self.prepare_latents(
	batch_size * num_images_per_prompt,
	num_channels_latents,
	height,
	width,
	prompt_embeds.dtype,
	device,
	generator,
	latents,
	)

	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:
	callback(i, 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)