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	#!/usr/bin/env python
	# coding=utf-8
	# Copyright 2024, Yahoo Research
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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

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

	from diffusers.image_processor import VaeImageProcessor
	from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
	from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
	from diffusers.schedulers import KarrasDiffusionSchedulers
	from diffusers.utils import (
	is_accelerate_available,
	is_accelerate_version,
	logging,
	replace_example_docstring,
	)
	from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
	from diffusers.pipelines.pipeline_utils import DiffusionPipeline
	from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
	from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
	from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel

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


	EXAMPLE_DOC_STRING = """
	Examples:
	```py
	>>> # !pip install transformers accelerate
	>>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler
	>>> from diffusers.utils import load_image
	>>> import numpy as np
	>>> import torch

	>>> init_image = load_image(
	... "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
	... )
	>>> init_image = init_image.resize((512, 512))

	>>> generator = torch.Generator(device="cpu").manual_seed(1)

	>>> mask_image = load_image(
	... "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
	... )
	>>> mask_image = mask_image.resize((512, 512))


	>>> def make_inpaint_condition(image, image_mask):
	... image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
	... image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0

	... assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
	... image[image_mask > 0.5] = -1.0 # set as masked pixel
	... image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
	... image = torch.from_numpy(image)
	... return image


	>>> control_image = make_inpaint_condition(init_image, mask_image)

	>>> controlnet = ControlNetModel.from_pretrained(
	... "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
	... )
	>>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
	... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
	... )

	>>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
	>>> pipe.enable_model_cpu_offload()

	>>> # generate image
	>>> image = pipe(
	... "a handsome man with ray-ban sunglasses",
	... num_inference_steps=20,
	... generator=generator,
	... eta=1.0,
	... image=init_image,
	... mask_image=mask_image,
	... control_image=control_image,
	... ).images[0]
	```
	"""


	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.prepare_mask_and_masked_image
	def prepare_mask_and_masked_image(image, mask, height, width, return_image=False):
	"""
	Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
	converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
	``image`` and ``1`` for the ``mask``.

	The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
	binarized (``mask > 0.5``) and cast to ``torch.float32`` too.

	Args:
	image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
	It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
	``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
	mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
	It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
	``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.


	Raises:
	ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
	should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
	TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
	(ot the other way around).

	Returns:
	tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
	dimensions: ``batch x channels x height x width``.
	"""

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

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

	if isinstance(image, torch.Tensor):
	if not isinstance(mask, torch.Tensor):
	raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")

	# Batch single image
	if image.ndim == 3:
	assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
	image = image.unsqueeze(0)

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

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

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

	assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
	assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
	assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"

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

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

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

	# Image as float32
	image = image.to(dtype=torch.float32)
	elif isinstance(mask, torch.Tensor):
	raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
	else:
	# preprocess image
	if isinstance(image, (PIL.Image.Image, np.ndarray)):
	image = [image]
	if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
	# resize all images w.r.t passed height an width
	image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in 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

	# preprocess mask
	if isinstance(mask, (PIL.Image.Image, np.ndarray)):
	mask = [mask]

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

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

	masked_image = image * (mask < 0.5)

	# n.b. ensure backwards compatibility as old function does not return image
	if return_image:
	return mask, masked_image, image

	return mask, masked_image


	class StableDiffusionControlNetInpaintPipeline(
	DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
	):
	r"""
	Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.

	This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
	library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

	In addition the pipeline inherits the following loading methods:
	- Textual-Inversion: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]

	<Tip>

	This pipeline can be used both with checkpoints that have been specifically fine-tuned for inpainting, such as
	[runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)
	as well as default text-to-image stable diffusion checkpoints, such as
	[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
	Default text-to-image stable diffusion checkpoints might be preferable for controlnets that have been fine-tuned on
	those, such as [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).

	</Tip>

	Args:
	vae ([`AutoencoderKL`]):
	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
	text_encoder ([`CLIPTextModel`]):
	Frozen text-encoder. Stable Diffusion uses the text portion of
	[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
	the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
	tokenizer (`CLIPTokenizer`):
	Tokenizer of class
	[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
	unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
	controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
	Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
	as a list, the outputs from each ControlNet are added together to create one combined additional
	conditioning.
	scheduler ([`SchedulerMixin`]):
	A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
	[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
	safety_checker ([`StableDiffusionSafetyChecker`]):
	Classification module that estimates whether generated images could be considered offensive or harmful.
	Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
	feature_extractor ([`CLIPImageProcessor`]):
	Model that extracts features from generated images to be used as inputs for the `safety_checker`.
	"""
	_optional_components = ["safety_checker", "feature_extractor"]

	def __init__(
	self,
	vae: AutoencoderKL,
	text_encoder: CLIPTextModel,
	tokenizer: CLIPTokenizer,
	unet: UNet2DConditionModel,
	controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
	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."
	)

	if isinstance(controlnet, (list, tuple)):
	controlnet = MultiControlNetModel(controlnet)

	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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
	self.control_image_processor = VaeImageProcessor(
	vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
	)
	self.register_to_config(requires_safety_checker=requires_safety_checker)

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

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

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

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

	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

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

	Args:
	prompt (`str` or `List[str]`, optional):
	prompt to be encoded
	device: (`torch.device`):
	torch device
	num_images_per_prompt (`int`):
	number of images that should be generated per prompt
	do_classifier_free_guidance (`bool`):
	whether to use classifier free guidance or not
	negative_prompt (`str` or `List[str]`, optional):
	The prompt or prompts not to guide the image generation. If not defined, one has to pass
	`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
	less than `1`).
	prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`torch.FloatTensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	lora_scale (`float`, optional):
	A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
	"""
	# set lora scale so that monkey patched LoRA
	# function of text encoder can correctly access it
	if lora_scale is not None and isinstance(self, LoraLoaderMixin):
	self._lora_scale = lora_scale

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

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

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

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

	if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
	attention_mask = text_inputs.attention_mask.to(device)
	else:
	attention_mask = None

	prompt_embeds = self.text_encoder(
	text_input_ids.to(device),
	attention_mask=attention_mask,
	)
	prompt_embeds = prompt_embeds[0]

	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 prompt is not None and type(prompt) is not type(negative_prompt):
	raise TypeError(
	f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
	f" {type(prompt)}."
	)
	elif isinstance(negative_prompt, str):
	uncond_tokens = [negative_prompt]
	elif batch_size != len(negative_prompt):
	raise ValueError(
	f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
	f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
	" the batch size of `prompt`."
	)
	else:
	uncond_tokens = negative_prompt

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

	max_length = prompt_embeds.shape[1]
	uncond_input = self.tokenizer(
	uncond_tokens,
	padding="max_length",
	max_length=max_length,
	truncation=True,
	return_tensors="pt",
	)

	if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
	attention_mask = uncond_input.attention_mask.to(device)
	else:
	attention_mask = None

	negative_prompt_embeds = self.text_encoder(
	uncond_input.input_ids.to(device),
	attention_mask=attention_mask,
	)
	negative_prompt_embeds = negative_prompt_embeds[0]

	if do_classifier_free_guidance:
	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
	seq_len = negative_prompt_embeds.shape[1]

	negative_prompt_embeds = negative_prompt_embeds.to(dtype=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

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
	def run_safety_checker(self, image, device, dtype):
	if self.safety_checker is None:
	has_nsfw_concept = None
	else:
	if torch.is_tensor(image):
	feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
	else:
	feature_extractor_input = self.image_processor.numpy_to_pil(image)
	safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
	image, has_nsfw_concept = self.safety_checker(
	images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
	)
	return image, has_nsfw_concept

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

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

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

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

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

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

	return timesteps, num_inference_steps - t_start

	def check_inputs(
	self,
	prompt,
	image,
	height,
	width,
	callback_steps,
	negative_prompt=None,
	prompt_embeds=None,
	negative_prompt_embeds=None,
	controlnet_conditioning_scale=1.0,
	control_guidance_start=0.0,
	control_guidance_end=1.0,
	):
	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}."
	)

	# `prompt` needs more sophisticated handling when there are multiple
	# conditionings.
	if isinstance(self.controlnet, MultiControlNetModel):
	if isinstance(prompt, list):
	logger.warning(
	f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
	" prompts. The conditionings will be fixed across the prompts."
	)

	# Check `image`
	is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
	self.controlnet, torch._dynamo.eval_frame.OptimizedModule
	)
	if (
	isinstance(self.controlnet, ControlNetModel)
	or is_compiled
	and isinstance(self.controlnet._orig_mod, ControlNetModel)
	):
	self.check_image(image, prompt, prompt_embeds)
	elif (
	isinstance(self.controlnet, MultiControlNetModel)
	or is_compiled
	and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
	):
	if not isinstance(image, list):
	raise TypeError("For multiple controlnets: `image` must be type `list`")

	# When `image` is a nested list:
	# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
	elif any(isinstance(i, list) for i in image):
	raise ValueError("A single batch of multiple conditionings are supported at the moment.")
	elif len(image) != len(self.controlnet.nets):
	raise ValueError(
	f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets."
	)

	for image_ in image:
	self.check_image(image_, prompt, prompt_embeds)
	else:
	assert False

	# Check `controlnet_conditioning_scale`
	if (
	isinstance(self.controlnet, ControlNetModel)
	or is_compiled
	and isinstance(self.controlnet._orig_mod, ControlNetModel)
	):
	if not isinstance(controlnet_conditioning_scale, float):
	raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
	elif (
	isinstance(self.controlnet, MultiControlNetModel)
	or is_compiled
	and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
	):
	if isinstance(controlnet_conditioning_scale, list):
	if any(isinstance(i, list) for i in controlnet_conditioning_scale):
	raise ValueError("A single batch of multiple conditionings are supported at the moment.")
	elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
	self.controlnet.nets
	):
	raise ValueError(
	"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
	" the same length as the number of controlnets"
	)
	else:
	assert False

	if len(control_guidance_start) != len(control_guidance_end):
	raise ValueError(
	f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
	)

	if isinstance(self.controlnet, MultiControlNetModel):
	if len(control_guidance_start) != len(self.controlnet.nets):
	raise ValueError(
	f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
	)

	for start, end in zip(control_guidance_start, control_guidance_end):
	if start >= end:
	raise ValueError(
	f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
	)
	if start < 0.0:
	raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
	if end > 1.0:
	raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")

	# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
	def check_image(self, image, prompt, prompt_embeds):
	image_is_pil = isinstance(image, PIL.Image.Image)
	image_is_tensor = isinstance(image, torch.Tensor)
	image_is_np = isinstance(image, np.ndarray)
	image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
	image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
	image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)

	if (
	not image_is_pil
	and not image_is_tensor
	and not image_is_np
	and not image_is_pil_list
	and not image_is_tensor_list
	and not image_is_np_list
	):
	raise TypeError(
	f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
	)

	if image_is_pil:
	image_batch_size = 1
	else:
	image_batch_size = len(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 image_batch_size != 1 and 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: {image_batch_size}, prompt batch size: {prompt_batch_size}"
	)

	# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
	def prepare_control_image(
	self,
	image,
	width,
	height,
	batch_size,
	num_images_per_prompt,
	device,
	dtype,
	do_classifier_free_guidance=False,
	guess_mode=False,
	):
	image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
	image_batch_size = 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

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

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

	if do_classifier_free_guidance and not guess_mode:
	image = torch.cat([image] * 2)

	return image

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_latents
	def prepare_latents(
	self,
	batch_size,
	num_channels_latents,
	height,
	width,
	dtype,
	device,
	generator,
	latents=None,
	image=None,
	timestep=None,
	is_strength_max=True,
	return_noise=False,
	return_image_latents=False,
	):
	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 (image is None or timestep is None) and not is_strength_max:
	raise ValueError(
	"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
	"However, either the image or the noise timestep has not been provided."
	)

	if return_image_latents or (latents is None and not is_strength_max):
	image = image.to(device=device, dtype=dtype)
	image_latents = self._encode_vae_image(image=image, generator=generator)

	if latents is None:
	noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
	# if strength is 1. then initialise the latents to noise, else initial to image + noise
	latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
	# if pure noise then scale the initial latents by the Scheduler's init sigma
	latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
	else:
	noise = latents.to(device)
	latents = noise * self.scheduler.init_noise_sigma

	outputs = (latents,)

	if return_noise:
	outputs += (noise,)

	if return_image_latents:
	outputs += (image_latents,)

	return outputs

	def _default_height_width(self, height, width, image):
	# NOTE: It is possible that a list of images have different
	# dimensions for each image, so just checking the first image
	# is not _exactly_ correct, but it is simple.
	while 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[2]

	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[3]

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

	return height, width

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents
	def prepare_mask_latents(
	self, mask, masked_image, batch_size, height, width, dtype, device, generator, 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 = torch.nn.functional.interpolate(
	mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
	)
	mask = mask.to(device=device, dtype=dtype)

	masked_image = masked_image.to(device=device, dtype=dtype)
	masked_image_latents = self._encode_vae_image(masked_image, generator=generator)

	# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
	if mask.shape[0] < batch_size:
	if not batch_size % mask.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.shape[0]} masks were passed. Make sure the number"
	" of masks that you pass is divisible by the total requested batch size."
	)
	mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
	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)

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

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
	def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
	if isinstance(generator, list):
	image_latents = [
	self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
	for i in range(image.shape[0])
	]
	image_latents = torch.cat(image_latents, dim=0)
	else:
	image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)

	image_latents = self.vae.config.scaling_factor * image_latents

	return image_latents

	@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,
	control_image: Union[
	torch.FloatTensor,
	PIL.Image.Image,
	np.ndarray,
	List[torch.FloatTensor],
	List[PIL.Image.Image],
	List[np.ndarray],
	] = None,
	height: Optional[int] = None,
	width: Optional[int] = None,
	strength: float = 1.0,
	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: Union[float, List[float]] = 0.5,
	guess_mode: bool = False,
	control_guidance_start: Union[float, List[float]] = 0.0,
	control_guidance_end: Union[float, List[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.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
	`List[List[torch.FloatTensor]]`, or `List[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 dimensions of the output image defaults to `image`'s dimensions. If
	height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
	specified in init, images must be passed as a list such that each element of the list can be correctly
	batched for input to a single controlnet.
	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.
	strength (`float`, optional, defaults to 1.):
	Conceptually, indicates how much to transform the masked portion of 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 the masked
	portion of the reference `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.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
	controlnet_conditioning_scale (`float` or `List[float]`, optional, defaults to 0.5):
	The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
	to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
	corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
	than for [`~StableDiffusionControlNetPipeline.__call__`].
	guess_mode (`bool`, optional, defaults to `False`):
	In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
	you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
	control_guidance_start (`float` or `List[float]`, optional, defaults to 0.0):
	The percentage of total steps at which the controlnet starts applying.
	control_guidance_end (`float` or `List[float]`, optional, defaults to 1.0):
	The percentage of total steps at which the controlnet stops applying.

	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`.
	"""
	controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet

	# 0. Default height and width to unet
	height, width = self._default_height_width(height, width, image)

	# align format for control guidance
	if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
	control_guidance_start = len(control_guidance_end) * [control_guidance_start]
	elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
	control_guidance_end = len(control_guidance_start) * [control_guidance_end]
	elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
	mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
	control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
	control_guidance_end
	]

	# 1. Check inputs. Raise error if not correct
	self.check_inputs(
	prompt,
	control_image,
	height,
	width,
	callback_steps,
	negative_prompt,
	prompt_embeds,
	negative_prompt_embeds,
	controlnet_conditioning_scale,
	control_guidance_start,
	control_guidance_end,
	)

	# 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

	if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
	controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)

	global_pool_conditions = (
	controlnet.config.global_pool_conditions
	if isinstance(controlnet, ControlNetModel)
	else controlnet.nets[0].config.global_pool_conditions
	)
	guess_mode = guess_mode or global_pool_conditions

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

	# 4. Prepare image
	if isinstance(controlnet, ControlNetModel):
	control_image = self.prepare_control_image(
	image=control_image,
	width=width,
	height=height,
	batch_size=batch_size * num_images_per_prompt,
	num_images_per_prompt=num_images_per_prompt,
	device=device,
	dtype=controlnet.dtype,
	do_classifier_free_guidance=do_classifier_free_guidance,
	guess_mode=guess_mode,
	)
	elif isinstance(controlnet, MultiControlNetModel):
	control_images = []

	for control_image_ in control_image:
	control_image_ = self.prepare_control_image(
	image=control_image_,
	width=width,
	height=height,
	batch_size=batch_size * num_images_per_prompt,
	num_images_per_prompt=num_images_per_prompt,
	device=device,
	dtype=controlnet.dtype,
	do_classifier_free_guidance=do_classifier_free_guidance,
	guess_mode=guess_mode,
	)

	control_images.append(control_image_)

	control_image = control_images
	else:
	assert False

	# 4. Preprocess mask and image - resizes image and mask w.r.t height and width
	mask, masked_image, init_image = prepare_mask_and_masked_image(
	image, mask_image, height, width, return_image=True
	)

	# 5. Prepare timesteps
	self.scheduler.set_timesteps(num_inference_steps, device=device)
	timesteps, num_inference_steps = self.get_timesteps(
	num_inference_steps=num_inference_steps, strength=strength, device=device
	)
	# at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
	latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
	# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
	is_strength_max = strength == 1.0

	# 6. Prepare latent variables
	num_channels_latents = self.vae.config.latent_channels
	num_channels_unet = self.unet.config.in_channels
	return_image_latents = num_channels_unet == 4
	latents_outputs = self.prepare_latents(
	batch_size * num_images_per_prompt,
	num_channels_latents,
	height,
	width,
	prompt_embeds.dtype,
	device,
	generator,
	latents,
	image=init_image,
	timestep=latent_timestep,
	is_strength_max=is_strength_max,
	return_noise=True,
	return_image_latents=return_image_latents,
	)

	if return_image_latents:
	latents, noise, image_latents = latents_outputs
	else:
	latents, noise = latents_outputs

	# 7. Prepare mask latent variables
	mask, masked_image_latents = self.prepare_mask_latents(
	mask,
	masked_image,
	batch_size * num_images_per_prompt,
	height,
	width,
	prompt_embeds.dtype,
	device,
	generator,
	do_classifier_free_guidance,
	)

	# 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)

	# 7.1 Create tensor stating which controlnets to keep
	controlnet_keep = []
	for i in range(len(timesteps)):
	keeps = [
	1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
	for s, e in zip(control_guidance_start, control_guidance_end)
	]
	controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)

	# 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
	latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
	latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

	# controlnet(s) inference
	if guess_mode and do_classifier_free_guidance:
	# Infer ControlNet only for the conditional batch.
	control_model_input = latents
	control_model_input = self.scheduler.scale_model_input(control_model_input, t)
	controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
	else:
	control_model_input = latent_model_input
	controlnet_prompt_embeds = prompt_embeds

	if isinstance(controlnet_keep[i], list):
	cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
	else:
	cond_scale = controlnet_conditioning_scale * controlnet_keep[i]

	# predict the noise residual
	if num_channels_unet == 9:
	latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)

	down_block_res_samples, mid_block_res_sample = self.controlnet(
	latent_model_input, #control_model_input,
	t,
	encoder_hidden_states=controlnet_prompt_embeds,
	controlnet_cond=control_image,
	conditioning_scale=cond_scale,
	guess_mode=guess_mode,
	return_dict=False,
	)

	if guess_mode and do_classifier_free_guidance:
	# Infered ControlNet only for the conditional batch.
	# To apply the output of ControlNet to both the unconditional and conditional batches,
	# add 0 to the unconditional batch to keep it unchanged.
	down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
	mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])

	noise_pred = self.unet(
	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,
	return_dict=False,
	)[0]

	# 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, return_dict=False)[0]

	if num_channels_unet == 4:
	init_latents_proper = image_latents[:1]
	init_mask = mask[:1]

	if i < len(timesteps) - 1:
	noise_timestep = timesteps[i + 1]
	init_latents_proper = self.scheduler.add_noise(
	init_latents_proper, noise, torch.tensor([noise_timestep])
	)

	latents = (1 - init_mask) * init_latents_proper + init_mask * latents

	# 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 not output_type == "latent":
	image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
	image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
	else:
	image = latents
	has_nsfw_concept = None

	if has_nsfw_concept is None:
	do_denormalize = [True] * image.shape[0]
	else:
	do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

	image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

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

	if not return_dict:
	return (image, has_nsfw_concept)

	return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)