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	# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
	# Copyright 2023 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


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

	import numpy as np
	import paddle
	import PIL.Image

	from paddlenlp.transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
	from ppdiffusers.image_processor import VaeImageProcessor
	from ppdiffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
	from ppdiffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
	from ppdiffusers.pipeline_utils import DiffusionPipeline
	from ppdiffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
	from ppdiffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import (
	MultiControlNetModel,
	)
	from ppdiffusers.pipelines.stable_diffusion.safety_checker import (
	StableDiffusionSafetyChecker,
	)
	from ppdiffusers.schedulers import KarrasDiffusionSchedulers
	from ppdiffusers.utils import (
	deprecate,
	logging,
	randn_tensor,
	replace_example_docstring,
	)


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


	EXAMPLE_DOC_STRING = """
	Examples:
	```py
	>>> # !pip install opencv-python paddlenlp
	>>> from ppdiffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler
	>>> from ppdiffusers.utils import load_image
	>>> import numpy as np
	>>> import paddle

	>>> import cv2
	>>> from PIL import Image

	>>> # download an image
	>>> image = load_image(
	... "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
	... )
	>>> np_image = np.array(image)

	>>> # get canny image
	>>> np_image = cv2.Canny(np_image, 100, 200)
	>>> np_image = np_image[:, :, None]
	>>> np_image = np.concatenate([np_image, np_image, np_image], axis=2)
	>>> canny_image = Image.fromarray(np_image)

	>>> # load control net and stable diffusion v1-5
	>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", paddle_dtype=paddle.float16)
	>>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
	... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, paddle_dtype=paddle.float16
	... )

	>>> # speed up diffusion process with faster scheduler and memory optimization
	>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

	>>> # generate image
	>>> generator = paddle.Generaotr().manual_seed(0)
	>>> image = pipe(
	... "futuristic-looking woman",
	... num_inference_steps=20,
	... generator=generator,
	... image=image,
	... control_image=canny_image,
	... ).images[0]
	>>> image.save("demo.png")
	```
	"""


	class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
	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`]

	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(
	f"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, do_convert_rgb=True)
	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)

	def _encode_prompt(
	self,
	prompt,
	num_images_per_prompt,
	do_classifier_free_guidance,
	negative_prompt=None,
	prompt_embeds: Optional[paddle.Tensor] = None,
	negative_prompt_embeds: Optional[paddle.Tensor] = 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
	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 (`paddle.Tensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`paddle.Tensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	lora_scale (`float`, optional):
	A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
	"""
	# 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="pd",
	)
	text_input_ids = text_inputs.input_ids
	untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pd").input_ids

	if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not paddle.equal_all(
	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}"
	)

	config = (
	self.text_encoder.config
	if isinstance(self.text_encoder.config, dict)
	else self.text_encoder.config.to_dict()
	)
	if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
	attention_mask = text_inputs.attention_mask
	else:
	attention_mask = None

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

	prompt_embeds = prompt_embeds.cast(dtype=self.text_encoder.dtype)

	bs_embed, seq_len, _ = prompt_embeds.shape
	# duplicate text embeddings for each generation per prompt, using mps friendly method
	prompt_embeds = prompt_embeds.tile([1, num_images_per_prompt, 1])
	prompt_embeds = prompt_embeds.reshape([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="pd",
	)

	config = (
	self.text_encoder.config
	if isinstance(self.text_encoder.config, dict)
	else self.text_encoder.config.to_dict()
	)
	if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
	attention_mask = uncond_input.attention_mask
	else:
	attention_mask = None

	negative_prompt_embeds = self.text_encoder(
	uncond_input.input_ids,
	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.cast(dtype=self.text_encoder.dtype)

	negative_prompt_embeds = negative_prompt_embeds.tile([1, num_images_per_prompt, 1])
	negative_prompt_embeds = negative_prompt_embeds.reshape([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 = paddle.concat([negative_prompt_embeds, prompt_embeds])

	return prompt_embeds

	def run_safety_checker(self, image, dtype):
	if self.safety_checker is None:
	has_nsfw_concept = None
	else:
	if paddle.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="pd")
	image, has_nsfw_concept = self.safety_checker(
	images=image, clip_input=safety_checker_input.pixel_values.cast(dtype)
	)
	return image, has_nsfw_concept

	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,
	callback_steps,
	negative_prompt=None,
	prompt_embeds=None,
	negative_prompt_embeds=None,
	controlnet_conditioning_scale=1.0,
	):
	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."
	)

	if isinstance(self.controlnet, ControlNetModel):
	self.check_image(image, prompt, prompt_embeds)
	elif isinstance(self.controlnet, 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):
	if not isinstance(controlnet_conditioning_scale, float):
	raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
	elif isinstance(self.controlnet, 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

	def check_image(self, image, prompt, prompt_embeds):
	image_is_pil = isinstance(image, PIL.Image.Image)
	image_is_tensor = isinstance(image, paddle.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], paddle.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, paddle 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}"
	)

	def prepare_control_image(
	self,
	image,
	width,
	height,
	batch_size,
	num_images_per_prompt,
	dtype,
	do_classifier_free_guidance=False,
	guess_mode=False,
	):
	image = self.control_image_processor.preprocess(image, height=height, width=width).cast(dtype=paddle.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, axis=0)

	image = image.cast(dtype)

	if do_classifier_free_guidance and not guess_mode:
	image = paddle.concat([image] * 2)

	return image

	def get_timesteps(self, num_inference_steps, strength):
	# 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 prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, generator=None):
	if not isinstance(image, (paddle.Tensor, PIL.Image.Image, list)):
	raise ValueError(
	f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
	)

	image = image.cast(dtype=dtype)

	batch_size = batch_size * num_images_per_prompt

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

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

	elif isinstance(generator, list):
	init_latents = [
	self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
	]
	init_latents = paddle.concat(init_latents, axis=0)
	else:
	init_latents = self.vae.encode(image).latent_dist.sample(generator)

	init_latents = self.vae.config.scaling_factor * init_latents

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

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

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

	return latents

	@paddle.no_grad()
	@replace_example_docstring(EXAMPLE_DOC_STRING)
	def __call__(
	self,
	prompt: Union[str, List[str]] = None,
	image: Union[
	paddle.Tensor,
	PIL.Image.Image,
	np.ndarray,
	List[paddle.Tensor],
	List[PIL.Image.Image],
	List[np.ndarray],
	] = None,
	control_image: Union[
	paddle.Tensor,
	PIL.Image.Image,
	np.ndarray,
	List[paddle.Tensor],
	List[PIL.Image.Image],
	List[np.ndarray],
	] = None,
	height: Optional[int] = None,
	width: Optional[int] = None,
	strength: float = 0.8,
	num_inference_steps: int = 50,
	guidance_scale: float = 7.5,
	negative_prompt: Optional[Union[str, List[str]]] = None,
	num_images_per_prompt: Optional[int] = 1,
	eta: float = 0.0,
	generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
	latents: Optional[paddle.Tensor] = None,
	prompt_embeds: Optional[paddle.Tensor] = None,
	negative_prompt_embeds: Optional[paddle.Tensor] = None,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
	callback_steps: int = 1,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
	guess_mode: bool = False,
	):
	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 (`paddle.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[paddle.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
	`List[List[paddle.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
	The initial image will be used as the starting point for the image generation process. Can also accpet
	image latents as `image`, if passing latents directly, it will not be encoded again.
	control_image (`paddle.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[paddle.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
	`List[List[paddle.Tensor]]`, `List[List[np.ndarray]]` 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 `paddle.Tensor`, 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.
	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 (`paddle.Tensor`, optional):
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will ge generated by sampling using the supplied random `generator`.
	prompt_embeds (`paddle.Tensor`, optional):
	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
	provided, text embeddings will be generated from `prompt` input argument.
	negative_prompt_embeds (`paddle.Tensor`, optional):
	Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
	weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
	argument.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generate image. Choose between
	[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.
	callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. The function will be
	called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`.
	callback_steps (`int`, optional, defaults to 1):
	The frequency at which the `callback` function will be called. If not specified, the callback will be
	called at every step.
	cross_attention_kwargs (`dict`, optional):
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
	`self.processor` in
	[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
	controlnet_conditioning_scale (`float` or `List[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. 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.

	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`.
	"""
	# 1. Check inputs. Raise error if not correct
	self.check_inputs(
	prompt,
	control_image,
	callback_steps,
	negative_prompt,
	prompt_embeds,
	negative_prompt_embeds,
	controlnet_conditioning_scale,
	)

	# 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]

	# 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

	controlnet = self.controlnet

	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,
	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
	image = self.image_processor.preprocess(image).cast(dtype=paddle.float32)

	# 5. Prepare controlnet_conditioning_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,
	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,
	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

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

	# 6. Prepare latent variables
	latents = self.prepare_latents(
	image,
	latent_timestep,
	batch_size,
	num_images_per_prompt,
	prompt_embeds.dtype,
	generator,
	)

	# 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
	latent_model_input = paddle.concat([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

	down_block_res_samples, mid_block_res_sample = self.controlnet(
	control_model_input,
	t,
	encoder_hidden_states=controlnet_prompt_embeds,
	controlnet_cond=control_image,
	conditioning_scale=controlnet_conditioning_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 = [paddle.concat([paddle.zeros_like(d), d]) for d in down_block_res_samples]
	mid_block_res_sample = paddle.concat(
	[paddle.zeros_like(mid_block_res_sample), mid_block_res_sample]
	)

	# predict the noise residual
	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]

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

	if not output_type == "latent":
	image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
	image, has_nsfw_concept = self.run_safety_checker(image, 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)

	if not return_dict:
	return (image, has_nsfw_concept)

	return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)