
"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_A				=					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'
def 						UpperCAmelCase   (						a_,		a_,		a_=8     ):





		'''simple docstring'''


		lowerCamelCase	:     List[str]            =       height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowerCamelCase	:     List[str]            =       width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor
def 						UpperCAmelCase   (						a_,		a_=512,		a_=512     ):





		'''simple docstring'''


		lowerCamelCase	:     Union[str, Any]            =       pil_image.resize((w, h),		resample=Image.BICUBIC,		reducing_gap=1     )
		lowerCamelCase	:     Tuple            =       np.array(pil_image.convert('RGB'     )     )
		lowerCamelCase	:     List[str]            =       arr.astype(np.floataa     ) / 1_2_7.5 - 1
		lowerCamelCase	:     Optional[int]            =       np.transpose(a_,		[2, 0, 1]     )
		lowerCamelCase	:     int            =       torch.from_numpy(a_     ).unsqueeze(0     )
		return image






class 			_lowercase  (							__UpperCAmelCase						):




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)						->				Optional[Any]:
			super().__init__()

			self.register_modules(
			    unet=UpperCAmelCase_						,					scheduler=UpperCAmelCase_						,					movq=UpperCAmelCase_						,					)
			lowerCamelCase	:     Any            =       2 ** (len(self.movq.config.block_out_channels   ) - 1)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				str:
			# get the original timestep using init_timestep
			lowerCamelCase	:     List[str]            =       min(int(num_inference_steps * strength   )						,					UpperCAmelCase_   )

			lowerCamelCase	:     Dict            =       max(num_inference_steps - init_timestep						,					0   )
			lowerCamelCase	:     Optional[Any]            =       self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None   )						->				Union[str, Any]:
			if not isinstance(UpperCAmelCase_						,					(torch.Tensor, PIL.Image.Image, list)   ):
					raise ValueError(
					    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_   )}"""   )

			lowerCamelCase	:     Optional[Any]            =       image.to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       batch_size * num_images_per_prompt

			if image.shape[1] == 4:
					lowerCamelCase	:     Any            =       image

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

					elif isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
							lowerCamelCase	:     List[Any]            =       [
							    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(UpperCAmelCase_   )
							]
							lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
					else:
							lowerCamelCase	:     Any            =       self.movq.encode(UpperCAmelCase_   ).latent_dist.sample(UpperCAmelCase_   )

					lowerCamelCase	:     List[Any]            =       self.movq.config.scaling_factor * init_latents

			lowerCamelCase	:     List[str]            =       torch.cat([init_latents]						,					dim=0   )

			lowerCamelCase	:     List[Any]            =       init_latents.shape
			lowerCamelCase	:     str            =       randn_tensor(UpperCAmelCase_						,					generator=UpperCAmelCase_						,					device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			# get latents
			lowerCamelCase	:     Union[str, Any]            =       self.scheduler.add_noise(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       init_latents

			return latents




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Tuple:
			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError('Please install accelerate via `pip install accelerate`'   )

			lowerCamelCase	:     Dict            =       torch.device(F"""cuda:{gpu_id}"""   )

			lowerCamelCase	:     Optional[int]            =       [
			    self.unet,
			    self.movq,
			]
			for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
							cpu_offload(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Optional[Any]:
			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.'   )

			lowerCamelCase	:     str            =       torch.device(F"""cuda:{gpu_id}"""   )

			if self.device.type != "cpu":
					self.to('cpu'						,					silence_dtype_warnings=UpperCAmelCase_   )
					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			lowerCamelCase	:     Optional[int]            =       None
			for cpu_offloaded_model in [self.unet, self.movq]:
					lowerCamelCase      ,       lowerCamelCase	:     Any            =       cpu_offload_with_hook(UpperCAmelCase_						,					UpperCAmelCase_						,					prev_module_hook=UpperCAmelCase_   )

			# We'll offload the last model manually.
			lowerCamelCase	:     Optional[Any]            =       hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      _UpperCamelCase       (					self   )						->				List[Any]:
			if not hasattr(self.unet						,					'_hf_hook'   ):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(UpperCAmelCase_						,					'_hf_hook'   )
					    and hasattr(module._hf_hook						,					'execution_device'   )
					    and module._hf_hook.execution_device is not None
					):
							return torch.device(module._hf_hook.execution_device   )
			return self.device




	@torch.no_grad()
	@replace_example_docstring(UpperCAmelCase_   )
	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 100						,					UpperCAmelCase_ = 4.0						,					UpperCAmelCase_ = 0.3						,					UpperCAmelCase_ = 1						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = "pil"						,					UpperCAmelCase_ = True						,					)						->				Any:
			lowerCamelCase	:     List[Any]            =       self._execution_device

			lowerCamelCase	:     int            =       guidance_scale > 1.0

			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[str]            =       torch.cat(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       image_embeds.shape[0]

			if do_classifier_free_guidance:
					lowerCamelCase	:     str            =       image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Tuple            =       negative_image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Optional[Any]            =       hint.repeat_interleave(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       torch.cat([negative_image_embeds, image_embeds]						,					dim=0   ).to(dtype=self.unet.dtype						,					device=UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       torch.cat([hint, hint]						,					dim=0   ).to(dtype=self.unet.dtype						,					device=UpperCAmelCase_   )

			if not isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       [image]
			if not all(isinstance(UpperCAmelCase_						,					(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
					raise ValueError(
					    F"""Input is in incorrect format: {[type(UpperCAmelCase_   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""   )

			lowerCamelCase	:     int            =       torch.cat([prepare_image(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   ) for i in image]						,					dim=0   )
			lowerCamelCase	:     Tuple            =       image.to(dtype=image_embeds.dtype						,					device=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       self.movq.encode(UpperCAmelCase_   )['latents']
			lowerCamelCase	:     Tuple            =       latents.repeat_interleave(UpperCAmelCase_						,					dim=0   )
			self.scheduler.set_timesteps(UpperCAmelCase_						,					device=UpperCAmelCase_   )
			lowerCamelCase      ,       lowerCamelCase	:     str            =       self.get_timesteps(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       timesteps[:1].repeat(batch_size * num_images_per_prompt   )
			lowerCamelCase      ,       lowerCamelCase	:     int            =       downscale_height_and_width(UpperCAmelCase_						,					UpperCAmelCase_						,					self.movq_scale_factor   )
			lowerCamelCase	:     List[Any]            =       self.prepare_latents(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					image_embeds.dtype						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			for i, t in enumerate(self.progress_bar(UpperCAmelCase_   )   ):
					# expand the latents if we are doing classifier free guidance
					lowerCamelCase	:     List[Any]            =       torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

					lowerCamelCase	:     Tuple            =       {'image_embeds': image_embeds, 'hint': hint}
					lowerCamelCase	:     List[Any]            =       self.unet(
					    sample=UpperCAmelCase_						,					timestep=UpperCAmelCase_						,					encoder_hidden_states=UpperCAmelCase_						,					added_cond_kwargs=UpperCAmelCase_						,					return_dict=UpperCAmelCase_						,					)[0]

					if do_classifier_free_guidance:
							lowerCamelCase      ,       lowerCamelCase	:     int            =       noise_pred.split(latents.shape[1]						,					dim=1   )
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.chunk(2   )
							lowerCamelCase      ,       lowerCamelCase	:     Any            =       variance_pred.chunk(2   )
							lowerCamelCase	:     Any            =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							lowerCamelCase	:     Any            =       torch.cat([noise_pred, variance_pred_text]						,					dim=1   )

					if not (
					    hasattr(self.scheduler.config						,					'variance_type'   )
					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
					):
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.split(latents.shape[1]						,					dim=1   )

					# compute the previous noisy sample x_t -> x_t-1

					lowerCamelCase	:     Union[str, Any]            =       self.scheduler.step(
					    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					generator=UpperCAmelCase_						,					)[0]

			# post-processing
			lowerCamelCase	:     Optional[Any]            =       self.movq.decode(UpperCAmelCase_						,					force_not_quantize=UpperCAmelCase_   )['sample']

			if output_type not in ["pt", "np", "pil"]:
					raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""   )

			if output_type in ["np", "pil"]:
					lowerCamelCase	:     Optional[Any]            =       image * 0.5 + 0.5
					lowerCamelCase	:     Any            =       image.clamp(0						,					1   )
					lowerCamelCase	:     Any            =       image.cpu().permute(0						,					2						,					3						,					1   ).float().numpy()

			if output_type == "pil":
					lowerCamelCase	:     Tuple            =       self.numpy_to_pil(UpperCAmelCase_   )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_   )








"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_A				=					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'
def 						UpperCAmelCase   (						a_,		a_,		a_=8     ):





		'''simple docstring'''


		lowerCamelCase	:     List[str]            =       height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowerCamelCase	:     List[str]            =       width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor
def 						UpperCAmelCase   (						a_,		a_=512,		a_=512     ):





		'''simple docstring'''


		lowerCamelCase	:     Union[str, Any]            =       pil_image.resize((w, h),		resample=Image.BICUBIC,		reducing_gap=1     )
		lowerCamelCase	:     Tuple            =       np.array(pil_image.convert('RGB'     )     )
		lowerCamelCase	:     List[str]            =       arr.astype(np.floataa     ) / 1_2_7.5 - 1
		lowerCamelCase	:     Optional[int]            =       np.transpose(a_,		[2, 0, 1]     )
		lowerCamelCase	:     int            =       torch.from_numpy(a_     ).unsqueeze(0     )
		return image






class 			_lowercase  (							__UpperCAmelCase						):




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)						->				Optional[Any]:
			super().__init__()

			self.register_modules(
			    unet=UpperCAmelCase_						,					scheduler=UpperCAmelCase_						,					movq=UpperCAmelCase_						,					)
			lowerCamelCase	:     Any            =       2 ** (len(self.movq.config.block_out_channels   ) - 1)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				str:
			# get the original timestep using init_timestep
			lowerCamelCase	:     List[str]            =       min(int(num_inference_steps * strength   )						,					UpperCAmelCase_   )

			lowerCamelCase	:     Dict            =       max(num_inference_steps - init_timestep						,					0   )
			lowerCamelCase	:     Optional[Any]            =       self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None   )						->				Union[str, Any]:
			if not isinstance(UpperCAmelCase_						,					(torch.Tensor, PIL.Image.Image, list)   ):
					raise ValueError(
					    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_   )}"""   )

			lowerCamelCase	:     Optional[Any]            =       image.to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       batch_size * num_images_per_prompt

			if image.shape[1] == 4:
					lowerCamelCase	:     Any            =       image

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

					elif isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
							lowerCamelCase	:     List[Any]            =       [
							    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(UpperCAmelCase_   )
							]
							lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
					else:
							lowerCamelCase	:     Any            =       self.movq.encode(UpperCAmelCase_   ).latent_dist.sample(UpperCAmelCase_   )

					lowerCamelCase	:     List[Any]            =       self.movq.config.scaling_factor * init_latents

			lowerCamelCase	:     List[str]            =       torch.cat([init_latents]						,					dim=0   )

			lowerCamelCase	:     List[Any]            =       init_latents.shape
			lowerCamelCase	:     str            =       randn_tensor(UpperCAmelCase_						,					generator=UpperCAmelCase_						,					device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			# get latents
			lowerCamelCase	:     Union[str, Any]            =       self.scheduler.add_noise(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       init_latents

			return latents




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Tuple:
			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError('Please install accelerate via `pip install accelerate`'   )

			lowerCamelCase	:     Dict            =       torch.device(F"""cuda:{gpu_id}"""   )

			lowerCamelCase	:     Optional[int]            =       [
			    self.unet,
			    self.movq,
			]
			for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
							cpu_offload(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Optional[Any]:
			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.'   )

			lowerCamelCase	:     str            =       torch.device(F"""cuda:{gpu_id}"""   )

			if self.device.type != "cpu":
					self.to('cpu'						,					silence_dtype_warnings=UpperCAmelCase_   )
					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			lowerCamelCase	:     Optional[int]            =       None
			for cpu_offloaded_model in [self.unet, self.movq]:
					lowerCamelCase      ,       lowerCamelCase	:     Any            =       cpu_offload_with_hook(UpperCAmelCase_						,					UpperCAmelCase_						,					prev_module_hook=UpperCAmelCase_   )

			# We'll offload the last model manually.
			lowerCamelCase	:     Optional[Any]            =       hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      _UpperCamelCase       (					self   )						->				List[Any]:
			if not hasattr(self.unet						,					'_hf_hook'   ):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(UpperCAmelCase_						,					'_hf_hook'   )
					    and hasattr(module._hf_hook						,					'execution_device'   )
					    and module._hf_hook.execution_device is not None
					):
							return torch.device(module._hf_hook.execution_device   )
			return self.device




	@torch.no_grad()
	@replace_example_docstring(UpperCAmelCase_   )
	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 100						,					UpperCAmelCase_ = 4.0						,					UpperCAmelCase_ = 0.3						,					UpperCAmelCase_ = 1						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = "pil"						,					UpperCAmelCase_ = True						,					)						->				Any:
			lowerCamelCase	:     List[Any]            =       self._execution_device

			lowerCamelCase	:     int            =       guidance_scale > 1.0

			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[str]            =       torch.cat(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       image_embeds.shape[0]

			if do_classifier_free_guidance:
					lowerCamelCase	:     str            =       image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Tuple            =       negative_image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Optional[Any]            =       hint.repeat_interleave(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       torch.cat([negative_image_embeds, image_embeds]						,					dim=0   ).to(dtype=self.unet.dtype						,					device=UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       torch.cat([hint, hint]						,					dim=0   ).to(dtype=self.unet.dtype						,					device=UpperCAmelCase_   )

			if not isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       [image]
			if not all(isinstance(UpperCAmelCase_						,					(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
					raise ValueError(
					    F"""Input is in incorrect format: {[type(UpperCAmelCase_   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""   )

			lowerCamelCase	:     int            =       torch.cat([prepare_image(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   ) for i in image]						,					dim=0   )
			lowerCamelCase	:     Tuple            =       image.to(dtype=image_embeds.dtype						,					device=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       self.movq.encode(UpperCAmelCase_   )['latents']
			lowerCamelCase	:     Tuple            =       latents.repeat_interleave(UpperCAmelCase_						,					dim=0   )
			self.scheduler.set_timesteps(UpperCAmelCase_						,					device=UpperCAmelCase_   )
			lowerCamelCase      ,       lowerCamelCase	:     str            =       self.get_timesteps(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       timesteps[:1].repeat(batch_size * num_images_per_prompt   )
			lowerCamelCase      ,       lowerCamelCase	:     int            =       downscale_height_and_width(UpperCAmelCase_						,					UpperCAmelCase_						,					self.movq_scale_factor   )
			lowerCamelCase	:     List[Any]            =       self.prepare_latents(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					image_embeds.dtype						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			for i, t in enumerate(self.progress_bar(UpperCAmelCase_   )   ):
					# expand the latents if we are doing classifier free guidance
					lowerCamelCase	:     List[Any]            =       torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

					lowerCamelCase	:     Tuple            =       {'image_embeds': image_embeds, 'hint': hint}
					lowerCamelCase	:     List[Any]            =       self.unet(
					    sample=UpperCAmelCase_						,					timestep=UpperCAmelCase_						,					encoder_hidden_states=UpperCAmelCase_						,					added_cond_kwargs=UpperCAmelCase_						,					return_dict=UpperCAmelCase_						,					)[0]

					if do_classifier_free_guidance:
							lowerCamelCase      ,       lowerCamelCase	:     int            =       noise_pred.split(latents.shape[1]						,					dim=1   )
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.chunk(2   )
							lowerCamelCase      ,       lowerCamelCase	:     Any            =       variance_pred.chunk(2   )
							lowerCamelCase	:     Any            =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							lowerCamelCase	:     Any            =       torch.cat([noise_pred, variance_pred_text]						,					dim=1   )

					if not (
					    hasattr(self.scheduler.config						,					'variance_type'   )
					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
					):
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.split(latents.shape[1]						,					dim=1   )

					# compute the previous noisy sample x_t -> x_t-1

					lowerCamelCase	:     Union[str, Any]            =       self.scheduler.step(
					    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					generator=UpperCAmelCase_						,					)[0]

			# post-processing
			lowerCamelCase	:     Optional[Any]            =       self.movq.decode(UpperCAmelCase_						,					force_not_quantize=UpperCAmelCase_   )['sample']

			if output_type not in ["pt", "np", "pil"]:
					raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""   )

			if output_type in ["np", "pil"]:
					lowerCamelCase	:     Optional[Any]            =       image * 0.5 + 0.5
					lowerCamelCase	:     Any            =       image.clamp(0						,					1   )
					lowerCamelCase	:     Any            =       image.cpu().permute(0						,					2						,					3						,					1   ).float().numpy()

			if output_type == "pil":
					lowerCamelCase	:     Tuple            =       self.numpy_to_pil(UpperCAmelCase_   )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_   )






