


'''simple docstring'''
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase__    					=      {
    '''sample_size''': 3_2,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [3_2, 6_4],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

UpperCamelCase__    					=      {
    '''sample_size''': 6_4,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

UpperCamelCase__    					=      {
    '''sample_size''': 2_5_6,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

UpperCamelCase__    					=      {
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.002,
    '''sigma_max''': 8_0.0,
}

UpperCamelCase__    					=      {
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 8_0.0,
}

UpperCamelCase__    					=      {
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 8_0.0,
}





def 					a__       (					lowerCAmelCase__						)    ->	Optional[int]:
    if isinstance(__lowerCamelCase  ,					__lowerCamelCase						):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError('''boolean value expected'''						)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=False						)    ->	List[Any]:
    UpperCAmelCase__			:		Tuple													=				checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
    UpperCAmelCase__			:		Tuple													=				checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
    UpperCAmelCase__			:		Union[str, Any]													=				checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
    UpperCAmelCase__			:		Union[str, Any]													=				checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
    UpperCAmelCase__			:		Union[str, Any]													=				checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
    UpperCAmelCase__			:		Optional[int]													=				checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
    UpperCAmelCase__			:		Any													=				checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
    UpperCAmelCase__			:		Union[str, Any]													=				checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
    UpperCAmelCase__			:		Optional[Any]													=				checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
    UpperCAmelCase__			:		Any													=				checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

    if has_skip:
        UpperCAmelCase__			:		List[Any]													=				checkpoint[F"""{old_prefix}.skip_connection.weight"""]
        UpperCAmelCase__			:		Tuple													=				checkpoint[F"""{old_prefix}.skip_connection.bias"""]

    return new_checkpoint





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=None						)    ->	int:
    UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Union[str, Any]													=				checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3  ,					dim=0						)
    UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Union[str, Any]													=				checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3  ,					dim=0						)

    UpperCAmelCase__			:		List[Any]													=				checkpoint[F"""{old_prefix}.norm.weight"""]
    UpperCAmelCase__			:		Dict													=				checkpoint[F"""{old_prefix}.norm.bias"""]

    UpperCAmelCase__			:		Union[str, Any]													=				weight_q.squeeze(-1						).squeeze(-1						)
    UpperCAmelCase__			:		Union[str, Any]													=				bias_q.squeeze(-1						).squeeze(-1						)
    UpperCAmelCase__			:		Any													=				weight_k.squeeze(-1						).squeeze(-1						)
    UpperCAmelCase__			:		List[Any]													=				bias_k.squeeze(-1						).squeeze(-1						)
    UpperCAmelCase__			:		Optional[Any]													=				weight_v.squeeze(-1						).squeeze(-1						)
    UpperCAmelCase__			:		Any													=				bias_v.squeeze(-1						).squeeze(-1						)

    UpperCAmelCase__			:		List[Any]													=				(
        checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1						).squeeze(-1						)
    )
    UpperCAmelCase__			:		Optional[Any]													=				checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1						).squeeze(-1						)

    return new_checkpoint





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	List[str]:
    UpperCAmelCase__			:		Dict													=				torch.load(__lowerCamelCase  ,					map_location='''cpu'''						)
    UpperCAmelCase__			:		Tuple													=				{}

    UpperCAmelCase__			:		Tuple													=				checkpoint['''time_embed.0.weight''']
    UpperCAmelCase__			:		List[Any]													=				checkpoint['''time_embed.0.bias''']
    UpperCAmelCase__			:		List[Any]													=				checkpoint['''time_embed.2.weight''']
    UpperCAmelCase__			:		List[Any]													=				checkpoint['''time_embed.2.bias''']

    if unet_config["num_class_embeds"] is not None:
        UpperCAmelCase__			:		List[Any]													=				checkpoint['''label_emb.weight''']

    UpperCAmelCase__			:		Union[str, Any]													=				checkpoint['''input_blocks.0.0.weight''']
    UpperCAmelCase__			:		Tuple													=				checkpoint['''input_blocks.0.0.bias''']

    UpperCAmelCase__			:		int													=				unet_config['''down_block_types''']
    UpperCAmelCase__			:		List[Any]													=				unet_config['''layers_per_block''']
    UpperCAmelCase__			:		List[Any]													=				unet_config['''attention_head_dim''']
    UpperCAmelCase__			:		Tuple													=				unet_config['''block_out_channels''']
    UpperCAmelCase__			:		int													=				1
    UpperCAmelCase__			:		List[Any]													=				channels_list[0]

    for i, layer_type in enumerate(__lowerCamelCase						):
        UpperCAmelCase__			:		Dict													=				channels_list[i]
        UpperCAmelCase__			:		List[str]													=				current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(__lowerCamelCase						):
                UpperCAmelCase__			:		Tuple													=				F"""down_blocks.{i}.resnets.{j}"""
                UpperCAmelCase__			:		Tuple													=				F"""input_blocks.{current_layer}.0"""
                UpperCAmelCase__			:		Dict													=				True if j == 0 and downsample_block_has_skip else False
                UpperCAmelCase__			:		int													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					has_skip=__lowerCamelCase						)
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(__lowerCamelCase						):
                UpperCAmelCase__			:		Optional[int]													=				F"""down_blocks.{i}.resnets.{j}"""
                UpperCAmelCase__			:		List[Any]													=				F"""input_blocks.{current_layer}.0"""
                UpperCAmelCase__			:		str													=				True if j == 0 and downsample_block_has_skip else False
                UpperCAmelCase__			:		List[Any]													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					has_skip=__lowerCamelCase						)
                UpperCAmelCase__			:		Optional[int]													=				F"""down_blocks.{i}.attentions.{j}"""
                UpperCAmelCase__			:		Optional[Any]													=				F"""input_blocks.{current_layer}.1"""
                UpperCAmelCase__			:		Union[str, Any]													=				convert_attention(
                    __lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)
                current_layer += 1

        if i != len(__lowerCamelCase						) - 1:
            UpperCAmelCase__			:		List[Any]													=				F"""down_blocks.{i}.downsamplers.0"""
            UpperCAmelCase__			:		str													=				F"""input_blocks.{current_layer}.0"""
            UpperCAmelCase__			:		Tuple													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)
            current_layer += 1

        UpperCAmelCase__			:		Tuple													=				current_channels

    # hardcoded the mid-block for now
    UpperCAmelCase__			:		Tuple													=				'''mid_block.resnets.0'''
    UpperCAmelCase__			:		Optional[int]													=				'''middle_block.0'''
    UpperCAmelCase__			:		int													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)
    UpperCAmelCase__			:		List[str]													=				'''mid_block.attentions.0'''
    UpperCAmelCase__			:		int													=				'''middle_block.1'''
    UpperCAmelCase__			:		Union[str, Any]													=				convert_attention(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)
    UpperCAmelCase__			:		List[str]													=				'''mid_block.resnets.1'''
    UpperCAmelCase__			:		Optional[int]													=				'''middle_block.2'''
    UpperCAmelCase__			:		str													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)

    UpperCAmelCase__			:		int													=				0
    UpperCAmelCase__			:		Optional[int]													=				unet_config['''up_block_types''']

    for i, layer_type in enumerate(__lowerCamelCase						):
        if layer_type == "ResnetUpsampleBlock2D":
            for j in range(layers_per_block + 1						):
                UpperCAmelCase__			:		int													=				F"""up_blocks.{i}.resnets.{j}"""
                UpperCAmelCase__			:		Union[str, Any]													=				F"""output_blocks.{current_layer}.0"""
                UpperCAmelCase__			:		Optional[int]													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					has_skip=__lowerCamelCase						)
                current_layer += 1

            if i != len(__lowerCamelCase						) - 1:
                UpperCAmelCase__			:		List[str]													=				F"""up_blocks.{i}.upsamplers.0"""
                UpperCAmelCase__			:		Optional[Any]													=				F"""output_blocks.{current_layer-1}.1"""
                UpperCAmelCase__			:		List[Any]													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1						):
                UpperCAmelCase__			:		Union[str, Any]													=				F"""up_blocks.{i}.resnets.{j}"""
                UpperCAmelCase__			:		List[Any]													=				F"""output_blocks.{current_layer}.0"""
                UpperCAmelCase__			:		Optional[int]													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					has_skip=__lowerCamelCase						)
                UpperCAmelCase__			:		Tuple													=				F"""up_blocks.{i}.attentions.{j}"""
                UpperCAmelCase__			:		Any													=				F"""output_blocks.{current_layer}.1"""
                UpperCAmelCase__			:		int													=				convert_attention(
                    __lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)
                current_layer += 1

            if i != len(__lowerCamelCase						) - 1:
                UpperCAmelCase__			:		Optional[int]													=				F"""up_blocks.{i}.upsamplers.0"""
                UpperCAmelCase__			:		Any													=				F"""output_blocks.{current_layer-1}.2"""
                UpperCAmelCase__			:		str													=				convert_resnet(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase						)

    UpperCAmelCase__			:		Optional[int]													=				checkpoint['''out.0.weight''']
    UpperCAmelCase__			:		Optional[int]													=				checkpoint['''out.0.bias''']
    UpperCAmelCase__			:		Tuple													=				checkpoint['''out.2.weight''']
    UpperCAmelCase__			:		List[str]													=				checkpoint['''out.2.bias''']

    return new_checkpoint


if __name__ == "__main__":
  UpperCamelCase__    					=      argparse.ArgumentParser()

  parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
  parser.add_argument(
      '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
  )
  parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

  UpperCamelCase__    					=      parser.parse_args()
  UpperCamelCase__    					=      strabool(args.class_cond)

  UpperCamelCase__    					=      os.path.basename(args.unet_path)
  print(F"""Checkpoint: {ckpt_name}""")

  # Get U-Net config
  if "imagenet64" in ckpt_name:
    UpperCamelCase__    					=      IMAGENET_64_UNET_CONFIG
  elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    UpperCamelCase__    					=      LSUN_256_UNET_CONFIG
  elif "test" in ckpt_name:
    UpperCamelCase__    					=      TEST_UNET_CONFIG
  else:
    raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

  if not args.class_cond:
    UpperCamelCase__    					=      None

  UpperCamelCase__    					=      con_pt_to_diffuser(args.unet_path, unet_config)

  UpperCamelCase__    					=      UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    UpperCamelCase__    					=      CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    UpperCamelCase__    					=      CT_IMAGENET_64_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    UpperCamelCase__    					=      CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

  UpperCamelCase__    					=      CMStochasticIterativeScheduler(**scheduler_config)

  UpperCamelCase__    					=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)







'''simple docstring'''

from typing import List, Optional, Union

import torch

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,
)


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

lowercase_					= """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[Any]=8    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _SCREAMING_SNAKE_CASE									=			width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		)		->   Union[str, Any]:
        super().__init__()

        self.register_modules(
            unet=A ,		scheduler=A ,		movq=A ,		)
        _SCREAMING_SNAKE_CASE									=			2 ** (len(self.movq.config.block_out_channels       ) - 1)


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        if latents is None:
            _SCREAMING_SNAKE_CASE									=			randn_tensor(A ,		generator=A ,		device=A ,		dtype=A       )
        else:
            if latents.shape != shape:
                raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'       )
            _SCREAMING_SNAKE_CASE									=			latents.to(A       )

        _SCREAMING_SNAKE_CASE									=			latents * scheduler.init_noise_sigma
        return latents


    def 			snake_case_(		self ,		A=0       )		->   Dict:
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

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

        _SCREAMING_SNAKE_CASE									=			[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                cpu_offload(A ,		A       )


    def 			snake_case_(		self ,		A=0       )		->   str:
        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."""       )

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

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

        _SCREAMING_SNAKE_CASE									=			None
        for cpu_offloaded_model in [self.unet, self.movq]:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cpu_offload_with_hook(A ,		A ,		prev_module_hook=A       )

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


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 			snake_case_(		self       )		->   Tuple:
        if not hasattr(self.unet ,		"""_hf_hook"""       ):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(A ,		"""_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(A       )
    def __call__(		self ,		A ,		A ,		A = 512 ,		A = 512 ,		A = 100 ,		A = 4.0 ,		A = 1 ,		A = None ,		A = None ,		A = "pil" ,		A = True ,		)		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self._execution_device

        _SCREAMING_SNAKE_CASE									=			guidance_scale > 1.0

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			torch.cat(A ,		dim=0       )
        _SCREAMING_SNAKE_CASE									=			image_embeds.shape[0] * num_images_per_prompt
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			torch.cat(A ,		dim=0       )

        if do_classifier_free_guidance:
            _SCREAMING_SNAKE_CASE									=			image_embeds.repeat_interleave(A ,		dim=0       )
            _SCREAMING_SNAKE_CASE									=			negative_image_embeds.repeat_interleave(A ,		dim=0       )

        _SCREAMING_SNAKE_CASE									=			torch.cat([negative_image_embeds, image_embeds] ,		dim=0       ).to(dtype=self.unet.dtype ,		device=A       )

        self.scheduler.set_timesteps(A ,		device=A       )
        _SCREAMING_SNAKE_CASE									=			self.scheduler.timesteps

        _SCREAMING_SNAKE_CASE									=			self.unet.config.in_channels

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			downscale_height_and_width(A ,		A ,		self.movq_scale_factor       )

        # create initial latent
        _SCREAMING_SNAKE_CASE									=			self.prepare_latents(
            (batch_size, num_channels_latents, height, width) ,		image_embeds.dtype ,		A ,		A ,		A ,		self.scheduler ,		)

        for i, t in enumerate(self.progress_bar(A       )       ):
            # expand the latents if we are doing classifier free guidance
            _SCREAMING_SNAKE_CASE									=			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

            _SCREAMING_SNAKE_CASE									=			{"""image_embeds""": image_embeds}
            _SCREAMING_SNAKE_CASE									=			self.unet(
                sample=A ,		timestep=A ,		encoder_hidden_states=A ,		added_cond_kwargs=A ,		return_dict=A ,		)[0]

            if do_classifier_free_guidance:
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.split(latents.shape[1] ,		dim=1       )
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			variance_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                _SCREAMING_SNAKE_CASE									=			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"]
            ):
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.split(latents.shape[1] ,		dim=1       )

            # compute the previous noisy sample x_t -> x_t-1
            _SCREAMING_SNAKE_CASE									=			self.scheduler.step(
                A ,		A ,		A ,		generator=A ,		)[0]
        # post-processing
        _SCREAMING_SNAKE_CASE									=			self.movq.decode(A ,		force_not_quantize=A       )["""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"]:
            _SCREAMING_SNAKE_CASE									=			image * 0.5 + 0.5
            _SCREAMING_SNAKE_CASE									=			image.clamp(0 ,		1       )
            _SCREAMING_SNAKE_CASE									=			image.cpu().permute(0 ,		2 ,		3 ,		1       ).float().numpy()

        if output_type == "pil":
            _SCREAMING_SNAKE_CASE									=			self.numpy_to_pil(A       )

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=A       )



