




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCAmelCase__						:       Union[str, Any]							=    {512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: List[str]						     =    F'pretrained_models/{model_name}'
  if not os.path.isfile(UpperCamelCase							):
    os.makedirs('pretrained_models'					,     exist_ok=UpperCamelCase							)
    _lowercase: List[Any]						     =    F'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
    download_url(UpperCamelCase					,     'pretrained_models'							)
  _lowercase: Optional[Any]						     =    torch.load(UpperCamelCase					,     map_location=lambda UpperCamelCase					,     UpperCamelCase							: storage							)
  return model


def 		_lowerCamelCase				(					UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: str						     =    download_model(pretrained_models[args.image_size]							)

  _lowercase: Any						     =    state_dict['x_embedder.proj.weight']
  _lowercase: str						     =    state_dict['x_embedder.proj.bias']
  state_dict.pop('x_embedder.proj.weight'							)
  state_dict.pop('x_embedder.proj.bias'							)

  for depth in range(28							):
    _lowercase: Tuple						     =    state_dict[
        't_embedder.mlp.0.weight'
    ]
    _lowercase: Dict						     =    state_dict[
        't_embedder.mlp.0.bias'
    ]
    _lowercase: str						     =    state_dict[
        't_embedder.mlp.2.weight'
    ]
    _lowercase: List[str]						     =    state_dict[
        't_embedder.mlp.2.bias'
    ]
    _lowercase: Tuple						     =    state_dict[
        'y_embedder.embedding_table.weight'
    ]

    _lowercase: Tuple						     =    state_dict[
        F'blocks.{depth}.adaLN_modulation.1.weight'
    ]
    _lowercase: Optional[Any]						     =    state_dict[
        F'blocks.{depth}.adaLN_modulation.1.bias'
    ]

    _lowercase     ,					_lowercase     ,					_lowercase: Union[str, Any]						     =    torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.weight']					,     3					,     dim=0							)
    _lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.bias']					,     3					,     dim=0							)

    _lowercase: str						     =    q
    _lowercase: List[str]						     =    q_bias
    _lowercase: Union[str, Any]						     =    k
    _lowercase: Optional[Any]						     =    k_bias
    _lowercase: Optional[Any]						     =    v
    _lowercase: str						     =    v_bias

    _lowercase: List[Any]						     =    state_dict[
        F'blocks.{depth}.attn.proj.weight'
    ]
    _lowercase: Dict						     =    state_dict[F'blocks.{depth}.attn.proj.bias']

    _lowercase: Any						     =    state_dict[F'blocks.{depth}.mlp.fc1.weight']
    _lowercase: Optional[Any]						     =    state_dict[F'blocks.{depth}.mlp.fc1.bias']
    _lowercase: Union[str, Any]						     =    state_dict[F'blocks.{depth}.mlp.fc2.weight']
    _lowercase: int						     =    state_dict[F'blocks.{depth}.mlp.fc2.bias']

    state_dict.pop(F'blocks.{depth}.attn.qkv.weight'							)
    state_dict.pop(F'blocks.{depth}.attn.qkv.bias'							)
    state_dict.pop(F'blocks.{depth}.attn.proj.weight'							)
    state_dict.pop(F'blocks.{depth}.attn.proj.bias'							)
    state_dict.pop(F'blocks.{depth}.mlp.fc1.weight'							)
    state_dict.pop(F'blocks.{depth}.mlp.fc1.bias'							)
    state_dict.pop(F'blocks.{depth}.mlp.fc2.weight'							)
    state_dict.pop(F'blocks.{depth}.mlp.fc2.bias'							)
    state_dict.pop(F'blocks.{depth}.adaLN_modulation.1.weight'							)
    state_dict.pop(F'blocks.{depth}.adaLN_modulation.1.bias'							)

  state_dict.pop('t_embedder.mlp.0.weight'							)
  state_dict.pop('t_embedder.mlp.0.bias'							)
  state_dict.pop('t_embedder.mlp.2.weight'							)
  state_dict.pop('t_embedder.mlp.2.bias'							)
  state_dict.pop('y_embedder.embedding_table.weight'							)

  _lowercase: Optional[int]						     =    state_dict['final_layer.adaLN_modulation.1.weight']
  _lowercase: Dict						     =    state_dict['final_layer.adaLN_modulation.1.bias']
  _lowercase: int						     =    state_dict['final_layer.linear.weight']
  _lowercase: Union[str, Any]						     =    state_dict['final_layer.linear.bias']

  state_dict.pop('final_layer.linear.weight'							)
  state_dict.pop('final_layer.linear.bias'							)
  state_dict.pop('final_layer.adaLN_modulation.1.weight'							)
  state_dict.pop('final_layer.adaLN_modulation.1.bias'							)

  # DiT XL/2
  _lowercase: Any						     =    TransformeraDModel(
      sample_size=args.image_size // 8					,     num_layers=28					,     attention_head_dim=72					,     in_channels=4					,     out_channels=8					,     patch_size=2					,     attention_bias=UpperCamelCase					,     num_attention_heads=16					,     activation_fn='gelu-approximate'					,     num_embeds_ada_norm=1_000					,     norm_type='ada_norm_zero'					,     norm_elementwise_affine=UpperCamelCase					,     )
  transformer.load_state_dict(UpperCamelCase					,     strict=UpperCamelCase							)

  _lowercase: Dict						     =    DDIMScheduler(
      num_train_timesteps=1_000					,     beta_schedule='linear'					,     prediction_type='epsilon'					,     clip_sample=UpperCamelCase					,     )

  _lowercase: Dict						     =    AutoencoderKL.from_pretrained(args.vae_model							)

  _lowercase: Optional[Any]						     =    DiTPipeline(transformer=UpperCamelCase					,     vae=UpperCamelCase					,     scheduler=UpperCamelCase							)

  if args.save:
    pipeline.save_pretrained(args.checkpoint_path							)


if __name__ == "__main__":
   UpperCAmelCase__						:       Union[str, Any]							=    argparse.ArgumentParser()

   parser.add_argument(
       '--image_size',
       default=256,
       type=int,
       required=False,
       help='Image size of pretrained model, either 256 or 512.',
   )
   parser.add_argument(
       '--vae_model',
       default='stabilityai/sd-vae-ft-ema',
       type=str,
       required=False,
       help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
   )
   parser.add_argument(
       '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
   )

   UpperCAmelCase__						:       List[Any]							=    parser.parse_args()
   main(args)






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
