





import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_UpperCAmelCase										=					{5_1_2: 'DiT-XL-2-512x512.pt', 2_5_6: 'DiT-XL-2-256x256.pt'}




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	F'''pretrained_models/{model_name}'''
       if not os.path.isfile(UpperCamelCase_	):
              os.makedirs("pretrained_models"       ,		exist_ok=UpperCamelCase_	)
              UpperCamelCase_ 							=	F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
              download_url(UpperCamelCase_       ,		"pretrained_models"	)
       UpperCamelCase_ 							=	torch.load(UpperCamelCase_       ,		map_location=lambda UpperCamelCase_       ,		UpperCamelCase_	: storage	)
       return model




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Any:
       UpperCamelCase_ 							=	download_model(pretrained_models[args.image_size]	)

       UpperCamelCase_ 							=	state_dict["x_embedder.proj.weight"]
       UpperCamelCase_ 							=	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	):
              UpperCamelCase_ 							=	state_dict[
                  "t_embedder.mlp.0.weight"
              ]
              UpperCamelCase_ 							=	state_dict[
                  "t_embedder.mlp.0.bias"
              ]
              UpperCamelCase_ 							=	state_dict[
                  "t_embedder.mlp.2.weight"
              ]
              UpperCamelCase_ 							=	state_dict[
                  "t_embedder.mlp.2.bias"
              ]
              UpperCamelCase_ 							=	state_dict[
                  "y_embedder.embedding_table.weight"
              ]

              UpperCamelCase_ 							=	state_dict[
                  F'''blocks.{depth}.adaLN_modulation.1.weight'''
              ]
              UpperCamelCase_ 							=	state_dict[
                  F'''blocks.{depth}.adaLN_modulation.1.bias'''
              ]

              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']       ,		3       ,		dim=0	)
              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']       ,		3       ,		dim=0	)

              UpperCamelCase_ 							=	q
              UpperCamelCase_ 							=	q_bias
              UpperCamelCase_ 							=	k
              UpperCamelCase_ 							=	k_bias
              UpperCamelCase_ 							=	v
              UpperCamelCase_ 							=	v_bias

              UpperCamelCase_ 							=	state_dict[
                  F'''blocks.{depth}.attn.proj.weight'''
              ]
              UpperCamelCase_ 							=	state_dict[F'''blocks.{depth}.attn.proj.bias''']

              UpperCamelCase_ 							=	state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
              UpperCamelCase_ 							=	state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
              UpperCamelCase_ 							=	state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
              UpperCamelCase_ 							=	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"	)

       UpperCamelCase_ 							=	state_dict["final_layer.adaLN_modulation.1.weight"]
       UpperCamelCase_ 							=	state_dict["final_layer.adaLN_modulation.1.bias"]
       UpperCamelCase_ 							=	state_dict["final_layer.linear.weight"]
       UpperCamelCase_ 							=	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
       UpperCamelCase_ 							=	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=1000       ,		norm_type="ada_norm_zero"       ,		norm_elementwise_affine=UpperCamelCase_       ,		)
       transformer.load_state_dict(UpperCamelCase_       ,		strict=UpperCamelCase_	)

       UpperCamelCase_ 							=	DDIMScheduler(
           num_train_timesteps=1000       ,		beta_schedule="linear"       ,		prediction_type="epsilon"       ,		clip_sample=UpperCamelCase_       ,		)

       UpperCamelCase_ 							=	AutoencoderKL.from_pretrained(args.vae_model	)

       UpperCamelCase_ 							=	DiTPipeline(transformer=UpperCamelCase_       ,		vae=UpperCamelCase_       ,		scheduler=UpperCamelCase_	)

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


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

     parser.add_argument(
         '--image_size',
         default=2_5_6,
         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										=					parser.parse_args()
     main(args)



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase__				:				List[str]						=      False






@nightly
@require_torch_gpu
class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )

													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE_      )

													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      generator.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													assert np.abs(image - new_image      ).sum() < 1E-5, "Models don't have the same forward pass"



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''cyberpunk 2077'''
													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )
													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt=SCREAMING_SNAKE_CASE_  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type='''numpy'''  ,						).images

													__UpperCamelCase  	=      image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase  	=      np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




