






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 lowerCamelCase     :							Dict          =argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )
 parser.add_argument(
     '''--original_config_file''',
     type=str,
     required=True,
     help='''The YAML config file corresponding to the original architecture.''',
 )
 parser.add_argument(
     '''--num_in_channels''',
     default=None,
     type=int,
     help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
 )
 parser.add_argument(
     '''--image_size''',
     default=512,
     type=int,
     help=(
         '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
         ''' Base. Use 768 for Stable Diffusion v2.'''
     ),
 )
 parser.add_argument(
     '''--extract_ema''',
     action='''store_true''',
     help=(
         '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
         ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
         ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
     ),
 )
 parser.add_argument(
     '''--upcast_attention''',
     action='''store_true''',
     help=(
         '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
         ''' diffusion 2.1.'''
     ),
 )
 parser.add_argument(
     '''--from_safetensors''',
     action='''store_true''',
     help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
 )
 parser.add_argument(
     '''--to_safetensors''',
     action='''store_true''',
     help='''Whether to store pipeline in safetensors format or not.''',
 )
 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')







 def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
      if string == "True":
           return True
      elif string == "False":
           return False
      else:
           raise ValueError(f'could not parse string as bool {string}'      )

 parser.add_argument(
     '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
 )

 parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

 lowerCamelCase     :							Optional[Any]          =parser.parse_args()

 lowerCamelCase     :							Any          =download_controlnet_from_original_ckpt(
     checkpoint_path=args.checkpoint_path,
     original_config_file=args.original_config_file,
     image_size=args.image_size,
     extract_ema=args.extract_ema,
     num_in_channels=args.num_in_channels,
     upcast_attention=args.upcast_attention,
     from_safetensors=args.from_safetensors,
     device=args.device,
     use_linear_projection=args.use_linear_projection,
     cross_attention_dim=args.cross_attention_dim,
 )




 controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







import gc
import tempfile
import unittest

import numpy as np
import torch

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


lowerCamelCase     :							Optional[Any]    =False





class       __a  (   unittest.TestCase	):
 pass







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







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)

      UpperCamelCase__      :       Dict	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       str	    =  pipe.dual_guided(
          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__      :       Tuple	    =  VersatileDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       int	    =  generator.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  pipe.dual_guided(
          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  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Tuple	    =  "cyberpunk 2077"
      UpperCamelCase__      :       Any	    =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__      :       int	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Any	    =  pipe.dual_guided(
          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__      :       Tuple	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Optional[int]	    =  np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]						)

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

      UpperCamelCase__      :       List[str]	    =  "A painting of a squirrel eating a burger "
      UpperCamelCase__      :       List[Any]	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  pipe.text_to_image(
          prompt=SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=50							,       output_type="numpy"						).images

      UpperCamelCase__      :       Union[str, Any]	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Dict	    =  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]						)

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

      UpperCamelCase__      :       Any	    =  pipe.image_variation(SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       output_type="numpy"						).images

      UpperCamelCase__      :       Optional[Any]	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       int	    =  np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]						)

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