



"""simple docstring"""






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

   parser.add_argument(
       '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
   )
   # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
   parser.add_argument(
       '''--original_config_file''',
       default=None,
       type=str,
       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(
       '''--scheduler_type''',
       default='''pndm''',
       type=str,
       help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
   )
   parser.add_argument(
       '''--pipeline_type''',
       default=None,
       type=str,
       help=(
           '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
           '''. If `None` pipeline will be automatically inferred.'''
       ),
   )
   parser.add_argument(
       '''--image_size''',
       default=None,
       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(
       '''--prediction_type''',
       default=None,
       type=str,
       help=(
           '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
           ''' Diffusion v2 Base. Use \'v_prediction\' 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.)''')
   parser.add_argument(
       '''--stable_unclip''',
       type=str,
       default=None,
       required=False,
       help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
   )
   parser.add_argument(
       '''--stable_unclip_prior''',
       type=str,
       default=None,
       required=False,
       help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
   )
   parser.add_argument(
       '''--clip_stats_path''',
       type=str,
       help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
       required=False,
   )
   parser.add_argument(
       '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
   )
   parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
   parser.add_argument(
       '''--vae_path''',
       type=str,
       default=None,
       required=False,
       help='''Set to a path, hub id to an already converted vae to not convert it again.''',
   )
   lowerCAmelCase__              =							parser.parse_args()

   lowerCAmelCase__              =							download_from_original_stable_diffusion_ckpt(
       checkpoint_path=args.checkpoint_path,
       original_config_file=args.original_config_file,
       image_size=args.image_size,
       prediction_type=args.prediction_type,
       model_type=args.pipeline_type,
       extract_ema=args.extract_ema,
       scheduler_type=args.scheduler_type,
       num_in_channels=args.num_in_channels,
       upcast_attention=args.upcast_attention,
       from_safetensors=args.from_safetensors,
       device=args.device,
       stable_unclip=args.stable_unclip,
       stable_unclip_prior=args.stable_unclip_prior,
       clip_stats_path=args.clip_stats_path,
       controlnet=args.controlnet,
       vae_path=args.vae_path,
   )

   if args.half:
      pipe.to(torch_dtype=torch.floataa)

   if args.controlnet:
      # only save the controlnet model
      pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
   else:
      pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)










'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Dict							     =  [tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:]				)


class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Union[str, Any]									=     StableDiffusionLatentUpscalePipeline
    __lowerCamelCase: List[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        'height',
        'width',
        'cross_attention_kwargs',
        'negative_prompt_embeds',
        'prompt_embeds',
    }
    __lowerCamelCase: List[Any]									=     PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
    __lowerCamelCase: List[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase: Union[str, Any]									=     frozenset(
        []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __lowerCamelCase: List[Any]									=     frozenset([]      )

    __lowerCamelCase: Dict									=     True




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  1
       lowercase_						:     Any							     =  4
       lowercase_						:     int							     =  (1_6, 1_6)

       lowercase_						:     List[Any]							     =  floats_tensor((batch_size, num_channels) + sizes	,  rng=random.Random(0      )      ).to(a      )
       return image




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       torch.manual_seed(0      )
       lowercase_						:     int							     =  UNetaDConditionModel(
           act_fn="gelu"	,  attention_head_dim=8	,  norm_num_groups=a	,  block_out_channels=[3_2, 3_2, 6_4, 6_4]	,  time_cond_proj_dim=1_6_0	,  conv_in_kernel=1	,  conv_out_kernel=1	,  cross_attention_dim=3_2	,  down_block_types=(
               "KDownBlock2D",
               "KCrossAttnDownBlock2D",
               "KCrossAttnDownBlock2D",
               "KCrossAttnDownBlock2D",
           )	,  in_channels=8	,  mid_block_type=a	,  only_cross_attention=a	,  out_channels=5	,  resnet_time_scale_shift="scale_shift"	,  time_embedding_type="fourier"	,  timestep_post_act="gelu"	,  up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")	,  )
       lowercase_						:     List[str]							     =  AutoencoderKL(
           block_out_channels=[3_2, 3_2, 6_4, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=[
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
           ]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
       lowercase_						:     Optional[int]							     =  EulerDiscreteScheduler(prediction_type="sample"      )
       lowercase_						:     List[str]							     =  CLIPTextConfig(
           bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  hidden_act="quick_gelu"	,  projection_dim=5_1_2	,  )
       lowercase_						:     Optional[int]							     =  CLIPTextModel(a      )
       lowercase_						:     List[str]							     =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

       lowercase_						:     int							     =  {
           "unet": model.eval(),
           "vae": vae.eval(),
           "scheduler": scheduler,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
       }

       return components




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							int	,  a	:							List[str]=0      ):






       '''simple docstring'''






       if str(a      ).startswith("mps"      ):
          lowercase_						:     Optional[int]							     =  torch.manual_seed(a      )
       else:
          lowercase_						:     Optional[int]							     =  torch.Generator(device=a      ).manual_seed(a      )
       lowercase_						:     Optional[Any]							     =  {
           "prompt": "A painting of a squirrel eating a burger",
           "image": self.dummy_image.cpu(),
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }
       return inputs




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  "cpu"

       lowercase_						:     Any							     =  self.get_dummy_components()
       lowercase_						:     Tuple							     =  self.pipeline_class(**a      )
       pipe.to(a      )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     List[Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     List[str]							     =  pipe(**a      ).images
       lowercase_						:     int							     =  image[0, -3:, -3:, -1]

       self.assertEqual(image.shape	,  (1, 2_5_6, 2_5_6, 3)      )
       lowercase_						:     int							     =  np.array(
           [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]      )
       lowercase_						:     int							     =  np.abs(image_slice.flatten() - expected_slice      ).max()
       self.assertLessEqual(a	,  1e-3      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       super().test_cpu_offload_forward_pass(expected_max_diff=3e-3      )




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       super().test_inference_batch_single_identical(expected_max_diff=7e-3      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3      )




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       super().test_save_load_local(expected_max_difference=3e-3      )




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       super().test_save_load_optional_components(expected_max_difference=3e-3      )





    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  [
           "DDIMScheduler",
           "DDPMScheduler",
           "PNDMScheduler",
           "HeunDiscreteScheduler",
           "EulerAncestralDiscreteScheduler",
           "KDPM2DiscreteScheduler",
           "KDPM2AncestralDiscreteScheduler",
           "DPMSolverSDEScheduler",
       ]
       lowercase_						:     Optional[int]							     =  self.get_dummy_components()
       lowercase_						:     int							     =  self.pipeline_class(**a      )

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=a      )

       pipe.to(a      )
       pipe.set_progress_bar_config(disable=a      )
       lowercase_						:     Dict							     =  self.get_dummy_inputs(a      )
       lowercase_						:     int							     =  2

       lowercase_						:     Dict							     =  []
       for scheduler_enum in KarrasDiffusionSchedulers:
          if scheduler_enum.name in skip_schedulers:
             # no sigma schedulers are not supported
             # no schedulers
             continue

          lowercase_						:     Optional[int]							     =  getattr(a	,  scheduler_enum.name      )
          lowercase_						:     Union[str, Any]							     =  scheduler_cls.from_config(pipe.scheduler.config      )
          lowercase_						:     Tuple							     =  pipe(**a      )[0]
          outputs.append(a      )

       assert check_same_shape(a      )


@require_torch_gpu
@slow
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     str							     =  torch.manual_seed(3_3      )

       lowercase_						:     Union[str, Any]							     =  StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	,  torch_dtype=torch.floataa      )
       pipe.to("cuda"      )

       lowercase_						:     List[Any]							     =  StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"	,  torch_dtype=torch.floataa      )
       upscaler.to("cuda"      )

       lowercase_						:     Dict							     =  "a photo of an astronaut high resolution, unreal engine, ultra realistic"

       lowercase_						:     Optional[Any]							     =  pipe(a	,  generator=a	,  output_type="latent"      ).images

       lowercase_						:     Tuple							     =  upscaler(
           prompt=a	,  image=a	,  num_inference_steps=2_0	,  guidance_scale=0	,  generator=a	,  output_type="np"	,  ).images[0]

       lowercase_						:     Any							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"      )
       assert np.abs((expected_image - image).mean()      ) < 5e-2





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  torch.manual_seed(3_3      )

       lowercase_						:     Optional[Any]							     =  StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"	,  torch_dtype=torch.floataa      )
       upscaler.to("cuda"      )

       lowercase_						:     Any							     =  "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

       lowercase_						:     str							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"      )

       lowercase_						:     int							     =  upscaler(
           prompt=a	,  image=a	,  num_inference_steps=2_0	,  guidance_scale=0	,  generator=a	,  output_type="np"	,  ).images[0]

       lowercase_						:     Union[str, Any]							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"      )
       assert np.abs((expected_image - image).max()      ) < 5e-2




