





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							UpperCAmelCase__  :     Dict   			=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.''',
							)
							UpperCAmelCase__  :     str   			=parser.parse_args()

							UpperCAmelCase__  :     Any   			=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)












import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class  __A       (  a      ,   a      ,   unittest.TestCase      ):
				__A								=			IFInpaintingSuperResolutionPipeline
				__A								=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__A								=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}      )
				__A								=			PipelineTesterMixin.required_optional_params - {"""latents"""}


				def _snake_case    (						self							):
								return self._get_superresolution_dummy_components()


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_=0							):
								if str(UpperCAmelCase_							).startswith("""mps"""							):
												lowerCamelCase			=torch.manual_seed(UpperCAmelCase_							)
								else:
												lowerCamelCase			=torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)

								lowerCamelCase			=floats_tensor((1, 3, 16, 16)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase			=floats_tensor((1, 3, 32, 32)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase			=floats_tensor((1, 3, 32, 32)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)

								lowerCamelCase			={
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": image,
								    """original_image""": original_image,
								    """mask_image""": mask_image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """output_type""": """numpy""",
								}

								return inputs


				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()				,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,	)
				def _snake_case    (						self							):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)


				def _snake_case    (						self							):
								self._test_save_load_optional_components()


				@unittest.skipIf(torch_device != """cuda"""				,	reason="""float16 requires CUDA"""							)
				def _snake_case    (						self							):
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1							)


				def _snake_case    (						self							):
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)


				def _snake_case    (						self							):
								self._test_save_load_local()


				def _snake_case    (						self							):
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2				,	)





