


'''simple docstring'''







import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
  a__  :					Tuple        =       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.',
  )
  a__  :					Any        =       parser.parse_args()

  a__  :					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)









from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class       A    (   A_ ):
							UpperCamelCase_   :							torch.FloatTensor





class       A    (   A_					,				A_ ):


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 2_5_6      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = None      ,				lowerCAmelCase = 0.1_82_15      ,				lowerCAmelCase = "group"      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			Encoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				down_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				double_z=lowerCAmelCase      ,				)

									__lowercase=			vq_embed_dim if vq_embed_dim is not None else latent_channels

									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)
									__lowercase=			VectorQuantizer(lowerCAmelCase      ,				lowerCAmelCase      ,				beta=0.25      ,				remap=lowerCAmelCase      ,				sane_index_shape=lowerCAmelCase					)
									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)

									# pass init params to Decoder
									__lowercase=			Decoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				up_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				norm_type=lowerCAmelCase      ,				)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			self.encoder(lowerCAmelCase					)
									__lowercase=			self.quant_conv(lowerCAmelCase					)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True					):
									# also go through quantization layer
									if not force_not_quantize:
											__lowercase,							__lowercase,							__lowercase=			self.quantize(lowerCAmelCase					)
									else:
											__lowercase=			h
									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase      ,				quant if self.config.norm_type == 'spatial' else None					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latents
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)






