






"""simple docstring"""







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase_	:    Optional[int]     =      [
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

lowerCAmelCase_	:    Dict     =      [
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

lowerCAmelCase_	:    Tuple     =      []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  lowerCAmelCase_	:    Tuple     =      F'down_blocks.{i}.resnets.{j}.'
  lowerCAmelCase_	:    List[Any]     =      F'input_blocks.{3*i + j + 1}.0.'
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   lowerCAmelCase_	:    Any     =      F'down_blocks.{i}.attentions.{j}.'
   lowerCAmelCase_	:    Union[str, Any]     =      F'input_blocks.{3*i + j + 1}.1.'
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  lowerCAmelCase_	:    Optional[int]     =      F'up_blocks.{i}.resnets.{j}.'
  lowerCAmelCase_	:    List[Any]     =      F'output_blocks.{3*i + j}.0.'
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   lowerCAmelCase_	:    Tuple     =      F'up_blocks.{i}.attentions.{j}.'
   lowerCAmelCase_	:    int     =      F'output_blocks.{3*i + j}.1.'
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  lowerCAmelCase_	:    str     =      F'down_blocks.{i}.downsamplers.0.conv.'
  lowerCAmelCase_	:    List[Any]     =      F'input_blocks.{3*(i+1)}.0.op.'
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  lowerCAmelCase_	:    Optional[Any]     =      F'up_blocks.{i}.upsamplers.0.'
  lowerCAmelCase_	:    List[Any]     =      F'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase_	:    List[Any]     =      "mid_block.attentions.0."
lowerCAmelCase_	:    Any     =      "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 lowerCAmelCase_	:    Tuple     =      F'mid_block.resnets.{j}.'
 lowerCAmelCase_	:    int     =      F'middle_block.{2*j}.'
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			{k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              UpperCAmelCase	    =			sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            UpperCAmelCase	    =			v.replace(_lowerCamelCase	,      _lowerCamelCase		)
                     UpperCAmelCase	    =			v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     UpperCAmelCase	    =			v.replace(_lowerCamelCase	,      _lowerCamelCase		)
              UpperCAmelCase	    =			v
       UpperCAmelCase	    =			{v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase_	:    str     =      [
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  lowerCAmelCase_	:    Union[str, Any]     =      F'encoder.down_blocks.{i}.resnets.{j}.'
  lowerCAmelCase_	:    Optional[Any]     =      F'encoder.down.{i}.block.{j}.'
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  lowerCAmelCase_	:    List[Any]     =      F'down_blocks.{i}.downsamplers.0.'
  lowerCAmelCase_	:    Tuple     =      F'down.{i}.downsample.'
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  lowerCAmelCase_	:    Any     =      F'up_blocks.{i}.upsamplers.0.'
  lowerCAmelCase_	:    Any     =      F'up.{3-i}.upsample.'
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  lowerCAmelCase_	:    Dict     =      F'decoder.up_blocks.{i}.resnets.{j}.'
  lowerCAmelCase_	:    Union[str, Any]     =      F'decoder.up.{3-i}.block.{j}.'
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 lowerCAmelCase_	:    Optional[Any]     =      F'mid_block.resnets.{i}.'
 lowerCAmelCase_	:    Tuple     =      F'mid.block_{i+1}.'
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase_	:    int     =      [
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       return w.reshape(*w.shape	,      1	,      1		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			{k: k for k in vae_state_dict.keys()}
       for k, v in mapping.items():
              for sd_part, hf_part in vae_conversion_map:
                     UpperCAmelCase	    =			v.replace(_lowerCamelCase	,      _lowerCamelCase		)
              UpperCAmelCase	    =			v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            UpperCAmelCase	    =			v.replace(_lowerCamelCase	,      _lowerCamelCase		)
                     UpperCAmelCase	    =			v
       UpperCAmelCase	    =			{v: vae_state_dict[k] for k, v in mapping.items()}
       UpperCAmelCase	    =			["q", "k", "v", "proj_out"]
       for k, v in new_state_dict.items():
              for weight_name in weights_to_convert:
                     if F'''mid.attn_1.{weight_name}.weight''' in k:
                            print(F'''Reshaping {k} for SD format'''		)
                            UpperCAmelCase	    =			reshape_weight_for_sd(_lowerCamelCase		)
       return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase_	:    List[Any]     =      [
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
lowerCAmelCase_	:    Dict     =      {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase_	:    Tuple     =      re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase_	:    Any     =      {"q": 0, "k": 1, "v": 2}






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			{}
       UpperCAmelCase	    =			{}
       UpperCAmelCase	    =			{}
       for k, v in text_enc_dict.items():
              if (
                  k.endswith(""".self_attn.q_proj.weight"""		)
                  or k.endswith(""".self_attn.k_proj.weight"""		)
                  or k.endswith(""".self_attn.v_proj.weight"""		)
              ):
                     UpperCAmelCase	    =			k[: -len(""".q_proj.weight"""		)]
                     UpperCAmelCase	    =			k[-len("""q_proj.weight"""		)]
                     if k_pre not in capture_qkv_weight:
                            UpperCAmelCase	    =			[None, None, None]
                     UpperCAmelCase	    =			v
                     continue

              if (
                  k.endswith(""".self_attn.q_proj.bias"""		)
                  or k.endswith(""".self_attn.k_proj.bias"""		)
                  or k.endswith(""".self_attn.v_proj.bias"""		)
              ):
                     UpperCAmelCase	    =			k[: -len(""".q_proj.bias"""		)]
                     UpperCAmelCase	    =			k[-len("""q_proj.bias"""		)]
                     if k_pre not in capture_qkv_bias:
                            UpperCAmelCase	    =			[None, None, None]
                     UpperCAmelCase	    =			v
                     continue

              UpperCAmelCase	    =			textenc_pattern.sub(lambda lowerCAmelCase		: protected[re.escape(m.group(0		)		)]	,      _lowerCamelCase		)
              UpperCAmelCase	    =			v

       for k_pre, tensors in capture_qkv_weight.items():
              if None in tensors:
                     raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""		)
              UpperCAmelCase	    =			textenc_pattern.sub(lambda lowerCAmelCase		: protected[re.escape(m.group(0		)		)]	,      _lowerCamelCase		)
              UpperCAmelCase	    =			torch.cat(_lowerCamelCase		)

       for k_pre, tensors in capture_qkv_bias.items():
              if None in tensors:
                     raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""		)
              UpperCAmelCase	    =			textenc_pattern.sub(lambda lowerCAmelCase		: protected[re.escape(m.group(0		)		)]	,      _lowerCamelCase		)
              UpperCAmelCase	    =			torch.cat(_lowerCamelCase		)

       return new_state_dict






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       return text_enc_dict


if __name__ == "__main__":
 lowerCAmelCase_	:    List[Any]     =      argparse.ArgumentParser()

 parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
 )

 lowerCAmelCase_	:    str     =      parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 lowerCAmelCase_	:    List[Any]     =      osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 lowerCAmelCase_	:    int     =      osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 lowerCAmelCase_	:    Dict     =      osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  lowerCAmelCase_	:    List[str]     =      load_file(unet_path, device='''cpu''')
 else:
  lowerCAmelCase_	:    int     =      osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  lowerCAmelCase_	:    Tuple     =      torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  lowerCAmelCase_	:    Union[str, Any]     =      load_file(vae_path, device='''cpu''')
 else:
  lowerCAmelCase_	:    Union[str, Any]     =      osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  lowerCAmelCase_	:    Union[str, Any]     =      torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  lowerCAmelCase_	:    Union[str, Any]     =      load_file(text_enc_path, device='''cpu''')
 else:
  lowerCAmelCase_	:    Dict     =      osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  lowerCAmelCase_	:    Tuple     =      torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 lowerCAmelCase_	:    int     =      convert_unet_state_dict(unet_state_dict)
 lowerCAmelCase_	:    List[str]     =      {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 lowerCAmelCase_	:    List[str]     =      convert_vae_state_dict(vae_state_dict)
 lowerCAmelCase_	:    List[str]     =      {"first_stage_model." + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 lowerCAmelCase_	:    List[str]     =      "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  lowerCAmelCase_	:    int     =      {"transformer." + k: v for k, v in text_enc_dict.items()}
  lowerCAmelCase_	:    Optional[Any]     =      convert_text_enc_state_dict_vaa(text_enc_dict)
  lowerCAmelCase_	:    List[Any]     =      {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
 else:
  lowerCAmelCase_	:    Dict     =      convert_text_enc_state_dict(text_enc_dict)
  lowerCAmelCase_	:    Tuple     =      {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 lowerCAmelCase_	:    Optional[int]     =      {**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  lowerCAmelCase_	:    Tuple     =      {k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  lowerCAmelCase_	:    Dict     =      {"state_dict": state_dict}
  torch.save(state_dict, args.checkpoint_path)








"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      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=5_1_2,
     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       _lowerCAmelCase       (      lowerCAmelCase		):







        '''simple docstring'''
        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_	:    int            =      parser.parse_args()

 lowerCAmelCase_	:    Optional[int]            =      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)
