from pathlib import Path
import torch
from safetensors.torch import load_file, save_file



def save_to_file(file_name, state_dict, dtype, metadata=None):
   for key in list(state_dict.keys()):
      if type(state_dict[key]) == torch.Tensor:
         state_dict[key] = state_dict[key].to(dtype)
   if metadata is None:
      from library.train_util import build_minimum_network_metadata
      metadata = build_minimum_network_metadata(str(False), "PDXL", "networks.lora", str(0), str(0), {})
      del metadata["ss_network_dim"]
      del metadata["ss_base_model_version"]
      del metadata["ss_network_alpha"]
      del metadata["ss_network_args"]
      del metadata["ss_v2"]
      del metadata["ss_network_module"]
   save_file(state_dict, file_name, metadata)

if __name__ == "__main__":
   state_dict = load_file(Path(R"X:\SD\voldy\models\Stable-diffusion\tPonynai3_v6.safetensors").absolute())
   state_dict2 = load_file(Path(R"X:\SD\voldy\models\Stable-diffusion\cosxl.safetensors").absolute())
   for k in state_dict.keys():
      if (
         False
          or "output_blocks.5.1" in k
          ):
         state_dict[k] = torch.lerp(state_dict[k], state_dict2[k], 0.75)
   save_to_file(R"X:\SD\voldy\models\Stable-diffusion\final1.safetensors", state_dict, torch.float16, None)



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# first_stage_model.post_quant_conv.bias
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