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| ''' | |
| It supports only SD-v2 models. | |
| usage: | |
| python simo2kohya.py --unet <simo's unet weight path> --text <simo's text_encoder weight path> --save_to <save path> | |
| (--text is optional) | |
| This code may no longer be available due to updates from both @kohya-ss and @cloneofsimo. | |
| ''' | |
| import torch | |
| import argparse | |
| from collections import OrderedDict | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--unet", type=str,required=True ,default=None, help="cloneofsimo氏製のUNet weight") | |
| parser.add_argument("--text", type=str,required=False ,default=None, help="cloneofsimo氏製のtext encoder weight") | |
| parser.add_argument("--save_to", type=str,required=True ,default=None, help="save先") | |
| args = parser.parse_args() | |
| def main(): | |
| simo_unet = torch.load(args.unet) | |
| if args.text is not None: | |
| simo_text = torch.load(args.text) | |
| simo = simo_text + simo_unet | |
| else: | |
| simo = simo_unet | |
| new_dic = OrderedDict() | |
| keys = [key for key in kohya_keys_v2 if "attn" in key or "ff_net_0_proj" in key] | |
| if args.text is None: | |
| keys = [key for key in keys if "text_model" not in key] | |
| for i in range(0,len(keys),2): | |
| print(f"copy:{keys[i][:-17]}!!!!^q^q^q^") | |
| new_dic[keys[i]] = simo[i+1] | |
| new_dic[keys[i+1]] = simo[i] | |
| torch.save(new_dic,args.save_to) | |
| #以下うんち | |
| kohya_keys_v2 = ['lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_0_self_attn_v_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_0_self_attn_v_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_0_self_attn_q_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_0_self_attn_q_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_0_self_attn_out_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_0_self_attn_out_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_0_mlp_fc1.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_0_mlp_fc2.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_0_mlp_fc2.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_1_self_attn_k_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_1_self_attn_k_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_1_self_attn_v_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_1_self_attn_v_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_1_self_attn_q_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_1_self_attn_q_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_1_self_attn_out_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_1_self_attn_out_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_1_mlp_fc1.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_1_mlp_fc1.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_1_mlp_fc2.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_1_mlp_fc2.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_2_self_attn_k_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_2_self_attn_k_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_2_self_attn_v_proj.lora_down.weight', | |
| 'lora_te_text_model_encoder_layers_2_self_attn_v_proj.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_2_self_attn_q_proj.lora_down.weight', | |
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| 'lora_te_text_model_encoder_layers_2_self_attn_out_proj.lora_down.weight', | |
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| 'lora_te_text_model_encoder_layers_2_mlp_fc2.lora_up.weight', | |
| 'lora_te_text_model_encoder_layers_3_self_attn_k_proj.lora_down.weight', | |
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| 'lora_unet_up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_0_proj_out.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_0_proj_out.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_proj_in.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_proj_in.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_proj_out.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_1_proj_out.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_proj_in.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_proj_in.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0.lora_up.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_proj_out.lora_down.weight', | |
| 'lora_unet_up_blocks_3_attentions_2_proj_out.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_proj_in.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_proj_in.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_q.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_q.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_k.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_k.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_v.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_v.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_ff_net_2.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_ff_net_2.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_q.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_q.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_k.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_k.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_v.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_v.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0.lora_up.weight', | |
| 'lora_unet_mid_block_attentions_0_proj_out.lora_down.weight', | |
| 'lora_unet_mid_block_attentions_0_proj_out.lora_up.weight'] | |
| if __name__ == "__main__": | |
| main() |
Really clever!
Can you please clarify the way to use this code, some readme?
Can you please clarify the way to use this code, some readme?
I added it to the top of the code. 🏯
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This is awesome, I didn't know it could be done this simple (?), amazing work!