simo2kohya.py

'''
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',
 'lora_te_text_model_encoder_layers_2_self_attn_q_proj.lora_up.weight',
 'lora_te_text_model_encoder_layers_2_self_attn_out_proj.lora_down.weight',
 'lora_te_text_model_encoder_layers_2_self_attn_out_proj.lora_up.weight',
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 'lora_te_text_model_encoder_layers_2_mlp_fc2.lora_down.weight',
 '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',
 'lora_te_text_model_encoder_layers_3_self_attn_k_proj.lora_up.weight',
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 '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()

This is awesome, I didn't know it could be done this simple (?), amazing work!

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. 🏯