converts ip-adapter-faceid_sd15.bin["ip_adapter"]["*"] to a lora that can be loaded in comfyui as a normal lora, maybe the order is wrong

ipadapter-faceid-loras-to-lora.py

import torch
import sys


all_lora_keys = [
	"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_k_proj.alpha",
	"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_v_proj.alpha",
	"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_q_proj.alpha",
	"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_self_attn_out_proj.alpha",
	"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_fc1.alpha",
	"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_0_mlp_fc2.alpha",
	"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_k_proj.alpha",
	"lora_te_text_model_encoder_layers_1_self_attn_v_proj.lora_down.weight",
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	"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_self_attn_out_proj.alpha",
	"lora_te_text_model_encoder_layers_1_mlp_fc1.lora_down.weight",
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	"lora_te_text_model_encoder_layers_1_mlp_fc1.alpha",
	"lora_te_text_model_encoder_layers_1_mlp_fc2.lora_down.weight",
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	"lora_te_text_model_encoder_layers_5_self_attn_k_proj.lora_down.weight",
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	"lora_unet_mid_block_resnets_1_conv1.lora_up.weight",
	"lora_unet_mid_block_resnets_1_conv1.alpha",
	"lora_unet_mid_block_resnets_1_time_emb_proj.lora_down.weight",
	"lora_unet_mid_block_resnets_1_time_emb_proj.lora_up.weight",
	"lora_unet_mid_block_resnets_1_time_emb_proj.alpha",
	"lora_unet_mid_block_resnets_1_conv2.lora_mid.weight",
	"lora_unet_mid_block_resnets_1_conv2.lora_down.weight",
	"lora_unet_mid_block_resnets_1_conv2.lora_up.weight",
	"lora_unet_mid_block_resnets_1_conv2.alpha",
	"lora_unet_conv_out.lora_mid.weight",
	"lora_unet_conv_out.lora_down.weight",
	"lora_unet_conv_out.lora_up.weight",
	"lora_unet_conv_out.alpha",
]

checkpoint = torch.load(sys.argv[1], map_location=torch.device('cpu'))

LORA_OUTPUT = {}

translate = {}

for key in checkpoint["ip_adapter"].keys():
	if not "_ip." in key:
		main_key = key.split(".")[0]
		index = int(main_key)
		if index not in translate:
			translate[index] = []
		translate[index].append(key)

index = -1
last_key = None
for key in all_lora_keys:
	if "unet" in key and ("_to_q" in key or "_to_k" in key or "_to_v" in key or "_to_out" in key) and not "alpha" in key:
		main_key = key.split(".")[0].replace("_to_q", "").replace("_to_k", "").replace("_to_v", "").replace("_to_out_0", "")


		if main_key != last_key:
				last_key = main_key
				index += 1

		for found_key in translate[index]:
			if "_to_out_0" in key and "to_out" in found_key:
				if "down.weight" in key and "down.weight" in found_key:
					LORA_OUTPUT[key] = checkpoint["ip_adapter"][found_key].clone()
					print(found_key, "    >>>>   ", key)
				if "up.weight" in key and "up.weight" in found_key:
					LORA_OUTPUT[key] = checkpoint["ip_adapter"][found_key].clone()
					print(found_key, "    >>>>   ", key)

			if "_to_q" in key and "_to_q" in found_key:
				if "down.weight" in key and "down.weight" in found_key:
					LORA_OUTPUT[key] = checkpoint["ip_adapter"][found_key].clone()
					print(found_key, "    >>>>   ", key)
				if "up.weight" in key and "up.weight" in found_key:
					LORA_OUTPUT[key] = checkpoint["ip_adapter"][found_key].clone()
					print(found_key, "    >>>>   ", key)

			if "_to_k" in key and "_to_k" in found_key:
				if "down.weight" in key and "down.weight" in found_key:
					LORA_OUTPUT[key] = checkpoint["ip_adapter"][found_key].clone()
					print(found_key, "    >>>>   ", key)
				if "up.weight" in key and "up.weight" in found_key:
					LORA_OUTPUT[key] = checkpoint["ip_adapter"][found_key].clone()
					print(found_key, "    >>>>   ", key)
			
			if "_to_v" in key and "_to_v" in found_key:
				if "down.weight" in key and "down.weight" in found_key:
					LORA_OUTPUT[key] = checkpoint["ip_adapter"][found_key].clone()
					print(found_key, "    >>>>   ", key)
				if "up.weight" in key and "up.weight" in found_key:
					LORA_OUTPUT[key] = checkpoint["ip_adapter"][found_key].clone()
					print(found_key, "    >>>>   ", key)
		 
		

from safetensors.torch import save_file

save_file(LORA_OUTPUT, "faceid.safetensors")