| import argparse |
| import os |
| import sys |
| from safetensors.torch import load_file, save_file |
|
|
| class C: |
| HEADER = '\033[95m' |
| BLUE = '\033[94m' |
| CYAN = '\033[96m' |
| GREEN = '\033[92m' |
| YELLOW = '\033[93m' |
| RED = '\033[91m' |
| GREY = '\033[90m' |
| RESET = '\033[0m' |
| BOLD = '\033[1m' |
|
|
| def convert_key(old_key): |
| parts = old_key.split('.') |
|
|
| if "attn" not in parts or "processor" not in parts or "weight" not in parts: |
| return None |
| |
| lora_type_idx = -1 |
| for i, part in enumerate(parts): |
| if part.endswith('_loras'): |
| lora_type_idx = i |
| break |
| |
| if lora_type_idx == -1: |
| return None |
|
|
| raw_type = parts[lora_type_idx].replace('_loras', '') |
| |
| try: |
| direction = parts[lora_type_idx + 2] |
| except IndexError: |
| return None |
|
|
| type_map = { |
| "q": "to_q", |
| "k": "to_k", |
| "v": "to_v", |
| "proj": "to_out.0" |
| } |
| |
| lora_map = { |
| "down": "lora_A", |
| "up": "lora_B" |
| } |
|
|
| if raw_type not in type_map or direction not in lora_map: |
| return None |
|
|
| new_type_str = type_map[raw_type] |
| new_lora_str = lora_map[direction] |
|
|
| prefix_parts = parts[:lora_type_idx - 1] |
| prefix_str = ".".join(prefix_parts) |
|
|
| new_key = f"transformer.{prefix_str}.{new_type_str}.{new_lora_str}.weight" |
| |
| return new_key |
|
|
| def main(): |
| parser = argparse.ArgumentParser(description="Colorful LoRA Key Converter") |
| parser.add_argument("input_file", type=str, help="Input safetensors file") |
| parser.add_argument("output_file", type=str, help="Output safetensors file") |
| |
| args = parser.parse_args() |
|
|
| if not os.path.exists(args.input_file): |
| print(f"{C.RED}Error: Input file '{args.input_file}' not found.{C.RESET}") |
| return |
|
|
| print(f"{C.HEADER}Loading weights from: {C.RESET}{C.BOLD}{args.input_file}{C.RESET}") |
| try: |
| tensors = load_file(args.input_file) |
| except Exception as e: |
| print(f"{C.RED}Failed to load file: {e}{C.RESET}") |
| return |
|
|
| new_tensors = {} |
| skipped_keys = [] |
| |
| print(f"{C.HEADER}{'-'*60}{C.RESET}") |
| print(f"{C.BOLD}Processing Keys:{C.RESET}") |
|
|
| for key, tensor in tensors.items(): |
| new_key = convert_key(key) |
| |
| if new_key: |
| print(f"{C.RED}{key}{C.RESET}") |
| print(f" ↓ {C.GREY}converted to{C.RESET}") |
| print(f"{C.GREEN}{new_key}{C.RESET}") |
| print(f"{C.GREY}-{C.RESET}" * 20) |
| |
| new_tensors[new_key] = tensor |
| else: |
| skipped_keys.append(key) |
|
|
| print(f"\n{C.HEADER}Saving...{C.RESET}") |
| save_file(new_tensors, args.output_file) |
| print(f"{C.BOLD}Saved converted model to:{C.RESET} {C.CYAN}{args.output_file}{C.RESET}") |
|
|
| print(f"\n{C.HEADER}{'-'*60}{C.RESET}") |
| print(f"{C.YELLOW}Skipped Keys (Not Converted): {len(skipped_keys)}{C.RESET}") |
| if skipped_keys: |
| for sk in skipped_keys: |
| print(f" {C.GREY}• {sk}{C.RESET}") |
| else: |
| print(f" {C.GREEN}(None - All keys were converted){C.RESET}") |
| |
| print(f"{C.HEADER}{'-'*60}{C.RESET}") |
| print(f"Total Original Keys: {len(tensors)}") |
| print(f"Converted Keys: {C.GREEN}{len(new_tensors)}{C.RESET}") |
| print(f"Skipped Keys: {C.YELLOW}{len(skipped_keys)}{C.RESET}") |
|
|
| if __name__ == "__main__": |
| main() |
|
|