from collections import OrderedDict import torch from safetensors.torch import load_file import argparse import os import json PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) keymap_path = os.path.join(PROJECT_ROOT, 'toolkit', 'keymaps', 'stable_diffusion_sdxl.json') # load keymap with open(keymap_path, 'r') as f: keymap = json.load(f) lora_keymap = OrderedDict() # convert keymap to lora key naming for ldm_key, diffusers_key in keymap['ldm_diffusers_keymap'].items(): if ldm_key.endswith('.bias') or diffusers_key.endswith('.bias'): # skip it continue # sdxl has same te for locon with kohya and ours if ldm_key.startswith('conditioner'): #skip it continue # ignore vae if ldm_key.startswith('first_stage_model'): continue ldm_key = ldm_key.replace('model.diffusion_model.', 'lora_unet_') ldm_key = ldm_key.replace('.weight', '') ldm_key = ldm_key.replace('.', '_') diffusers_key = diffusers_key.replace('unet_', 'lora_unet_') diffusers_key = diffusers_key.replace('.weight', '') diffusers_key = diffusers_key.replace('.', '_') lora_keymap[f"{ldm_key}.alpha"] = f"{diffusers_key}.alpha" lora_keymap[f"{ldm_key}.lora_down.weight"] = f"{diffusers_key}.lora_down.weight" lora_keymap[f"{ldm_key}.lora_up.weight"] = f"{diffusers_key}.lora_up.weight" parser = argparse.ArgumentParser() parser.add_argument("input", help="input file") parser.add_argument("input2", help="input2 file") args = parser.parse_args() # name = args.name # if args.sdxl: # name += '_sdxl' # elif args.sd2: # name += '_sd2' # else: # name += '_sd1' name = 'stable_diffusion_locon_sdxl' locon_save = load_file(args.input) our_save = load_file(args.input2) our_extra_keys = list(set(our_save.keys()) - set(locon_save.keys())) locon_extra_keys = list(set(locon_save.keys()) - set(our_save.keys())) print(f"we have {len(our_extra_keys)} extra keys") print(f"locon has {len(locon_extra_keys)} extra keys") save_dtype = torch.float16 print(f"our extra keys: {our_extra_keys}") print(f"locon extra keys: {locon_extra_keys}") def export_state_dict(our_save): converted_state_dict = OrderedDict() for key, value in our_save.items(): # test encoders share keys for some reason if key.startswith('lora_te'): converted_state_dict[key] = value.detach().to('cpu', dtype=save_dtype) else: converted_key = key for ldm_key, diffusers_key in lora_keymap.items(): if converted_key == diffusers_key: converted_key = ldm_key converted_state_dict[converted_key] = value.detach().to('cpu', dtype=save_dtype) return converted_state_dict def import_state_dict(loaded_state_dict): converted_state_dict = OrderedDict() for key, value in loaded_state_dict.items(): if key.startswith('lora_te'): converted_state_dict[key] = value.detach().to('cpu', dtype=save_dtype) else: converted_key = key for ldm_key, diffusers_key in lora_keymap.items(): if converted_key == ldm_key: converted_key = diffusers_key converted_state_dict[converted_key] = value.detach().to('cpu', dtype=save_dtype) return converted_state_dict # check it again converted_state_dict = export_state_dict(our_save) converted_extra_keys = list(set(converted_state_dict.keys()) - set(locon_save.keys())) locon_extra_keys = list(set(locon_save.keys()) - set(converted_state_dict.keys())) print(f"we have {len(converted_extra_keys)} extra keys") print(f"locon has {len(locon_extra_keys)} extra keys") print(f"our extra keys: {converted_extra_keys}") # convert back cycle_state_dict = import_state_dict(converted_state_dict) cycle_extra_keys = list(set(cycle_state_dict.keys()) - set(our_save.keys())) our_extra_keys = list(set(our_save.keys()) - set(cycle_state_dict.keys())) print(f"we have {len(our_extra_keys)} extra keys") print(f"cycle has {len(cycle_extra_keys)} extra keys") # save keymap to_save = OrderedDict() to_save['ldm_diffusers_keymap'] = lora_keymap with open(os.path.join(PROJECT_ROOT, 'toolkit', 'keymaps', f'{name}.json'), 'w') as f: json.dump(to_save, f, indent=4)