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https://github.com/ostris/ai-toolkit.git
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Corrected key saving and loading to better match kohya
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130
testing/generate_lora_mapping.py
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130
testing/generate_lora_mapping.py
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from collections import OrderedDict
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import torch
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from safetensors.torch import load_file
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import argparse
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import os
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import json
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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keymap_path = os.path.join(PROJECT_ROOT, 'toolkit', 'keymaps', 'stable_diffusion_sdxl.json')
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# load keymap
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with open(keymap_path, 'r') as f:
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keymap = json.load(f)
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lora_keymap = OrderedDict()
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# convert keymap to lora key naming
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for ldm_key, diffusers_key in keymap['ldm_diffusers_keymap'].items():
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if ldm_key.endswith('.bias') or diffusers_key.endswith('.bias'):
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# skip it
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continue
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# sdxl has same te for locon with kohya and ours
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if ldm_key.startswith('conditioner'):
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#skip it
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continue
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# ignore vae
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if ldm_key.startswith('first_stage_model'):
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continue
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ldm_key = ldm_key.replace('model.diffusion_model.', 'lora_unet_')
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ldm_key = ldm_key.replace('.weight', '')
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ldm_key = ldm_key.replace('.', '_')
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diffusers_key = diffusers_key.replace('unet_', 'lora_unet_')
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diffusers_key = diffusers_key.replace('.weight', '')
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diffusers_key = diffusers_key.replace('.', '_')
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lora_keymap[f"{ldm_key}.alpha"] = f"{diffusers_key}.alpha"
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lora_keymap[f"{ldm_key}.lora_down.weight"] = f"{diffusers_key}.lora_down.weight"
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lora_keymap[f"{ldm_key}.lora_up.weight"] = f"{diffusers_key}.lora_up.weight"
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parser = argparse.ArgumentParser()
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parser.add_argument("input", help="input file")
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parser.add_argument("input2", help="input2 file")
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args = parser.parse_args()
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# name = args.name
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# if args.sdxl:
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# name += '_sdxl'
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# elif args.sd2:
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# name += '_sd2'
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# else:
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# name += '_sd1'
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name = 'stable_diffusion_locon_sdxl'
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locon_save = load_file(args.input)
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our_save = load_file(args.input2)
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our_extra_keys = list(set(our_save.keys()) - set(locon_save.keys()))
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locon_extra_keys = list(set(locon_save.keys()) - set(our_save.keys()))
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print(f"we have {len(our_extra_keys)} extra keys")
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print(f"locon has {len(locon_extra_keys)} extra keys")
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save_dtype = torch.float16
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print(f"our extra keys: {our_extra_keys}")
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print(f"locon extra keys: {locon_extra_keys}")
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def export_state_dict(our_save):
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converted_state_dict = OrderedDict()
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for key, value in our_save.items():
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# test encoders share keys for some reason
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if key.startswith('lora_te'):
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converted_state_dict[key] = value.detach().to('cpu', dtype=save_dtype)
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else:
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converted_key = key
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for ldm_key, diffusers_key in lora_keymap.items():
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if converted_key == diffusers_key:
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converted_key = ldm_key
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converted_state_dict[converted_key] = value.detach().to('cpu', dtype=save_dtype)
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return converted_state_dict
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def import_state_dict(loaded_state_dict):
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converted_state_dict = OrderedDict()
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for key, value in loaded_state_dict.items():
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if key.startswith('lora_te'):
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converted_state_dict[key] = value.detach().to('cpu', dtype=save_dtype)
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else:
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converted_key = key
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for ldm_key, diffusers_key in lora_keymap.items():
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if converted_key == ldm_key:
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converted_key = diffusers_key
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converted_state_dict[converted_key] = value.detach().to('cpu', dtype=save_dtype)
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return converted_state_dict
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# check it again
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converted_state_dict = export_state_dict(our_save)
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converted_extra_keys = list(set(converted_state_dict.keys()) - set(locon_save.keys()))
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locon_extra_keys = list(set(locon_save.keys()) - set(converted_state_dict.keys()))
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print(f"we have {len(converted_extra_keys)} extra keys")
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print(f"locon has {len(locon_extra_keys)} extra keys")
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print(f"our extra keys: {converted_extra_keys}")
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# convert back
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cycle_state_dict = import_state_dict(converted_state_dict)
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cycle_extra_keys = list(set(cycle_state_dict.keys()) - set(our_save.keys()))
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our_extra_keys = list(set(our_save.keys()) - set(cycle_state_dict.keys()))
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print(f"we have {len(our_extra_keys)} extra keys")
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print(f"cycle has {len(cycle_extra_keys)} extra keys")
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# save keymap
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to_save = OrderedDict()
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to_save['ldm_diffusers_keymap'] = lora_keymap
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with open(os.path.join(PROJECT_ROOT, 'toolkit', 'keymaps', f'{name}.json'), 'w') as f:
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json.dump(to_save, f, indent=4)
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