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import os
import torch
from safetensors.torch import load_file
from collections import OrderedDict
from toolkit.kohya_model_util import load_vae, convert_diffusers_back_to_ldm, vae_keys_squished_on_diffusers
import json
# this was just used to match the vae keys to the diffusers keys
# you probably wont need this. Unless they change them.... again... again
# on second thought, you probably will
device = torch.device('cpu')
dtype = torch.float32
vae_path = '/mnt/Models/stable-diffusion/models/VAE/vae-ft-mse-840000-ema-pruned/vae-ft-mse-840000-ema-pruned.safetensors'
find_matches = False
state_dict_ldm = load_file(vae_path)
diffusers_vae = load_vae(vae_path, dtype=torch.float32).to(device)
ldm_keys = state_dict_ldm.keys()
matched_keys = {}
duplicated_keys = {
}
if find_matches:
# find values that match with a very low mse
for ldm_key in ldm_keys:
ldm_value = state_dict_ldm[ldm_key]
for diffusers_key in list(diffusers_vae.state_dict().keys()):
diffusers_value = diffusers_vae.state_dict()[diffusers_key]
if diffusers_key in vae_keys_squished_on_diffusers:
diffusers_value = diffusers_value.clone().unsqueeze(-1).unsqueeze(-1)
# if they are not same shape, skip
if ldm_value.shape != diffusers_value.shape:
continue
mse = torch.nn.functional.mse_loss(ldm_value, diffusers_value)
if mse < 1e-6:
if ldm_key in list(matched_keys.keys()):
print(f'{ldm_key} already matched to {matched_keys[ldm_key]}')
if ldm_key in duplicated_keys:
duplicated_keys[ldm_key].append(diffusers_key)
else:
duplicated_keys[ldm_key] = [diffusers_key]
continue
matched_keys[ldm_key] = diffusers_key
is_matched = True
break
print(f'Found {len(matched_keys)} matches')
dif_to_ldm_state_dict = convert_diffusers_back_to_ldm(diffusers_vae)
dif_to_ldm_state_dict_keys = list(dif_to_ldm_state_dict.keys())
keys_in_both = []
keys_not_in_diffusers = []
for key in ldm_keys:
if key not in dif_to_ldm_state_dict_keys:
keys_not_in_diffusers.append(key)
keys_not_in_ldm = []
for key in dif_to_ldm_state_dict_keys:
if key not in ldm_keys:
keys_not_in_ldm.append(key)
keys_in_both = []
for key in ldm_keys:
if key in dif_to_ldm_state_dict_keys:
keys_in_both.append(key)
# sort them
keys_not_in_diffusers.sort()
keys_not_in_ldm.sort()
keys_in_both.sort()
# print(f'Keys in LDM but not in Diffusers: {len(keys_not_in_diffusers)}{keys_not_in_diffusers}')
# print(f'Keys in Diffusers but not in LDM: {len(keys_not_in_ldm)}{keys_not_in_ldm}')
# print(f'Keys in both: {len(keys_in_both)}{keys_in_both}')
json_data = {
"both": keys_in_both,
"ldm": keys_not_in_diffusers,
"diffusers": keys_not_in_ldm
}
json_data = json.dumps(json_data, indent=4)
remaining_diffusers_values = OrderedDict()
for key in keys_not_in_ldm:
remaining_diffusers_values[key] = dif_to_ldm_state_dict[key]
# print(remaining_diffusers_values.keys())
remaining_ldm_values = OrderedDict()
for key in keys_not_in_diffusers:
remaining_ldm_values[key] = state_dict_ldm[key]
# print(json_data)
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
json_save_path = os.path.join(project_root, 'config', 'keys.json')
json_matched_save_path = os.path.join(project_root, 'config', 'matched.json')
json_duped_save_path = os.path.join(project_root, 'config', 'duped.json')
with open(json_save_path, 'w') as f:
f.write(json_data)
if find_matches:
with open(json_matched_save_path, 'w') as f:
f.write(json.dumps(matched_keys, indent=4))
with open(json_duped_save_path, 'w') as f:
f.write(json.dumps(duplicated_keys, indent=4))
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