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import argparse
import os
# add project root to sys path
import sys
from tqdm import tqdm
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
from diffusers.loaders import LoraLoaderMixin
from safetensors.torch import load_file
from collections import OrderedDict
import json
from toolkit.config_modules import ModelConfig
from toolkit.paths import KEYMAPS_ROOT
from toolkit.saving import convert_state_dict_to_ldm_with_mapping, get_ldm_state_dict_from_diffusers
from toolkit.stable_diffusion_model import StableDiffusion
# 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
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
device = torch.device('cpu')
dtype = torch.float32
parser = argparse.ArgumentParser()
# require at lease one config file
parser.add_argument(
'file_1',
nargs='+',
type=str,
help='Path an LDM model'
)
parser.add_argument(
'--is_xl',
action='store_true',
help='Is the model an XL model'
)
parser.add_argument(
'--is_v2',
action='store_true',
help='Is the model a v2 model'
)
args = parser.parse_args()
find_matches = False
print("Loading model")
state_dict_file_1 = load_file(args.file_1[0])
state_dict_1_keys = list(state_dict_file_1.keys())
print("Loading model into diffusers format")
model_config = ModelConfig(
name_or_path=args.file_1[0],
is_xl=args.is_xl
)
sd = StableDiffusion(
model_config=model_config,
device=device,
)
sd.load_model()
# load our base
base_path = os.path.join(KEYMAPS_ROOT, 'stable_diffusion_sdxl_ldm_base.safetensors')
mapping_path = os.path.join(KEYMAPS_ROOT, 'stable_diffusion_sdxl.json')
print("Converting model back to LDM")
version_string = '1'
if args.is_v2:
version_string = '2'
if args.is_xl:
version_string = 'sdxl'
# convert the state dict
state_dict_file_2 = get_ldm_state_dict_from_diffusers(
sd.state_dict(),
version_string,
device='cpu',
dtype=dtype
)
# state_dict_file_2 = load_file(args.file_2[0])
state_dict_2_keys = list(state_dict_file_2.keys())
keys_in_both = []
keys_not_in_state_dict_2 = []
for key in state_dict_1_keys:
if key not in state_dict_2_keys:
keys_not_in_state_dict_2.append(key)
keys_not_in_state_dict_1 = []
for key in state_dict_2_keys:
if key not in state_dict_1_keys:
keys_not_in_state_dict_1.append(key)
keys_in_both = []
for key in state_dict_1_keys:
if key in state_dict_2_keys:
keys_in_both.append(key)
# sort them
keys_not_in_state_dict_2.sort()
keys_not_in_state_dict_1.sort()
keys_in_both.sort()
if len(keys_not_in_state_dict_2) == 0 and len(keys_not_in_state_dict_1) == 0:
print("All keys match!")
print("Checking values...")
mismatch_keys = []
loss = torch.nn.MSELoss()
tolerance = 1e-6
for key in tqdm(keys_in_both):
if loss(state_dict_file_1[key], state_dict_file_2[key]) > tolerance:
print(f"Values for key {key} don't match!")
print(f"Loss: {loss(state_dict_file_1[key], state_dict_file_2[key])}")
mismatch_keys.append(key)
if len(mismatch_keys) == 0:
print("All values match!")
else:
print("Some valued font match!")
print(mismatch_keys)
mismatched_path = os.path.join(project_root, 'config', 'mismatch.json')
with open(mismatched_path, 'w') as f:
f.write(json.dumps(mismatch_keys, indent=4))
exit(0)
else:
print("Keys don't match!, generating info...")
json_data = {
"both": keys_in_both,
"not_in_state_dict_2": keys_not_in_state_dict_2,
"not_in_state_dict_1": keys_not_in_state_dict_1
}
json_data = json.dumps(json_data, indent=4)
remaining_diffusers_values = OrderedDict()
for key in keys_not_in_state_dict_1:
remaining_diffusers_values[key] = state_dict_file_2[key]
# print(remaining_diffusers_values.keys())
remaining_ldm_values = OrderedDict()
for key in keys_not_in_state_dict_2:
remaining_ldm_values[key] = state_dict_file_1[key]
# print(json_data)
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')
state_dict_1_filename = os.path.basename(args.file_1[0])
# state_dict_2_filename = os.path.basename(args.file_2[0])
# save key names for each in own file
with open(os.path.join(project_root, 'config', f'{state_dict_1_filename}.json'), 'w') as f:
f.write(json.dumps(state_dict_1_keys, indent=4))
with open(os.path.join(project_root, 'config', f'{state_dict_1_filename}_loop.json'), 'w') as f:
f.write(json.dumps(state_dict_2_keys, indent=4))
with open(json_save_path, 'w') as f:
f.write(json_data)
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