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add safetensors support for model merging #4869

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AUTOMATIC
2022-11-27 15:51:29 +03:00
parent 6074175faa
commit dac9b6f15d
3 changed files with 35 additions and 24 deletions
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26
modules/extras.py
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@@ -20,6 +20,7 @@ import modules.codeformer_model
import piexif
import piexif.helper
import gradio as gr
import safetensors.torch
class LruCache(OrderedDict):
@@ -249,7 +250,7 @@ def run_pnginfo(image):
return '', geninfo, info
def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
def weighted_sum(theta0, theta1, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
@@ -264,19 +265,15 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
print(f"Loading {primary_model_info.filename}...")
primary_model = torch.load(primary_model_info.filename, map_location='cpu')
theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
print(f"Loading {secondary_model_info.filename}...")
secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
if teritary_model_info is not None:
print(f"Loading {teritary_model_info.filename}...")
teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
theta_2 = sd_models.read_state_dict(teritary_model_info.filename, map_location='cpu')
else:
teritary_model = None
theta_2 = None
theta_funcs = {
@@ -295,7 +292,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
theta_1[key] = theta_func1(theta_1[key], t2)
else:
theta_1[key] = torch.zeros_like(theta_1[key])
del theta_2, teritary_model
del theta_2
for key in tqdm.tqdm(theta_0.keys()):
if 'model' in key and key in theta_1:
@@ -314,12 +311,17 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
filename = filename if custom_name == '' else (custom_name + '.ckpt')
filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.' + checkpoint_format
filename = filename if custom_name == '' else (custom_name + '.' + checkpoint_format)
output_modelname = os.path.join(ckpt_dir, filename)
print(f"Saving to {output_modelname}...")
torch.save(primary_model, output_modelname)
_, extension = os.path.splitext(output_modelname)
if extension.lower() == ".safetensors":
safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
else:
torch.save(theta_0, output_modelname)
sd_models.list_models()