Mirror/stable-diffusion-webui
1
0
Fork 0
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git synced 2024-06-07 21:20:49 +00:00
Code Issues Packages Projects Releases Wiki Activity

add safetensors support for model merging #4869

Browse Source
This commit is contained in:
AUTOMATIC 2022-11-27 15:51:29 +03:00
parent 6074175faa
commit dac9b6f15d
3 changed files with 35 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

26
modules/extras.py
View File

@ -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()

26
modules/sd_models.py
View File

@ -160,6 +160,20 @@ def get_state_dict_from_checkpoint(pl_sd):
return pl_sd
def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
_, extension = os.path.splitext(checkpoint_file)
if extension.lower() == ".safetensors":
pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location)
else:
pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
if print_global_state and "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = get_state_dict_from_checkpoint(pl_sd)
return sd
def load_model_weights(model, checkpoint_info, vae_file="auto"):
checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash
@ -174,17 +188,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
# load from file
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
_, extension = os.path.splitext(checkpoint_file)
if extension.lower() == ".safetensors":
pl_sd = safetensors.torch.load_file(checkpoint_file, device=shared.weight_load_location)
else:
pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = get_state_dict_from_checkpoint(pl_sd)
del pl_sd
sd = read_state_dict(checkpoint_file)
model.load_state_dict(sd, strict=False)
del sd

7
modules/ui.py
View File

@ -1164,7 +1164,11 @@ def create_ui(wrap_gradio_gpu_call):
custom_name = gr.Textbox(label="Custom Name (Optional)")
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
save_as_half = gr.Checkbox(value=False, label="Save as float16")
with gr.Row():
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format")
save_as_half = gr.Checkbox(value=False, label="Save as float16")
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
with gr.Column(variant='panel'):
@ -1692,6 +1696,7 @@ def create_ui(wrap_gradio_gpu_call):
interp_amount,
save_as_half,
custom_name,
checkpoint_format,
],
outputs=[
submit_result,