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added console outputs, more clear indication of progress, and ability to specify full filename to checkpoint merger

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restore "Loading..." text
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AUTOMATIC
2022-09-27 10:44:00 +03:00
parent a9dc307a21
commit ada901ed66
2 changed files with 35 additions and 16 deletions
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48
modules/extras.py
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@@ -4,6 +4,7 @@ import numpy as np
from PIL import Image
import torch
import tqdm
from modules import processing, shared, images, devices
from modules.shared import opts
@@ -149,28 +150,45 @@ def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
alpha = alpha * alpha * (3 - (2 * alpha))
return theta0 + ((theta1 - theta0) * alpha)
model_0 = torch.load('models/' + modelname_0 + '.ckpt')
model_1 = torch.load('models/' + modelname_1 + '.ckpt')
if os.path.exists(modelname_0):
model0_filename = modelname_0
modelname_0 = os.path.splitext(os.path.basename(modelname_0))[0]
else:
model0_filename = 'models/' + modelname_0 + '.ckpt'
if os.path.exists(modelname_1):
model1_filename = modelname_1
modelname_1 = os.path.splitext(os.path.basename(modelname_1))[0]
else:
model1_filename = 'models/' + modelname_1 + '.ckpt'
print(f"Loading {model0_filename}...")
model_0 = torch.load(model0_filename, map_location='cpu')
print(f"Loading {model1_filename}...")
model_1 = torch.load(model1_filename, map_location='cpu')
theta_0 = model_0['state_dict']
theta_1 = model_1['state_dict']
theta_func = weighted_sum
if interp_method == "Weighted Sum":
theta_func = weighted_sum
if interp_method == "Sigmoid":
theta_func = sigmoid
for key in theta_0.keys():
theta_funcs = {
"Weighted Sum": weighted_sum,
"Sigmoid": sigmoid,
}
theta_func = theta_funcs[interp_method]
print(f"Merging...")
for key in tqdm.tqdm(theta_0.keys()):
if 'model' in key and key in theta_1:
theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)
for key in theta_1.keys():
if 'model' in key and key not in theta_0:
theta_0[key] = theta_1[key]
output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt';
output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt'
print(f"Saving to {output_modelname}...")
torch.save(model_0, output_modelname)
return "<p>Model saved to " + output_modelname + "</p>"
print(f"Checkpoint saved.")
return "Checkpoint saved to " + output_modelname