Merge remote-tracking branch 'upstream/master' into ModelLoader

modules/extras.py

@@ -3,6 +3,9 @@ import os
 import numpy as np
 from PIL import Image
 
+import torch
+import tqdm
+
 from modules import processing, shared, images, devices
 from modules.shared import opts
 import modules.gfpgan_model
@@ -137,3 +140,57 @@ def run_pnginfo(image):
         info = f"<div><p>{message}<p></div>"
 
     return '', geninfo, info
+
+
+def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
+    # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
+    def weighted_sum(theta0, theta1, alpha):
+        return ((1 - alpha) * theta0) + (alpha * theta1)
+
+    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
+    def sigmoid(theta0, theta1, alpha):
+        alpha = alpha * alpha * (3 - (2 * alpha))
+        return theta0 + ((theta1 - theta0) * alpha)
+
+    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_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'
+    print(f"Saving to {output_modelname}...")
+    torch.save(model_0, output_modelname)
+
+    print(f"Checkpoint saved.")
+    return "Checkpoint saved to " + output_modelname