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

restore "Loading..." text

1 files changed, 33 insertions, 15 deletions

diff --git a/modules/extras.py b/modules/extras.py
index a9788e7d..15873204 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -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