Fix variable conversion code issue

1 files changed, 14 insertions, 14 deletions

diff --git a/modules/extras.py b/modules/extras.py
index 90968352..f6704382 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -150,26 +150,26 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
         alpha = alpha * alpha * (3 - (2 * alpha))

         return theta0 + ((theta1 - theta0) * alpha)

 

-    if os.path.exists(secondary_model_name):

-        secondary_model_filename = secondary_model_name

-        secondary_model_name = os.path.splitext(os.path.basename(secondary_model_name))[0]

-    else:

-        secondary_model_filename = 'models/' + secondary_model_name + '.ckpt'

-

     if os.path.exists(primary_model_name):

         primary_model_filename = primary_model_name

         primary_model_name = os.path.splitext(os.path.basename(primary_model_name))[0]

     else:

         primary_model_filename = 'models/' + primary_model_name + '.ckpt'

 

-    print(f"Loading {secondary_model_filename}...")

-    model_0 = torch.load(secondary_model_filename, map_location='cpu')

+    if os.path.exists(secondary_model_name):

+        secondary_model_filename = secondary_model_name

+        secondary_model_name = os.path.splitext(os.path.basename(secondary_model_name))[0]

+    else:

+        secondary_model_filename = 'models/' + secondary_model_name + '.ckpt'

 

     print(f"Loading {primary_model_filename}...")

-    model_1 = torch.load(primary_model_filename, map_location='cpu')

-    

-    theta_0 = model_0['state_dict']

-    theta_1 = model_1['state_dict']

+    primary_model = torch.load(primary_model_filename, map_location='cpu')

+

+    print(f"Loading {secondary_model_filename}...")

+    secondary_model = torch.load(secondary_model_filename, map_location='cpu')

+   

+    theta_0 = primary_model['state_dict']

+    theta_1 = secondary_model['state_dict']

 

     theta_funcs = {

         "Weighted Sum": weighted_sum,

@@ -180,7 +180,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
     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)

+            theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint

     

     for key in theta_1.keys():

         if 'model' in key and key not in theta_0:

@@ -188,7 +188,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
 

     output_modelname = 'models/' + primary_model_name + '_' + str(interp_amount) + '-' + secondary_model_name + '_' + str(float(1.0) - interp_amount) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'

     print(f"Saving to {output_modelname}...")

-    torch.save(model_0, output_modelname)

+    torch.save(primary_model, output_modelname)

 

     print(f"Checkpoint saved.")

     return "Checkpoint saved to " + output_modelname