- Add gradio dropdown list to select checkpoints

- Update checkpoint model fields labels

1 files changed, 14 insertions, 14 deletions

diff --git a/modules/extras.py b/modules/extras.py
index 9e1efeda..90968352 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -140,7 +140,7 @@ def run_pnginfo(image):
     return '', geninfo, info

 

 

-def run_modelmerger(from_model_name, to_model_name, interp_method, interp_amount):

+def run_modelmerger(primary_model_name, secondary_model_name, 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)

@@ -150,23 +150,23 @@ def run_modelmerger(from_model_name, to_model_name, interp_method, interp_amount
         alpha = alpha * alpha * (3 - (2 * alpha))

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

 

-    if os.path.exists(to_model_name):

-        to_model_filename = to_model_name

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

+    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:

-        to_model_filename = 'models/' + to_model_name + '.ckpt'

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

 

-    if os.path.exists(from_model_name):

-        from_model_filename = from_model_name

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

+    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:

-        from_model_filename = 'models/' + from_model_name + '.ckpt'

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

 

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

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

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

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

 

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

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

+    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']

@@ -186,7 +186,7 @@ def run_modelmerger(from_model_name, to_model_name, interp_method, interp_amount
         if 'model' in key and key not in theta_0:

             theta_0[key] = theta_1[key]

 

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

+    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)