add progress bar to modelmerger

1 files changed, 15 insertions, 3 deletions

diff --git a/modules/extras.py b/modules/extras.py
index 367c15cc..034f28e4 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -274,14 +274,15 @@ def create_config(ckpt_result, config_source, a, b, c):
     shutil.copyfile(cfg, checkpoint_filename)

 

 

-def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source):

+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source):

     shared.state.begin()

     shared.state.job = 'model-merge'

+    shared.state.job_count = 1

 

     def fail(message):

         shared.state.textinfo = message

         shared.state.end()

-        return [message, *[gr.update() for _ in range(4)]]

+        return [*[gr.update() for _ in range(4)], message]

 

     def weighted_sum(theta0, theta1, alpha):

         return ((1 - alpha) * theta0) + (alpha * theta1)

@@ -320,9 +321,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')

 

     if theta_func1:

+        shared.state.job_count += 1

+

         print(f"Loading {tertiary_model_info.filename}...")

         theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')

 

+        shared.state.sampling_steps = len(theta_1.keys())

         for key in tqdm.tqdm(theta_1.keys()):

             if 'model' in key:

                 if key in theta_2:

@@ -330,8 +334,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                     theta_1[key] = theta_func1(theta_1[key], t2)

                 else:

                     theta_1[key] = torch.zeros_like(theta_1[key])

+

+            shared.state.sampling_step += 1

         del theta_2

 

+        shared.state.nextjob()

+

     shared.state.textinfo = f"Loading {primary_model_info.filename}..."

     print(f"Loading {primary_model_info.filename}...")

     theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')

@@ -340,6 +348,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
 

     chckpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]

 

+    shared.state.sampling_steps = len(theta_0.keys())

     for key in tqdm.tqdm(theta_0.keys()):

         if 'model' in key and key in theta_1:

 

@@ -367,6 +376,8 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
             if save_as_half:

                 theta_0[key] = theta_0[key].half()

 

+        shared.state.sampling_step += 1

+

     # I believe this part should be discarded, but I'll leave it for now until I am sure

     for key in theta_1.keys():

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

@@ -393,6 +404,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
 

     output_modelname = os.path.join(ckpt_dir, filename)

 

+    shared.state.nextjob()

     shared.state.textinfo = f"Saving to {output_modelname}..."

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

 

@@ -410,4 +422,4 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     shared.state.textinfo = "Checkpoint saved to " + output_modelname

     shared.state.end()

 

-    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]

+    return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname]