prepare override_uses correctly.

 * check best_para

sd_modelmixer/hyper.py

@@ -327,15 +327,20 @@ def hyper_score(localargs):
     if variable_models is not None and len(variable_models) == 0:
         variable_models = None
 
+    # setup override uses
+    override_uses = uses.copy()
+
     k = 0
     for i in range(len(uses)):
         if uses[i] is not True:
             continue
 
         # is this model in the variable_models? e.g.) chr(0+66) == B
-        if variable_models is not None and chr(i+66) not in variable_models:
-            k += 1
-            continue
+        if variable_models is not None:
+            if chr(i+66) not in variable_models:
+                override_uses[i] = False
+                k += 1
+                continue
 
         name = f"model_{chr(i + 98)}"
         weight = weights[k]
@@ -352,7 +357,7 @@ def hyper_score(localargs):
                 search_space[f"{name}.{_BLOCKS[j]}"] = [*np.round(np.linspace(lower, upper, 5), 8)]
         k += 1
 
-    #print(" - search_space keys =", search_space.keys())
+    print(" - search_space keys =", search_space.keys())
 
     # setup warm_start
     if warm_start:
@@ -420,7 +425,7 @@ def hyper_score(localargs):
         pass_through = {
             "tunables": [*search_space.keys()],
             "weights": weights,
-            "uses": uses,
+            "uses": override_uses,
             "classifier": classifier,
             "payload_path": payload_path,
             "tally_type": tally_type,
@@ -470,37 +475,46 @@ def hyper_score(localargs):
 
         # run main optimizer
         shared.state.begin(job="modelmixer-auto-merger")
+        best_para = None
         try:
             hyper.run(search_time*60)
+            best_para = hyper.best_para(hyper_score)
         except Exception as e:
             print(f"Error: {e}")
         finally:
             shared.state.end()
 
-        best_para = hyper.best_para(hyper_score)
-        best_weights = para_to_weights(best_para, isxl)
-        print(" - Best weights para = ", best_weights)
+        if best_para is not None:
+            best_weights = para_to_weights(best_para, isxl)
+            print(" - Best weights para = ", best_weights, override_uses)
 
-        # setup override weights. will be replaced with mm_weights
-        shared.modelmixer_overrides = {"weights": best_weights, "uses": uses}
+            # setup override weights. will be replaced with mm_weights
 
-        # generate image with the optimized parameter
-        ret = None
-        ret = txt2img.txt2img(*txt2img_args)
-        if ret and ret[0] is not None:
-            gallery = ret[0] # gallery
+            shared.modelmixer_overrides = {"weights": best_weights, "uses": override_uses}
 
-            score = score_func(classifier, gallery[0], prompt)
-            print("Result score =", score)
+            # generate image with the optimized parameter
+            ret = None
+            ret = txt2img.txt2img(*txt2img_args)
+            if ret and ret[0] is not None:
+                gallery = ret[0] # gallery
+
+                score = score_func(classifier, gallery[0], prompt)
+                print("Result score =", score)
 
-        shared._memory_warm_start = hyper.search_data(hyper_score)
-        shared._memory_warm_hash = warm_hash
+            shared._memory_warm_start = hyper.search_data(hyper_score)
+            shared._memory_warm_hash = warm_hash
+            msg = "merge completed."
+        else:
+            shared._memory_warm_start = None
+            shared._memory_warm_hash = None
+            msg = "Failed to call hyper.run()"
 
     # search data save
     #timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%I-%M%p-%S")
     #collector = SearchDataCollector(os.path.join(folder_path, f"{model_O}-{pass}-{timestamp}.csv"))
     #collector.save(hyper.search_data(hyper_score, times=True))
 
-    delattr(shared, "modelmixer_overrides")
+    if hasattr(shared, "modelmixer_overrides"):
+        delattr(shared, "modelmixer_overrides")
 
-    return "merge completed."
+    return msg