Fix regression with model merging.

comfy/model_management.py

@@ -319,16 +319,14 @@ class LoadedModel:
 def minimum_inference_memory():
     return (1024 * 1024 * 1024)
 
-def unload_model_clones(loaded_model, unload_weights_only=True):
-    model = loaded_model.model
-
+def unload_model_clones(model, unload_weights_only=True, force_unload=True):
     to_unload = []
     for i in range(len(current_loaded_models)):
         if model.is_clone(current_loaded_models[i].model):
             to_unload = [i] + to_unload
 
     if len(to_unload) == 0:
-        return
+        return None
 
     same_weights = 0
     for i in to_unload:
@@ -340,14 +338,15 @@ def unload_model_clones(loaded_model, unload_weights_only=True):
     else:
         unload_weight = True
 
+    if not force_unload:
         if unload_weights_only and unload_weight == False:
-        return
+            return None
 
     for i in to_unload:
         logging.debug("unload clone {} {}".format(i, unload_weight))
         current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight)
 
-    loaded_model.weights_loaded = not unload_weight
+    return unload_weight
 
 def free_memory(memory_required, device, keep_loaded=[]):
     unloaded_model = False
@@ -402,7 +401,7 @@ def load_models_gpu(models, memory_required=0):
 
     total_memory_required = {}
     for loaded_model in models_to_load:
-        unload_model_clones(loaded_model, unload_weights_only=True) #unload clones where the weights are different
+        unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) #unload clones where the weights are different
         total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
 
     for device in total_memory_required:
@@ -410,7 +409,9 @@ def load_models_gpu(models, memory_required=0):
             free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
 
     for loaded_model in models_to_load:
-        unload_model_clones(loaded_model, unload_weights_only=False) #unload the rest of the clones where the weights can stay loaded
+        weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded
+        if weights_unloaded is not None:
+            loaded_model.weights_loaded = not weights_unloaded
 
     for loaded_model in models_to_load:
         model = loaded_model.model