Update app.py

app.py

@@ -154,28 +154,30 @@ def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, progress=
     full_path_lora = state_dicts[repo_name]["saved_name"]
     loaded_state_dict = state_dicts[repo_name]["state_dict"]
     cross_attention_kwargs = None
+    
     print("Last LoRA:", last_lora, "Was it last merged? ", last_merged, "Was it last fused?", last_fused)
     print("Current LoRA: ", repo_name)
+    
     if last_lora != repo_name:
-        if last_merged:
-            del pipe
-            gc.collect()
-            pipe = copy.deepcopy(original_pipe)
-            pipe.to(device)
-        elif(last_fused):
+        #if last_merged:
+        del pipe
+        gc.collect()
+        pipe = copy.deepcopy(original_pipe)
+        pipe.to(device)
+        #elif(last_fused):
             #pipe.unfuse_lora()
-            pipe.unload_lora_weights()
+            #pipe.unload_lora_weights()
         is_compatible = sdxl_loras[selected_state.index]["is_compatible"]
         
         if is_compatible:
             pipe.load_lora_weights(loaded_state_dict)
-            #pipe.fuse_lora(lora_scale)
+            pipe.fuse_lora(lora_scale)
             last_fused = True
         else:
             is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
             if(is_pivotal):
                 pipe.load_lora_weights(loaded_state_dict)
-                #pipe.fuse_lora(lora_scale)
+                pipe.fuse_lora(lora_scale)
                 last_fused = True
                 
                 #Add the textual inversion embeddings from pivotal tuning models