Update app.py

app.py

@@ -65,37 +65,27 @@ def generate(prompt, negative_prompt, width=1024, height=1024, num_inference_ste
     #     pipe.unload_lora_weights()
     #     pipe.load_lora_weights(lora_id.strip())
 
-    global CURRENTLY_LOADED_CUSTOM_LORA
-
-    active_adapters = []
-    adapter_weights = []
+    clean_lora_id = lora_id.strip() if lora_id else ""
 
-    # Always add the default LoRA if it was loaded successfully
-    if DEFAULT_LORA_NAME:
-        active_adapters.append(DEFAULT_LORA_NAME)
-        adapter_weights.append(1.0) # Strength for base LoRA
+    try:
+        # --- IF LORA ID IS NONE, USE ONE LORA ---
+        if not clean_lora_id:
+            print("No custom LoRA provided. Using ONLY the base LoRA.")
+            # Activate just the default LoRA
+            pipe.set_adapters([DEFAULT_LORA_NAME], adapter_weights=[1.0])
+
+        # --- OTHERWISE, LOAD AND USE TWO LORAS ---
+        else:
+            print(f"Custom LoRA provided. Loading '{clean_lora_id}' and combining with base LoRA.")
+            # Load the custom LoRA fresh for this run
+            pipe.load_lora_weights(clean_lora_id, adapter_name=CUSTOM_LORA_NAME)
+
+            # Activate BOTH LoRAs together
+            pipe.set_adapters(
+                [DEFAULT_LORA_NAME, CUSTOM_LORA_NAME],
+                adapter_weights=[1.0, 1.0]  # Strength for base, strength for custom
+            )
 
-    # Handle the user-provided custom LoRA
-    clean_lora_id = lora_id.strip() if lora_id else ""
-    if clean_lora_id:
-        try:
-            # If the requested LoRA is different from the one in memory, swap it
-            if clean_lora_id != CURRENTLY_LOADED_CUSTOM_LORA:
-                print(f"Switching custom LoRA to: {clean_lora_id}")
-                # Unload the old custom LoRA to save memory
-                if CURRENTLY_LOADED_CUSTOM_LORA is not None:
-                    pipe.unload_lora_weights(CUSTOM_LORA_NAME)
-                
-                # Load the new one with its unique name
-                pipe.load_lora_weights(clean_lora_id, adapter_name=CUSTOM_LORA_NAME)
-                CURRENTLY_LOADED_CUSTOM_LORA = clean_lora_id
-            
-            # Add the custom LoRA to the active list for this generation
-            active_adapters.append(CUSTOM_LORA_NAME)
-            adapter_weights.append(1.0) # Strength for custom LoRA
-
-        except Exception as e:
-            print(f"⚠️ Failed to load custom LoRA '{clean_lora_id}'. Error: {e}")
 
     pipe.to("cuda")
     # apply_first_block_cache(pipe.transformer, FirstBlockCacheConfig(threshold=0.2))