Update app.py

app.py

@@ -62,63 +62,55 @@ OPTIONAL_LORA_MAP = {
 }
 OPTIONAL_LORA_CHOICES = {k: v for k, v in OPTIONAL_LORA_MAP.items() if v in SUCCESSFULLY_LOADED_LORAS}
 
-# At startup, disable all adapters. They will be selectively enabled during each run.
-print("Disabling all LoRAs at startup. They will be activated on-demand.")
-pipe.disable_lora()
+# At startup, we set a known initial state. Setting no adapters is the cleanest start.
+print("Setting a clean initial state (no adapters active).")
+pipe.set_adapters([], adapter_weights=[])
 
 print("Initialization complete. Gradio is starting...")
 
 @spaces.GPU()
 def generate(prompt, negative_prompt, width, height, num_inference_steps, optional_lora_id, progress=gr.Progress(track_tqdm=True)):
     
-    # Using a try...finally block is robust for state management in apps.
-    try:
-        # --- Step 1: Build the list of ACTIVE adapters and their weights for THIS run ---
-        
-        active_adapters = []
-        adapter_weights = []
+    # --- Step 1: ALWAYS build the desired state from scratch for THIS run ---
+    
+    active_adapters = []
+    adapter_weights = []
+    
+    # Always include the base LoRA if it was loaded successfully
+    if CAUSVID_NAME in SUCCESSFULLY_LOADED_LORAS:
+        active_adapters.append(CAUSVID_NAME)
+        adapter_weights.append(1.0)
         
-        # Always include the base LoRA if it was loaded successfully
-        if CAUSVID_NAME in SUCCESSFULLY_LOADED_LORAS:
-            active_adapters.append(CAUSVID_NAME)
+    # If an optional LoRA is selected, add it to the list
+    if optional_lora_id and optional_lora_id != "None":
+        internal_name_to_add = OPTIONAL_LORA_CHOICES.get(optional_lora_id)
+        if internal_name_to_add:
+            active_adapters.append(internal_name_to_add)
             adapter_weights.append(1.0)
-            
-        # If an optional LoRA is selected, add it to the list
-        if optional_lora_id and optional_lora_id != "None":
-            internal_name_to_add = OPTIONAL_LORA_CHOICES.get(optional_lora_id)
-            if internal_name_to_add:
-                active_adapters.append(internal_name_to_add)
-                adapter_weights.append(1.0)
-
-        # --- Step 2: Apply the adapters and weights for this run using the correct function ---
-        if active_adapters:
-            print(f"Activating adapters: {active_adapters} with weights: {adapter_weights}")
-            # This is the correct, modern way to set adapters and their weights.
-            pipe.set_adapters(active_adapters, adapter_weights=adapter_weights)
-        else:
-            print("No LoRAs are active for this run.")
-            # ensure all are disabled if for some reason none were selected
-            pipe.disable_lora()
 
-        apply_cache_on_pipe(pipe)
-        
-        # --- Step 3: Run inference ---
-        output = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            height=height,
-            width=width,
-            num_frames=1,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=1.0, 
-        )
-        image = output.frames[0][0]
-        image = (image * 255).astype(np.uint8)
-        return Image.fromarray(image)
+    # --- Step 2: Apply the calculated state, OVERWRITING any previous state ---
+    # This single call is the source of truth for the run.
+    print(f"Setting adapters for this run: {active_adapters} with weights: {adapter_weights}")
+    pipe.set_adapters(active_adapters, adapter_weights=adapter_weights)
+
+    apply_cache_on_pipe(pipe)
+    
+    # --- Step 3: Run inference ---
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        height=height,
+        width=width,
+        num_frames=1,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=1.0, 
+    )
+    image = output.frames[0][0]
+    image = (image * 255).astype(np.uint8)
+    return Image.fromarray(image)
+
+    # --- No cleanup step is needed, as the next run will set its own state ---
 
-    finally:
-        print("Disabling LoRAs after run to reset state.")
-        pipe.disable_lora()
 
 # --- Gradio Interface ---
 iface = gr.Interface(