Update app.py

app.py

@@ -27,8 +27,6 @@ OVIMX2_NAME = "ovimx2_optional"
 OVIMX3_NAME = "ovimx3_optional"
 PRTHE_NAME = "prthe_optional"
 
-SUCCESSFULLY_LOADED_LORAS = {}
-
 lora_definitions = {
     CAUSVID_NAME: ("joerose/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32", "Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors"),
     PERSONVID_NAME: ("ovi054/p3r5onVid1000", None),
@@ -39,6 +37,9 @@ lora_definitions = {
     PRTHE_NAME: ("ovi054/prwthxVid", None)
 }
 
+# --- THIS ORDERED LIST IS NOW CRITICAL ---
+# It defines the consistent order for the weight vector.
+ALL_ADAPTER_NAMES = []
 for name, (repo, filename) in lora_definitions.items():
     print(f"Attempting to load LoRA '{name}'...")
     try:
@@ -48,7 +49,7 @@ for name, (repo, filename) in lora_definitions.items():
         else:
             pipe.load_lora_weights(repo, adapter_name=name, device_map="auto")
         print(f"✅ LoRA '{name}' loaded successfully.")
-        SUCCESSFULLY_LOADED_LORAS[name] = repo
+        ALL_ADAPTER_NAMES.append(name)
     except Exception as e:
         print(f"⚠️ LoRA '{name}' could not be loaded: {e}")
 
@@ -60,38 +61,59 @@ OPTIONAL_LORA_MAP = {
     "ovi054/ovimxVid2500": OVIMX3_NAME,
     "ovi054/prwthxVid": PRTHE_NAME,
 }
-OPTIONAL_LORA_CHOICES = {k: v for k, v in OPTIONAL_LORA_MAP.items() if v in SUCCESSFULLY_LOADED_LORAS}
+# Filter choices to only include LoRAs that actually loaded
+OPTIONAL_LORA_CHOICES = {k: v for k, v in OPTIONAL_LORA_MAP.items() if v in ALL_ADAPTER_NAMES}
+
+
+# --- SET INITIAL STATE AT STARTUP ---
+# Set ALL adapters as active, but control them with weights.
+if ALL_ADAPTER_NAMES:
+    print(f"Setting up all {len(ALL_ADAPTER_NAMES)} loaded adapters in the pipeline.")
+    
+    # Start with all weights at 0.0
+    initial_weights = [0.0] * len(ALL_ADAPTER_NAMES)
+    
+    # Set the base LoRA's weight to 1.0
+    try:
+        base_lora_index = ALL_ADAPTER_NAMES.index(CAUSVID_NAME)
+        initial_weights[base_lora_index] = 1.0
+    except ValueError:
+        print(f"Warning: Base LoRA '{CAUSVID_NAME}' not found in the loaded list. All weights start at 0.")
 
-# 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(f"Setting initial state: adapters={ALL_ADAPTER_NAMES}, weights={initial_weights}")
+    pipe.set_adapters(ALL_ADAPTER_NAMES, adapter_weights=initial_weights)
+else:
+    print("No LoRAs were loaded.")
 
 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)):
     
-    # --- Step 1: ALWAYS build the desired state from scratch for THIS run ---
-    
-    active_adapters = []
-    adapter_weights = []
+    # --- Step 1: ALWAYS build the full weight vector from scratch for THIS run ---
     
-    # 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)
-        
-    # If an optional LoRA is selected, add it to the list
+    # Start with the default state: base LoRA on, others off.
+    adapter_weights = [0.0] * len(ALL_ADAPTER_NAMES)
+    try:
+        base_lora_index = ALL_ADAPTER_NAMES.index(CAUSVID_NAME)
+        adapter_weights[base_lora_index] = 1.0
+    except ValueError:
+        pass # Base lora was not loaded, so its weight remains 0.
+
+    # If an optional LoRA is selected, turn its weight on.
     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)
+            try:
+                optional_lora_index = ALL_ADAPTER_NAMES.index(internal_name_to_add)
+                adapter_weights[optional_lora_index] = 1.0
+            except ValueError:
+                print(f"Warning: Could not find index for selected LoRA '{internal_name_to_add}'. It will not be applied.")
 
     # --- 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)
+    # We always pass the FULL list of adapters, just with different weights.
+    print(f"Setting weights for this run: {list(zip(ALL_ADAPTER_NAMES, adapter_weights))}")
+    pipe.set_adapters(ALL_ADAPTER_NAMES, adapter_weights=adapter_weights)
 
     apply_cache_on_pipe(pipe)
     
@@ -109,8 +131,6 @@ def generate(prompt, negative_prompt, width, height, num_inference_steps, option
     image = (image * 255).astype(np.uint8)
     return Image.fromarray(image)
 
-    # --- No cleanup step is needed, as the next run will set its own state ---
-
 
 # --- Gradio Interface ---
 iface = gr.Interface(