Update app.py

app.py

@@ -45,7 +45,8 @@ MAX_NUM_FRAMES = 257
 FPS = 30.0
 MIN_DIM_SLIDER = 256
 TARGET_FIXED_SIDE = 768
-
+last_lora = ""
+last_fused=False
 
 class calculateDuration:
     def __init__(self, activity_name=""):
@@ -110,9 +111,6 @@ def get_huggingface_safetensors_for_ltx(link): # Renamed for clarity
     try:
         list_of_files = fs.ls(link, detail=False)
         safetensors_name = None
-        # Simplified logic: find first .safetensors, or prioritize specific names if needed
-        # For LoRAs, usually there's one main .safetensors file.
-        # The complex step-based selection from app(2) might be overkill unless LTX LoRAs follow that pattern.
         
         # Prioritize files common for LoRAs
         common_lora_filenames = ["lora.safetensors", "pytorch_lora_weights.safetensors"]
@@ -311,13 +309,13 @@ def get_duration(prompt, negative_prompt, image, video, height, width, mode, ste
                  frames_to_use, seed, randomize_seed, guidance_scale, duration_input, improve_texture,
                  # New LoRA params
                  selected_lora_index, lora_scale_value,
-                 progress): # Add selected_lora_index and lora_scale_value if they affect duration
+                 progress):
     if duration_input > 7:
-        return 85
+        return 95
     else:
-        return 75
+        return 85
 
-@spaces.GPU(duration=get_duration) # Needs selected_lora_index and lora_scale_value if get_duration uses them
+@spaces.GPU(duration=get_duration) 
 def generate(prompt,
              negative_prompt,
              image,
@@ -339,16 +337,13 @@ def generate(prompt,
              progress=gr.Progress(track_tqdm=True)):
     
     effective_prompt = prompt
+    global last_fused, last_lora
+
     
     # --- LoRA Handling ---
     # Unload any existing LoRAs from main pipes first to prevent conflicts
-    # This should ideally be more granular if LoRAs are very large or loading is slow.
-    with calculateDuration("Unloading previous LoRAs"):
-        try:
-            pipe.unload_lora_weights()
-            print("Previous LoRAs unloaded if any.")
-        except Exception as e:
-            print(f"Note: Could not unload LoRAs (maybe none were loaded): {e}")
+    
+    
 
     if selected_lora_index is not None and 0 <= selected_lora_index < len(loras):
         selected_lora_data = loras[selected_lora_index]
@@ -356,7 +351,30 @@ def generate(prompt,
         lora_weights_name = selected_lora_data.get("weights", None)
         lora_trigger = selected_lora_data.get("trigger_word", "")
 
+        print("Last LoRA: ", last_lora)
+        print("Current LoRA: ", repo_name)
+        print("Last fused: ", last_fused)
+
         print(f"Selected LoRA: {selected_lora_data['title']} from {lora_repo_id}")
+
+        if last_lora != lora_repo_id:
+            if(last_fused):
+                with calculateDuration("Unloading previous LoRAs"):
+                    pipe.unfuse_lora()
+                    pipe.unload_lora_weights()
+                    print("Previous LoRAs unloaded if any.")
+                    
+            with calculateDuration(f"Loading LoRA weights for {selected_lora_data['title']}"):
+                pipe.load_lora_weights(
+                    lora_repo_id, 
+                    weight_name=lora_weights_name,
+                )
+                #pipe.set_adapters(["active_lora"], adapter_weights=[lora_scale_value])
+                pipe.fuse_lora(lora_scale_value)
+                print(f"LoRA loaded into main pipe with scale {lora_scale_value}")
+                last_fused = True
+                last_lora = lora_repo_id
+
         if lora_trigger:
             print(f"Applying trigger word: {lora_trigger}")
 
@@ -364,25 +382,7 @@ def generate(prompt,
                  effective_prompt = f"{lora_trigger} {prompt}"
             else: # Default to append or if not specified
                  effective_prompt = f"{prompt} {lora_trigger}"
-
-        with calculateDuration(f"Loading LoRA weights for {selected_lora_data['title']}"):
-            try:
-                # Load into main generation pipe
-                pipe.load_lora_weights(
-                    lora_repo_id, 
-                    weight_name=lora_weights_name,
-                    adapter_name="active_lora" # Use a consistent adapter name
-                )
-                pipe.set_adapters(["active_lora"], adapter_weights=[lora_scale_value])
-                print(f"LoRA loaded into main pipe with scale {lora_scale_value}")
                 
-            except Exception as e:
-                gr.Warning(f"Failed to load LoRA '{selected_lora_data['title']}': {e}. Proceeding without LoRA.")
-                print(f"Error loading LoRA: {e}")
-                # Ensure pipes are clean if loading failed mid-way (though unload_lora_weights should handle this)
-                try:
-                    pipe.unload_lora_weights()
-                except: pass # Ignore errors here
     else:
         print("No LoRA selected or invalid index.")
     # --- End LoRA Handling ---
@@ -431,7 +431,7 @@ def generate(prompt,
         timesteps_first_pass = None 
         timesteps_second_pass = None
     
-    with calculateDuration("Main pipe generation"):
+    with calculateDuration("video generation"):
         latents = pipe(
             conditions=condition1,
             prompt=effective_prompt, # Use prompt with trigger word