lset inclusion

app.py

@@ -64,29 +64,29 @@ except Exception as e:
     traceback.print_exc()
 
 # --- LoRA Discovery ---
-def get_available_loras(repo_id, subfolder):
+def get_available_presets(repo_id, subfolder):
     """
-    Fetches the list of available LoRA files from a Hugging Face Hub repo subfolder.
-    This version is compatible with older huggingface_hub libraries that don't support the 'subfolder' argument.
+    Fetches the list of available LoRA presets by looking for .lset files.
+    This is more robust as it ensures a preset and prompt info exists.
     """
     try:
         # Fetch all files from the repo to maintain compatibility with older library versions.
         all_files = list_repo_files(repo_id=repo_id, repo_type='model')
 
-        # Manually filter for .safetensors files within the specified subfolder.
+        # Manually filter for .lset files and get their names without the extension.
         subfolder_path = f"{subfolder}/"
-        safetensors_files = [
-            f.split('/')[-1]
+        lset_files = [
+            os.path.splitext(f.split('/')[-1])[0] # Get filename without extension
             for f in all_files
-            if f.startswith(subfolder_path) and f.endswith('.safetensors')
+            if f.startswith(subfolder_path) and f.endswith('.lset')
         ]
-        print(f"✅ Discovered {len(safetensors_files)} LoRAs in {repo_id}/{subfolder}")
-        return ["None"] + sorted(safetensors_files)
+        print(f"✅ Discovered {len(lset_files)} LoRA presets in {repo_id}/{subfolder}")
+        return ["None"] + sorted(lset_files)
     except Exception as e:
-        print(f"⚠️ Warning: Could not fetch LoRAs from {repo_id}. LoRA selection will be disabled. Error: {e}")
+        print(f"⚠️ Warning: Could not fetch LoRA presets from {repo_id}. LoRA selection will be disabled. Error: {e}")
         return ["None"]
 
-available_i2v_loras = get_available_loras(I2V_LORA_REPO_ID, I2V_LORA_SUBFOLDER) if i2v_pipe else ["None"]
+available_i2v_presets = get_available_presets(I2V_LORA_REPO_ID, I2V_LORA_SUBFOLDER) if i2v_pipe else ["None"]
 
 
 # --- Constants and Configuration ---
@@ -124,17 +124,17 @@ def parse_lset_prompt(lset_prompt):
         
     return resolved_prompt
 
-def handle_lora_selection_change(lora_name, current_prompt):
+def handle_lora_selection_change(preset_name, current_prompt):
     """
-    When a LoRA is selected, this function tries to find a corresponding .lset file,
+    When a preset is selected, this function finds the corresponding .lset file,
     parses it, and appends the generated prompt to the current prompt.
     """
-    if not lora_name or lora_name == "None":
-        return gr.update() # No LoRA selected, do not change the prompt.
+    if not preset_name or preset_name == "None":
+        return gr.update() # No preset selected, do not change the prompt.
 
     try:
-        # Construct the .lset filename from the .safetensors filename
-        lset_filename = os.path.splitext(lora_name)[0] + ".lset"
+        # The preset_name is the filename without extension
+        lset_filename = f"{preset_name}.lset"
         
         # Download the .lset file from the same subfolder as the LoRA
         lset_path = hf_hub_download(
@@ -156,9 +156,9 @@ def handle_lora_selection_change(lora_name, current_prompt):
         gr.Info(f"✅ Appended prompt from '{lset_filename}'")
         return gr.update(value=new_prompt)
     except Exception as e:
-        # This is expected if a .lset file doesn't exist for the selected LoRA.
-        print(f"Info: Could not process .lset for '{lora_name}'. Reason: {e}")
-        gr.Info(f"ℹ️ No prompt preset found for '{lora_name}'.")
+        # This should be less common now, but good to keep for safety.
+        print(f"Info: Could not process .lset for '{preset_name}'. Reason: {e}")
+        gr.Info(f"ℹ️ Error processing preset '{preset_name}'.")
         return gr.update()
 
 # --- Helper Functions ---
@@ -244,8 +244,8 @@ def get_t2v_duration(steps, duration_seconds):
 @spaces.GPU(duration_from_args=get_i2v_duration)
 def generate_i2v_video(input_image, prompt, height, width,
                       negative_prompt, duration_seconds,
-                      guidance_scale, steps, seed, randomize_seed,
-                      lora_name, lora_weight,
+                      guidance_scale, steps, seed, randomize_seed,                      
+                      preset_name, lora_weight,
                       progress=gr.Progress(track_tqdm=True)):
     """Generates a video from an initial image and a prompt."""
     if input_image is None:
@@ -264,18 +264,41 @@ def generate_i2v_video(input_image, prompt, height, width,
     resized_image = input_image.resize((target_w, target_h))
     enhanced_prompt = f"{prompt}, cinematic quality, smooth motion, detailed animation, dynamic lighting"
 
+    lora_filename = None # Will be extracted from the .lset file
     adapter_name = "i2v_lora"
     try:
-        # Dynamically load the selected LoRA
-        if lora_name and lora_name != "None":
-            print(f"🚀 Loading LoRA: {lora_name} with weight {lora_weight}")
-            i2v_pipe.load_lora_weights(
-                I2V_LORA_REPO_ID,
-                weight_name=lora_name,
-                adapter_name=adapter_name,
-                subfolder=I2V_LORA_SUBFOLDER
-            )
-            i2v_pipe.set_adapters([adapter_name], adapter_weights=[float(lora_weight)])
+        # If a preset is selected, load the corresponding LoRA
+        if preset_name and preset_name != "None":
+            lset_filename = f"{preset_name}.lset"
+            print(f"🚀 Processing preset: {preset_name}")
+            try:
+                lset_path = hf_hub_download(
+                    repo_id=I2V_LORA_REPO_ID,
+                    filename=lset_filename,
+                    subfolder=I2V_LORA_SUBFOLDER,
+                    repo_type='model'
+                )
+                with open(lset_path, 'r', encoding='utf-8') as f:
+                    lset_data = json.load(f)
+                
+                # Extract the LoRA filename from the .lset file
+                loras_list = lset_data.get("loras")
+                if not loras_list or not isinstance(loras_list, list) or len(loras_list) == 0:
+                    raise gr.Error(f"Preset file '{lset_filename}' is invalid or does not specify a LoRA file.")
+                
+                lora_filename = loras_list[0] # Use the first LoRA in the list
+                print(f"   - Found LoRA file: {lora_filename}")
+                
+                i2v_pipe.load_lora_weights(
+                    I2V_LORA_REPO_ID,
+                    weight_name=lora_filename,
+                    adapter_name=adapter_name,
+                    subfolder=I2V_LORA_SUBFOLDER
+                )
+                i2v_pipe.set_adapters([adapter_name], adapter_weights=[float(lora_weight)])
+                print(f"   - LoRA '{lora_filename}' loaded successfully with weight {lora_weight}.")
+            except Exception as e:
+                raise gr.Error(f"Failed to load LoRA for preset '{preset_name}'. Reason: {e}")
 
         with torch.inference_mode():
             output_frames_list = i2v_pipe(
@@ -291,8 +314,8 @@ def generate_i2v_video(input_image, prompt, height, width,
             ).frames[0]
     finally:
         # Unload the LoRA to ensure a clean state for the next run
-        if lora_name and lora_name != "None" and hasattr(i2v_pipe, "unload_lora_weights"):
-            print(f"🧹 Unloading LoRA: {lora_name}")
+        if lora_filename and hasattr(i2v_pipe, "unload_lora_weights"):
+            print(f"🧹 Unloading LoRA: {lora_filename}")
             i2v_pipe.unload_lora_weights()
         # Clear GPU cache to free up memory for the next run
         if torch.cuda.is_available():
@@ -336,7 +359,7 @@ with gr.Blocks() as demo:
                             i2v_neg_prompt = gr.Textbox(label="❌ Negative Prompt", value=default_negative_prompt, lines=4)
                             i2v_seed = gr.Slider(label="🎲 Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
                             i2v_rand_seed = gr.Checkbox(label="🔀 Randomize seed", value=True, interactive=True)
-                            i2v_lora_name = gr.Dropdown(label="🎨 LoRA Style", choices=available_i2v_loras, value="None", info="Dynamically loaded from Hugging Face.", interactive=len(available_i2v_loras) > 1)
+                            i2v_preset_name = gr.Dropdown(label="🎨 LoRA Preset", choices=available_i2v_presets, value="None", info="Select a preset to apply a LoRA and a suggested prompt.", interactive=len(available_i2v_presets) > 1)
                             i2v_lora_weight = gr.Slider(label="💪 LoRA Weight", minimum=0.0, maximum=2.0, step=0.1, value=0.8, interactive=True)
                             with gr.Row():
                                 i2v_height = gr.Slider(minimum=SLIDER_MIN_H, maximum=SLIDER_MAX_H, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label=f"📏 Height ({MOD_VALUE}px steps)")
@@ -355,9 +378,9 @@ with gr.Blocks() as demo:
 
     # --- Event Handlers ---
     # I2V Handlers
-    i2v_lora_name.change(
+    i2v_preset_name.change(
         fn=handle_lora_selection_change,
-        inputs=[i2v_lora_name, i2v_prompt],
+        inputs=[i2v_preset_name, i2v_prompt],
         outputs=[i2v_prompt]
     )
     i2v_input_image.upload(
@@ -372,7 +395,7 @@ with gr.Blocks() as demo:
     )
     i2v_generate_btn.click(
         fn=generate_i2v_video,
-        inputs=[i2v_input_image, i2v_prompt, i2v_height, i2v_width, i2v_neg_prompt, i2v_duration, i2v_guidance, i2v_steps, i2v_seed, i2v_rand_seed, i2v_lora_name, i2v_lora_weight],
+        inputs=[i2v_input_image, i2v_prompt, i2v_height, i2v_width, i2v_neg_prompt, i2v_duration, i2v_guidance, i2v_steps, i2v_seed, i2v_rand_seed, i2v_preset_name, i2v_lora_weight],
         outputs=[i2v_output_video, i2v_seed, i2v_download]
     )
     i2v_height.release(