Update model.py

model.py

@@ -8,6 +8,11 @@ from diffusers import (
     ControlNetModel, 
     LCMScheduler
 )
+# --- START FIX ---
+# Import the MultiControlNetModel wrapper
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+# --- END FIX ---
+
 # Import the custom pipeline from your local file
 from pipeline_stable_diffusion_xl_instantid_img2img import StableDiffusionXLInstantIDImg2ImgPipeline
 
@@ -82,6 +87,13 @@ class ModelHandler:
         cn_zoe = ControlNetModel.from_pretrained(Config.CN_ZOE_REPO, torch_dtype=Config.DTYPE)
         cn_lineart = ControlNetModel.from_pretrained(Config.CN_LINEART_REPO, torch_dtype=Config.DTYPE)
 
+        # --- START FIX for AssertionError ---
+        # Manually wrap the list of models in a MultiControlNetModel
+        print("Wrapping ControlNets in MultiControlNetModel...")
+        controlnet_list = [cn_instantid, cn_zoe, cn_lineart]
+        controlnet = MultiControlNetModel(controlnet_list)
+        # --- END FIX ---
+
         # 3. Load SDXL Pipeline
         print(f"Loading SDXL Pipeline ({Config.CHECKPOINT_FILENAME})...")
         
@@ -100,7 +112,7 @@ class ModelHandler:
         print(f"Loading pipeline from local file: {checkpoint_local_path}")
         self.pipeline = StableDiffusionXLInstantIDImg2ImgPipeline.from_single_file(
             checkpoint_local_path, # Pass the local path
-            controlnet=[cn_instantid, cn_zoe, cn_lineart], 
+            controlnet=controlnet, # <-- Pass the single, wrapped object
             torch_dtype=Config.DTYPE,
             use_safetensors=True
         )
@@ -147,9 +159,7 @@ class ModelHandler:
             
         try:
             # Convert PIL to CV2
-            # --- START FIX ---
-            cv2_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) # <-- Corrected typo
-            # --- END FIX ---
+            cv2_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) 
             faces = self.app.get(cv2_img)
             
             if len(faces) == 0: