Update model.py

model.py

@@ -118,8 +118,13 @@ class ModelHandler:
 
         # 4. Set TCD Scheduler
         print("Configuring TCDScheduler...")
-        self.pipeline.scheduler = TCDScheduler.from_config(self.pipeline.scheduler.config)
-        print("  [OK] TCDScheduler loaded.")
+        # --- FIX: Enable Karras Sigmas for cleaner TCD generation ---
+        self.pipeline.scheduler = TCDScheduler.from_config(
+            self.pipeline.scheduler.config,
+            use_karras_sigmas=True 
+        )
+        # --- END FIX ---
+        print("  [OK] TCDScheduler loaded (Karras enabled).")
 
         # 5. Load Adapters (IP-Adapter, TCD-LoRA & Style LoRA)
         print("Loading Adapters...")
@@ -140,9 +145,9 @@ class ModelHandler:
         print(f"Loading IP-Adapter from local file: {ip_adapter_local_path}")
         self.pipeline.load_ip_adapter_instantid(ip_adapter_local_path)
         
-        # 5b. Load TCD LoRA (Fixed Filename)
+        # 5b. Load TCD LoRA
         print("Loading TCD-SDXL-LoRA...")
-        tcd_lora_filename = "pytorch_lora_weights.safetensors" # <-- CORRECTED FILENAME
+        tcd_lora_filename = "pytorch_lora_weights.safetensors"
         tcd_lora_path = os.path.join("./models", tcd_lora_filename)
         
         if not os.path.exists(tcd_lora_path):
@@ -156,7 +161,7 @@ class ModelHandler:
         self.pipeline.fuse_lora(lora_scale=1.0) 
         print("  [OK] TCD LoRA fused.")
 
-        # 5c. Load Style LoRA (Your existing one)
+        # 5c. Load Style LoRA
         print("Loading Style LoRA weights...")
         self.pipeline.load_lora_weights(Config.REPO_ID, weight_name=Config.LORA_FILENAME)