Update generator.py

generator.py

@@ -751,15 +751,15 @@ class RetroArtConverter:
         
         pipe_kwargs["generator"] = generator
         
+        # --- START FIX: Handle CPU Offload for Compel ---
         # Use Compel for prompt encoding if available
         if self.use_compel and self.compel is not None:
             try:
                 print("Encoding prompts with Compel...")
                 
-                # --- FIX: Move text encoders to GPU for Compel ---
+                # Move text encoders to GPU
                 self.pipe.text_encoder.to(self.device)
                 self.pipe.text_encoder_2.to(self.device)
-                # --- END FIX ---
                 
                 conditioning = self.compel(prompt)
                 negative_conditioning = self.compel(negative_prompt)
@@ -775,16 +775,16 @@ class RetroArtConverter:
                 pipe_kwargs["prompt"] = prompt
                 pipe_kwargs["negative_prompt"] = negative_prompt
             finally:
-                # --- FIX: Move text encoders back to CPU to save VRAM ---
+                # Move text encoders back to CPU
                 try:
                     self.pipe.text_encoder.to("cpu")
                     self.pipe.text_encoder_2.to("cpu")
                 except Exception as e:
                     print(f"Could not move text encoders back to CPU: {e}")
-                # --- END FIX ---
         else:
             pipe_kwargs["prompt"] = prompt
             pipe_kwargs["negative_prompt"] = negative_prompt
+        # --- END FIX ---
         
         # Add CLIP skip
         if hasattr(self.pipe, 'text_encoder'):
@@ -878,7 +878,7 @@ class RetroArtConverter:
             pipe_kwargs["controlnet_conditioning_scale"] = conditioning_scales
             print(f"Active ControlNets: {len(control_images)} (all {target_width}x{target_height})")
         else:
-            print("No active ControlNets, running standard Img2Img")
+            print("No active ControlNfets, running standard Img2Img")
         
         # Generate
         print(f"Generating with LCM: Steps={num_inference_steps}, CFG={guidance_scale}, Strength={strength}")