Fix component-only model loading for LTX-Video fine-tunes

- Set pipeline_loaded_successfully=True when component loading succeeds
- Fix indentation for LoRA and offloading code blocks
- Define 'off' variable inside the correct scope
- This fixes loading models like Muinez/ltxvideo-2b-nsfw which are
  fine-tuned transformer weights without a full pipeline

videogen

@@ -8576,6 +8576,7 @@ def main(args):
                                         )
                                         print(f"   ✅ Fine-tuned transformer loaded successfully!")
                                         loaded_with_base = True
+                                        pipeline_loaded_successfully = True
                                     elif class_name == "AutoencoderKLLTXVideo":
                                         from diffusers import AutoencoderKLLTXVideo
                                         print(f"   Loading fine-tuned VAE...")
@@ -8586,6 +8587,7 @@ def main(args):
                                         )
                                         print(f"   ✅ Fine-tuned VAE loaded successfully!")
                                         loaded_with_base = True
+                                        pipeline_loaded_successfully = True
                     except Exception as component_e:
                         if debug:
                             print(f"   [DEBUG] Component detection failed: {component_e}")
@@ -8692,16 +8694,19 @@ def main(args):
                                         if components_loaded:
                                             print(f"   ✅ Loaded components: {components_loaded}")
                                             loaded_with_base = True
+                                            pipeline_loaded_successfully = True
                                         else:
                                             print(f"   ⚠️ No components could be loaded from fine-tuned model")
                                             print(f"   Using base model: {base_model}")
                                             loaded_with_base = True  # Still use base model
+                                            pipeline_loaded_successfully = True
                                             
                                     except Exception as ft_e:
                                         if debug:
                                             print(f"   [DEBUG] Fine-tuned loading failed: {ft_e}")
                                         print(f"   Using base model: {base_model}")
                                         loaded_with_base = True
+                                        pipeline_loaded_successfully = True
                                     break
                                 except Exception as base_e:
                                     if debug:
@@ -8717,6 +8722,7 @@ def main(args):
                             print(f"  ✅ Successfully loaded I2V model with DiffusionPipeline")
                             PipelineClass = DiffusionPipeline
                             loaded_with_base = True
+                            pipeline_loaded_successfully = True
                         except Exception as generic_e:
                             if debug:
                                 print(f"   [DEBUG] Generic loader also failed: {generic_e}")
@@ -8730,7 +8736,7 @@ def main(args):
                     raise e  # Re-raise for non-404 errors
         
         # Apply LoRA if this is a LoRA model
-                if is_lora and lora_id:
+        if is_lora and lora_id and pipeline_loaded_successfully:
             print(f"  Loading LoRA adapter: {lora_id}")
             try:
                 pipe.load_lora_weights(lora_id)
@@ -8739,10 +8745,13 @@ def main(args):
                 print(f"  ⚠️ LoRA loading failed: {lora_e}")
                 print(f"     Continuing with base model...")
         
+        # Apply safety checker and offloading only if pipeline loaded successfully
+        if pipeline_loaded_successfully:
             if args.no_filter and hasattr(pipe, "safety_checker"):
                 pipe.safety_checker = None
 
-                # Re-apply offloading strategy
+            # Offloading
+            off = args.offload_strategy
             if off == "auto_map":
                 pipe.enable_model_cpu_offload()
             elif off == "sequential":