Update ltx_manager_helpers.py

ltx_manager_helpers.py

@@ -123,13 +123,18 @@ class LtxPoolManager:
                 "conditioning_items": final_conditioning_items, "is_video": True, "vae_per_channel_normalize": True,
                 "prompt": kwargs['motion_prompt'], "negative_prompt": "blurry, distorted, static, bad quality",
                 "guidance_scale": kwargs['guidance_scale'], "stg_scale": kwargs['stg_scale'], 
-                "rescaling_scale": kwargs['rescaling_scale'], "num_inference_steps": kwargs['num_inference_steps']
+                "rescaling_scale": kwargs['rescaling_scale'], "num_inference_steps": kwargs['num_inference_steps'],
+                "output_type": "latent"
             }
             if worker_to_use.is_distilled:
                 pipeline_params["timesteps"] = worker_to_use.config.get("first_pass", {}).get("timesteps")
                 pipeline_params["num_inference_steps"] = len(pipeline_params["timesteps"]) if pipeline_params["timesteps"] else 20
 
-            result = worker_to_use.generate_video_fragment_internal(**pipeline_params)
+            if isinstance(worker_to_use.pipeline, LTXMultiScalePipeline):
+                result = worker_to_use.pipeline.video_pipeline(**pipeline_params).images
+            else:
+                result = worker_to_use.generate_video_fragment_internal(**pipeline_params)
+            
             return result, padding_vals
         except Exception as e:
             logger.error(f"LTX POOL MANAGER: Erro durante a geração em {worker_to_use.device}: {e}", exc_info=True)
@@ -158,18 +163,18 @@ class LtxPoolManager:
 
             logger.info("LTX POOL MANAGER: Iniciando passe de refinamento (denoise)...")
             
-            # --- [INÍCIO DA CORREÇÃO] ---
-            # Para o refinamento, usamos a pipeline de vídeo base diretamente,
-            # ignorando o invólucro MultiScale que causa o erro de processamento de imagem.
-            if isinstance(worker_to_use.pipeline, LTXMultiScalePipeline):
-                result = worker_to_use.pipeline.video_pipeline(**pipeline_params).images
-            else:
-                result = worker_to_use.generate_video_fragment_internal(**pipeline_params)
-            # --- [FIM DA CORREÇÃO] ---
-            
-            return result, None
+            pipeline_to_call = worker_to_use.pipeline.video_pipeline if isinstance(worker_to_use.pipeline, LTXMultiScalePipeline) else worker_to_use.pipeline
+
+            try:
+                result = pipeline_to_call(**pipeline_params).images
+                return result, None
+            except torch.cuda.OutOfMemoryError as e:
+                logger.error(f"FALHA DE MEMÓRIA DURANTE O REFINAMENTO em {worker_to_use.device}: {e}")
+                logger.warning("Limpando VRAM e retornando None para sinalizar a falha.")
+                gc.collect(); torch.cuda.empty_cache()
+                return None, None
         except Exception as e:
-            logger.error(f"LTX POOL MANAGER: Erro durante o refinamento em {worker_to_use.device}: {e}", exc_info=True)
+            logger.error(f"LTX POOL MANAGER: Erro inesperado durante o refinamento em {worker_to_use.device}: {e}", exc_info=True)
             raise e
         finally:
             if worker_to_use and worker_to_use.device.type == 'cuda':