Update api/ltx_server_refactored.py

api/ltx_server_refactored.py

@@ -303,7 +303,6 @@ class VideoService:
             self.finalize(keep_paths=[])
             
     def generate_upscale_denoise(self, latents_path, prompt, negative_prompt, guidance_scale, seed):
-        try:
             used_seed = random.randint(0, 2**32 - 1) if seed is None else int(seed)
             seed_everething(used_seed)
             temp_dir = tempfile.mkdtemp(prefix="ltxv_up_"); self._register_tmp_dir(temp_dir)
@@ -340,21 +339,21 @@ class VideoService:
                     else:
                         final_latents_list.append(refined_chunk)
     
-                final_latents = torch.cat(final_latents_list, dim=2)
-                log_tensor_info(final_latents, "Latentes Upscaled/Refinados Finais")
+            final_latents = torch.cat(final_latents_list, dim=2)
+            log_tensor_info(final_latents, "Latentes Upscaled/Refinados Finais")
     
-                latents_cpu = final_latents.detach().to("cpu")
-                tensor_path = os.path.join(results_dir, f"latents_refined_{used_seed}.pt")
-                torch.save(latents_cpu, tensor_path)
-                pixel_tensor = vae_manager_singleton.decode(final_latents, decode_timestep=float(self.config.get("decode_timestep", 0.05)))
-                video_path = self._save_and_log_video(pixel_tensor, "refined_video", 24.0, temp_dir, results_dir, used_seed)
-                return video_path, tensor_path        except Exception as e:
-        except Exception as e:   
-            pass
-        finally:
-            torch.cuda.empty_cache()
-            torch.cuda.ipc_collect()
-            self.finalize(keep_paths=[])
+            latents_cpu = final_latents.detach().to("cpu")
+            tensor_path = os.path.join(results_dir, f"latents_refined_{used_seed}.pt")
+            torch.save(latents_cpu, tensor_path)
+            pixel_tensor = vae_manager_singleton.decode(final_latents, decode_timestep=float(self.config.get("decode_timestep", 0.05)))
+            video_path = self._save_and_log_video(pixel_tensor, "refined_video", 24.0, temp_dir, results_dir, used_seed)
+            return video_path, tensor_path        except Exception as e:
+            except Exception as e:   
+                pass
+            finally:
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+                self.finalize(keep_paths=[])
 
     def encode_mp4(self, latents_path: str, fps: int = 24):
         latents = torch.load(latents_path)