Update api/ltx_server_refactored.py

api/ltx_server_refactored.py

@@ -200,11 +200,11 @@ class VideoService:
 
             
     def _save_and_log_video(self, pixel_tensor, base_filename, fps, temp_dir, results_dir, used_seed, progress_callback=None):
-        output_path = os.path.join(temp_dir, f"{base_filename}_{used_seed}.mp4")
+        output_path = os.path.join(temp_dir, f"{base_filename}_.mp4")
         video_encode_tool_singleton.save_video_from_tensor(
             pixel_tensor, output_path, fps=fps, progress_callback=progress_callback
         )
-        final_path = os.path.join(results_dir, f"{base_filename}_{used_seed}.mp4")
+        final_path = os.path.join(results_dir, f"{base_filename}_.mp4")
         shutil.move(output_path, final_path)
         print(f"[DEBUG] Vídeo salvo em: {final_path}")
         return final_path
@@ -278,6 +278,9 @@ class VideoService:
         """
         print("\n" + "-"*20 + " INÍCIO: _generate_single_chunk_low " + "-"*20)
 
+        used_seed = random.randint(0, 2**32 - 1) if seed is None else int(seed)
+        seed_everething(used_seed)
+        
         # --- NÓ 1.1: SETUP DE PARÂMETROS ---
         height_padded = ((height - 1) // 8 + 1) * 8
         width_padded = ((width - 1) // 8 + 1) * 8
@@ -330,7 +333,7 @@ class VideoService:
             with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device.type == 'cuda'):
                 latents_bruto = self.pipeline(**first_pass_kwargs).images
                 latents_cpu_bruto = latents_bruto.detach().to("cpu")
-                tensor_path_cpu = os.path.join(results_dir, f"latents_low_res_{used_seed}.pt")
+                tensor_path_cpu = os.path.join(results_dir, f"latents_low_res.pt")
                 torch.save(latents_cpu_bruto, tensor_path_cpu)
                 log_tensor_info(latents_bruto, f"Latente Bruto Gerado para: '{prompt[:40]}...'")
         
@@ -359,6 +362,7 @@ class VideoService:
         print("======           INICIANDO GERAÇÃO NARRATIVA EM CHUNKS (LOW-RES)           ======")
         print("="*80)
         
+        
         used_seed = random.randint(0, 2**32 - 1) if seed is None else int(seed)
         seed_everething(used_seed)
         FPS = 24.0
@@ -435,7 +439,7 @@ class VideoService:
                 pixel_tensor = vae_manager_singleton.decode(final_latents.clone(), decode_timestep=float(self.config.get("decode_timestep", 0.05)))
                 video_path = self._save_and_log_video(pixel_tensor, "narrative_video", FPS, temp_dir, results_dir, used_seed)
                 latents_cpu = latents.detach().to("cpu")
-                tensor_path = os.path.join(results_dir, f"latents_low_res_{used_seed}.pt")
+                tensor_path = os.path.join(results_dir, f"latents_low_res.pt")
                 torch.save(latents_cpu, tensor_path)
             return video_path, tensor_path, used_seed
         
@@ -487,7 +491,7 @@ class VideoService:
                 pixel_tensor = vae_manager_singleton.decode(final_latents.clone(), decode_timestep=float(self.config.get("decode_timestep", 0.05)))
                 video_path = self._save_and_log_video(pixel_tensor, "single_video", FPS, temp_dir, results_dir, used_seed)
                 latents_cpu = latents.detach().to("cpu")
-                tensor_path = os.path.join(results_dir, f"latents_single_{used_seed}.pt")
+                tensor_path = os.path.join(results_dir, f"latents_single.pt")
                 torch.save(latents_cpu, tensor_path)
             return video_path, tensor_path, used_seed
         except Exception as e: