Update api/ltx/ltx_aduc_pipeline.py

api/ltx/ltx_aduc_pipeline.py

@@ -552,7 +552,7 @@ def generate_narrative_low(self, prompt: str, **kwargs) -> Tuple[Optional[str],
 
     # (O resto das funções de _finalize_generation, _save_and_log_video, etc., permanecem as mesmas)
     @log_function_io
-    def _finalize_generation(self, final_latents_cpu: torch.Tensor, base_filename: str, seed: int) -> Tuple[str, str]:
+    def _finalize_generation1(self, final_latents_cpu: torch.Tensor, base_filename: str, seed: int) -> Tuple[str, str]:
         final_latents_path = RESULTS_DIR / f"latents_{base_filename}_{seed}.pt"
         torch.save(final_latents_cpu, final_latents_path)
         logging.info(f"Final latents saved to: {final_latents_path}")
@@ -577,19 +577,19 @@ def generate_narrative_low(self, prompt: str, **kwargs) -> Tuple[Optional[str],
             logging.info(f"Video saved successfully to: {final_path}")
             return final_path
     
-    def _apply_precision_policy(self):
+    def _apply_precision_policy1(self):
         precision = str(self.config.get("precision", "bfloat16")).lower()
         if precision in ["float8_e4m3fn", "bfloat16"]: self.runtime_autocast_dtype = torch.bfloat16
         elif precision == "mixed_precision": self.runtime_autocast_dtype = torch.float16
         else: self.runtime_autocast_dtype = torch.float32
         logging.info(f"Runtime precision policy set for autocast: {self.runtime_autocast_dtype}")
 
-    def _align(self, dim: int, alignment: int = FRAMES_ALIGNMENT, alignment_rule: str = 'default') -> int:
+    def _align1(self, dim: int, alignment: int = FRAMES_ALIGNMENT, alignment_rule: str = 'default') -> int:
         if alignment_rule == 'n*8+1':
              return ((dim - 1) // alignment) * alignment + 1
         return ((dim - 1) // alignment + 1) * alignment
     
-    def _calculate_aligned_frames(self, duration_s: float, min_frames: int = 1) -> int:
+    def _calculate_aligned_frames1(self, duration_s: float, min_frames: int = 1) -> int:
         num_frames = int(round(duration_s * DEFAULT_FPS))
         aligned_frames = self._align(num_frames, alignment=FRAMES_ALIGNMENT)
         return max(aligned_frames, min_frames)
@@ -598,7 +598,7 @@ def generate_narrative_low(self, prompt: str, **kwargs) -> Tuple[Optional[str],
         return random.randint(0, 2**32 - 1)
 
 
-        def _finalize_generation(self, latents_paths: List[Path], base_filename: str, seed: int) -> Tuple[str, str, int]:
+    def _finalize_generation(self, latents_paths: List[Path], base_filename: str, seed: int) -> Tuple[str, str, int]:
         """Loads latents, concatenates, decodes to video, and saves both."""
         logging.info("Finalizing generation: decoding latents to video.")
         all_tensors_cpu = [torch.load(p) for p in latents_paths]