Update float_lipsync.py

float_lipsync.py

@@ -229,7 +229,6 @@ class FloatLipsync:
             img = self._crop_face(img)
 
         self.preprocessed_ref_image = self.transform(image=img)['image'].unsqueeze(0).to(self.device)
-        self._original_ref_image = self.preprocessed_ref_image.clone()
         logger.info(f"[FLOAT] Reference image preprocessed: {self.preprocessed_ref_image.shape}")
 
     def _crop_face(self, img: np.ndarray) -> np.ndarray:
@@ -274,7 +273,7 @@ class FloatLipsync:
             return False
 
     @torch.no_grad()
-    def generate(self, audio_path: str, output_path: str = None, emo: str = 'S2E', chain_frames: bool = False) -> str:
+    def generate(self, audio_path: str, output_path: str = None, emo: str = 'S2E') -> str:
         """
         Generate a lipsync video from an audio file.
 
@@ -282,7 +281,6 @@ class FloatLipsync:
             audio_path: Path to the input WAV audio file
             output_path: Where to save the output video (auto-generated if None)
             emo: Emotion mode ('S2E' for speech-to-emotion auto-detect)
-            chain_frames: If True, use the last frame as reference for next call
 
         Returns:
             Path to the generated video file
@@ -326,25 +324,13 @@ class FloatLipsync:
         )['d_hat']
         logger.info(f"[FLOAT] Model inference: {time.time() - gen_start:.2f}s")
 
-        # 3. Chain frames: use last frame as reference for next chunk
-        if chain_frames and d_hat.shape[0] > 0:
-            # d_hat is (T, C, H, W) in [-1, 1] range — same as preprocessed_ref_image
-            self.preprocessed_ref_image = d_hat[-1:].clone().to(self.device)
-            logger.info(f"[FLOAT] Chained last frame as next reference")
-
-        # 4. Save video with audio
+        # 3. Save video with audio
         self._save_video(d_hat, output_path, audio_path)
 
         logger.info(f"[FLOAT] ✓ Total generation: {time.time() - inference_start:.2f}s")
         logger.info(f"[FLOAT] ✓ Output: {output_path}")
         return output_path
 
-    def reset_reference(self):
-        """Reset the reference image back to the original ref.png."""
-        if hasattr(self, '_original_ref_image') and self._original_ref_image is not None:
-            self.preprocessed_ref_image = self._original_ref_image.clone()
-            logger.info("[FLOAT] Reference reset to original")
-
     def _save_video(self, vid_tensor: torch.Tensor, video_path: str, audio_path: str):
         """Save video frames tensor to mp4 with audio using ffmpeg."""
         # Prepare frames: (T, C, H, W) -> (T, H, W, C) uint8