[Refactor] save video (#316)

lightx2v/models/runners/wan/wan_audio_runner.py

 import gc
 import os
-import subprocess
+import warnings
 from dataclasses import dataclass
 from typing import Dict, List, Optional, Tuple
 
@@ -12,6 +12,7 @@ import torchvision.transforms.functional as TF
 from PIL import Image
 from einops import rearrange
 from loguru import logger
+from torchvision.io import write_video
 from torchvision.transforms import InterpolationMode
 from torchvision.transforms.functional import resize
 
@@ -27,7 +28,9 @@ from lightx2v.models.video_encoders.hf.wan.vae_2_2 import Wan2_2_VAE
 from lightx2v.utils.envs import *
 from lightx2v.utils.profiler import *
 from lightx2v.utils.registry_factory import RUNNER_REGISTER
-from lightx2v.utils.utils import find_torch_model_path, load_weights, save_to_video, vae_to_comfyui_image
+from lightx2v.utils.utils import find_torch_model_path, load_weights, vae_to_comfyui_image
+
+warnings.filterwarnings("ignore", category=UserWarning, module="torchvision.io._video_deprecation_warning")
 
 
 def get_optimal_patched_size_with_sp(patched_h, patched_w, sp_size):
@@ -664,34 +667,26 @@ class WanAudioRunner(WanRunner):  # type:ignore
         self.run_input_encoder = self._run_input_encoder_local_r2v_audio
 
     def _save_video_with_audio(self, images, audio_array, fps):
-        """Save video with audio"""
-        import tempfile
-
-        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as video_tmp:
-            video_path = video_tmp.name
-
-        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as audio_tmp:
-            audio_path = audio_tmp.name
-
-        try:
-            save_to_video(images, video_path, fps)
-            ta.save(audio_path, torch.tensor(audio_array[None]), sample_rate=self._audio_processor.audio_sr)  # type: ignore
-
         output_path = self.config.get("save_video_path")
         parent_dir = os.path.dirname(output_path)
         if parent_dir and not os.path.exists(parent_dir):
             os.makedirs(parent_dir, exist_ok=True)
 
-            subprocess.call(["/usr/bin/ffmpeg", "-y", "-i", video_path, "-i", audio_path, output_path])
+        sample_rate = self._audio_processor.audio_sr
 
-            logger.info(f"Saved video with audio to: {output_path}")
+        if images.dtype != torch.uint8:
+            images = (images * 255).clamp(0, 255).to(torch.uint8)
 
-        finally:
-            # Clean up temp files
-            if os.path.exists(video_path):
-                os.remove(video_path)
-            if os.path.exists(audio_path):
-                os.remove(audio_path)
+        write_video(
+            filename=output_path,
+            video_array=images,
+            fps=fps,
+            video_codec="libx264",
+            audio_array=torch.tensor(audio_array[None]),
+            audio_fps=sample_rate,
+            audio_codec="aac",
+            options={"preset": "medium", "crf": "23"},  # 可调整视频输出质量
+        )
 
     def load_transformer(self):
         """Load transformer with LoRA support"""