fix: update default video dimensions and maximum duration in LightX2VInferenceConfig; improve code formatting and comments
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gaclove
@@ -81,7 +81,7 @@ class LightX2VInferenceConfig:
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"height": (
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"INT",
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{
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"default": 480,
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"default": 1280,
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"min": 64,
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"max": 2048,
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"step": 8,
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@@ -91,7 +91,7 @@ class LightX2VInferenceConfig:
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"width": (
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"INT",
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{
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"default": 832,
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"default": 720,
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"min": 64,
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"max": 2048,
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"step": 8,
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@@ -103,7 +103,7 @@ class LightX2VInferenceConfig:
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{
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"default": 5.0,
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"min": 1.0,
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"max": 10.0,
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"max": 999,
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"step": 0.1,
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"tooltip": "Video duration in seconds",
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},
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@@ -149,22 +149,22 @@ class LightX2VInferenceConfig:
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):
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"""Create basic inference configuration."""
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model_path = get_model_full_path(model_name)
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if model_cls == "hunyuan":
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fps = 24
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else:
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fps = 16
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fps = 16
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video_length = int(round(duration * fps))
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if video_length < 16:
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video_length = 16
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remainder = (video_length - 1) % 4
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if remainder != 0:
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video_length = video_length + (4 - remainder)
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#TODO(xxx):
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# TODO(xxx):
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if "wan2.1_audio" in [model_cls]:
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video_length = 81
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@@ -655,15 +655,11 @@ class LightX2VModularInference:
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temp_files.append(tmp.name)
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logging.info(f"Image saved to {tmp.name}")
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if (
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audio is not None
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and hasattr(config, "model_cls")
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and "audio" in config.model_cls
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):
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if isinstance(audio, dict) and 'waveform' in audio and 'sample_rate' in audio:
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waveform = audio['waveform']
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sample_rate = audio['sample_rate']
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if audio is not None and hasattr(config, "model_cls") and "audio" in config.model_cls:
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if isinstance(audio, dict) and "waveform" in audio and "sample_rate" in audio:
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waveform = audio["waveform"]
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sample_rate = audio["sample_rate"]
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# Handle different waveform shapes
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if isinstance(waveform, torch.Tensor):
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if waveform.dim() == 3: # [batch, channels, samples]
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@@ -680,43 +676,34 @@ class LightX2VModularInference:
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else:
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raise ValueError(f"Unsupported audio format: {type(audio)}")
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with tempfile.NamedTemporaryFile(
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suffix=".wav", delete=False
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) as tmp:
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try:
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import scipy.io.wavfile as wavfile
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except ImportError:
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import wave
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
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try:
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import scipy.io.wavfile as wavfile
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except ImportError:
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import wave
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with wave.open(tmp.name, "wb") as wav_file:
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wav_file.setnchannels(
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1 if waveform.ndim == 1 else waveform.shape[-1]
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)
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wav_file.setsampwidth(2) # 16-bit
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wav_file.setframerate(sample_rate)
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if waveform.dtype != np.int16:
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waveform = (waveform * 32767).astype(np.int16)
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wav_file.writeframes(waveform.tobytes())
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with wave.open(tmp.name, "wb") as wav_file:
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wav_file.setnchannels(1 if waveform.ndim == 1 else waveform.shape[-1])
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wav_file.setsampwidth(2) # 16-bit
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wav_file.setframerate(sample_rate)
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if waveform.dtype != np.int16:
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waveform = (waveform * 32767).astype(np.int16)
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wav_file.writeframes(waveform.tobytes())
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else:
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if waveform.ndim == 1:
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wavfile.write(tmp.name, sample_rate, waveform)
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else:
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if waveform.ndim == 1:
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wavfile.write(tmp.name, sample_rate, waveform)
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else:
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if waveform.shape[0] < waveform.shape[1]:
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waveform = waveform.T
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wavfile.write(tmp.name, sample_rate, waveform)
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if waveform.shape[0] < waveform.shape[1]:
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waveform = waveform.T
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wavfile.write(tmp.name, sample_rate, waveform)
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config.audio_path = tmp.name
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temp_files.append(tmp.name)
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config.audio_path = tmp.name
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temp_files.append(tmp.name)
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logging.info(f"Audio saved to {tmp.name}")
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logging.info(f"Audio saved to {tmp.name}")
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config_hash = self._get_config_hash(config)
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needs_reinit = (
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self._current_runner is None
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or self._current_config_hash != config_hash
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or getattr(config, "lazy_load", False)
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)
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needs_reinit = self._current_runner is None or self._current_config_hash != config_hash or getattr(config, "lazy_load", False)
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if needs_reinit:
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if self._current_runner is not None:
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@@ -739,9 +726,8 @@ class LightX2VModularInference:
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if hasattr(self._current_runner, "set_progress_callback"):
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self._current_runner.set_progress_callback(update_progress)
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images, audio = self._current_runner.run_pipeline(save_video=False)
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if getattr(config, "unload_after_inference", False):
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del self._current_runner
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self._current_runner = None
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@@ -750,7 +736,6 @@ class LightX2VModularInference:
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torch.cuda.empty_cache()
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gc.collect()
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return (images, audio)
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except Exception as e:
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