chore: update lightx2v submodule to latest commit and remove unused LightX2VModularInference class
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gaclove
+1
-1
Submodule lightx2v updated: a75f590678...3ea2d3dc50
@@ -728,224 +728,6 @@ class LightX2VConfigCombiner:
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return (config,)
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class LightX2VModularInference:
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_current_runner = None
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_current_config_hash = None
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def __init__(self):
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if not hasattr(self.__class__, "_current_runner"):
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self.__class__._current_runner = None
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if not hasattr(self.__class__, "_current_config_hash"):
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self.__class__._current_config_hash = None
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self.config_builder = ConfigBuilder()
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self.temp_manager = TempFileManager()
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self.image_handler = ImageFileHandler()
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self.audio_handler = AudioFileHandler()
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self.resolver = ComfyUIFileResolver()
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self.http_downloader = HTTPFileDownloader()
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"combined_config": (
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"COMBINED_CONFIG",
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{"tooltip": "Combined configuration from config combiner"},
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),
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"prompt": (
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"STRING",
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{"multiline": True, "default": "", "tooltip": "Generation prompt"},
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),
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"negative_prompt": (
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"STRING",
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{"multiline": True, "default": "", "tooltip": "Negative prompt"},
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),
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},
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"optional": {
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"image": ("IMAGE", {"tooltip": "Input image for i2v or s2v task"}),
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"audio": (
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"AUDIO",
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{"tooltip": "Input audio for audio-driven generation for s2v task"},
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),
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},
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}
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RETURN_TYPES = ("IMAGE", "AUDIO")
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RETURN_NAMES = ("images", "audio")
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FUNCTION = "generate"
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CATEGORY = "LightX2V/Inference"
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def _get_config_hash(self, config) -> str:
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"""Get hash of configuration to detect changes."""
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return self.config_builder.get_config_hash(config)
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def generate(
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self,
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combined_config,
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prompt,
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negative_prompt,
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image=None,
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audio=None,
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**kwargs,
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):
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# config type is EasyDict
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config = combined_config
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config.prompt = prompt
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config.negative_prompt = negative_prompt
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if config.task in ["i2v", "s2v", "rs2v"] and image is None:
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raise ValueError(f"{config.task} task requires input image")
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try:
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# Handle image input
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if config.task in ["i2v", "s2v", "rs2v"] and image is not None:
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image_np = (image[0].cpu().numpy() * 255).astype(np.uint8)
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pil_image = Image.fromarray(image_np)
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temp_path = self.temp_manager.create_temp_file(suffix=".png")
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pil_image.save(temp_path)
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config.image_path = temp_path
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logging.info(f"Image saved to {temp_path}")
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# Handle audio input for seko models
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if audio is not None and hasattr(config, "model_cls") and "seko" in config.model_cls:
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temp_path = self.temp_manager.create_temp_file(suffix=".wav")
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self.audio_handler.save(audio, temp_path)
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config.audio_path = temp_path
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logging.info(f"Audio saved to {temp_path}")
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# Handle talk objects
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if hasattr(config, "talk_objects") and config.talk_objects:
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talk_objects = config.talk_objects
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processed_talk_objects = []
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for talk_obj in talk_objects:
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processed_obj = {}
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if "audio" in talk_obj:
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processed_obj["audio"] = talk_obj["audio"]
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if "mask" in talk_obj:
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processed_obj["mask"] = talk_obj["mask"]
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if "audio" in processed_obj:
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processed_talk_objects.append(processed_obj)
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# Resolve paths and download URLs
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for obj in processed_talk_objects:
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if "audio" in obj and obj["audio"]:
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audio_path = obj["audio"]
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# Check if it's a URL and download if needed
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if self.http_downloader.is_url(audio_path):
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try:
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downloaded_path = self.http_downloader.download_if_url(audio_path, prefix="audio")
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obj["audio"] = downloaded_path
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logging.info(f"Downloaded audio from URL: {audio_path} -> {downloaded_path}")
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except Exception as e:
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logging.error(f"Failed to download audio from {audio_path}: {e}")
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continue
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# Handle relative paths
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elif not os.path.isabs(audio_path) and not audio_path.startswith("/tmp"):
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obj["audio"] = self.resolver.resolve_input_path(audio_path)
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logging.info(f"Resolved audio path: {audio_path} -> {obj['audio']}")
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# Check if file exists
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if not os.path.exists(obj["audio"]):
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logging.warning(f"Audio file not found: {obj['audio']}")
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if "mask" in obj and obj["mask"]:
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mask_path = obj["mask"]
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# Check if it's a URL and download if needed
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if self.http_downloader.is_url(mask_path):
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try:
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downloaded_path = self.http_downloader.download_if_url(mask_path, prefix="mask")
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obj["mask"] = downloaded_path
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logging.info(f"Downloaded mask from URL: {mask_path} -> {downloaded_path}")
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except Exception as e:
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logging.error(f"Failed to download mask from {mask_path}: {e}")
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# Don't skip the object if mask download fails (mask is optional)
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# Handle relative paths
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elif not os.path.isabs(mask_path) and not mask_path.startswith("/tmp"):
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obj["mask"] = self.resolver.resolve_input_path(mask_path)
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logging.info(f"Resolved mask path: {mask_path} -> {obj['mask']}")
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# Check if file exists
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if not os.path.exists(obj["mask"]):
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logging.warning(f"Mask file not found: {obj['mask']}")
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if processed_talk_objects:
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config.talk_objects = processed_talk_objects
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logging.info(f"Processed {len(processed_talk_objects)} talk objects")
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logging.info("lightx2v config: " + json.dumps(config, indent=2, ensure_ascii=False))
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config_hash = self._get_config_hash(config)
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current_runner = getattr(self.__class__, "_current_runner", None)
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current_config_hash = getattr(self.__class__, "_current_config_hash", None)
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needs_reinit = current_runner is None or current_config_hash != config_hash or getattr(config, "lazy_load", False)
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logging.info(f"Needs reinit: {needs_reinit}, old config hash: {current_config_hash}, new config hash: {config_hash}")
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if needs_reinit:
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if current_runner is not None:
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# current_runner.end_run()
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del self.__class__._current_runner
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torch.cuda.empty_cache()
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gc.collect()
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formated_config = set_config(config)
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self.__class__._current_runner = init_runner(formated_config)
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self.__class__._current_config_hash = config_hash
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progress = ProgressBar(100)
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def update_progress(current_step, _total):
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progress.update_absolute(current_step)
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current_runner = getattr(self.__class__, "_current_runner", None)
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if hasattr(current_runner, "set_progress_callback"):
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current_runner.set_progress_callback(update_progress)
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config["return_result_tensor"] = True
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config["save_result_path"] = ""
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config["negative_prompt"] = config.get("negative_prompt", "")
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input_info = init_empty_input_info(config.task)
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update_input_info_from_dict(input_info, config)
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current_runner.set_config(config)
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result_dict = current_runner.run_pipeline(input_info)
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images = result_dict.get("video", None)
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audio = result_dict.get("audio", None)
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if images is not None and images.numel() > 0:
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images = images.cpu()
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if images.dtype != torch.float32:
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images = images.float()
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if getattr(config, "unload_after_inference", False):
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if hasattr(self.__class__, "_current_runner"):
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del self.__class__._current_runner
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self.__class__._current_runner = None
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self.__class__._current_config_hash = None
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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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logging.error(f"Error during inference: {e}")
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raise
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finally:
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# Cleanup is handled by TempFileManager destructor
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pass
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class LightX2VConfigCombinerV2:
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"""Config combiner that also handles data preparation (image/audio/prompts)."""
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@@ -1816,10 +1598,10 @@ class LightX2VModularInferenceV2:
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# current_runner.set_config(config)
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result_dict = current_runner.run_pipeline(config)
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else:
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input_info = init_empty_input_info(config.task)
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update_input_info_from_dict(input_info, config)
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input_data = init_empty_input_info(config.task)
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update_input_info_from_dict(input_data, config)
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current_runner.set_config(config)
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result_dict = current_runner.run_pipeline(input_info)
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result_dict = current_runner.run_pipeline(input_data)
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images = result_dict.get("video", None)
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audio = result_dict.get("audio", None)
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@@ -1856,7 +1638,6 @@ NODE_CLASS_MAPPINGS = {
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"LightX2VMemoryOptimization": LightX2VMemoryOptimization,
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"LightX2VLoRALoader": LightX2VLoRALoader,
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"LightX2VConfigCombiner": LightX2VConfigCombiner,
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"LightX2VModularInference": LightX2VModularInference,
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"LightX2VConfigCombinerV2": LightX2VConfigCombinerV2,
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"LightX2VConfigCombinerV3": LightX2VConfigCombinerV3,
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"LightX2VModularInferenceV2": LightX2VModularInferenceV2,
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@@ -1873,7 +1654,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"LightX2VMemoryOptimization": "LightX2V Memory Optimization",
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"LightX2VLoRALoader": "LightX2V LoRA Loader",
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"LightX2VConfigCombiner": "LightX2V Config Combiner",
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"LightX2VModularInference": "LightX2V Modular Inference",
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"LightX2VConfigCombinerV2": "LightX2V Config Combiner V2",
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"LightX2VConfigCombinerV3": "LightX2V Config Combiner V3",
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"LightX2VModularInferenceV2": "LightX2V Modular Inference V2",
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