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training_pipeline

Classes

fastvideo.training.training_pipeline.TrainingPipeline 

TrainingPipeline(model_path: str, fastvideo_args: TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: LoRAPipeline, ABC

A pipeline for training a model. All training pipelines should inherit from this class. All reusable components and code should be implemented in this class.

Source code in fastvideo/training/training_pipeline.py 
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def __init__(
        self,
        model_path: str,
        fastvideo_args: TrainingArgs,
        required_config_modules: list[str] | None = None,
        loaded_modules: dict[str, torch.nn.Module] | None = None) -> None:
    fastvideo_args.inference_mode = False
    self.lora_training = fastvideo_args.lora_training
    if self.lora_training and fastvideo_args.lora_rank is None:
        raise ValueError("lora rank must be set when using lora training")

    set_random_seed(fastvideo_args.seed)  # for lora param init
    super().__init__(model_path, fastvideo_args, required_config_modules,
                     loaded_modules)  # type: ignore
    self.tracker = DummyTracker()

Functions

fastvideo.training.training_pipeline.TrainingPipeline.visualize_intermediate_latents 
visualize_intermediate_latents(training_batch: TrainingBatch, training_args: TrainingArgs, step: int)

Add visualization data to tracker logging and save frames to disk.

Source code in fastvideo/training/training_pipeline.py 
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def visualize_intermediate_latents(self, training_batch: TrainingBatch,
                                   training_args: TrainingArgs, step: int):
    """Add visualization data to tracker logging and save frames to disk."""
    raise NotImplementedError(
        "Visualize intermediate latents is not implemented for training pipeline"
    )

Functions