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ode_causal_pipeline

Classes

fastvideo.training.ode_causal_pipeline.ODEInitTrainingPipeline 

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

Bases: TrainingPipeline

Training pipeline for ODE-init using precomputed denoising trajectories.

Supervision: predict the next latent in the stored trajectory by - feeding current latent at timestep t into the transformer to predict noise - stepping the scheduler with the predicted noise - minimizing MSE to the stored next latent at timestep t_next

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