What automated methods exist for hyperparameter search in diffusion modeling?

Automated hyperparameter search methods for diffusion models help optimize training efficiency and model performance without manual tuning. Common approaches include grid search, random search, Bayesian optimization, and population-based training. These methods systematically explore combinations of hyperparameters like learning rates, diffusion steps, noise schedules, and batch sizes to identify configurations that improve metrics such as sample quality or training speed. Given the computational cost of training diffusion models, efficient hyperparameter search is critical to avoid wasted resources.

Grid search and random search are foundational methods. Grid search exhaustively tests predefined hyperparameter combinations, which works for small search spaces but becomes impractical for complex models like diffusion due to high dimensionality. For example, tuning the number of diffusion steps (e.g., 100 vs. 1,000) alongside noise schedules (linear vs. cosine) would require training many models, making grid search inefficient. Random search samples hyperparameters randomly, often outperforming grid search in high-dimensional spaces by focusing on diverse regions. For instance, randomly sampling learning rates (e.g., 1e-4 to 1e-6) and batch sizes (e.g., 32 to 256) can yield better results faster. However, both methods lack adaptability, making them less ideal for resource-intensive diffusion models.

Advanced techniques like Bayesian optimization and population-based training (PBT) offer more efficiency. Bayesian optimization uses probabilistic models to predict promising hyperparameters based on past evaluations. For example, a Gaussian process can model the relationship between noise schedule parameters and validation loss, guiding the search toward optimal values with fewer trials. Tools like Hyperopt or Optuna implement this approach. PBT, used in frameworks like Ray Tune, trains multiple models in parallel and dynamically adjusts hyperparameters during training. For diffusion models, this could involve evolving the learning rate or dropout rates based on intermediate results. Another method, Hyperband, combines random search with early stopping, allocating more resources to promising configurations. For example, it might terminate a diffusion model training run early if the loss plateaus, saving compute time. These methods balance exploration and exploitation, making them well-suited for the iterative nature of diffusion training.