How do learning rate schedules impact the training of diffusion models?

Learning rate schedules play a critical role in training diffusion models by balancing stability, convergence speed, and final performance. Diffusion models learn to reverse a noise-adding process through iterative refinement, which involves training a network to predict noise or data at multiple timesteps. Because each timestep corresponds to a different noise level, the optimization landscape varies significantly across training. A well-designed learning rate schedule adapts to these variations, helping the model navigate complex gradients and avoid instability while maintaining efficient training.

One key impact of learning rate schedules is managing the trade-off between training speed and stability. Early in training, higher learning rates can accelerate initial convergence by allowing larger updates to the model parameters. For example, a linear warmup schedule gradually increases the learning rate during the first few epochs, preventing early overshooting in the high-noise timesteps where gradients might be erratic. Conversely, decay-based schedules (like cosine or step decay) reduce the learning rate later in training to refine the model’s ability to handle low-noise steps, which require precise adjustments. Without such decay, the model might oscillate or diverge when fine-tuning details in later stages. This balance ensures the model progresses efficiently without sacrificing stability.

Practical implementation choices depend on the diffusion model’s architecture and dataset. For instance, the original DDPM paper used a constant learning rate, but modern variants often employ adaptive schedules. A cosine decay schedule, which smoothly reduces the rate over time, can help the model transition from coarse to fine-grained learning phases. Developers might also experiment with per-timestep learning rate adjustments, where noisier timesteps (early in the diffusion process) use higher rates than later ones. Monitoring loss curves and gradient magnitudes across timesteps can inform adjustments—sudden spikes might indicate the need for a slower rate. Combining schedules with techniques like gradient clipping or EMA (exponential moving averages) further stabilizes training. Ultimately, the right schedule depends on empirical testing but is critical for achieving high-quality, stable diffusion models.