The choice of optimizer significantly impacts the training dynamics, stability, and final performance of diffusion models. Optimizers determine how the model’s parameters are updated during training based on the gradients of the loss function. Diffusion models involve iterative processes where noise is gradually added to data and then reversed, creating a complex optimization landscape. The optimizer must handle varying gradient magnitudes, avoid getting stuck in local minima, and balance computational efficiency. A poorly chosen optimizer can lead to slow convergence, unstable training, or suboptimal generation quality.
For example, Adam is a popular choice for training diffusion models due to its adaptive learning rates, which adjust per parameter based on gradient history. This adaptability helps manage the varying scales of gradients across different timesteps in the diffusion process. However, Adam’s memory overhead—storing first and second moment estimates—can be a drawback for very large models. In contrast, SGD with momentum might require careful tuning of the learning rate schedule but can offer more stable updates in scenarios where Adam’s adaptability leads to overshooting optimal parameters. Some implementations also use hybrid approaches, like AdamW, which decouples weight decay from parameter updates, improving generalization. Developers might experiment with optimizers like RMSProp for specific use cases, such as when training with limited hardware resources, as it can be less memory-intensive than Adam.
The optimizer’s hyperparameters, such as learning rate and momentum, also interact with the diffusion process. For instance, a high initial learning rate with Adam might cause instability in early training when the model is learning coarse-grained noise patterns, while a low rate could slow down convergence. Learning rate warmup—gradually increasing the rate during initial steps—is often used to mitigate this. Additionally, gradient clipping is sometimes applied to prevent exploding gradients, especially in architectures with many timesteps. The choice of optimizer also affects how well the model generalizes across different noise levels; adaptive methods might better handle the shifting loss landscape compared to non-adaptive ones. Developers should prioritize testing optimizers with their specific dataset and architecture, starting with established defaults (e.g., Adam with a learning rate of 1e-4) and iterating based on training metrics like loss curves and sample quality.