Debugging diffusion model training issues involves systematically identifying and resolving problems that may arise during the training process. Some best practices include monitoring key metrics, adjusting hyperparameters, and examining data quality. Regularly checking the loss metrics can help pinpoint when issues begin to surface. If the loss suddenly spikes or stagnates, it may indicate issues with learning rates or data quality.
One effective approach is to start by visualizing model outputs at various stages of the training process. This can reveal how the model is learning and whether it is generating meaningful outputs. Implementing logging mechanisms to capture model performance, such as loss and accuracy, can provide insight into the training process. Versioning your model, data, and configurations can also be helpful. By keeping track of different training sessions, you can isolate combinations of parameters or datasets that lead to good or poor performance.
Finally, consider simplifying your model or dataset to identify the root cause of the issue. For instance, if you are working with a highly complex model, try using a smaller subset of data or reducing the model's architecture. If the issues persist in the simpler setup, this may indicate a fundamental problem with the training process or datasets. Collaborating with peers for code reviews can also bring a new perspective and help catch errors that might be overlooked when working alone.