Debugging diffusion model training issues requires systematic checks focused on monitoring, hyperparameters, and data integrity. Start by closely tracking training metrics. The loss curve should show a gradual decrease, but if it plateaus or spikes, investigate potential causes like incorrect noise scheduling or unstable gradients. For example, using mixed-precision training without proper scaling can lead to NaN values in loss. Validation metrics (e.g., FID scores) are equally critical—if they stagnate while training loss improves, your model might overfit. Tools like TensorBoard or WandB can visualize these trends and help spot anomalies early. Additionally, monitor hardware metrics (GPU memory, utilization) to rule out bottlenecks or memory leaks.
Next, review hyperparameters and model architecture. Diffusion models are sensitive to learning rates and noise schedules. A learning rate too high might cause divergence, while one too low can stall progress—start with values like 1e-4 and adjust based on loss behavior. Batch size also matters: smaller batches may introduce noise, while larger ones risk memory issues. For architecture, ensure components like attention layers or time-step embeddings are correctly implemented. For instance, a missing connection in a residual block can degrade sample quality. Experiment with gradient clipping (e.g., limiting gradients to 1.0) if you encounter exploding gradients. If samples appear blurry or incoherent, tweak the noise schedule (e.g., linear vs. cosine) to better align with your data distribution.
Finally, perform sanity checks on data and model initialization. Verify that input data is normalized correctly (e.g., scaling images to [-1, 1] or [0, 1]) and that augmentations (e.g., random crops) aren’t distorting content. A common pitfall is mismatched data preprocessing between training and inference. Test the model with a single batch: if loss doesn’t decrease, check for frozen layers or incorrect parameter updates. For initialization, pretrained components (e.g., a U-Net backbone) should match the expected input dimensions. Debugging tools like saving intermediate samples during training can reveal issues (e.g., all samples turning black due to activation saturation). Unit tests for key components, such as the noise prediction network, ensure they behave as expected before full training runs.