To prevent mode collapse in diffusion models, developers can focus on three main strategies: diversifying training data and objectives, modifying model architecture, and adjusting training and sampling techniques. Mode collapse occurs when the model generates limited variations, often due to overfitting to specific patterns in the data or insufficient exploration during training. By addressing these root causes, developers can encourage the model to produce diverse and high-quality outputs.
First, diversifying training data and refining the loss function are foundational steps. Using a dataset that broadly represents the target distribution helps the model learn diverse features. For example, if training an image generator, include variations in object poses, lighting, and backgrounds. Additionally, adjusting the training objective can reduce mode collapse. Diffusion models typically minimize the mean squared error (MSE) between predicted and actual noise at each denoising step. Introducing regularization terms, such as KL divergence, can penalize overly confident predictions and encourage exploration. For instance, adding a term that rewards the model for maintaining uncertainty in its predictions during early training stages can prevent premature convergence to a subset of modes.
Second, architectural modifications can improve the model’s capacity to capture diversity. For example, using U-Net architectures with attention mechanisms allows the model to focus on both local and global features, which helps generate coherent yet varied outputs. Incorporating multi-scale features or adaptive normalization layers (e.g., conditional batch normalization) can also help the model adapt to different modes in the data. A practical example is using spatial attention layers to prioritize different regions of an image during generation, ensuring that the model doesn’t fixate on repetitive patterns. These changes enable the model to handle complex data distributions more effectively.
Finally, training and sampling strategies play a critical role. Extending the training duration with a carefully tuned noise schedule ensures the model learns to denoise across all levels of corruption, preventing over-reliance on a narrow subset of data. During sampling, introducing stochasticity—such as varying the number of denoising steps or using ancestral sampling with randomized noise—can help explore different modes. For example, alternating between deterministic (DDIM) and stochastic (Langevin) sampling steps during inference can balance output quality and diversity. Additionally, techniques like temperature scaling in the noise prediction step can control the trade-off between sample variety and fidelity. By combining these approaches, developers can mitigate mode collapse while maintaining the model’s generative performance.