To handle vanishing gradients in deep diffusion networks, the primary approach involves architectural and training adjustments that maintain gradient flow across the network. Vanishing gradients occur when backpropagated error signals become too small to update early layers in deep models, which is especially problematic in diffusion models due to their iterative, multi-step nature. Two key strategies are using residual connections and normalization techniques. Residual connections allow gradients to bypass layers via skip connections, preserving their magnitude. For example, in diffusion models, each denoising step’s neural network often employs residual blocks, ensuring that even deep networks can propagate gradients effectively. Layer normalization or batch normalization can stabilize activations between layers, reducing the risk of gradients shrinking during training.
Another solution is modifying the loss function or training dynamics to address gradient decay. Diffusion models train by predicting noise at each timestep, but gradients can vanish if later timesteps dominate the learning process. Techniques like loss weighting—assigning higher weights to gradients from early timesteps—ensure balanced updates across all steps. For instance, some implementations use a cosine-weighted schedule to prioritize mid-range timesteps where the denoising task is neither too trivial nor too noisy. Additionally, progressive training, where the model first learns to handle fewer timesteps and gradually scales to full depth, helps stabilize initial learning. This phased approach prevents the network from being overwhelmed by complex dependencies early on.
Finally, parameter initialization and activation functions play a role. Initializing weights to avoid saturation (e.g., He initialization for ReLU layers) ensures gradients start with viable magnitudes. Using activation functions with non-zero derivatives, like SiLU (Swish), instead of ReLU, helps maintain gradient flow. In diffusion models, some architectures replace standard convolutions with gated linear units that combine activation paths, balancing nonlinearity and gradient preservation. By combining these methods—residual blocks, weighted losses, and careful initialization—developers can train deeper diffusion networks effectively without losing gradient signal, enabling models to learn complex data distributions across many iterative steps.