How can self-attention be integrated into the diffusion process?

Self-attention can be integrated into the diffusion process by embedding it within the neural network architecture responsible for denoising. In diffusion models, a U-Net is typically used to predict and remove noise at each step of the generation process. Self-attention layers are added to the U-Net to help the model capture long-range dependencies in the data, which convolutional layers alone might miss. For example, in image generation, self-attention allows pixels or feature map regions to directly influence one another regardless of their spatial proximity, enabling the model to maintain global coherence in structures like faces or objects.

The integration involves inserting self-attention layers at specific resolutions in the U-Net. For instance, after downsampling the input to a manageable spatial size (e.g., 16x16 or 8x8), self-attention is applied to the flattened feature map. Each position in the feature map computes attention scores relative to all other positions, weighted by their importance. This process helps the model understand relationships between distant elements, such as ensuring symmetry in a generated face or aligning textures across an image. To reduce computational cost, self-attention is often applied at lower resolutions, and techniques like grouped attention or sparse attention patterns may be used. Additionally, timestep embeddings (indicating the current denoising step) are injected into the network, often by modulating the attention weights or feature maps, ensuring the model adapts its behavior across diffusion steps.

A practical example is Stable Diffusion, which uses self-attention in its U-Net to handle spatial relationships. The model applies self-attention at intermediate layers after downsampling, balancing local detail processing (via convolutions) with global context modeling. For instance, when generating an image of a dog, the self-attention mechanism might link the position of the tail to the body, even if they are far apart in the feature map. This integration improves the model’s ability to generate coherent, high-quality outputs by combining the strengths of convolutional operations (local patterns) and attention (global structure). Developers can implement this by adding self-attention modules to existing U-Net codebases, ensuring compatibility with the diffusion framework’s noise prediction and training loop.