DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention

Figure 1. Image generation with the proposed Diffusion Gated Linear Attention Transformers (DiG). We show selected samples from our class-conditional DiG-XL/2 models trained on ImageNet at 512 × 512 and 256 × 256 resolution, respectively.