Pooling Representation Autoencoders for Efficient Diffusion

Representation autoencoders (RAEs) generate images from pretrained visual features, but their dense token grids make generative modeling expensive. Motivated by local feature correlations, we introduce PoolDINO, a learned affine pooling operator that merges neighboring tokens. Training the pooling operator jointly with the RGB decoder preserves the standard two-stage RAE procedure without a separate feature autoencoder. On ImageNet-256, $4\times$ token compression retains comparable generation quality under internal guidance, while $16\times$ compression trades some quality for greater efficiency. At a fixed budget of 100 sampling steps, latent-sampling throughput increases by $3.7\times$ and $9.0\times$, respectively, relative to the unpooled baseline. Classification and dense prediction evaluations show that comparable guided generation quality can coexist with weaker performance on other tasks.