DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models

We introduce DiverseVAR, a test-time framework that enhances the output diversity of text-conditioned visual autoregressive models (VAR) without additional training or substantial computational overhead. While VAR models have recently emerged as strong competitors of diffusion and flow models for image and video generation, they suffer from a critical diversity limitation: even simple prompts often produce nearly identical outputs. This issue has largely gone unnoticed amid the field’s predominant focus on image quality. We address this limitation in two stages. First, we systematically explore diversity enhancement techniques by injecting noise into different components of VAR at test time, finding that noise injection into the text embedding yields the best diversity gains. However, this comes at the cost of sharp degradation in image quality. To recover quality, we propose scale-travel: a latent refinement technique that leverages a multi-scale autoencoder to extract coarsescale tokens to resume generation from intermediate stages. Extensive experiments demonstrate that combining text-embedding noise injection with scale-travel refinement substantially improves diversity while minimizing quality degradation, advancing the diversity-quality Pareto front. Project page: https://diverse-var.github.io/