UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling

• URL: http://arxiv.org/abs/2607.24157v1
• Date: Mon, 27 Jul 2026 08:35:09 GMT
• Title: UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling
• Abstract summary: We study Unified Visual Generation (UVG), where a single model produces diverse image-valued outputs through a unified multimodal interface.<n>We propose UniGen-AR, a framework that pairs a general-purpose multi-modal language model (MLLM) with an efficient visual auto-regressive decoder.<n>In our framework, the MLLM encodes free-form instructions and control signals into a unified sequence, which guides the VAR decoder to generate image-valued outputs for over 15 tasks spanning four families.
• Score: 57.126059929740926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern computer vision pipelines remain fragmented, with tasks such as text-to-image generation, editing, restoration, and classical perception handled by separate models. We study Unified Visual Generation (UVG), where a single model produces diverse image-valued outputs through a unified multimodal interface. While diffusion-based systems dominate UVG due to strong quality and controllability, their iterative sampling incurs substantial inference latency, limiting practical deployment. To address these limitations, we propose UniGen-AR, a framework that pairs a general-purpose multi-modal language model (MLLM) with an efficient next-scale visual auto-regressive (VAR) decoder. This design retains the flexibility of MLLM-based conditioning while leveraging the sampling efficiency and latent unification properties of VAR models. In our framework, the MLLM encodes free-form instructions and control signals into a unified sequence, which guides the VAR decoder to generate image-valued outputs for over 15 tasks spanning four families. Empirically, UniGen-AR achieves up to $19 \times$ lower inference latency than diffusion-based baselines while maintaining or improving output quality. Our ablations further reveal that VQ-VAE tokenizer design, particularly codebook size and hierarchy, is a critical factor for VAR scalability in UVG. These results establish visual auto-regressive modeling as a compelling and efficient backbone for unified visual generation. Our project page is at https://zpbao.github.io/projects/unigenar.

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