MADFormer: Mixed Autoregressive and Diffusion Transformers for Continuous Image Generation

• URL: http://arxiv.org/abs/2506.07999v1
• Date: Mon, 09 Jun 2025 17:59:01 GMT
• Title: MADFormer: Mixed Autoregressive and Diffusion Transformers for Continuous Image Generation
• Authors: Junhao Chen, Yulia Tsvetkov, Xiaochuang Han,
• Abstract summary: We introduce MADFormer, a Mixed Autoregressive Diffusion and Transformer that serves as a testbed for analyzing AR-diffusion trade-offs.<n>We identify two key insights: (1) block-wise partitioning significantly improves performance on high-resolution images, and (2) vertically mixing AR and diffusion layers yields better quality-efficiency balances--improving FID by up to 75% under constrained inference compute.
• Score: 32.945437908689286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in multimodal generation has increasingly combined autoregressive (AR) and diffusion-based approaches, leveraging their complementary strengths: AR models capture long-range dependencies and produce fluent, context-aware outputs, while diffusion models operate in continuous latent spaces to refine high-fidelity visual details. However, existing hybrids often lack systematic guidance on how and why to allocate model capacity between these paradigms. In this work, we introduce MADFormer, a Mixed Autoregressive and Diffusion Transformer that serves as a testbed for analyzing AR-diffusion trade-offs. MADFormer partitions image generation into spatial blocks, using AR layers for one-pass global conditioning across blocks and diffusion layers for iterative local refinement within each block. Through controlled experiments on FFHQ-1024 and ImageNet, we identify two key insights: (1) block-wise partitioning significantly improves performance on high-resolution images, and (2) vertically mixing AR and diffusion layers yields better quality-efficiency balances--improving FID by up to 75% under constrained inference compute. Our findings offer practical design principles for future hybrid generative models.

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