SCAN: Self-Calibrated AutoregressioN for High-Quality Visual Generation

Human artists can continuously refine their coarse sketches during artistic creation. This is quiet different from existing autoregressive generation, where a token is determined once sampled. Aiming to flexibly refine the generated contents, this paper presents a Self-Calibrated AutoregressioN (SCAN) model capable of self-evaluating and refining generation quality without regenerating the entire image. We unify image token generation and quality evaluation into a single autoregressive model, formulating both tasks as categorical prediction problems. During inference, the model first generates a coarse initial image, then iteratively refines the lowest-quality patches until satisfactory image quality is achieved. Experimental results demonstrate that SCAN effectively handles diverse real-world generation errors and achieves a promising balance between image quality and speed. For example, SCAN-XL achieves an FID of 2.10 and an IS of 326.1, surpassing the LlamaGen-XL by 1.29 (+38\%) in FID and 99.0 (+43.6\%) in IS, with a 5.6× speedup (19.76s → 3.56s). Compared to recent works, SCAN improves FID and speed by +18.3\% and +23\% over VAR-d20, and by +7\% and +46\% over RandAR-XL. Code and models will be released to facilitate further exploration in self-calibrated content generation.