Paper page - Unlocking Any-Order Generation in Pretrained Autoregressive Image Models

Source: https://huggingface.co/papers/2610.39293

Abstract

Pretrained raster-scan autoregressive image models can be adapted into flexible any-order generators via future-aware positional embeddings and an ordering curriculum, enabling inpainting and outpainting with minimal extra compute.

Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fixed raster-scan order hardcodes a single factorization of the image distribution into the model, leaving it unable to inpaint, outpaint, or edit at inference time. Existing solutions, including the modern discrete-diffusion family, train from scratch on a mixture of token orderings, paying roughly 3x the compute of standard raster-scan training;discrete diffusionfurther sacrifices compatibility with the AR inference ecosystem (KV caching,vLLM) by construction. We argue that this cost is unnecessary. A pretrained raster-scan AR model has already learned the joint distribution over image tokens; what it lacks is not knowledge but the ability to express that knowledge through non-sequential conditional pathways. We propose a recipe for adapting pretrained raster-scan models into any-order generators using two components:future-aware positional embeddingsthat resolve a target-position identifiability problem at negligible overhead, and anordering curriculumthat traces a stable optimization path from the pretrained solution to a good any-order basin. Applied to four base models across two architectures and two scales (LlamaGen-L/XL,RAR-L/XL), the recipe substantially outperformsMaskGITon ImageNet inpainting (FID 6.21 vs. 10.44) and outpainting, is competitive withMAR-L, trades a 1.2-1.9 increase in class-conditional FID for this flexibility, and costs 13-33% additional compute beyond pretraining, while leaving the standard AR inference stack untouched. Inpainting, outpainting, and editing, our results suggest, need not be added to autoregressive image models. They can be unlocked from the ones that exist. Code is available at https://github.com/Ordered-AR/unlockedadaptation.

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