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arXiv · 2602.07564

SIGMA: Selective-Interleaved Generation with Multi-Attribute Tokens

Xiaoyan Zhang·Zechen Bai·Haofan Wang·Yiren Song
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Abstract

Recent unified models such as Bagel demonstrate that paired image-edit data can effectively align multiple visual tasks within a single diffusion transformer. However, these models remain limited to single-condition inputs and lack the flexibility needed to synthesize results from multiple heterogeneous sources. We present SIGMA (Selective-Interleaved Generation with Multi-Attribute Tokens), a unified post-training framework that enables interleaved multi-condition generation within diffusion transformers. SIGMA introduces selective multi-attribute tokens, including style, content, subject, and identity tokens, which allow the model to interpret and compose multiple visual conditions in an interleaved text-image sequence. Through post-training on the Bagel unified backbone with 700K interleaved examples, SIGMA supports compositional editing, selective attribute transfer, and fine-grained multimodal alignment. Extensive experiments show that SIGMA improves controllability, cross-condition consistency, and visual quality across diverse editing and generation tasks, with substantial gains over Bagel on compositional tasks.

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Xiaoyan Zhang, Zechen Bai, Haofan Wang, Yiren Song. 2026-02-07. SIGMA: Selective-Interleaved Generation with Multi-Attribute Tokens. https://arxiv.org/abs/2602.07564

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