DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation
Recent unified multimodal large language models (MLLMs) have shown impressive capabilities, incorporating chain-of-thought (CoT) reasoning for enhanced text-to-image generation. However, existing approaches remain limited, either treating the model merely as a standalone generator or relying on abstract textual planning. To this end, we propose Draft-as-CoT (DraCo), a novel interleaved reasoning paradigm that fully leverages both textual and visual contents in CoT for better planning and verification. Our method first generates a low-resolution draft image as preview, providing more concrete and structural visual planning and guidance. Then, we employ the model's inherent understanding capability to verify potential semantic misalignments between the draft and input prompt, and performs refinement through selective corrections with super-resolution. In this way, our approach addresses two fundamental challenges: the coarse-grained nature of textual planning and the difficulty in generating rare attribute combinations. To support training, we curate DraCo-240K, aiming to enhance three atomic capabilities spanning general correction, instance manipulation, and layout reorganization. Supported by DraCo-CFG, a specialized classifier-free guidance (CFG) strategy for interleaved reasoning, DraCo achieves a tremendous increase on GenEval (+8%), Imagine-Bench (+0.91), and GenEval++ (+3%), significantly outperforming direct generation and other generation methods empowered by CoT.
69324e4e6d1060ca587a271b
Dongzhi Jiang
claimed_verified
2025-12-05T08:29:21.214Z
6349214f8146350b3a4c5cdf
/avatars/cfd24caac9a87efb528d0f4c375932bc.svg
Dongzhi Jiang
CaraJ
user
CaraJ
69324e4e6d1060ca587a271c
Renrui Zhang
69324e4e6d1060ca587a271d
Haodong Li
claimed_verified
2025-12-05T08:29:23.300Z
65ddea8b2d26e59a5a33330f
/avatars/3104ddafd6dda3c05ea9a771dbf2deeb.svg
li haodong
mickyhimself
user
mickyhimself
69324e4e6d1060ca587a271e
Zhuofan Zong
69324e4e6d1060ca587a271f
Ziyu Guo
69324e4e6d1060ca587a2720
Jun He
69324e4e6d1060ca587a2721
Claire Guo
69324e4e6d1060ca587a2722
Junyan Ye
69324e4e6d1060ca587a2723
Rongyao Fang
69324e4e6d1060ca587a2724
Weijia Li
69324e4e6d1060ca587a2725
Rui Liu
69324e4e6d1060ca587a2726
Hongsheng Li
DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation
Recent unified multimodal large language models (MLLMs) have shown impressive capabilities, incorporating chain-of-thought (CoT) reasoning for enhanced text-to-image generation. However, existing approaches remain limited, either treating the model merely as a standalone generator or relying on abstract textual planning. To this end, we propose Draft-as-CoT (DraCo), a novel interleaved reasoning paradigm that fully leverages both textual and visual contents in CoT for better planning and verification. Our method first generates a low-resolution draft image as preview, providing more concrete and structural visual planning and guidance. Then, we employ the model's inherent understanding capability to verify potential semantic misalignments between the draft and input prompt, and performs refinement through selective corrections with super-resolution. In this way, our approach addresses two fundamental challenges: the coarse-grained nature of textual planning and the difficulty in generating rare attribute combinations. To support training, we curate DraCo-240K, aiming to enhance three atomic capabilities spanning general correction, instance manipulation, and layout reorganization. Supported by DraCo-CFG, a specialized classifier-free guidance (CFG) strategy for interleaved reasoning, DraCo achieves a tremendous increase on GenEval (+8%), Imagine-Bench (+0.91), and GenEval++ (+3%), significantly outperforming direct generation and other generation methods empowered by CoT.
DraCo, a novel interleaved reasoning paradigm, enhances text-to-image generation by integrating both textual and visual content, generating low-resolution drafts, verifying semantic alignment, and refining images with super-resolution to address challenges in textual planning and rare attribute generation.
unified multimodal large language models MLLMs chain-of-thought CoT reasoning text-to-image generation Draft-as-CoT DraCo interleaved reasoning low-resolution draft image visual planning semantic misalignments super-resolution DraCo-240K classifier-free guidance CFG GenEval Imagine-Bench GenEval++