I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models
Zhenxing Mi, Kuan-Chieh Wang, Guocheng Qian, Hanrong Ye, Runtao Liu, Sergey Tulyakov, Kfir Aberman, Dan Xu
Abstract
Predict what the next image is: monkey + + cat + (a) Multimodal in-context reasoning generation (b) Multimodal composing "The girl holds a board showing 'Stop copy & paste. Let's Think DIFFERENT.'." + Ground truth reasoning answer: flying zebra + zebra + Figure 1. (a) Our ThinkDiff reasons over interleaved images (a flying monkey and a flying cat) and text prompts (monkey, cat, and zebra) to generate a logically correct and high-quality image (a flying zebra). The ground truth reasoning answer is provided as a reference for readers. (b) ThinkDiff composes images and texts into a coherent and reasonable image.
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Cited by top-tier papers2
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- Unified Thinker: A General Reasoning Core for Image GenerationSashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang et al.ACL 2026
Builds on23
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Visual Instruction TuningHaotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae LeeNeurIPS 2023 · 11,349 citations
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