ThinkGen: Generalized Thinking for Visual Generation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915698535563264 |
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| author | Jiao, Siyu Lin, Yiheng Zhong, Yujie She, Qi Zhou, Wei Lan, Xiaohan Huang, Zilong Yu, Fei Yu, Yingchen Zhao, Yunqing Zhao, Yao Wei, Yunchao |
| author_facet | Jiao, Siyu Lin, Yiheng Zhong, Yujie She, Qi Zhou, Wei Lan, Xiaohan Huang, Zilong Yu, Fei Yu, Yingchen Zhao, Yunqing Zhao, Yao Wei, Yunchao |
| contents | Recent progress in Multimodal Large Language Models (MLLMs) demonstrates that Chain-of-Thought (CoT) reasoning enables systematic solutions to complex understanding tasks. However, its extension to generation tasks remains nascent and limited by scenario-specific mechanisms that hinder generalization and adaptation. In this work, we present ThinkGen, the first think-driven visual generation framework that explicitly leverages MLLM's CoT reasoning in various generation scenarios. ThinkGen employs a decoupled architecture comprising a pretrained MLLM and a Diffusion Transformer (DiT), wherein the MLLM generates tailored instructions based on user intent, and DiT produces high-quality images guided by these instructions. We further propose a separable GRPO-based training paradigm (SepGRPO), alternating reinforcement learning between the MLLM and DiT modules. This flexible design enables joint training across diverse datasets, facilitating effective CoT reasoning for a wide range of generative scenarios. Extensive experiments demonstrate that ThinkGen achieves robust, state-of-the-art performance across multiple generation benchmarks. Code is available: https://github.com/jiaosiyuu/ThinkGen |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23568 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ThinkGen: Generalized Thinking for Visual Generation Jiao, Siyu Lin, Yiheng Zhong, Yujie She, Qi Zhou, Wei Lan, Xiaohan Huang, Zilong Yu, Fei Yu, Yingchen Zhao, Yunqing Zhao, Yao Wei, Yunchao Computer Vision and Pattern Recognition Recent progress in Multimodal Large Language Models (MLLMs) demonstrates that Chain-of-Thought (CoT) reasoning enables systematic solutions to complex understanding tasks. However, its extension to generation tasks remains nascent and limited by scenario-specific mechanisms that hinder generalization and adaptation. In this work, we present ThinkGen, the first think-driven visual generation framework that explicitly leverages MLLM's CoT reasoning in various generation scenarios. ThinkGen employs a decoupled architecture comprising a pretrained MLLM and a Diffusion Transformer (DiT), wherein the MLLM generates tailored instructions based on user intent, and DiT produces high-quality images guided by these instructions. We further propose a separable GRPO-based training paradigm (SepGRPO), alternating reinforcement learning between the MLLM and DiT modules. This flexible design enables joint training across diverse datasets, facilitating effective CoT reasoning for a wide range of generative scenarios. Extensive experiments demonstrate that ThinkGen achieves robust, state-of-the-art performance across multiple generation benchmarks. Code is available: https://github.com/jiaosiyuu/ThinkGen |
| title | ThinkGen: Generalized Thinking for Visual Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.23568 |