Wan-Image: Pushing the Boundaries of Generative Visual Intelligence
Chaojie Mao, Chen-Wei Xie, Chongyang Zhong, Haoyou Deng, Jiaxing Zhao, Jie Xiao, Jinbo Xing, Jingfeng Zhang, Jingren Zhou, Jingyi Zhang, Jun Dan, Kai Zhu, Kang Zhao, Keyu Yan, Minghui Chen, Pandeng Li, Shuangle Chen, Tong Shen, Yu Liu, Yue Jiang, Yulin Pan, Yuxiang Tuo

TL;DR
Wan-Image is a unified multi-modal visual generation system that combines large language models and diffusion transformers to deliver professional-grade, controllable, and high-resolution images for diverse applications.
Contribution
It introduces a novel architecture integrating language understanding with high-fidelity image synthesis, enabling expert-level visual generation and editing capabilities.
Findings
Wan-Image surpasses Seedream 5.0 Lite and GPT Image 1.5 in performance.
Achieves parity with Nano Banana Pro on challenging tasks.
Supports diverse professional visual synthesis features.
Abstract
We present Wan-Image, a unified visual generation system explicitly engineered to paradigm-shift image generation models from casual synthesizers into professional-grade productivity tools. While contemporary diffusion models excel at aesthetic generation, they frequently encounter critical bottlenecks in rigorous design workflows that demand absolute controllability, complex typography rendering, and strict identity preservation. To address these challenges, Wan-Image features a natively unified multi-modal architecture by synergizing the cognitive capabilities of large language models with the high-fidelity pixel synthesis of diffusion transformers, which seamlessly translates highly nuanced user intents into precise visual outputs. It is fundamentally powered by large-scale multi-modal data scaling, a systematic fine-grained annotation engine, and curated reinforcement learning data…
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