LLaDA-Image：以全开放训练方案构建强大的图像生成器

LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes

September 3, 2026

作者: Chuyan Chen, Haoxing Chen, Kun Chen, Zhenglin Cheng, Long Cui, Ruishan Fang, Zhangxuan Gu, Zhicheng Huang, Zhenzhong Lan, Yuanting Lei, Haoquan Li, Jianguo Li, Rongchuan Li, Sidu Li, Tao Lin, Deyuan Liu, Jiacheng Liu, Lin Liu, Yuxuan Lou, Zhisheng Lu, Yuxin Ma, Shuheng Shen, Peng Sun, Chaoyang Wang, Hongjun Wang, Xiaomei Wang, Yongxin Wang, Chengzhang Wu, Hongru Wu, Jun Xie

cs.AI

摘要

我们提出了LLaDA-Image，一个统一框架，将一个从零训练的6B扩散Transformer（DiT）与一个基于LLaDA2.0-Mini扩散语言模型主干构建的冻结视觉-语言理解模块相结合。我们并非从一开始就严重依赖图文配对数据，而是首先通过仅图像预训练和中期训练建立一个强大的视觉生成先验。该生成流程包含220M个样本，其中98%为真实图像。为实现高效且可扩展的优化，我们在整个DiT中使用无参数RMSNorm，并采用Muon优化器。得到的统一模型能够生成高度逼真的图像，同时准确遵循细粒度编辑指令。我们进一步将LLaDA-Image蒸馏为LLaDA-Image-Turbo，使其只需2-4步采样即可快速推理。在Qwen-Image-Bench上，LLaDA-Image在英文和中文赛道分别取得53.53和53.38的综合得分，在这两个赛道均刷新了开源模型的最佳成绩。为支持对能力强且高效的生成模型的进一步研究，我们发布了模型权重、训练代码及详细训练方案。

English

We introduce LLaDA-Image, a unified framework that pairs a 6B Diffusion Transformer (DiT) trained from scratch with a frozen vision-language understanding module built on the LLaDA2.0-Mini diffusion language model backbone. Instead of relying heavily on paired image-text data from the beginning, we first build a strong visual generative prior through image-only pre-training and mid-training. The generation pipeline comprises 220M samples, 98 of which are real images. For efficient and scalable optimization, we use parameter-free RMSNorm throughout the DiT together with the Muon optimizer. The resulting unified model produces highly photorealistic images while accurately following fine-grained editing instructions. We further distill LLaDA-Image into LLaDA-Image-Turbo, enabling fast inference in 2-4 sampling steps. On Qwen-Image-Bench, LLaDA-Image achieves overall scores of 53.53 and 53.38 on the English and Chinese tracks, respectively, setting a new state-of-the-art among open-source models on both tracks. To support further research on capable and efficient generative models, we release our model weights, training code, and detailed recipes.