Poster

UniGen-1.5: Enhancing Image Generation and Editing through Reward Unification in RL

Rui Tian ⋅ Mingfei Gao ⋅ Haiming Gang ⋅ Jiasen Lu ⋅ Zhe Gan ⋅ Yinfei Yang ⋅ Zuxuan Wu ⋅ Afshin Dehghan

Abstract

We present UniGen-1.5, a unified multimodal large language model (MLLM) for advanced image understanding, generation and editing. Building upon UniGen, we comprehensively enhance the model architecture and training pipeline to strengthen the image understanding and generation capabilities while unlocking strong image editing ability. Especially, we propose a unified Reinforcement Learning (RL) strategy that improves both image generation and image editing jointly via shared reward models. To further enhance image editing performance, we propose a light Edit Instruction Alignment stage that significantly improves the editing instruction comprehension that is essential for the success of the RL training. Experimental results show that UniGen-1.5 demonstrates competitive understanding and generation performance. Specifically, UniGen-1.5 achieves 0.89 and 4.31 overall scores on GenEval and ImgEdit benchmarks that surpass the state-of-the-art models such as BAGEL and reaching performance comparable to proprietary models such as GPT-Image-1.