DyRef lifts Qwen-Image-Edit-2511 from 4.97 to 8.38 on multi-reference editing

量子位 · wechat · 2026-07-29

DyRef targets complex multi-reference image generation, where a model must combine identity, background, pose, lighting, and style from multiple reference images without letting them conflict.
The team built OmniRef-Bench, a 395-sample benchmark covering 5 reference types, 2 to 7 reference images per sample, and up to 4 simultaneous reference categories.
DyRef uses a two-stage training recipe: supervised fine-tuning first, then reinforcement learning with dynamic reward optimization so harder samples get more weight.
On OmniRef-Bench, the post says Qwen-Image-Edit-2511 improved from 4.97 to 8.38 in MLLM evaluation average, narrowing the gap to NanoBananaPro and beating Seedream4.5.
The article also claims the method improves performance on MultiBanana, OmniContext, and even single-image editing benchmarks such as ImgEdit and DreamBench++.

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