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arXiv · 2504.04903

Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision

Yuandong Pu·Le Zhuo·Kaiwen Zhu·Liangbin Xie·Wenlong Zhang·Xiangyu Chen·Peng Gao·Yu Qiao·Chao Dong·Yihao Liu
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Abstract

We present Lunima-OmniLV (abbreviated as OmniLV), a universal multimodal multi-task framework for low-level vision that addresses over 100 sub-tasks across four major categories: image restoration, image enhancement, weak-semantic dense prediction, and stylization. OmniLV leverages both textual and visual prompts to offer flexible and user-friendly interactions. Built on Diffusion Transformer (DiT)-based generative priors, our framework supports arbitrary resolutions -- achieving optimal performance at 1K resolution -- while preserving fine-grained details and high fidelity. Through extensive experiments, we demonstrate that separately encoding text and visual instructions, combined with co-training using shallow feature control, is essential to mitigate task ambiguity and enhance multi-task generalization. Our findings also reveal that integrating high-level generative tasks into low-level vision models can compromise detail-sensitive restoration. These insights pave the way for more robust and generalizable low-level vision systems.

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Yuandong Pu, Le Zhuo, Kaiwen Zhu, Liangbin Xie, Wenlong Zhang, Xiangyu Chen, Peng Gao, Yu Qiao, Chao Dong, Yihao Liu. 2026-09-10. Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision. https://arxiv.org/abs/2504.04903

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