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

arXiv:2504.04903 · cs.CV, cs.AI · Submitted 2025-04-07 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-04-07

Updated: 2026-09-10

Code: https://github.com/black-forest-labs/flux

License: http://creativecommons.org/licenses/by/4.0/

The gist: 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,

Terminology

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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