OmniAlpha: A Sequence-to-Sequence Framework for Unified Multi-Task RGBA Generation
Generative models have excelled in RGB synthesis, but real-world applications require RGBA manipulation. This has led to a fragmented landscape: specialized, single-task models handle alpha but lack versatility, while unified multi-task frameworks are confined to the RGB domain. To bridge this critical gap, we propose OmniAlpha, the first unified, multi-task generative framework for sequence-to-sequence RGBA image generation and editing. Its architecture features MSRoPE-BiL, a novel RoPE method with a bi-directionally extendable layer axis for its Diffusion Transformer (DiT) backbone, enabling the concurrent processing of multiple input and target RGBA layers. To power this framework, we introduce AlphaLayers, a new dataset of 1,000 high-quality, multi-layer triplets, built via a novel automated synthesis and filter pipeline. Jointly training OmniAlpha on this dataset across a comprehensive suite of 21 diverse tasks, extensive experiments demonstrate that our unified approach consistently outperforms strong, specialized baselines. Most notably, OmniAlpha achieves a dramatic 84.8% relative reduction in SAD for mask-free matting on AIM-500 and wins over 90% of human preferences in layer-conditioned completion. Our work proves that a unified, multi-task model can learn a superior shared representation for RGBA, paving the way for more powerful, layer-aware generative systems.
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Hao Yu
claimed_verified
2025-11-26T09:27:10.152Z
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Hao Yu
Longin-Yu
user
Longin-Yu
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Jiabo Zhan
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Zile Wang
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Jinglin Wang
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Huaisong Zhang
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Hongyu Li
claimed_verified
2025-12-08T08:35:55.818Z
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Hongyu Li
appletea2333
user
appletea2333
6926ae80243b2216fb75cb41
Xinrui Chen
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Yongxian Wei
6926ae80243b2216fb75cb43
Chun Yuan
OmniAlpha: A Sequence-to-Sequence Framework for Unified Multi-Task RGBA Generation
Generative models have excelled in RGB synthesis, but real-world applications require RGBA manipulation. This has led to a fragmented landscape: specialized, single-task models handle alpha but lack versatility, while unified multi-task frameworks are confined to the RGB domain. To bridge this critical gap, we propose OmniAlpha, the first unified, multi-task generative framework for sequence-to-sequence RGBA image generation and editing. Its architecture features MSRoPE-BiL, a novel RoPE method with a bi-directionally extendable layer axis for its Diffusion Transformer (DiT) backbone, enabling the concurrent processing of multiple input and target RGBA layers. To power this framework, we introduce AlphaLayers, a new dataset of 1,000 high-quality, multi-layer triplets, built via a novel automated synthesis and filter pipeline. Jointly training OmniAlpha on this dataset across a comprehensive suite of 21 diverse tasks, extensive experiments demonstrate that our unified approach consistently outperforms strong, specialized baselines. Most notably, OmniAlpha achieves a dramatic 84.8% relative reduction in SAD for mask-free matting on AIM-500 and wins over 90% of human preferences in layer-conditioned completion. Our work proves that a unified, multi-task model can learn a superior shared representation for RGBA, paving the way for more powerful, layer-aware generative systems.
OmniAlpha, a unified multi-task generative framework, excels in RGBA image generation and editing using a Diffusion Transformer with a novel MSRoPE-BiL method, outperforming specialized models across various tasks.
generative models RGBA manipulation OmniAlpha MSRoPE-BiL Diffusion Transformer AlphaLayers mask-free matting layer-conditioned completion