Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
We introduce Lumina-Image 2.0, an advanced text-to-image generation framework
that achieves significant progress compared to previous work, Lumina-Next.
Lumina-Image 2.0 is built upon two key principles: (1) Unification - it adopts
a unified architecture (Unified Next-DiT) that treats text and image tokens as
a joint sequence, enabling natural cross-modal interactions and allowing
seamless task expansion. Besides, since high-quality captioners can provide
semantically well-aligned text-image training pairs, we introduce a unified
captioning system, Unified Captioner (UniCap), specifically designed for T2I
generation tasks. UniCap excels at generating comprehensive and accurate
captions, accelerating convergence and enhancing prompt adherence. (2)
Efficiency - to improve the efficiency of our proposed model, we develop
multi-stage progressive training strategies and introduce inference
acceleration techniques without compromising image quality. Extensive
evaluations on academic benchmarks and public text-to-image arenas show that
Lumina-Image 2.0 delivers strong performances even with only 2.6B parameters,
highlighting its scalability and design efficiency. We have released our
training details, code, and models at
https://github.com/Alpha-VLLM/Lumina-Image-2.0.
67e6092a40fb111ac9342c39
Qi Qin
claimed_verified
2025-03-28T08:37:18.440Z
66bb136002fd8eb58bc84ffb
/avatars/122cb8f59c502392768099b3c2afe043.svg
qinqi
user
Dakerqi
67e6092a40fb111ac9342c3a
Le Zhuo
admin_assigned
2025-03-28T08:50:03.385Z
6358a167f56b03ec9147074d
/avatars/e54ea7bf0c240cf76d538296efb3976c.svg
Le Zhuo
user
JackyZhuo
67e6092a40fb111ac9342c3b
Yi Xin
67e6092a40fb111ac9342c3c
Ruoyi Du
admin_assigned
2025-03-28T08:50:09.220Z
64a54586c0f13de8e7093314
/avatars/389e43e9a32cf2fc95f8f3a23b8f0508.svg
Ruoyi Du
user
RuoyiDu
67e6092a40fb111ac9342c3d
Zhen Li
claimed_verified
2025-03-28T08:58:25.783Z
6285a9133ab6642179158944
/avatars/6e10fa07c94141fcdbe0cab02bb731ca.svg
Zhen Li
user
Paper99
67e6092a40fb111ac9342c3e
Bin Fu
67e6092a40fb111ac9342c3f
Yiting Lu
admin_assigned
2025-03-28T08:50:17.491Z
6467ec2f374fe5728d4216e0
/avatars/4d14f64572c9c0707edb54993e331a49.svg
Yiting Lu
user
luyiting
67e6092a40fb111ac9342c40
Jiakang Yuan
admin_assigned
2025-03-28T08:50:23.000Z
64a3d1ddb3239f3e3892b24b
/avatars/7ce585f5fc1d077fb1d70cc18c4da2c1.svg
Jiakang Yuan
user
JiakangYuan
67e6092a40fb111ac9342c41
Xinyue Li
claimed_verified
2025-03-28T08:37:20.809Z
66aba287b0f0b7411f511a47
/avatars/1450f182c38e80066ae5ea5df4fa218f.svg
Xinyue Li
user
Xxxy13
67e6092a40fb111ac9342c42
Dongyang Liu
admin_assigned
2025-03-28T08:50:37.419Z
646f1bef075e11ca78da3bb7
https://cdn-avatars.huggingface.co/v1/production/uploads/646f1bef075e11ca78da3bb7/gNS-ikyZXYeMrf4a7HTQE.jpeg
Dongyang Liu (Chris Liu)
user
Cxxs
67e6092a40fb111ac9342c43
Xiangyang Zhu
67e6092a40fb111ac9342c44
Manyuan Zhang
67e6092a40fb111ac9342c45
Will Beddow
admin_assigned
2025-03-28T08:51:18.703Z
629aed605ab4232a3fe266f7
/avatars/53e1f4a9fc2bad17b05a80b14118442e.svg
Will Beddow
user
willbeddow
67e6092a40fb111ac9342c46
Erwann Millon
admin_assigned
2025-03-28T08:51:24.290Z
62a2712903bf94c3ac3ae004
/avatars/2f11b73ecd7a4cb561b42c676b70b7f8.svg
Erwann Millon
user
erwann
67e6092a40fb111ac9342c47
Victor Perez
67e6092a40fb111ac9342c48
Wenhai Wang
admin_assigned
2025-03-28T08:51:42.390Z
64d1c560c0c627dfa71bdbe0
/avatars/f42794fe25bffcd870a1bcee69b95298.svg
wenhai.wang
user
wangwhcore
67e6092a40fb111ac9342c49
Conghui He
admin_assigned
2025-03-28T08:51:48.715Z
63f9fca8d4349b157a109eec
/avatars/fa1f2ae7972d7cde99dab178136ccbb0.svg
Conghui He
user
conghui
67e6092a40fb111ac9342c4a
Bo Zhang
claimed_verified
2025-03-31T08:14:48.701Z
643dfd235aafbdca3a5792c0
/avatars/ce8553cf5936012c692e08054ee27937.svg
Bo Zhang
user
BoZhang
67e6092a40fb111ac9342c4b
Xiaohong Liu
67e6092a40fb111ac9342c4c
Hongsheng Li
admin_assigned
2025-03-28T08:52:25.823Z
65c04e9c27a5fdca81abcbd9
/avatars/12a155683c824fa23da4a9e2bed4f64e.svg
Hongsheng LI
user
hsli-cuhk
67e6092a40fb111ac9342c4d
Yu Qiao
67e6092a40fb111ac9342c4e
Chang Xu
admin_assigned
2025-03-28T08:52:36.782Z
6391fa34e110d51320389b06
https://cdn-avatars.huggingface.co/v1/production/uploads/1670510944260-noauth.jpeg
chang xu
user
changxu-2022
67e6092a40fb111ac9342c4f
Peng Gao
Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
We introduce Lumina-Image 2.0, an advanced text-to-image generation framework
that achieves significant progress compared to previous work, Lumina-Next.
Lumina-Image 2.0 is built upon two key principles: (1) Unification - it adopts
a unified architecture (Unified Next-DiT) that treats text and image tokens as
a joint sequence, enabling natural cross-modal interactions and allowing
seamless task expansion. Besides, since high-quality captioners can provide
semantically well-aligned text-image training pairs, we introduce a unified
captioning system, Unified Captioner (UniCap), specifically designed for T2I
generation tasks. UniCap excels at generating comprehensive and accurate
captions, accelerating convergence and enhancing prompt adherence. (2)
Efficiency - to improve the efficiency of our proposed model, we develop
multi-stage progressive training strategies and introduce inference
acceleration techniques without compromising image quality. Extensive
evaluations on academic benchmarks and public text-to-image arenas show that
Lumina-Image 2.0 delivers strong performances even with only 2.6B parameters,
highlighting its scalability and design efficiency. We have released our
training details, code, and models at
https://github.com/Alpha-VLLM/Lumina-Image-2.0.
Lumina-Image 2.0, a text-to-image framework, achieves significant advancements through a unified architecture and efficient training, enabling high-quality image generation with reduced parameters.
Unified Next-DiT text-to-image cross-modal interactions Unified Captioner (UniCap) multi-stage progressive training inference acceleration