SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder
Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perception, and generation. Despite this potential, training large-scale text-to-image diffusion models entirely within the VFM representation space remains largely unexplored. To bridge this gap, we scale the SVG (Self-supervised representations for Visual Generation) framework, proposing SVG-T2I to support high-quality text-to-image synthesis directly in the VFM feature domain. By leveraging a standard text-to-image diffusion pipeline, SVG-T2I achieves competitive performance, reaching 0.75 on GenEval and 85.78 on DPG-Bench. This performance validates the intrinsic representational power of VFMs for generative tasks. We fully open-source the project, including the autoencoder and generation model, together with their training, inference, evaluation pipelines, and pre-trained weights, to facilitate further research in representation-driven visual generation.
693f754ef516c693246811c3
Minglei Shi
claimed_verified
2025-12-15T08:08:57.314Z
662887715d246621f33d2ce6
/avatars/3e0b1017b1e1bf284758ce840c174290.svg
Shi Minglei
MingleiShi
user
MingleiShi
693f754ef516c693246811c4
Haolin Wang
claimed_verified
2025-12-15T08:08:54.781Z
641d373d353524fe41f1d453
/avatars/6fa9a0a4ba9818a221f835174a14be2d.svg
Haolin Wang
howlin
user
howlin
693f754ef516c693246811c5
Borui Zhang
693f754ef516c693246811c6
Wenzhao Zheng
693f754ef516c693246811c7
Bohan Zeng
claimed_verified
2025-12-17T10:09:40.522Z
6671214c92412fd4640714eb
/avatars/48fa84e7bc3bb92ad0192aa26b32de10.svg
bohan zeng
zbhpku
user
zbhpku
693f754ef516c693246811c8
Ziyang Yuan
693f754ef516c693246811c9
Xiaoshi Wu
693f754ef516c693246811ca
Yuanxing Zhang
693f754ef516c693246811cb
Huan Yang
693f754ef516c693246811cc
Xintao Wang
693f754ef516c693246811cd
Pengfei Wan
693f754ef516c693246811ce
Kun Gai
693f754ef516c693246811cf
Jie Zhou
693f754ef516c693246811d0
Jiwen Lu
SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder
Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perception, and generation. Despite this potential, training large-scale text-to-image diffusion models entirely within the VFM representation space remains largely unexplored. To bridge this gap, we scale the SVG (Self-supervised representations for Visual Generation) framework, proposing SVG-T2I to support high-quality text-to-image synthesis directly in the VFM feature domain. By leveraging a standard text-to-image diffusion pipeline, SVG-T2I achieves competitive performance, reaching 0.75 on GenEval and 85.78 on DPG-Bench. This performance validates the intrinsic representational power of VFMs for generative tasks. We fully open-source the project, including the autoencoder and generation model, together with their training, inference, evaluation pipelines, and pre-trained weights, to facilitate further research in representation-driven visual generation.
SVG-T2I framework enables high-quality text-to-image synthesis by training diffusion models within visual foundation model representation space, achieving competitive performance on benchmark datasets.
text-to-image diffusion models visual foundation models representation space SVG framework GenEval DPG-Bench autoencoder diffusion pipeline