Hyper-Bagel: A Unified Acceleration Framework for Multimodal
  Understanding and Generation
Unified multimodal models have recently attracted considerable attention for
their remarkable abilities in jointly understanding and generating diverse
content. However, as contexts integrate increasingly numerous interleaved
multimodal tokens, the iterative processes of diffusion denoising and
autoregressive decoding impose significant computational overhead. To address
this, we propose Hyper-Bagel, a unified acceleration framework designed to
simultaneously speed up both multimodal understanding and generation tasks. Our
approach uses a divide-and-conquer strategy, employing speculative decoding for
next-token prediction and a multi-stage distillation process for diffusion
denoising. The framework delivers substantial performance gains, achieving over
a 2x speedup in multimodal understanding. For generative tasks, our resulting
lossless 6-NFE model yields a 16.67x speedup in text-to-image generation and a
22x speedup in image editing, all while preserving the high-quality output of
the original model. We further develop a highly efficient 1-NFE model that
enables near real-time interactive editing and generation. By combining
advanced adversarial distillation with human feedback learning, this model
achieves ultimate cost-effectiveness and responsiveness, making complex
multimodal interactions seamless and instantaneous.
68d350cf0e215259d193b1c4
Yanzuo Lu
claimed_verified
2025-09-24T13:53:29.233Z
6614cbd40bbea65e71db4e1f
/avatars/ca8ff74887bbf8eb3f5b04ae9bb6d05b.svg
Yanzuo Lu
user
oliveryanzuolu
68d350cf0e215259d193b1c5
Xin Xia
claimed_verified
2025-09-24T13:53:32.477Z
63089a78ff78e2aead8d10e7
/avatars/f326fd08abee7e31599a78923be30003.svg
XinXia
user
XiaXin-Aloys
68d350cf0e215259d193b1c6
Manlin Zhang
admin_assigned
2025-09-24T15:26:55.019Z
670dd3b3488201d7944325b9
/avatars/25f2db41615f3ed87907a8735acc1249.svg
Manlin Zhang
user
cclim
68d350cf0e215259d193b1c7
Huafeng Kuang
68d350cf0e215259d193b1c8
Jianbin Zheng
admin_assigned
2025-09-24T15:27:11.042Z
641870135d6f3d15c64d074e
/avatars/35f4b33fd1d4db326e4ee4300b26db72.svg
Jianbin Zheng
user
jabir-zheng
68d350cf0e215259d193b1c9
Yuxi Ren
68d350cf0e215259d193b1ca
Xuefeng Xiao
Hyper-Bagel: A Unified Acceleration Framework for Multimodal
  Understanding and Generation
Unified multimodal models have recently attracted considerable attention for
their remarkable abilities in jointly understanding and generating diverse
content. However, as contexts integrate increasingly numerous interleaved
multimodal tokens, the iterative processes of diffusion denoising and
autoregressive decoding impose significant computational overhead. To address
this, we propose Hyper-Bagel, a unified acceleration framework designed to
simultaneously speed up both multimodal understanding and generation tasks. Our
approach uses a divide-and-conquer strategy, employing speculative decoding for
next-token prediction and a multi-stage distillation process for diffusion
denoising. The framework delivers substantial performance gains, achieving over
a 2x speedup in multimodal understanding. For generative tasks, our resulting
lossless 6-NFE model yields a 16.67x speedup in text-to-image generation and a
22x speedup in image editing, all while preserving the high-quality output of
the original model. We further develop a highly efficient 1-NFE model that
enables near real-time interactive editing and generation. By combining
advanced adversarial distillation with human feedback learning, this model
achieves ultimate cost-effectiveness and responsiveness, making complex
multimodal interactions seamless and instantaneous.
Hyper-Bagel accelerates multimodal understanding and generation tasks using speculative decoding and multi-stage distillation, achieving significant speedups while maintaining high-quality outputs.
diffusion denoising autoregressive decoding speculative decoding multi-stage distillation text-to-image generation image editing adversarial distillation human feedback learning