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.