DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed
  Latent Space
Authors: name: Wenkun He, name: Yuchao Gu, name: Junyu Chen, name: Dongyun Zou, name: Yujun Lin, name: Zhekai Zhang, name: Haocheng Xi, name: Muyang Li, name: Ligeng Zhu, name: Jincheng Yu, name: Junsong Chen, name: Enze Xie, name: Song Han, name: Han Cai
Organization: NVIDIA

Existing text-to-image diffusion models excel at generating high-quality
images, but face significant efficiency challenges when scaled to high
resolutions, like 4K image generation. While previous research accelerates
diffusion models in various aspects, it seldom handles the inherent redundancy
within the latent space. To bridge this gap, this paper introduces DC-Gen, a
general framework that accelerates text-to-image diffusion models by leveraging
a deeply compressed latent space. Rather than a costly training-from-scratch
approach, DC-Gen uses an efficient post-training pipeline to preserve the
quality of the base model. A key challenge in this paradigm is the
representation gap between the base model's latent space and a deeply
compressed latent space, which can lead to instability during direct
fine-tuning. To overcome this, DC-Gen first bridges the representation gap with
a lightweight embedding alignment training. Once the latent embeddings are
aligned, only a small amount of LoRA fine-tuning is needed to unlock the base
model's inherent generation quality. We verify DC-Gen's effectiveness on SANA
and FLUX.1-Krea. The resulting DC-Gen-SANA and DC-Gen-FLUX models achieve
quality comparable to their base models but with a significant speedup.
Specifically, DC-Gen-FLUX reduces the latency of 4K image generation by 53x on
the NVIDIA H100 GPU. When combined with NVFP4 SVDQuant, DC-Gen-FLUX generates a
4K image in just 3.5 seconds on a single NVIDIA 5090 GPU, achieving a total
latency reduction of 138x compared to the base FLUX.1-Krea model. Code:
https://github.com/dc-ai-projects/DC-Gen.