DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed
  Latent Space
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.
68dc82714159d1f2418f99ec
Wenkun He
68dc82714159d1f2418f99ed
Yuchao Gu
68dc82714159d1f2418f99ee
Junyu Chen
68dc82714159d1f2418f99ef
Dongyun Zou
68dc82714159d1f2418f99f0
Yujun Lin
68dc82714159d1f2418f99f1
Zhekai Zhang
68dc82714159d1f2418f99f2
Haocheng Xi
68dc82714159d1f2418f99f3
Muyang Li
68dc82714159d1f2418f99f4
Ligeng Zhu
68dc82714159d1f2418f99f5
Jincheng Yu
68dc82714159d1f2418f99f6
Junsong Chen
claimed_verified
2025-10-01T10:22:32.337Z
645b5b09bc7518912e1f9733
/avatars/4d35f728b41f93881a9b67c337f4d1df.svg
Chen
user
Lawrence-cj
68dc82714159d1f2418f99f7
Enze Xie
68dc82714159d1f2418f99f8
Song Han
68dc82714159d1f2418f99f9
Han Cai
DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed
  Latent Space
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.
DC-Gen accelerates text-to-image diffusion models by compressing the latent space, reducing 4K image generation latency significantly while maintaining quality.
diffusion models latent space DC-Gen embedding alignment LoRA fine-tuning SANA FLUX.1-Krea NVFP4 SVDQuant