DC-Gen delivers high-quality, high-resolution visual generation across text-to-image, text-to-video, image-to-video, and image editing tasks, achieving up to 53.8× speedup over pre-trained diffusion models.
- (🔥 New) [2026/6/18] DC-Gen is accepted by ECCV 2026! 🎉
- (🔥 New) [2025/9/30] We released the DC-Gen technical report on arXiv.
- (🔥 New) [2025/9/30] We released DC-AE-Lite.
- [2025/6] DC-AE 1.5 is accepted by ICCV 2025!
DC-Gen is a new acceleration framework for diffusion models. DC-Gen works with any pre-trained diffusion model, boosting efficiency by transferring it into a deeply compressed latent space with lightweight post-training. For example, applying DC-Gen to FLUX.1-Krea takes just 31 H100 GPU days. The resulting DC-Gen-FLUX delivers the same quality as the base model while achieving dramatic gains—53.8× faster inference on H100 at 4K resolution. DC-Gen has been validated across text-to-image, text-to-video, image-to-video, and image editing tasks.
Modern visual diffusion models are recognized for their superior realism and text-rendering capabilities but suffer from low throughput. DC-Gen models successfully preserve these qualities while delivering a significant speedup over corresponding base models.
- Comparison to Previous Models on 1024×1024 Resolution.
- Comparison to pre-trained Wan2.1 on 720×1280 Resolution, 81 frames.
Text-to-Video
| Wan2.1-T2V-14B (27.52 mins/video) |
DC-Gen-Wan2.1-T2V-14B (3.58 mins/video) |
Wan2.1-T2V-14B (27.52 mins/video) |
DC-Gen-Wan2.1-T2V-14B (3.58 mins/video) |
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Image-to-Video
| Wan2.1-I2V-14B (27.88 mins/video) |
DC-Gen-Wan2.1-I2V-14B (3.67 mins/video) |
Wan2.1-I2V-14B (27.88 mins/video) |
DC-Gen-Wan2.1-I2V-14B (3.67 mins/video) |
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- Comparison to pre-trained Qwen-Image-Edit on 1K image editing tasks.
- The relative speedup of DC-Gen is more significant at higher resolutions, achieving up to 53.8× acceleration on DC-Gen-FLUX.
- FLUX and Z-Image do not support native 4K image generation due to prohibitive training and inference costs. DC-Gen-FLUX and DC-Gen-Z-Image address this limitation by reducing token redundancy with DC-AE-f64c128.
Previously, changing the autoencoder required retraining diffusion models from scratch, which was highly inefficient. DC-Gen introduces Embedding Alignment to transfer the base model's knowledge to the new latent space. After this alignment, the model can perform visual generation with correct semantics in the new latent space without fine-tuning the diffusion model's weights. With only a few steps of end-to-end fine-tuning, the DC-Gen models can already achieve visual generation quality comparable to pre-trained models.
Following embedding alignment, we can fully recover the quality through LoRA fine-tuning.
conda create -n dc_ai python=3.10
conda activate dc_ai
pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128 # please install torch according to your cuda version
# for arm architecture, use
# pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu129
pip install -U -r requirements.txt
pip install flash-attn --no-build-isolation
# if you failed to import flash_attn, please uninstall and build from source
# pip uninstall flash_attn
# pip install ninja
# git clone git@github.com:Dao-AILab/flash-attention.git
# cd flash-attention
# python setup.py install
pre-commit installApart from the Online Demo we provided, you can also run DC-Gen locally with a simple command-line interface. This codebase supports text-to-image (1K and 4K), text-to-video, image-to-video, and instruction-based image editing. See applications/dc_gen/inference_demo/README.md for details.
We provide step-by-step guides for applying DC-Gen to FLUX, Qwen-Image-Edit, Wan2.1, and Z-Image, covering data preparation, embedding alignment, and LoRA fine-tuning. See projects/DC-Gen for details.
- [ICLR 2025] DC-AE 1.0: Getting Started, Website
- [ICCV 2025] DC-AE 1.5: Getting Started, Website, AE Demo, T2I Demo
- DC-AE-Lite: Getting Started
@article{he2025dc,
title={DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space},
author={He, Wenkun and Gu, Yuchao and Chen, Junyu and Zou, Dongyun and Lin, Yujun and Zhang, Zhekai and Xi, Haocheng and Li, Muyang and Zhu, Ligeng and Yu, Jincheng and others},
journal={arXiv preprint arXiv:2509.25180},
year={2025}
}
@article{chen2024deep,
title={Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models},
author={Chen, Junyu and Cai, Han and Chen, Junsong and Xie, Enze and Yang, Shang and Tang, Haotian and Li, Muyang and Lu, Yao and Han, Song},
journal={arXiv preprint arXiv:2410.10733},
year={2024}
}
@article{chen2025dc,
title={DC-AE 1.5: Accelerating Diffusion Model Convergence with Structured Latent Space},
author={Chen, Junyu and Zou, Dongyun and He, Wenkun and Chen, Junsong and Xie, Enze and Han, Song and Cai, Han},
journal={arXiv preprint arXiv:2508.00413},
year={2025}
}
@misc{zou2025dcaelite,
title = {DC-AE-Lite},
author = {Zou, Dongyun and Chen, Junyu and He, Wenkun and Chen, Junsong and Xie, Enze and Han, Song and Cai, Han},
url = {https://github.com/dc-ai-projects/DC-Gen/blob/main/projects/DC-AE-Lite.md},
month = Sep,
year = {2025}
}