DDT-XL-22en6de-R256

DDT: Decoupled Diffusion Transformer

<div style="text-align: center;"> <a href="https://arxiv.org/abs/2504.05741"><img src="https://img.shields.io/badge/arXiv-2504.05741-b31b1b.svg" alt="arXiv"></a> <a href="https://huggingface.co/papers/2504.05741"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/paper-page-sm.svg" alt="Paper page"></a> </div> <div style="text-align: center;"> <a href="https://paperswithcode.com/sota/image-generation-on-imagenet-256x256?p=ddt-decoupled-diffusion-transformer"><img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ddt-decoupled-diffusion-transformer/image-generation-on-imagenet-256x256" alt="PWC"></a>

<a href="https://paperswithcode.com/sota/image-generation-on-imagenet-512x512?p=ddt-decoupled-diffusion-transformer"><img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ddt-decoupled-diffusion-transformer/image-generation-on-imagenet-512x512" alt="PWC"></a>

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Introduction

We decouple diffusion transformer into encoder-decoder design, and surpresingly that a more substantial encoder yields performance improvements as model size increases.

• We achieves 1.26 FID on ImageNet256x256 Benchmark with DDT-XL/2(22en6de).
• We achieves 1.28 FID on ImageNet512x512 Benchmark with DDT-XL/2(22en6de).
• As a byproduct, our DDT can reuse encoder among adjacent steps to accelerate inference.

Visualizations

Checkpoints

We take the off-shelf VAE to encode image into latent space, and train the decoder with DDT.

Online Demos

Coming soon.

Usages

We use ADM evaluation suite to report FID.

# for installation
pip install -r requirements.txt

# for inference
python main.py predict -c configs/repa_improved_ddt_xlen22de6_256.yaml --ckpt_path=XXX.ckpt

# for training
# extract image latent (optional)
python3 tools/cache_imlatent4.py
# train
python main.py fit -c configs/repa_improved_ddt_xlen22de6_256.yaml

Reference

@ARTICLE{ddt,
  title         = "DDT: Decoupled Diffusion Transformer",
  author        = "Wang, Shuai and Tian, Zhi and Huang, Weilin and Wang, Limin",
  month         =  apr,
  year          =  2025,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CV",
  eprint        = "2504.05741"
}