Logic/DDT
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wangshuai6
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[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ddt-decoupled-diffusion-transformer-1/image-generation-on-imagenet-256x256)](https://paperswithcode.com/sota/image-generation-on-imagenet-256x256?p=ddt-decoupled-diffusion-transformer-1) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ddt-decoupled-diffusion-transformer-1/image-generation-on-imagenet-256x256)](https://paperswithcode.com/sota/image-generation-on-imagenet-256x256?p=ddt-decoupled-diffusion-transformer-1)
## Introduction ## Introduction
We decouple diffusion transformer into encoder-decoder design, and surpresingly that a **more substantial encoder yields performance improvements as model size increases**. We decouple diffusion transformer into encoder-decoder design, and surprisingly that a **more substantial encoder yields performance improvements as model size increases**.
![](./figs/main.png) ![](./figs/main.png)
* We achieves **1.26 FID** on ImageNet256x256 Benchmark with DDT-XL/2(22en6de). * 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). * We achieves **1.28 FID** on ImageNet512x512 Benchmark with DDT-XL/2(22en6de).
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``` ```
## Acknowledgement ## Acknowledgement
The code is mainly built upon [FlowDCN](https://github.com/MCG-NJU/FlowDCN), we also borrow ideas from the [REPA](https://github.com/sihyun-yu/REPA), [MAR](https://github.com/LTH14/mar) and [SiT](https://github.com/willisma/SiT). The code is mainly built upon [FlowDCN](https://github.com/MCG-NJU/FlowDCN), we also borrow ideas from the [REPA](https://github.com/sihyun-yu/REPA), [MAR](https://github.com/LTH14/mar) and [SiT](https://github.com/willisma/SiT).