63 lines
2.8 KiB
Markdown
63 lines
2.8 KiB
Markdown
# DDT: Decoupled Diffusion Transformer
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<div style="text-align: center;">
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<a href="https://arxiv.org/abs/2504.05741"><img src="https://img.shields.io/badge/arXiv-2504.05741-b31b1b.svg" alt="arXiv"></a>
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<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>
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</div>
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<div style="text-align: center;">
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<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>
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<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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</div>
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## Introduction
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We decouple diffusion transformer into encoder-decoder design, and surpresingly that a **more substantial encoder yields performance improvements as model size increases**.
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* We achieves **1.26 FID** on ImageNet256x256 Benchmark with DDT-XL/2(22en6de).
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* We achieves **1.28 FID** on ImageNet512x512 Benchmark with DDT-XL/2(22en6de).
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* As a byproduct, our DDT can reuse encoder among adjacent steps to accelerate inference.
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## Visualizations
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## Checkpoints
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We take the off-shelf [VAE](https://huggingface.co/stabilityai/sd-vae-ft-ema) to encode image into latent space, and train the decoder with DDT.
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| Dataset | Model | Params | FID | HuggingFace |
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|-------------|-------------------|-----------|------|----------------------------------------------------------|
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| ImageNet256 | DDT-XL/2(22en6de) | 675M | 1.26 | [🤗](https://huggingface.co/MCG-NJU/DDT-XL-22en6de-R256) |
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| ImageNet512 | DDT-XL/2(22en6de) | 675M | 1.28 | [🤗](https://huggingface.co/MCG-NJU/DDT-XL-22en6de-R512) |
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## Online Demos
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Coming soon.
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## Usages
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We use ADM evaluation suite to report FID.
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```bash
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# for installation
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pip install -r requirements.txt
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```
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```bash
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# for inference
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python main.py predict -c configs/repa_improved_ddt_xlen22de6_256.yaml --ckpt_path=XXX.ckpt
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```
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```bash
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# extract image latent (optional)
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python3 tools/cache_imlatent4.py
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```
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```bash
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# for training
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python main.py fit -c configs/repa_improved_ddt_xlen22de6_256.yaml
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```
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## Reference
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```bibtex
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@ARTICLE{ddt,
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title = "DDT: Decoupled Diffusion Transformer",
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author = "Wang, Shuai and Tian, Zhi and Huang, Weilin and Wang, Limin",
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month = apr,
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year = 2025,
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archivePrefix = "arXiv",
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primaryClass = "cs.CV",
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eprint = "2504.05741"
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}
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``` |