Awesome

DnD-Transformer: ✨ A Spark of Vision-Language Intelligence

🤗 <a href="https://huggingface.co/leonardPKU/DnD-Transformer">Model</a>&nbsp&nbsp | 🤗 <a href=""> Dataset (Coming Soon)</a>&nbsp&nbsp| &nbsp&nbsp📑 <a href="https://arxiv.org/abs/2410.01912">Paper</a> | &nbsp&nbsp💻<a href="https://github.com/chenllliang/DnD-Transformer"> Github</a> <img src="./llama-dnd.png" width=40%> </div>

Updates 🎈

2024-10-11: Release training code.
2024-10-8: Release models and inference code.
2024-10-4: Release paper.

What's New?

A better AR image genenation paradigm and transformer model structure based on 2D autoregression. It generates images of higher quality without increasing computation budget.
A spark of vision-language intelligence for the first time, enabling unconditional rich-text image generation, outperforming diffusion models like DDPM and Stable Diffusion on dedicated rich-text image datasets, highlighting the distinct advantage of autoregressive models for multimodal modeling.

Models

DnD-Tokenizers (VQVAE-Checkpoints)

ImageNet

Code Size	Codebook Size	Link	rFID
16x16x2	16384	🤗	0.92 (256x256 f16 SoTA!)
32x32x2	16384	🤗	0.42 (512x512 f16 SoTA!)

Text-Image

Code Size	Codebook Size	Link
24x24x1	4096	🤗

arXiv-Image

Code Size	Codebook Size	Link
16x16x8	16384	🤗

DnD-Transformers (GPT-Checkpoints)

ImageNet

Code Shape	Image Size	Model Size	Link	gFID
16x16x2	256x256	XXL	🤗	2.58 (cfg=2)
16x16x2	256x256	XXXL	🤗	2.21 (cfg=1.7)

Text-Image

Code Shape	Image Size	Model Size	Link
24x24x1	384x384	XXL	🤗

arXiv-Image

Code Shape	Image Size	Model Size	Link
16x16x8	256x256	XXL	🤗

Setup

conda create -n DnD python=3.10
conda activate DnD
pip install -r requirements.txt

Inference

First download all vq-tokenizers and model checkpoints from <a href="https://huggingface.co/leonardPKU/DnD-Transformer">🤗</a>

Sampling ImageNet Examples and Compute FID

cd ./src
bash ./scripts/sampling_dnd_transformer_imagenet.sh # edit the address for vq model checkpoint and dnd-transformer checkpoint

# An npz would be saved after genearting 50k images, you can follow https://github.com/openai/guided-diffusion/tree/main/evaluations to compute the generated FID.

Sampling Text-Image Examples

cd ./src
bash ./scripts/sampling_dnd_transformer_text_image.sh # edit the address for vq model checkpoint and dnd-transformer checkpoint

Sampling arXiv-Image Examples

cd ./src
bash ./scripts/sampling_dnd_transformer_arxiv_image.sh # edit the address for vq model checkpoint and dnd-transformer checkpoint

Training

Training VQVAEs

We refer to RQVAE for training the multi-depth VQVAEs.

Extract Codes for Training

cd ./src
bash ./scripts/extract_codes_tencrop_c2i.sh

Training DnD-Transformers

cd ./src
bash ./scripts/train_dnd_transformer_imagenet.sh

Evaluation

ReconstructionOCR (rOCR)

sh ./scripts/rOCR.sh

python infer_gt_ModelParallel_qwen.py \
--batch_size 16 \
--gt_dir <gt_dir> \
--images_dir <images_dir> \
--model_dir <model_dir> \
--output_dir <output_dir>

python metrics.py \
--predict_dir <predict_dir> \
--gt_dir <gt_dir> \
--output_dir <output_dir>

PPL-OCR

coming soon~

Acknowledgements

Thanks to RQVAE and LlamaGen for providing the open source codebase.

Reference

@misc{chen2024sparkvisionlanguageintelligence2dimensional,
      title={A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation}, 
      author={Liang Chen and Sinan Tan and Zefan Cai and Weichu Xie and Haozhe Zhao and Yichi Zhang and Junyang Lin and Jinze Bai and Tianyu Liu and Baobao Chang},
      year={2024},
      eprint={2410.01912},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.01912}, 
}