OPEN-MAGVIT2: An Open-source Project Toward Democratizing Auto-Regressive Visual Generation

 

OPEN-MAGVIT2: An Open-source Project Toward Democratizing Auto-Regressive Visual Generation
Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, Ying Shan
ARC Lab Tencent PCG, Tsinghua University, Nanjing University

We present Open-MAGVIT2, a family of auto-regressive image generation models ranging from 300M to 1.5B. The Open-MAGVIT2 project produces an open-source replication of Google's MAGVIT-v2 tokenizer, a tokenizer with a super-large codebook (i.e., $2^{18}$ codes), and achieves the state-of-the-art reconstruction performance (1.17 rFID) on ImageNet $256 \times 256$. Furthermore, we explore its application in plain auto-regressive models and validate scalability properties. To assist auto-regressive models in predicting with a super-large vocabulary, we factorize it into two sub-vocabulary of different sizes by asymmetric token factorization, and further introduce ''next sub-token prediction'' to enhance sub-token interaction for better generation quality. We release all models and codes to foster innovation and creativity in the field of auto-regressive visual generation. 💖

📰 News

• We thank Marina Vinyes for making a package. Enjoy it for more convenience.
• [2024.09.09] 🔥🔥🔥 We release a better image tokenizer and a family of auto-regressive models ranging from 300M to 1.5B.
• [2024.06.17] 🔥🔥🔥 We release the training code of the image tokenizer and checkpoints for different resolutions, achieving state-of-the-art performance (0.39 rFID for 8x downsampling) compared to VQGAN, MaskGIT, and recent TiTok, LlamaGen, and OmniTokenizer.

🎤 TODOs

• [ ✔ ] Better image tokenizer with scale-up training.
• [ ✔ ] Finalize the training of the autoregressive model.
• Video tokenizer and the corresponding autoregressive model.

🤗 Open-MAGVIT2 is still at an early stage and under active development. Stay tuned for the update!

📖 Implementations

Note that our experments are all using Ascned 910B for training. But we have tested our models on V100. The performance gap is narrow.

Figure 1. The framework of the Open-MAGVIT2.

🛠️ Installation

GPU

• Env: We have tested on Python 3.8.8 and CUDA 11.8 (other versions may also be fine).
• Dependencies: pip install -r requirements.txt

NPU

• Env: Python 3.9.16 and CANN 8.0.T13
• Main Dependencies: torch=2.1.0+cpu + torch-npu=2.1.0.post3-20240523 + Lightning
• Other Dependencies: see in requirements.txt

Datasets

We use Imagenet2012 as our dataset.

imagenet
└── train/
    ├── n01440764
        ├── n01440764_10026.JPEG
        ├── n01440764_10027.JPEG
        ├── ...
    ├── n01443537
    ├── ...
└── val/
    ├── ...

Stage I: Training of Visual Tokenizer

🚀 Training Scripts

• $128\times 128$ Tokenizer Training

bash scripts/train_tokenizer/run_128_L.sh MASTER_ADDR MASTER_PORT NODE_RANK

• $256\times 256$ Tokenizer Training

bash scripts/train_tokenizer/run_256_L.sh MASTER_ADDR MASTER_PORT NODE_RANK

🚀 Evaluation Scripts

• $128\times 128$ Tokenizer Evaluation

bash scripts/evaluation/evaluation_128.sh

• $256\times 256$ Tokenizer Evaluation

bash scripts/evaluation/evaluation_256.sh

🍺 Performance and Models

Tokenizer

Method	Token Type	#Tokens	Train Data	Codebook Size	rFID	PSNR	Codebook Utilization	Checkpoint
Open-MAGVIT2-20240617	2D	16 $\times$ 16	256 $\times$ 256 ImageNet	262144	1.53	21.53	100%	-
Open-MAGVIT2-20240617	2D	16 $\times$ 16	128 $\times$ 128 ImageNet	262144	1.56	24.45	100%	-
Open-MAGVIT2	2D	16 $\times$ 16	256 $\times$ 256 ImageNet	262144	1.17	21.90	100%	IN256_Large
Open-MAGVIT2	2D	16 $\times$ 16	128 $\times$ 128 ImageNet	262144	1.18	25.08	100%	IN128_Large
Open-MAGVIT2*	2D	32 $\times$ 32	128 $\times$ 128 ImageNet	262144	0.34	26.19	100%	above

(*) denotes that the results are from the direct inference using the model trained with $128 \times 128$ resolution without fine-tuning.

Stage II: Training of Auto-Regressive Models

🚀 Training Scripts

Please see in scripts/train_autogressive/run.sh for different model configurations.

bash scripts/train_autogressive/run.sh MASTER_ADDR MASTER_PORT NODE_RANK

🚀 Sample Scripts

Please see in scripts/train_autogressive/run.sh for different sampling hyper-parameters for different scale of models.

bash scripts/evaluation/sample_npu.sh or scripts/evaluation/sample_gpu.sh Your_Total_Rank

🍺 Performance and Models

Method	Params	#Tokens	FID	IS	Checkpoint
Open-MAGVIT2	343M	16 $\times$ 16	3.08	258.26	AR_256_B
Open-MAGVIT2	804M	16 $\times$ 16	2.51	271.70	AR_256_L
Open-MAGVIT2	1.5B	16 $\times$ 16	2.33	271.77	AR_256_XL

❤️ Acknowledgement

We thank Lijun Yu for his encouraging discussions. We refer a lot from VQGAN and MAGVIT. We also refer to LlamaGen, VAR and RQVAE. Thanks for their wonderful work.

✏️ Citation

If you found the codebase and our work helpful, please cite it and give us a star ⭐.

@article{luo2024open,
  title={Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation},
  author={Luo, Zhuoyan and Shi, Fengyuan and Ge, Yixiao and Yang, Yujiu and Wang, Limin and Shan, Ying},
  journal={arXiv preprint arXiv:2409.04410},
  year={2024}
}

@inproceedings{yu2024language,
  title={Language Model Beats Diffusion - Tokenizer is key to visual generation},
  author={Lijun Yu and Jose Lezama and Nitesh Bharadwaj Gundavarapu and Luca Versari and Kihyuk Sohn and David Minnen and Yong Cheng and Agrim Gupta and Xiuye Gu and Alexander G Hauptmann and Boqing Gong and Ming-Hsuan Yang and Irfan Essa and David A Ross and Lu Jiang},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://openreview.net/forum?id=gzqrANCF4g}
}