slz1/wxy_var

VAR: a new visual generation method elevates GPT-style models beyond diffusion🚀 & Scaling laws observed📈

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&nbsp; &nbsp; &nbsp; 

</div> <p align="center" style="font-size: larger;"> <a href="https://arxiv.org/abs/2404.02905">Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction</a> </p>

<div> <p align="center" style="font-size: larger;"> <strong>NeurIPS 2024 Best Paper</strong> </p> </div>

<p align="center"> <img src="https://github.com/FoundationVision/VAR/assets/39692511/9850df90-20b1-4f29-8592-e3526d16d755" width=95%> <p>

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News

• —2024-12: 🏆 VAR received NeurIPS 2024 Best Paper Award.
• —2024-12: 🔥 We Release our Text-to-Image research based on VAR, please check Infinity.
• —2024-09: VAR is accepted as NeurIPS 2024 Oral Presentation.
• —2024-04: Visual AutoRegressive modeling is released.

🕹️ Try and Play with VAR!

~~We provide a demo website for you to play with VAR models and generate images interactively. Enjoy the fun of visual autoregressive modeling!~~

We provide a demo website for you to play with VAR Text-to-Image and generate images interactively. Enjoy the fun of visual autoregressive modeling!

We also provide demo_sample.ipynb for you to see more technical details about VAR.

[//]: # (<p align="center">) [//]: # (<img src="https://user-images.githubusercontent.com/39692511/226376648-3f28a1a6-275d-4f88-8f3e-cd1219882488.png" width=50%) [//]: # (<p>)

What's New?

🔥 Introducing VAR: a new paradigm in autoregressive visual generation✨:

Visual Autoregressive Modeling (VAR) redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction".

<p align="center"> <img src="https://github.com/FoundationVision/VAR/assets/39692511/3e12655c-37dc-4528-b923-ec6c4cfef178" width=93%> <p>

🔥 For the first time, GPT-style autoregressive models surpass diffusion models🚀:

<p align="center"> <img src="https://github.com/FoundationVision/VAR/assets/39692511/cc30b043-fa4e-4d01-a9b1-e50650d5675d" width=55%> <p>

🔥 Discovering power-law Scaling Laws in VAR transformers📈:

<p align="center"> <img src="https://github.com/FoundationVision/VAR/assets/39692511/c35fb56e-896e-4e4b-9fb9-7a1c38513804" width=85%> <p> <p align="center"> <img src="https://github.com/FoundationVision/VAR/assets/39692511/91d7b92c-8fc3-44d9-8fb4-73d6cdb8ec1e" width=85%> <p>

🔥 Zero-shot generalizability🛠️:

<p align="center"> <img src="https://github.com/FoundationVision/VAR/assets/39692511/a54a4e52-6793-4130-bae2-9e459a08e96a" width=70%> <p>

For a deep dive into our analyses, discussions, and evaluations, check out our paper.

VAR zoo

We provide VAR models for you to play with, which are on <a href='https://huggingface.co/FoundationVision/var'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-FoundationVision/var-yellow'></a> or can be downloaded from the following links:

You can load these models to generate images via the codes in demo_sample.ipynb. Note: you need to download vae_ch160v4096z32.pth first.

Installation

1. 1.Install torch>=2.0.0.
2. 2.Install other pip packages via pip3 install -r requirements.txt.
3. 3.Prepare the ImageNet dataset <details> <summary> assume the ImageNet is in /path/to/imagenet. It should be like this:</summary>

    /path/to/imagenet/:
        train/:
            n01440764: 
                many_images.JPEG ...
            n01443537:
                many_images.JPEG ...
        val/:
            n01440764:
                ILSVRC2012_val_00000293.JPEG ...
            n01443537:
                ILSVRC2012_val_00000236.JPEG ...

NOTE: The arg `--data_path=/path/to/imagenet` should be passed to the training script. </details>

1. 1.(Optional) install and compile flash-attn and xformers for faster attention computation. Our code will automatically use them if installed. See models/basic_var.py#L15-L30.

Training Scripts

To train VAR-{d16, d20, d24, d30, d36-s} on ImageNet 256x256 or 512x512, you can run the following command:

# d16, 256x256
torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \
  --depth=16 --bs=768 --ep=200 --fp16=1 --alng=1e-3 --wpe=0.1
# d20, 256x256
torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \
  --depth=20 --bs=768 --ep=250 --fp16=1 --alng=1e-3 --wpe=0.1
# d24, 256x256
torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \
  --depth=24 --bs=768 --ep=350 --tblr=8e-5 --fp16=1 --alng=1e-4 --wpe=0.01
# d30, 256x256
torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \
  --depth=30 --bs=1024 --ep=350 --tblr=8e-5 --fp16=1 --alng=1e-5 --wpe=0.01 --twde=0.08
# d36-s, 512x512 (-s means saln=1, shared AdaLN)
torchrun --nproc_per_node=8 --nnodes=... --node_rank=... --master_addr=... --master_port=... train.py \
  --depth=36 --saln=1 --pn=512 --bs=768 --ep=350 --tblr=8e-5 --fp16=1 --alng=5e-6 --wpe=0.01 --twde=0.08

A folder named local_output will be created to save the checkpoints and logs. You can monitor the training process by checking the logs in local_output/log.txt and local_output/stdout.txt, or using tensorboard --logdir=local_output/.

If your experiment is interrupted, just rerun the command, and the training will automatically resume from the last checkpoint in local_output/ckpt*.pth (see utils/misc.py#L344-L357).

Sampling & Zero-shot Inference

For FID evaluation, use var.autoregressive_infer_cfg(..., cfg=1.5, top_p=0.96, top_k=900, more_smooth=False) to sample 50,000 images (50 per class) and save them as PNG (not JPEG) files in a folder. Pack them into a .npz file via create_npz_from_sample_folder(sample_folder) in utils/misc.py#L344. Then use the OpenAI's FID evaluation toolkit and reference ground truth npz file of 256x256 or 512x512 to evaluate FID, IS, precision, and recall.

Note a relatively small cfg=1.5 is used for trade-off between image quality and diversity. You can adjust it to cfg=5.0, or sample with autoregressive_infer_cfg(..., more_smooth=True) for better visual quality. We'll provide the sampling script later.

Third-party Usage and Research

*In this pargraph, we cross link third-party repositories or research which use VAR and report results. You can let us know by raising an issue*

(Note please report accuracy numbers and provide trained models in your new repository to facilitate others to get sense of correctness and model behavior)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If our work assists your research, feel free to give us a star ⭐ or cite us using:

@Article{VAR,
      title={Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction}, 
      author={Keyu Tian and Yi Jiang and Zehuan Yuan and Bingyue Peng and Liwei Wang},
      year={2024},
      eprint={2404.02905},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{Infinity,
    title={Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis}, 
    author={Jian Han and Jinlai Liu and Yi Jiang and Bin Yan and Yuqi Zhang and Zehuan Yuan and Bingyue Peng and Xiaobing Liu},
    year={2024},
    eprint={2412.04431},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2412.04431}, 
}