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

     

Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

NeurIPS 2024 Oral

News

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

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".

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

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

🔥 Zero-shot generalizability🛠️:

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 or can be downloaded from the following links:

model	reso.	FID	rel. cost	#params	HF weights🤗
VAR-d16	256	3.55	0.4	310M	var_d16.pth
VAR-d20	256	2.95	0.5	600M	var_d20.pth
VAR-d24	256	2.33	0.6	1.0B	var_d24.pth
VAR-d30	256	1.97	1	2.0B	var_d30.pth
VAR-d30-re	256	1.80	1	2.0B	var_d30.pth

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. Install torch>=2.0.0.

2. Install other pip packages via pip3 install -r requirements.txt.

3. Prepare the ImageNet dataset

   assume the ImageNet is in `/path/to/imagenet`. It should be like this:

   /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.

4. (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.

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}
}