README.md

December 8, 2024 · View on GitHub

ImageFolder🚀: Autoregressive Image Generation with Folded Tokens

Updates

(2024.12.02) Code released. Also try our new work XQ-GAN for more extensions of ImageFolder.
(2024.10.03) We are working on advanced training for the ImageFolder tokenizer.
(2024.10.01) Repo created. Code and checkpoints will be released soon.

Model Zoo

We provide pre-trained tokenizers for image reconstruction on ImageNet.

Training	Eval	Codebook Size	rFID ↓	Link	Resolution	Utilization
ImageNet	ImageNet	4096	0.80	Huggingface	256x256	100%
ImageNet	ImageNet	8192	0.70	Huggingface	256x256	100%
ImageNet	ImageNet	16384	0.67	Huggingface	256x256	100%

We provide a pre-trained generator for class-conditioned image generation on ImageNet 256x256 resolution.

Type	Dataset	Model Size	gFID ↓	Link	Resolution
VAR	ImageNet	362M	2.60	Huggingface	256x256

Installation

Install all packages as

conda env create -f environment.yml

Dataset

We download the ImageNet2012 from the website and collect it as

ImageNet2012
├── train
└── val

If you want to train or finetune on other datasets, collect them in the format that ImageFolder (pytorch's ImageFolder) can recognize.

Dataset
├── train
│   ├── Class1
│   │   ├── 1.png
│   │   └── 2.png
│   ├── Class2
│   │   ├── 1.png
│   │   └── 2.png
├── val

Training code for tokenizer

Please login to Wandb first using

wandb login

rFID will be automatically evaluated and reported on Wandb. The best checkpoint on the val set will be saved.

torchrun --nproc_per_node=8 tokenizer/tokenizer_image/msvq_train.py --config configs/tokenizer.yaml

Please modify the configuration file as needed for your specific dataset. We list some important ones here.

vq_ckpt: ckpt_best.pt                # resume
cloud_save_path: output/exp-xx       # output dir
data_path: ImageNet2012/train        # training set dir
val_data_path: ImageNet2012/val      # val set dir
enc_tuning_method: 'full'            # ['full', 'lora', 'frozen']
dec_tuning_method: 'full'            # ['full', 'lora', 'frozen']
codebook_embed_dim: 32               # codebook dim
codebook_size: 4096                  # codebook size
product_quant: 2                     # branch number
codebook_drop: 0.1                   # quantizer dropout rate
semantic_guide: dinov2               # ['none', 'dinov2']

Tokenizer linear probing

torchrun --nproc_per_node=8 tokenizer/tokenizer_image/linear_probing.py --config configs/tokenizer.yaml

Training code for VAR

We follow the VAR training code and our training cmd for reproducibility is

torchrun --nproc_per_node=8 train.py --bs=768 --alng=1e-4 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --vae_ckpt /path/to/ckpt.pt --sem_half True

Inference code for ImageFolder

torchrun --nproc_per_node=8 inference.py --infer_ckpt /path/to/ckpt --data_path /path/to/ImageNet --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --sem_half True --cfg 3.25 3.25 --top_k 750 --top_p 0.95

Ablation

ID	Method	Length	rFID ↓	gFID ↓	ACC ↑
🔶1	Multi-scale residual quantization (Tian et al., 2024)	680	1.92	7.52	-
🔶2	+ Quantizer dropout	680	1.71	6.03	-
🔶3	+ Smaller patch size K = 11	265	3.24	6.56	-
🔶4	+ Product quantization & Parallel decoding	265	2.06	5.96	-
🔶5	+ Semantic regularization on all branches	265	1.97	5.21	-
🔶6	+ Semantic regularization on one branch	265	1.57	3.53	40.5
🔷7	+ Stronger discriminator	265	1.04	2.94	50.2
🔷8	+ Equilibrium enhancement	265	0.80	2.60	58.0

🔶1-6 are already in the released paper, and after that 🔷7+ are advanced training settings used similar to VAR (gFID 3.30).

@misc{li2024imagefolderautoregressiveimagegeneration,
      title={ImageFolder: Autoregressive Image Generation with Folded Tokens}, 
      author={Xiang Li and Hao Chen and Kai Qiu and Jason Kuen and Jiuxiang Gu and Bhiksha Raj and Zhe Lin},
      year={2024},
      eprint={2410.01756},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.01756}, 
}