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OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation

Official pytorch implementation of the following paper:

We introduce OmniTokenizer, a joint image-video tokenizer which features the following properties:

• 🚀 One model and one weight for joint image and video tokenization;
• 🥇 State-of-the-art reconstruction performance on both image and video datasets;
• ⚡ High adaptability to high resolution and long video inputs;
• 🔥 Equipped with it, both language model and diffusion model could achieve competitive visual generation results.

Please refer to our project page for the reconstruction and generation results by OmniTokenizer.

Setup

Please setup the environment using the following commands:

pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu118
pip3 install -r requirements.txt

Then download the datasets from the official websites. You can download the annotation.zip processed by us and put them under ./annotations.

Model Zoo for VQVAE and VAE

We release both VQVAE and VAE version of OmniTokenizer, that are pretrained on a wide range of image and video datasets:

[^1] We train this model w/o scaled_dot_product_attention, please comment line 446-460 in OmniTokenizer/modules/attention.py to reproduce this result.

We recommand you to try imagenet_k600.ckpt as it is trained on large-scale image and video data.

You can easily incorporate OmniTokenizer into your language model or diffusion model with:

from OmniTokenizer import OmniTokenizer_VQGAN
vqgan = OmniTokenizer_VQGAN.load_from_checkpoint(vqgan_ckpt, strict=False)

# tokens = vqgan.encode(img)
# recons = vqgan.decode(tokens)

Tokenizer (VQVAE and VAE)

The training of VQVAE includes two stages: image-only training on a fixed resolution, and image-video joint training on multiple resolutions. After this, finetune the VQVAE model w/ KL loss to obtain a VAE model.

Please refer to scripts/recons/train.sh for the training of omnitokenizer. Explanation of the flags that are opted to change according to different settings:

• patch_size & temporal_patch_size: shape of the patches in patch embedding layer, also determine the downsample ratio
• enc_block: type of encoder blocks, 't' indices plain attention and 'w' indicates window attention
• n_codes: codebook size
• spatial_pos: type of spatial positional encoding
• use_vae: train in VAE mode or VQVAE mode
• resolution & sequence_length: spatial and temporal resolution for training
• resolution_scale: for multiple resolution training, proportion of the specificed resolution

For the evaluation of omnitokenizer, please refer to scripts/recons/eval_image_inet.sh, scripts/recons/eval_image_face.sh, scripts/recons/eval_video.sh.

LM-based Visual Synthesis

Please refer to scripts/lm_train and scripts/lm_gen for the training and evaluation of language model. We provide the checkpoints for ImageNet[imagenet_class_lm.ckpt], UCF [ucf_class_lm.ckpt], and Kinetics-600 [k600_fp_lm.ckpt].

Diffusion-based Visual Synthesis

We adopt DiT and Latte for diffusion-based visual generation. Please refer to diffusion.md for the training and evaluation instructions.

Evaluation

Please refer to evaluation.md for how to evaluate the reconstruction or generation results.

Acknowledgments

Our code is partially built upon VQGAN and TATS. We also appreciate the wonderful tools provided by pytorch-fid and common_metrics_on_video_quality.

License

This project is licensed under the MIT license, as found in the LICENSE file.