Token Merging: Your ViT But Faster
(2022)cite arxiv:2210.09461Comment: Accepted ICLR 2023 Oral (top 5%) final v2. This version includes stable diffusion experiments. See code at https://github.com/facebookresearch/ToMe.Abstract
We introduce Token Merging (ToMe), a simple method to increase the throughput
of existing ViT models without needing to train. ToMe gradually combines
similar tokens in a transformer using a general and light-weight matching
algorithm that is as fast as pruning while being more accurate. Off-the-shelf,
ToMe can 2x the throughput of state-of-the-art ViT-L @ 512 and ViT-H @ 518
models on images and 2.2x the throughput of ViT-L on video with only a 0.2-0.3%
accuracy drop in each case. ToMe can also easily be applied during training,
improving in practice training speed up to 2x for MAE fine-tuning on video.
Training with ToMe further minimizes accuracy drop, leading to 2x the
throughput of ViT-B on audio for only a 0.4% mAP drop. Qualitatively, we find
that ToMe merges object parts into one token, even over multiple frames of
video. Overall, ToMe's accuracy and speed are competitive with state-of-the-art
on images, video, and audio.
Description
Token Merging: Your ViT But Faster