MergeVQ: A Unified Framework for Visual Generation and Representation
  with Disentangled Token Merging and Quantization
Authors: name: Siyuan Li; name: Luyuan Zhang; name: Zedong Wang; name: Juanxi Tian; name: Cheng Tan; name: Zicheng Liu; name: Chang Yu; name: Qingsong Xie; name: Haonan Lu; name: Haoqian Wang; name: Zhen Lei
Published: 2025-04-01 17:39:19

Abstract: Masked Image Modeling (MIM) with Vector Quantization (VQ) has achieved great
success in both self-supervised pre-training and image generation. However,
most existing methods struggle to address the trade-off in shared latent space
for generation quality vs. representation learning and efficiency. To push the
limits of this paradigm, we propose MergeVQ, which incorporates token merging
techniques into VQ-based generative models to bridge the gap between image
generation and visual representation learning in a unified architecture. During
pre-training, MergeVQ decouples top-k semantics from latent space with the
token merge module after self-attention blocks in the encoder for subsequent
Look-up Free Quantization (LFQ) and global alignment and recovers their
fine-grained details through cross-attention in the decoder for reconstruction.
As for the second-stage generation, we introduce MergeAR, which performs KV
Cache compression for efficient raster-order prediction. Extensive experiments
on ImageNet verify that MergeVQ as an AR generative model achieves competitive
performance in both visual representation learning and image generation tasks
while maintaining favorable token efficiency and inference speed. The code and
model will be available at https://apexgen-x.github.io/MergeVQ.