MergeVQ: A Unified Framework for Visual Generation and Representation
  with Disentangled Token Merging and Quantization
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.
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Siyuan Li
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Luyuan Zhang
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Luyuan Zhang
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Zedong Wang
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Zedong Wang
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JackyWangAI
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Juanxi Tian
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Cheng Tan
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Cheng Tan
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Zicheng Liu
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Chang Yu
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Y
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Qingsong Xie
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2025-04-03T13:34:47.666Z
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qingsong xie
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kongming11
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Haonan Lu
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2025-04-03T13:34:57.853Z
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Haonan Lu
user
luhaonan20
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Haoqian Wang
67ecc3973d267d266649e07f
Zhen Lei
MergeVQ: A Unified Framework for Visual Generation and Representation
  with Disentangled Token Merging and Quantization
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.
MergeVQ and MergeAR enhance MIM with VQ by integrating token merging and KV cache compression, improving both image generation and visual representation learning.
MIM Vector Quantization MergeVQ token merging Look-up Free Quantization self-attention cross-attention decoder MergeAR KV Cache compression AR generative model visual representation learning image generation