Diversity Has Always Been There in Your Visual Autoregressive Models
Visual Autoregressive (VAR) models have recently garnered significant attention for their innovative next-scale prediction paradigm, offering notable advantages in both inference efficiency and image quality compared to traditional multi-step autoregressive (AR) and diffusion models. However, despite their efficiency, VAR models often suffer from the diversity collapse i.e., a reduction in output variability, analogous to that observed in few-step distilled diffusion models. In this paper, we introduce DiverseVAR, a simple yet effective approach that restores the generative diversity of VAR models without requiring any additional training. Our analysis reveals the pivotal component of the feature map as a key factor governing diversity formation at early scales. By suppressing the pivotal component in the model input and amplifying it in the model output, DiverseVAR effectively unlocks the inherent generative potential of VAR models while preserving high-fidelity synthesis. Empirical results demonstrate that our approach substantially enhances generative diversity with only neglectable performance influences. Our code will be publicly released at https://github.com/wangtong627/DiverseVAR.
6923f826b5612535ed955a06
Tong Wang
claimed_verified
2025-11-24T07:54:29.468Z
66d16a015ab9ab8cb4ee4f94
https://cdn-avatars.huggingface.co/v1/production/uploads/66d16a015ab9ab8cb4ee4f94/6ISRsgVL9u3qJ96c0qDDx.jpeg
TongWang
TongWang-NJ
user
TongWang-NJ
6923f826b5612535ed955a07
Guanyu Yang
6923f826b5612535ed955a08
Nian Liu
6923f826b5612535ed955a09
Kai Wang
6923f826b5612535ed955a0a
Yaxing Wang
6923f826b5612535ed955a0b
Abdelrahman M Shaker
6923f826b5612535ed955a0c
Salman Khan
6923f826b5612535ed955a0d
Fahad Shahbaz Khan
6923f826b5612535ed955a0e
Senmao Li
Diversity Has Always Been There in Your Visual Autoregressive Models
Visual Autoregressive (VAR) models have recently garnered significant attention for their innovative next-scale prediction paradigm, offering notable advantages in both inference efficiency and image quality compared to traditional multi-step autoregressive (AR) and diffusion models. However, despite their efficiency, VAR models often suffer from the diversity collapse i.e., a reduction in output variability, analogous to that observed in few-step distilled diffusion models. In this paper, we introduce DiverseVAR, a simple yet effective approach that restores the generative diversity of VAR models without requiring any additional training. Our analysis reveals the pivotal component of the feature map as a key factor governing diversity formation at early scales. By suppressing the pivotal component in the model input and amplifying it in the model output, DiverseVAR effectively unlocks the inherent generative potential of VAR models while preserving high-fidelity synthesis. Empirical results demonstrate that our approach substantially enhances generative diversity with only neglectable performance influences. Our code will be publicly released at https://github.com/wangtong627/DiverseVAR.
DiverseVAR enhances generative diversity in Visual Autoregressive models by modifying the pivotal component of feature maps without additional training, improving synthesis quality.
Visual Autoregressive (VAR) models next-scale prediction inference efficiency image quality multi-step autoregressive (AR) models diffusion models diversity collapse DiverseVAR feature map generative diversity high-fidelity synthesis