CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation

• URL: http://arxiv.org/abs/2506.23347v2
• Date: Mon, 07 Jul 2025 16:20:58 GMT
• Title: CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
• Authors: Yi Liu, Shengqian Li, Zuzeng Lin, Feng Wang, Si Liu,
• Abstract summary: Current conditional autoregressive image generation methods have shown promising results, yet their potential remains largely unexplored in the practical unsupervised image translation domain.<n>A critical limitation stems from the discrete quantization inherent in traditional Vector Quantization-based frameworks.<n>We propose Softmax Relaxed Quantization, a novel approach that reformulates codebook selection as a continuous probability mixing process.
• Score: 9.628074306577851
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The current conditional autoregressive image generation methods have shown promising results, yet their potential remains largely unexplored in the practical unsupervised image translation domain, which operates without explicit cross-domain correspondences. A critical limitation stems from the discrete quantization inherent in traditional Vector Quantization-based frameworks, which disrupts gradient flow between the Variational Autoencoder decoder and causal Transformer, impeding end-to-end optimization during adversarial training in image space. To tackle this issue, we propose using Softmax Relaxed Quantization, a novel approach that reformulates codebook selection as a continuous probability mixing process via Softmax, thereby preserving gradient propagation. Building upon this differentiable foundation, we introduce CycleVAR, which reformulates image-to-image translation as image-conditional visual autoregressive generation by injecting multi-scale source image tokens as contextual prompts, analogous to prefix-based conditioning in language models. CycleVAR exploits two modes to generate the target image tokens, including (1) serial multi-step generation, enabling iterative refinement across scales, and (2) parallel one-step generation synthesizing all resolution outputs in a single forward pass. Experimental findings indicate that the parallel one-step generation mode attains superior translation quality with quicker inference speed than the serial multi-step mode in unsupervised scenarios. Furthermore, both quantitative and qualitative results indicate that CycleVAR surpasses previous state-of-the-art unsupervised image translation models, \textit{e}.\textit{g}., CycleGAN-Turbo.

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