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Visual Autoregressive Modeling for Image Super-Resolution
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Image Super-Resolution (ISR) has seen significant progress with the introduction of remarkable generative models. However, challenges such as the trade-off issues between fidelity and realism, as well as computational complexity, have also posed limitations on their application. Building upon the tremendous success of autoregressive models in the language domain, we propose \textbf{VARSR}, a novel visual autoregressive modeling for ISR framework with the form of next-scale prediction. To effectively integrate and preserve semantic information in low-resolution images, we propose using prefix tokens to incorporate the condition. Scale-aligned Rotary Positional Encodings are introduced to capture spatial structures and the diffusion refiner is utilized for modeling quantization residual loss to achieve pixel-level fidelity. Image-based Classifier-free Guidance is proposed to guide the generation of more realistic images. Furthermore, we collect large-scale data and design a training process to obtain robust generative priors. Quantitative and qualitative results show that VARSR is capable of generating high-fidelity and high-realism images with more efficiency than diffusion-based methods. Our codes will be released at https://github.com/qyp2000/VARSR.
Forward citations
Cited by 6 Pith papers
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Structure-Detail Decoupled Autoregressive Generation for Fast and High-Fidelity Virtual Try-On
STAR-VTON decouples latent VAR structure synthesis from pixel-space matching-based detail recovery, yielding faster high-fidelity virtual try-on than diffusion baselines.
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Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling
Next-dense-stride prediction enables coarse-to-fine autoregressive image generation on a single-scale grid and unifies multi-contrast MRI translation, generation, and segmentation in one model.
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Detail Continuation over a Trustworthy Coarse Scale for Autoregressive Super-Resolution
In VAR-based super-resolution, K2N predicts the first three coarse scales in parallel from the low-resolution input and generates only the remaining fine scales autoregressively, reducing hallucination while staying c...
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UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling
An MLLM-conditioned next-scale VAR decoder handles 15+ unified visual generation tasks with competitive quality and substantially lower latency than diffusion baselines.
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TanDiT: Tangent-Plane Diffusion Transformer for High-Quality 360{\deg} Panorama Generation
TanDiT generates high-quality 360-degree panoramas by jointly generating grids of tangent-plane views with a single diffusion transformer and refining them with a pretrained model.
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Adaptive Visual Autoregressive Acceleration via Dual-Linkage Entropy Analysis
A training-free entropy-guided token-pruning framework accelerates VAR image generation up to 2.9× with negligible benchmark loss by activating pruning at an adaptive entropy-growth inflection point and adjusting rati...
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