Pathological image has been recognized as the gold standard for cancer diagnosis for more than a century. However, some internal regions of pathological images may inevitably exhibit various degradation issues, including low resolution, image blurring, and image noising, which will affect disease diagnosis, staging, and risk stratification.Existing pathological image restoration methods were mainly based on generative adversarial networks (GANs) to improve image quality, which are limited by the inherent instability and loss of structural details, often resulting in artifacts in the restored images.Large scale of whole slide images (WSIs) also makes it hard for efficient processing and restoration. To address these limitations, we propose a conditional visual autoregressive model (CVARPath) for next-scale token prediction, guided by the degraded tokens from the current scale. We introduce a novel framework that employs quantified encoders specifically designed for pathological image generation, which learns consistent sparse vocabulary tokens through self-supervised contrastive learning. Furthermore, our method efficiently compresses image patches into compact degraded sparse tokens at smaller scales and reconstructs high-quality large-scale whole slide images (WSIs). This is achieved using only an 8×8 vocabulary index for 256×256 images while maintaining minimal reconstruction loss. Experimental results demonstrate that our approach significantly enhances image quality, achieving an approximately 30% improvement in mean Fréchet inception distance (FID) compared to popular conditional GANs and diffusion models across various degradation scenarios in pathological images.