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README.md

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 license: mit
+datasets:
+- imagenet-1k
+language:
+- en
+- zh
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+# Model Card for VAR (Visual AutoRegressive) Transformers 🔥
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+<!-- Provide a quick summary of what the model is/does. -->
+
+[![arXiv](https://img.shields.io/badge/arXiv%20papr-2404.02905-b31b1b.svg)](https://arxiv.org/abs/2404.02905)[![arXiv](https://img.shields.io/badge/demo%20website-VAR%20demo-lightblue)](https://var.vision)
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+VAR is a new visual generation framework that makes GPT-style models surpass diffusion models **for the first time**🚀, and exhibits clear power-law Scaling Laws📈 like large language models (LLMs).
+
+<p align="center">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/60e73ffd06ad9ae5bbcfc52c/FusWBHW8uJgYWO02HFNGz.png" width=93%>
+<p>
+  
+VAR redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction".
+
+<p align="center">
+<img src="https://github.com/FoundationVision/VAR/assets/39692511/3e12655c-37dc-4528-b923-ec6c4cfef178" width=93%>
+<p>
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+This repo is used for hosting VAR's checkpoints.
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+For more details or tutorials see https://github.com/FoundationVision/VAR.
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+