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![AINativeF Avatar](https://lunarcrush.com/gi/w:24/cr:twitter::1795402815298486272.png) AI Native Foundation [@AINativeF](/creator/twitter/AINativeF) on x 1913 followers
Created: 2025-07-22 00:51:30 UTC

X. CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models

🔑 Keywords: Visual Autoregressive Modeling, Content-Style Decomposition, Scale-aware optimization, SVD-based rectification, Augmented K-V memory

💡 Category: Generative Models

🌟 Research Objective:
   - The paper aims to enhance content-style decomposition using the CSD-VAR model, which outperforms diffusion models in both content preservation and stylization.

🛠️ Research Methods:
   - The researchers introduced a scale-aware optimization strategy, SVD-based rectification, and augmented K-V memory to improve content and style separation.

💬 Research Conclusions:
   - Experiments with the CSD-100 dataset demonstrate that CSD-VAR provides superior results in content preservation and stylization fidelity compared to existing methods.

👉 Paper link:

![](https://pbs.twimg.com/ext_tw_video_thumb/1947459414899990528/pu/img/hnRTU3nWcD0IxqCS.jpg)

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[Post Link](https://x.com/AINativeF/status/1947459453764440248)