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@lixin4ever

🚀🚀Excited to share DiGIT, a new image tokenizer for autoregressive image modeling. 💡Super simple idea: - SSL representations + K-Means clustering as discrete image tokenizer - Autoregressive modeling over image tokens 💗 Discriminative SSL model (e.g., Dinov2) is critical 🚀 Strong results (understanding & generation): - 80.3% Top-1 Accuracy on ImageNet (Linear probe) - Class-unconditional FID score of 4.59 - Class-conditional FID score of 3.39 Paper: https://t.co/y4JSBd7DnQ Repo: https://t.co/WdvNkzVYUl Model: https://t.co/poqMhrJcph

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  "full_text": "🚀🚀Excited to share DiGIT, a new image tokenizer for autoregressive image modeling.\n\n💡Super simple idea: \n     - SSL representations + K-Means clustering as discrete image tokenizer\n     - Autoregressive modeling over image tokens\n💗 Discriminative SSL model (e.g., Dinov2) is critical \n🚀 Strong results (understanding & generation):\n     - 80.3% Top-1 Accuracy on ImageNet (Linear probe)\n     - Class-unconditional FID score of 4.59\n     - Class-conditional FID score of 3.39\n\nPaper: https://t.co/y4JSBd7DnQ\nRepo: https://t.co/WdvNkzVYUl\nModel: https://t.co/poqMhrJcph",
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