✨KLASS: KL-Guided Fast Inference in Masked Diffusion Models
📝 Summary:
KLASS accelerates masked diffusion model inference by using KL divergence to identify stable, high-confidence predictions. It unmasks multiple tokens per iteration, significantly speeding up generation and improving quality across text, image, and molecular tasks.
🔹 Publication Date: Published on Nov 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05664
• PDF: https://arxiv.org/pdf/2511.05664
• Github: https://github.com/shkim0116/KLASS
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#DiffusionModels #GenerativeAI #MachineLearning #AIResearch #ModelAcceleration
📝 Summary:
KLASS accelerates masked diffusion model inference by using KL divergence to identify stable, high-confidence predictions. It unmasks multiple tokens per iteration, significantly speeding up generation and improving quality across text, image, and molecular tasks.
🔹 Publication Date: Published on Nov 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05664
• PDF: https://arxiv.org/pdf/2511.05664
• Github: https://github.com/shkim0116/KLASS
==================================
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✓ https://t.me/DataScienceT
#DiffusionModels #GenerativeAI #MachineLearning #AIResearch #ModelAcceleration
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✨Glance: Accelerating Diffusion Models with 1 Sample
📝 Summary:
Glance accelerates diffusion models with a phase-aware strategy using lightweight LoRA adapters. This method applies varying speedups across denoising stages, achieving up to 5x acceleration and strong generalization with minimal retraining on just 1 sample.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02899
• PDF: https://arxiv.org/pdf/2512.02899
==================================
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#DiffusionModels #ModelAcceleration #LoRA #DeepLearning #GenerativeAI
📝 Summary:
Glance accelerates diffusion models with a phase-aware strategy using lightweight LoRA adapters. This method applies varying speedups across denoising stages, achieving up to 5x acceleration and strong generalization with minimal retraining on just 1 sample.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02899
• PDF: https://arxiv.org/pdf/2512.02899
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#DiffusionModels #ModelAcceleration #LoRA #DeepLearning #GenerativeAI
✨StageVAR: Stage-Aware Acceleration for Visual Autoregressive Models
📝 Summary:
StageVAR accelerates visual autoregressive models by recognizing early stages are critical while later detail-refinement stages can be pruned or approximated. This plug-and-play framework achieves up to 3.4x speedup with minimal quality loss, outperforming existing methods.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16483
• PDF: https://arxiv.org/pdf/2512.16483
• Github: https://github.com/sen-mao/StageVAR
==================================
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#ComputerVision #DeepLearning #ModelAcceleration #AI #NeuralNetworks
📝 Summary:
StageVAR accelerates visual autoregressive models by recognizing early stages are critical while later detail-refinement stages can be pruned or approximated. This plug-and-play framework achieves up to 3.4x speedup with minimal quality loss, outperforming existing methods.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16483
• PDF: https://arxiv.org/pdf/2512.16483
• Github: https://github.com/sen-mao/StageVAR
==================================
For more data science resources:
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#ComputerVision #DeepLearning #ModelAcceleration #AI #NeuralNetworks
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