✨DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion

📝 Summary:
DyPE enhances diffusion transformers for ultra-high-resolution image generation by dynamically adjusting positional encodings. This training-free method allows pre-trained models to synthesize images far beyond their training resolution, achieving state-of-the-art fidelity without extra sampling ...

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.20766
• PDF: https://arxiv.org/pdf/2510.20766
• Project Page: https://noamissachar.github.io/DyPE/
• Github: https://github.com/guyyariv/DyPE

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For more data science resources:
✓ https://t.me/DataScienceT

#DiffusionModels #ImageGeneration #HighResolution #DeepLearning #ComputerVision

✨UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers

📝 Summary:
UltraImage tackles content repetition and quality degradation in high-resolution image generation by correcting dominant frequency periodicity and applying entropy-guided attention. It achieves extreme extrapolation, producing high-fidelity images up to 6Kx6K without low-resolution guidance.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04504
• PDF: https://arxiv.org/pdf/2512.04504
• Project Page: https://thu-ml.github.io/ultraimage.github.io/
• Github: https://thu-ml.github.io/ultraimage.github.io/

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For more data science resources:
✓ https://t.me/DataScienceT

#ImageGeneration #DiffusionModels #Transformers #HighResolution #DeepLearning