✨Image-Free Timestep Distillation via Continuous-Time Consistency with Trajectory-Sampled Pairs

📝 Summary:
TBCM is a self-contained method that distills diffusion models by extracting latent representations directly from the teacher model trajectory. This eliminates external data, greatly improving efficiency and quality for few-step generation with reduced resources.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20410
• PDF: https://arxiv.org/pdf/2511.20410
• Github: https://github.com/hustvl/TBCM

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#DiffusionModels #ModelDistillation #GenerativeAI #AIResearch #MachineLearning

✨Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield

📝 Summary:
This study challenges the understanding of Distribution Matching Distillation DMD for text-to-image generation. It reveals that CFG Augmentation is the primary driver of few-step distillation, while distribution matching acts as a regularizer. This new insight enables improved distillation method...

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22677
• PDF: https://arxiv.org/pdf/2511.22677
• Project Page: https://tongyi-mai.github.io/Z-Image-blog/
• Github: https://github.com/Tongyi-MAI/Z-Image/tree/main

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#TextToImage #GenerativeAI #DiffusionModels #ModelDistillation #AIResearch

✨Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning

📝 Summary:
Flash-DMD accelerates generative diffusion models via efficient timestep-aware distillation and joint reinforcement learning. This framework achieves faster convergence, high-fidelity few-step generation, and stabilizes RL training using distillation as a regularizer, all with reduced computation...

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20549
• PDF: https://arxiv.org/pdf/2511.20549

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#DiffusionModels #ImageGeneration #ReinforcementLearning #ModelDistillation #GenerativeAI