✨Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration 📝 Summary: Calibri enhances Diffusion Transformers by adding a single learned scaling parameter to improve generative quality. This parameter-efficient method, optimizing only ~100 parameters, reduces inference steps across various text-to-image models while maintaining high-quality outputs. 🔹 Publication Date: Published on Mar 25 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2603.24800 • PDF: https://arxiv.org/pdf/2603.24800 • Project Page: https://v-gen-ai.github.io/Calibri-page/ • Github: https://github.com/v-gen-ai/Calibri 🔹 Models citing this paper: • https://huggingface.co/v-gen-ai/flux-calibri-gates • https://huggingface.co/v-gen-ai/qwen-calibri ================================== For more data science resources: ✓ https://t.me/DataScienceT #DiffusionModels #GenerativeAI #AIResearch #MachineLearning #DeepLearning