Explore the blog post for a concise and insightful overview of the CAME optimizer! Congrats to the first author @YangYoungLL! 🔥 Blog: zhengzangw.github.io/blogs/came ArXiv: arxiv.org/abs/2307.02047 Code: github.com/huawei-noah/Pr… (a plug-and-play optimizer repo will be released soon)

We are pleased to announce that PIXART-Σ is trained by our CAME optimizer(arxiv.org/abs/2307.02047). Glad to see that our work has a real-world impact on the training of DiT models! Code: github.com/yangluo7/CAME

Generating ~200 million parameters in just minutes! 🥳 Excited to share our work with Doven Tang , ZHAO WANGBO , and Yang You: 'Recurrent Diffusion for Large-Scale Parameter Generation' (RPG for short). Example: Obtain customized models using prompts (see below). (🧵1/8)

🚀Towards efficient Diffusion Transformers! 😆We are happy to introduce RAS, the first diffusion sampling strategy that allows for regional variability in sampling ratios, achieving up to 2x+ speedup! 🔌Training-free, plug and play! 💪Nice work with Microsoft Research Yang You

Glad to see CAME's efficiency in the training of SANA 1.5!

Customizing Your LLMs in seconds using prompts🥳! Excited to share our latest work with HPC-AI Lab, VITA Group, Konstantin Schürholt, Yang You, Michael Bronstein, Damian Borth : Drag-and-Drop LLMs(DnD). 2 features: tuning-free, comparable or even better than full-shot tuning.(🧵1/8)

Customize your LLM in seconds!