Super excited to share that I’ve received the Google PhD Fellowship for 2024 🥳🥳🥳🥳🥳🥳
Heartfelt thanks to my advisor, @XinchaoWang3, and to all my incredible collaborators and friends who’ve supported me along the way.
Also thanks @GoogleAI for this amazing recognition!
Xinyin Ma
63 posts
Ph.D. Candidate at National University of Singapore @NUSingapore. Interested in efficient learning for Diffusion Models, LLM, ... || Google PhD Fellowship 2024
- Our new work: 0.1% Data Makes Segment Anything Slim Page: huggingface.co/papers/2312.05… Github: github.com/czg1225/SlimSAM Compared to SAM-H, SlimSAM achieves approaching performance while reducing parameter counts to merely 0.9%(5.7M), MACs to 0.8%(21G), and requiring only 0.1% data.
- Hi everyone! I will present our work DeepCache today at @CVPR in the afternoon poster session! POSTER: Arch 4A-E Poster #111 Come stop by our poster today😀! We are very welcome for discussions. You can also drop me an email if you want to discuss more.
- 🥳Excited to share our new work: Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching. Our method nearly losslessly removes ~46% of the layers without updating model parameters. - paper: arxiv.org/abs/2406.01733 - code: github.com/horseee/learni…
- Thanks @_akhaliq for sharing our work! DeepCache is a training-free and almost lossless acceleration of diffusion models. Check our Github for more results😉:DeepCache: Accelerating Diffusion Models for Free paper page: huggingface.co/papers/2312.00… Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities. Notwithstanding their prowess, these models often
- DeepCache is integrated into OneDiff, and DeepCache with OneDiff can achieve 3x speed up😎 See Github: github.com/Oneflow-Inc/on… and Reddit: reddit.com/r/StableDiffus… Big Thanks for Oneflow and OneDiff!
- Excited to announce that DeepCache got accepted to #CVPR2024! 🎉 Huge thanks to @XinchaoWang3 and Gongfan! 🍊Paper: arxiv.org/abs/2312.00858 🦒Code: github.com/horseee/DeepCa… 🐳 Diffusers: huggingface.co/docs/diffusers…DeepCache: Accelerating Diffusion Models for Free paper page: huggingface.co/papers/2312.00… Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities. Notwithstanding their prowess, these models often
- Introducing CoDe: our new work on accelerating VAR! Exciting observations: 1. Downsizing models for larger scale has minimal impact on performance! 2. Different scales serve unique roles, so a unified model for all scales is not optimal. Check out our paper and code🥰Excited to introduce our new work! CoDe: Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient CoDe achieves 1.7x speedup and 0.5x Memory with only a negligible impact to quality! 🚀 GitHub: github.com/czg1225/CoDe 🚀 Project Page: czg1225.github.io/CoDe_page/
- A doc page for DeepCache has been added in Diffusers! Huge thanks to the Diffusers team🥰 Check here for more information:
- Replying to @horseeeMa(1/3) We introduce SlimSAM, a novel SAM compression method that achieves superior performance with remarkably low training costs. This is achieved by the efficient reuse of pre-trained SAMs through a unified pruning-distillation framework.
- SO COOL!!!!!🚀 Introducing Video-Infinity! Our new distributed framework revolutionizes long video generation. 🎥✨ 🌟 Generate videos up to 2,300 frames in just 5 minutes—100x faster than previous methods!#AI #Video #AIGC Project Page: video-infinity.tanzhenxiong.com Paper: arxiv.org/abs/2406.16260
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00:00 - Introducing another new work: DeepCache Paper: arxiv.org/abs/2312.00858 Github: github.com/horseee/DeepCa…… 🚀 Training-free and almost lossless 🚀Accelerate Stable Diffusion by 2.3X and Stable Diffusion XL by 2.6X😀 🚀Compatible with sampling algorithms like DDIM and PLMS
GIF - Replying to @horseeeMa(3/3) SlimSAM yields significant performance improvements while demanding over 10X less training costs than existing methods. Compared to SAM-H, SlimSAM achieves approaching performance while reducing parameters to merely 0.9%, MACs to 0.8%, and requiring only 0.1% (10k) data.










