Yang Luo

18 posts

Yang Luo

@YangL_7

Ph.D. candidate @NUSingapore | Efficient ML | ACL Outstanding Paper Award

Singapore Katılım Aralık 2021

37 Takip Edilen127 Takipçiler

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Yang Luo@YangL_7·20 Ara

Training-free Video Enhancement: Achieved 🎉 Nice work with @oahzxl @shaowenqi126301 @VictorKaiWang1 @VitaGroupUT @YangYou1991 et al. Non-trivial enhancement, training-free, and plug-and-play 🥳 Blog: oahzxl.github.io/Enhance_A_Vide… (🧵1/6)

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Yang Luo@YangL_7·5 Mar

Amazing work on parameter generation!!

Tencent HY@TencentHunyuan

One static model does not fit all😭 We just dropped our latest work: Functional Neural Memory. Instead of static models, we generate custom "parameters" for every single input. ✅Prompt your model anytime ✅Instant personalization ✅Better instruction following ✅Flexible & dynamic memory (w/o memory bank✌️) (🧵1/6)

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Yang Luo@YangL_7·23 Haz

Customize your LLM in seconds!

AK@_akhaliq

Drag-and-Drop LLMs Zero-Shot Prompt-to-Weights

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Victor.Kai Wang@VictorKaiWang1·20 Haz

Customizing Your LLMs in seconds using prompts🥳! Excited to share our latest work with @HPCAILab, @VITAGroupUT, @k_schuerholt, @YangYou1991, @mmbronstein, @damianborth : Drag-and-Drop LLMs(DnD). 2 features: tuning-free, comparable or even better than full-shot tuning.(🧵1/8)

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Ziqiao Wang@ZiqiaoWang63428·27 May

Representation Alignment (REPA) is NOT ALWAYS helpful for diffusion training!🤷 Sharing latest work w/ @HPCAILab and @VITAGroupUT: "REPA Works Until It Doesn't: Early-Stopped, Holistic Alignment Supercharges Diffusion Training". Acceleration up to 28x w/o performance drop.(🧵1/7)

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Yang Luo@YangL_7·28 Şub

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

Enze Xie@xieenze_jr

🔥 SANA 1.5: A linear Diffusion Transformer pushes SOTA in text-to-image generation! Key innovations: • Depth-growth training: 1.6B → 4.8B params • Memory-efficient 8-bit optimizer • Flexible model pruning • Inference scaling for better quality Achieves 0.80 on GenEval! 🚀

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Ziming Liu@lzm_mlsys·17 Şub

🚀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 @MSFTResearch @YangYou1991 @Yif_Yang et al. 📜Paper: huggingface.co/papers/2502.10… 📖Blog: aka.ms/ras-dit ⌨️Code: github.com/microsoft/RAS (1/5)

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Victor.Kai Wang@VictorKaiWang1·20 Oca

Generating ~200 million parameters in just minutes! 🥳 Excited to share our work with @MTDovent , @heisejiasuo96 , and @YangYou1991: 'Recurrent Diffusion for Large-Scale Parameter Generation' (RPG for short). Example: Obtain customized models using prompts (see below). (🧵1/8)

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Yang Luo@YangL_7·22 Ara

@nirzabari @oahzxl @shaowenqi126301 @VictorKaiWang1 @VITAGroupUT @YangYou1991 Thank you! Glad to hear the improvements worked well with LTXV too. Your observation about the enhanced coherence in movement and detail preservation is super helpful feedback.

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Nir Zabari‎@nirzabari·22 Ara

@YangL_7 @oahzxl @shaowenqi126301 @VictorKaiWang1 @VITAGroupUT @YangYou1991 Excellent analysis and improvements! I implemented this for LTXV and noticed enhancements as well. The most notable improvements are the reduction in people/animals/etc splitting into multiple parts (leading to more coherent movement) and finer details. Great work!

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Yang Luo@YangL_7·20 Ara

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Yang Luo@YangL_7·22 Ara

@ai_kimera @oahzxl @shaowenqi126301 @VictorKaiWang1 @VITAGroupUT @YangYou1991 Thanks! Really appreciate you noticing the progress, and we're excited to keep pushing forward.

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Ken Kimera@ai_kimera·21 Ara

@YangL_7 @oahzxl @shaowenqi126301 @VictorKaiWang1 @VITAGroupUT @YangYou1991 Congratulations on this. I've seen some real examples before and after and it looks like you guys achieved a huge milestone.

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Yang Luo@YangL_7·20 Ara

For more details, check out our blog: oahzxl.github.io/Enhance_A_Video Our code is open-source on GitHub: github.com/NUS-HPC-AI-Lab… More results and the technique report will be coming soon! (🧵6/6)

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Yang Luo@YangL_7·20 Ara

Our approach: we compute the average of non-diagonal elements of temporal attention maps as cross-frame intensity (CFI). An enhanced temperature parameter multiplies the CFI to enhance the temporal attention output.

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Yang Luo@YangL_7·18 Mar

@oahzxl Lei God

Norsk

Xuanlei Zhao@oahzxl·18 Mar

@YangL_7 No wonder it's ACL 2023 Outstanding Paper Award winner - being able to train impressive DiT models like PIXART-Σ with your CAME optimizer shows the real-world impact of your work!

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Yang Luo@YangL_7·17 Mar

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

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Zangwei Zheng@ZangweiZheng·15 Tem

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)

Zangwei Zheng@ZangweiZheng

🎉 We're thrilled and honored to have received the ACL Outstanding Paper Award! 🏆 Stay tuned, as we'll be publishing a repository for the CAME optimizer very soon. #ACL2023

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Keşfet

@HPCAILab @VITAGroupUT @k_schuerholt @YangYou1991 @mmbronstein @damianborth @MSFTResearch @Yif_Yang