Hila Chefer @hila_chefer
PhD candidate @TelAvivUni, student researcher @GoogleAI, interested in Deep Learning, Computer Vision, explainable AI hila-chefer.github.io Joined December 2020-
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After two years in stealth mode, we're thrilled to unveil Mentee Robotics and our humanoid robot, @MenteeBot! With AI integration at every layer, from Sim2Real machine learning to NeRF-based algorithms and LLMs, we've achieved a complete end-to-end cycle for tasks. This is just…
📢 Announcing MoA !! TL;DR - We introduce Mixture-of-Attention (MoA) a new architecture for personalization of generative models that disentangles the generation of given subjects and the context from the prior. snap-research.github.io/mixture-of-att… #snap #GenerativeAI
For my first community article, I made a little overview of recent & popular zero-shot pipelines for personalization 👩🏻🎨🎭 Check it out 🤗▶️ huggingface.co/blog/linoyts/z…
Announcing our new work: lcm-lookahead.github.io 🥳 We improve IP-Adapter by training with a new identity-lookahead loss and synthetic, consistent data. The key idea is to propagate image-space losses (ID, CLIP) through LCM-LoRA instead of single-step DDPM approximations. 1/5
InstandID's facial feature preservation + sdxl style LoRAs = portrait magic ✨ Check out 🎭Face-to-all🎭 (inspired by @fofrAI face-to-many 🔥) ▶️ huggingface.co/spaces/multimo…
The 3rd Transformers for Vision workshop will be back at #CVPR2024! We have a great speaker lineup covering diverse Transformer topics! Papers Due: Apr 15. Website: sites.google.com/view/t4v-cvpr24 Organized w/ @_rohitgirdhar_, @ZhidingYu, @giffmana, @gulvarol, @mohitban47 and others!
We're excited to share this groundbreaking work - MyVLM! I usually reserve ‘groundbreaking’ for truly exceptional works, but I think that this one's gonna shake up how AI understands users. Get ready for a whole new level of personalized image understanding and interaction! 🚀
Amazing opportunity to work with @MokadyRon 🤩
🚨New Dataset Distillation paper 🚨 We propose PoDD, a new dataset distillation setting for a tiny🤏under 1 image-per-class (IPC) budget❗In this CIFAR100 example, current SoTA is 35.5% acc using ~100k pixels, PoDD gets 35.7% with ~40k pixels Project: vision.huji.ac.il/podd 🧵👇
Happy to share a new work led by @oren_sultan, accepted to #NAACL2024. Leveraging LLMs to generate ProPara-Logy, a dataset of analogies between scientific processes.
Happy to share a new work led by @oren_sultan, accepted to #NAACL2024. Leveraging LLMs to generate ProPara-Logy, a dataset of analogies between scientific processes.
Thank you @_akhaliq for sharing our recent work, Emu Video Edit, on video editing! Project page: fdd-video-edit.github.io An amazing collaboration with @amit_zhr, @YKirstain, @ShellySheynin, @adam_polyak90, @deviparikh, and @taigman
Thank you @_akhaliq for sharing our recent work, Emu Video Edit, on video editing! Project page: fdd-video-edit.github.io An amazing collaboration with @amit_zhr, @YKirstain, @ShellySheynin, @adam_polyak90, @deviparikh, and @taigman
Meta presents Video Editing via Factorized Diffusion Distillation We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separately train an image editing
In the 36th session of #MultimodalWeekly, we welcome @hila_chefer - a Ph.D. candidate @TelAvivUni and research intern @GoogleAI. She is a second-time speaker at our webinar.
Slides from @sourab_m and my session "Building a personal coding assistant" at the Build with AI roadshow: docs.google.com/presentation/d…
Now you can boost the quality of your 3D generations using cascaded models. CSD code is live! 🚀
[1/4] Can we sample 3D from a 2D diffusion models instead of optimizing with SDS? In "MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion" [CVPR2024🥳] we show that for 3D animation, the answer is - Yes!
2024 is the year of text-to-video models 🎥 Join me tomorrow as we dive into ✨Lumiere✨our T2V model from @GoogleAI! We'll discuss Lumiere's architecture, applications, and more🤩 ⏰ tomorrow at 20:00 IDT (10:00 PDT) 🚀 sign up now: docs.google.com/forms/d/e/1FAI…
2024 is the year of text-to-video models 🎥 Join me tomorrow as we dive into ✨Lumiere✨our T2V model from @GoogleAI! We'll discuss Lumiere's architecture, applications, and more🤩 ⏰ tomorrow at 20:00 IDT (10:00 PDT) 🚀 sign up now: docs.google.com/forms/d/e/1FAI… https://t.co/j7TjuYv4jp
After you get great base models like #SD3 what comes next? Control, composition, collaboration.. More soon.. @Nitrosocke
Announcing Stable Diffusion 3, our most capable text-to-image model, utilizing a diffusion transformer architecture for greatly improved performance in multi-subject prompts, image quality, and spelling abilities. Today, we are opening the waitlist for early preview. This phase…
🚨We uncover a new vulnerability- Pre-Fine-Tuning Weight Recovery With a few LoRA fine-tuned models we recover the pre-fine-tuning weights🏋️of SoTA models, undoing Stable Diffusion personalization training and Mistral alignment😈 Project: vision.huji.ac.il/spectral_detun… 🧵👇
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4K Followers 311 Following Dad, Data Science, Math, GenAI, LLMs, Diffusion Models, Gym Lover, RightWing Liberal, Anti-Socialism, Anti-Islamism, Russia delenda est, AntiPolitCorr, AntiWokeMyVLM’s code is out! 💥 We've also released a new benchmark and pretrained models, enabling you to evaluate your personalized Vision Language Models! github.com/snap-research/…
We've just published the code, models, and data for MyVLM! 🤩 Check it out here: 💻 github.com/snap-research/… We're really excited to see where the community will take the personalization of VLMs! 💪🚀
I will give a talk at @ETH_en in person on 6th May. This will be about diffusion models and of course, the diffusers library. Come, say hi, if you're around :) linkedin.com/events/diffusi…
Thanks @_akhaliq! MAIA solves diverse interpretability tasks by iteratively designing experiments on other AI systems. …imodal-interpretability.csail.mit.edu/maia/ w/ @cogconfluence @f_x_wang @AchyutaBot @evanqed @jacobandreas and Antonio Torralba Stay tuned for more from Interpretability Agents!
A Multimodal Automated Interpretability Agent This paper describes MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery. It equips a
Thanks @MIT_CSAIL for covering our #ICLR2024 paper! w/ @nikhil, @Tal_Ha535, @boknilev and @davidbau web: finetuning.baulab.info arxiv: arxiv.org/abs/2402.14811
Fine-tuning turns generic pretrained language models into specialists. But how? In their #ICLR2024 work, researchers from @MIT_CSAIL, @KhouryCollege, and @TechnionLive reveal it's not about introducing new capabilities, but rather enhancing the existing ones!…
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1000+ citations 🍾 AMAZING collaboration with AMAZING people! ❤️ @geopavlakos @XiaoweiZhou5 @KostasPenn
📢 Announcing MoA !! TL;DR - We introduce Mixture-of-Attention (MoA) a new architecture for personalization of generative models that disentangles the generation of given subjects and the context from the prior. snap-research.github.io/mixture-of-att… #snap #GenerativeAI
Great work! And thanks @kcjacksonwang for this cool image, I do smile like that when I drink boba 🤭
💫 Happy to share MoA: a *Mixture-of-Attention* architecture for personalization of generative models! Imagine YOU🫵🏻 drinking boba🧋in a night market, or scuba diving🤿, and more! 📕: arxiv.org/abs/2404.11565 🔗: snap-research.github.io/mixture-of-att… 🎥: youtu.be/Ckv5ttqGJNA
💫 Happy to share MoA: a *Mixture-of-Attention* architecture for personalization of generative models! Imagine YOU🫵🏻 drinking boba🧋in a night market, or scuba diving🤿, and more! 📕: arxiv.org/abs/2404.11565 🔗: snap-research.github.io/mixture-of-att… 🎥: youtu.be/Ckv5ttqGJNA
Code and demo are now available for Bounded Attention! 🐶🐱 "Be yourself" and try it 😁
Excited to share Bounded Attention, our new training-free technique for generating multi-subject images 😀 Now with available code & demo (on SDXL!) Website: omer11a.github.io/bounded-attent… Paper: arxiv.org/abs/2403.16990 Code: github.com/omer11a/bounde… Demo: huggingface.co/spaces/omer11a…
#MDM is now 40X faster 🤩🤩🤩 (~0.4 sec/sample) How come?!? (1) We released the 50 diffusion steps model (instead of 1000 steps) which runs 20X faster. (2) Calling CLIP just once and caching the result runs 2X faster for all models. github.com/GuyTevet/motio…
For my first community article, I made a little overview of recent & popular zero-shot pipelines for personalization 👩🏻🎨🎭 Check it out 🤗▶️ huggingface.co/blog/linoyts/z…
Announcing our new work: lcm-lookahead.github.io 🥳 We improve IP-Adapter by training with a new identity-lookahead loss and synthetic, consistent data. The key idea is to propagate image-space losses (ID, CLIP) through LCM-LoRA instead of single-step DDPM approximations. 1/5
Took my stylized portrait from 🎭 Face-to-All , gave it to AniPortrait 🎭→📹 (+ me saying "Hugging Face") And it's even better than I expected ✨ ▶️AniPortrait: huggingface.co/spaces/ZJYang/… ▶️Face-to-All: huggingface.co/spaces/multimo…
InstandID's facial feature preservation + sdxl style LoRAs = portrait magic ✨ Check out 🎭Face-to-all🎭 (inspired by @fofrAI face-to-many 🔥) ▶️ huggingface.co/spaces/multimo…
#IMVC2024 was great! A big thank you to everyone who came to my talk. Loved catching up with old friends from the vision community! 😊
The 3rd Transformers for Vision workshop will be back at #CVPR2024! We have a great speaker lineup covering diverse Transformer topics! Papers Due: Apr 15. Website: sites.google.com/view/t4v-cvpr24 Organized w/ @_rohitgirdhar_, @ZhidingYu, @giffmana, @gulvarol, @mohitban47 and others!
Paper decisions are now available at OpenReview🕺
We invite you to submit your Motion Generation papers to the HuMoGen @CVPR workshop! The deadline is on March 19 More details @ humogen.github.io
Check out our recent work that studies the use of audio-visual siamese networks (AVSiam) for efficient and scalable audio-visual pretraining. Our model is fast, scalable, memory-efficient, and it achieves SOTA results on multiple classification and retrieval benchmarks.
🔊Check out our preprint, AVSiam 🤝. We use a single shared ViT to process audio and visual inputs, improving its parameter efficiency⚡, reducing the GPU memory footprint💻, and allowing us to scale📈 to larger datasets and model sizes. arxiv.org/abs/2403.19638 w. @gberta227 👇
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