ObjectDrop is accepted to #ECCV2024! 🥳 In this work from Google AI we tackle photorealistic object removal and insertion. Congrats to the team: Matan Cohen Shlomi Fruchter Yael Pritch  Alex Rav-Acha Yedid Hoshen Checkout our project page: objectdrop.github.io

With friends at Google we announce 💜 Magic Insert 💜 - a generative AI method that allows you to drag-and-drop a subject into an image with a vastly different style achieving a style-harmonized and realistic insertion of the subject (Thread 🧵) web: magicinsert.github.io

Giving a talk about common neurons in vision models and emergent representations in diffusion model weights today at European Conference on Computer Vision #ECCV2026 ☺️

I'm sharing something unique we've been making at Google (w/ UNC). We are releasing our work on a new class of interactive experiences that we call generative infinite games, essentially video games where the game mechanics and graphics are fully subsumed by generative models 🧵

🚨 Excited to share ObjectMate our latest from Google AI for zero-shot subject-driven generation & insertion 🚨 🔗 project page: object-mate.com 📄Arxiv: arxiv.org/abs/2412.08645

Crazy work by colleagues in my team!

🚨 New paper alert! 🚨 Millions of neural networks now populate public repositories like Hugging Face 🤗, but most lack documentation. So, we decided to build an Atlas 🗺️ Project: horwitz.ai/model-atlas Demo: huggingface.co/spaces/Eliahu/… 🧵👇🏻 Here's what we found:

In our #ICLR2025 paper, we introduce WIND 🌬️ A method that embeds a distortion watermark directly in the diffusion noise! Our method ensures that the watermark in one image does not reveal information about the watermark in other images 🤫 📝 arxiv.org/abs/2412.04653 (1/5)

Google presents LightLab Controlling Light Sources in Images with Diffusion Models

A quick look on what we were working on in the past months 🎉 more exciting things to come 🙌🏻

When we "erase" a concept from a diffusion model, is that knowledge truly gone? 🤔 We investigated, and the answer is often 'no'! Using simple probing techniques, the knowledge traces of the erased concept can be easily resurfaced 🔍 Here is what we learned 🧵👇

Andrej Karpathy Thanks for the inspiring talk (as always!). I'm the author of the Model Atlas. I'm delighted you liked our work, seeing the figure in your slides felt like an "achievement unlocked"🙌Would really appreciate a link to our work in your slides/tweet arxiv.org/abs/2503.10633

We are releasing a paper I'm very excited about. We know test-time scaling is a path to greatly improved results, and achieves reasoning in the case of LLMs. We present a new and promising way to amortize it into training using HyperNetworks for image generation models.