We are also releasing self-contained lecture notes that explain... flow matching and diffusion models from scratch. This goes from "zero" to the state-of-the-art in modern Generative AI. 📖 Read the notes here: Joint work with Ezra Erives.show more

This account posting fake community notes in all my... posts needs to be banned from X and removed from Community Notes These notes are wrong and I’ve posted proof in the replies to other people already.show more

Sell art with ZERO fees 🤯 You read that... right: ZERO selling fees. When we say we are here to empower artists, we mean it! Keep 100% of the listing price.show more

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper... page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.show more

Harness the power of generative AI without just prompting... and hoping for the best. From Unbound Slop to Tripo🔜SIGGRAPH2026 to Unreal Engine, we love this workflow for generating 3D models.show more

THIS GUY TURNS NOTES, DOCUMENTS, AND IDEAS INTO AN... AI SECOND BRAIN the system stores all the context of your work and helps claude not forget anything how to build one yourself: install obsidian connect claude code keep your projects, notes, and sessions in one vault link similar ideas together use the graph to discover new connections as a result, claude gets memory of your projects and understands the context without you constantly having to explain everything the more you work, the smarter the system gets don’t collect notes build connections between them insteadshow more

Just gave my first academic lecture in French at... the amazing conference (OK so I had to read from my translated lecture notes, but still!) Covered chlorine dioxide (“dioxy de chlor”) and DMSO in just under an hour too! Vive La France 🇫🇷show more

That was quick! Valentine's cross update is available NOW... in the Skullgirls 2nd Encore Beta on Steam! Why are we making these changes? The short version is: we had to! For the long version, read the full update notes here:show more

The Hidden Language of Diffusion Models paper page: tackle... the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulationshow more

1/ Happy to share UniDisc - Unified Multimodal Discrete... Diffusion – We train a 1.5 billion parameter transformer model from scratch on 250 million image/caption pairs using a **discrete diffusion objective**. Our model has all the benefits of diffusion models but now in multimodal space! - flexible compute-quality tradeoff, zero-shot inpainting and editing, better control via classifier-free guidance and lower latency! We open source everything - our code, weights and the training dataset.show more

Today, we are releasing Stable Video Diffusion, our first... foundation model for generative AI video based on the image model, Stable Diffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our waitlist to access a new upcoming web experience featuring a Text-To-Video interface. To access the model & sign up for our waitlist, visit our website here:show more

Generative models can’t discover what they can’t reach. We’re... excited to introduce ActFlow: a continued pre-training scheme that actively expands the valid design space reachable by flow and diffusion models. We call this generable set expansion — a new learning principle for out-of-distribution generative modeling, and a step toward evolvable search spaces for scientific discovery. (1/5)show more

Big news from Boltz - our biggest update yet!... 🚀 Today we’re releasing two new state-of-the-art models for protein and small molecule design with extensive wet lab validation and a new API to run all of our models on scalable GPUs wherever you (or your agents) work! 🔥show more

Okay, this is seriously cool. A team from Google... DeepMind, including DeepMind Cofounder Shane Legg, published a paper "From AGI to ASI". In the paper, they include instructions for an AI agent to read along with you. You can open the paper in Codex's in-app browser and have GPT-5.5 read it with you and explain all the concepts. This is the future. AI agents will be part of the target audience, and help us to understand anything we want.show more

Introducing benchmark tracking across all of arXiv With thousands... of models and benchmarks out there, it's impossible to track what's actually state-of-the-art We now index results from millions of papers to surface which models are SOTA according to open source research!show more

Are you worried about AI deep fakes becoming the... norm over the next few years? Yesterday this deep fake video of Mr. Beast began circulating. It directed users to a link that could have scammed them. The video is virtually indistinguishable from reality unless you are Mr Beasts’ Mom. So what are the answer on how to stop this? 1) Community notes will be a powerful tool if also combined with machine learning to tag any and all of the fake videos on the platform. 2) If a video is found to be a deep fake via Community notes X should highlight the fact. 3) We need Continued development of good AI to push back on the bad AI. Thoughts?show more

A major question in multimodal modeling is how to... leverage strong pre-trained models, such as industry-scale LLMs and VLMs, during training to avoid starting from scratch. This is important as the demand for rich and domain-specific multimodal models continues to increase, while training them from scratch is obviously impractical due to limited data and compute. The so-called “any-to-any” multimodal models can model a large diverse dictionary of modalities. That’s good. Their downside is that their architectures are often not decoder-only, which has limited their performance in practice and prevents them from leveraging strong pre-trained decoder-only models as priors. We are releasing MODUS (ICML Conference '26) a decoder-only any-to-any multimodal model to address some of these questions. A single transformer decoder predicts any modality from any others with no modality-specific heads, losses, or task pipelines. We show efficient adaptation of established models, e.g., BAGEL, to rich any-to-any multimodal modeling. We are releasing 14B to 77B parameter models. All materials are open-source. The download links and demos here 🧵show more

Version 1.13b just released! Replacing Workspaces with a new... way to manage your tabs: Spaces. This update also delivers multiple fixes for more stability and security. Please read the full release notes hereshow more

Shout-out to the artist community We love art, images,... and ideas that make the world feel different, even strange at times. As a team, we are always curious about new art and enjoy sharing the gems we stumble upon with our community. From photography to AI and code-based art, here are some of the works we have loved over the past few weeks. We hope you enjoy them as much as we do! ↓show more