[1/4] Ever wondered what it would be like to use images—rather than text—to generate object and background compositions? We introduce VisualComposer, a method for compositional image generation with object-level visual prompts.

Text prompts have shaped how we compose images with foundation models. But what if we could simply inject Visual Prompts instead? We introduce 🌟Visual Composer🌟 which achieves high-fidelity compositions of subjects and backgrounds with visual prompts! snap-research.github.io/visual-compose…

Introducing ⚗️ Video Alchemist Our new video model supporting 👪 Multi-subject open-set personalization 🏞️ Foreground & background personalization 🚀 Without the need of inference-time tuning snap-research.github.io/open-set-video… [Results] 1. Sora girl rides a dinosaur on a savanna 🧵👇

🚀In my last project, I developed a simple interactive WebUI tool, #VisCompare, to compare images/videos side-by-side across different models and methods as in the video. 🌟It's now open-source at github.com/mit-han-lab/Vi…! 🙌Hope it can benefit the community—feedback and

Excited to bring back the 2nd Workshop on Visual Concepts at #CVPR2025 2025, this time with a call for papers! We welcome submissions on the following topics. See our website for more info: sites.google.com/stanford.edu/w… Join us & a fantastic lineup of speakers in Tennessee!

Explore SVDQuant, it's time for 4bit inference: forbes.com/sites/johnwern…

Can we generate a training dataset of the same object in different contexts for customization? Check out our work SynCD, which uses Objaverse assets and shared attention in text-to-image models for the same. cs.cmu.edu/~syncd-project/ w/ Xi Yin Jun-Yan Zhu Ishan Misra Samaneh Azadi

Holy Crap!! The journal extension of Expressive Image Generation with Rich Text has been accepted to IJCV! This extension expands the capability of rich text by enabling hyperlinks, texture fill, semantic image editing, and a new benchmark (yay, table with numbers)! Congrats

Check out our @gradio demo based on Black Forest Labs's FLUX model!! We fine-tune the model using our generated dataset to achieve tuning-free customization on new reference objects. huggingface.co/spaces/nupurkm…

We shared some early work towards a multi-modal and multi-task 3D foundation model at Roblox. First release is a discrete shape tokenizer compatible with autoregressive modeling for text-to-shape. More to come soon Github: github.com/Roblox/cube Arxiv: arxiv.org/abs/2503.15475

Today's visual generative models are mere stochastic parrots of imagery, much like early language models, which could only statistically mimic short sentences with little reasoning. In contrast, modern large language models (LLMs) can comprehend long documents, keep track of

Excited to come out of stealth at Reve! Today's text-to-image/video models, in contrast to LLMs, lack logic. Images seem plausible initially but fall apart under scrutiny: painting techniques don't match, props don't carry meaning, and compositions lack intention. (1/4)

I've updated my blog post to walk through the remaining technical details of our Surface Winding Numbers algorithm: now the calculus of the algorithm is explained a bit more in detail. The post, paper, code, etc. is all here: nzfeng.github.io/research/WNoDS…

Hi there, Phillip Isola and I wrote a short article (500 words) on Generative Modeling for the Open Encyclopedia of Cognitive Science. We briefly discuss the basic concepts of generative models and their applications. Don't miss out Phillip Isola's hand-drawn cats in Figure 1!

🚀 How to run 12B FLUX.1 on your local laptop with 2-3× speedup? Come check out our #SVDQuant (#ICLR2025 Spotlight) poster session! 🎉 🗓️ When: Friday, Apr 25, 10–12:30 (Singapore time) 📍 Where: Hall 3 + Hall 2B, Poster 169 📌 Poster: tinyurl.com/poster-svdquant 🎮 Demo:

Thank you, Coach Pop, for your brilliance on and off the court. We look forward to our next chapter together.

Wrapped up Stanford CS336 (Language Models from Scratch), taught with an amazing team Tatsunori Hashimoto Marcel Rød Neil Band Rohith Kuditipudi. Researchers are becoming detached from the technical details of how LMs work. In CS336, we try to fix that by having students build everything:

🚀 #Nunchaku now supports FLUX.1-Kontext-dev! Edit images with just one sentence — style transfer, face swap, and more — now 2–3× faster and using 1/4 VRAM. ✅ Works with ComfyUI & Diffusers 🔗 Demo: svdquant.mit.edu/kontext/ 📂 Code: github.com/mit-han-lab/nu… 🤗 4-bit #SVDQuant