Pablo Pernías
@pabloppp
Making some dreams become realities... AI, DL, ML, CV, Generative Models, Magic ✨
They make it sound easy. I guess they think this all happens by magic or something. So, I laid it out straight for them. I said, "Yes, I can do it."
What is a reasonable amount of GPU hours to train to convergence a "small" t2i diffusion model? 🤔 What would be considered groundbreaking in your opinion?
nvidia could do the most viral ai competition in history: start with 10,000 researchers and give each a free gpu to work on a public leaderboard but do rounds of elimination where the winners take the remaining hardware. the final winner gets all the gpus for a year.
What would be the expected quality of an 8B params t2i diffusion model nowadays? Would you expect a perfect prompt following, anatomy, long text generation, very fine details etc? Or would you still consider 8B to not be enough for all of that?
Heading to ICML in Vancouver this week? Swing by the Luma AI Happy Hour! Meet our team, network with fellow innovators in AI & ML, and enjoy drinks on us. Space is limited, RSVP now. lu.ma/7b3nyhvb
@linqi_zhou and I will be presenting IMM (lumalabs.ai/news/imm) @ ICML on Tuesday 4pm (oral) and 4:30pm-6:00pm (poster). After that, join us at The Lamplighter Public House (6:00pm - 10:00pm) if you want to chat more! lu.ma/7b3nyhvb
Kira (Short Film on Human Cloning) My new AI-assisted short film is here. Kira explores human cloning and the search for identity in today’s world. It took nearly 600 prompts, 12 days (during my free time), and a $500 budget to bring this project to life. The entire film was…
I am encountering BRAND NEW DATA BUG once every 2 ~ 3 days for past 3 months and its driving me fucking crazy You dont need brand new attention arch im telling you its other way around : YOUR DATASET needs your attention
Install `diffusers` from source and start using Kontext from @bfl_ml 🧨 Use your favorite optims, too :) Training is also supported (@linoy_tsaban and yours truly) 🤗
These are not real photos of me 🤯 I think we can declare the "AI look" is officially dead, thanks to Krea's new model. It's insanely good at photorealism, and you can use trained styles and characters.
who wants early access? 👀
today, we're introducing our first image model: Krea 1. Krea 1 offers superior aesthetic control and image quality. It has a wide range of artistic knowledge and supports style references and custom trainings. learn how to try our free beta 👇
today, we're introducing our first image model: Krea 1. Krea 1 offers superior aesthetic control and image quality. It has a wide range of artistic knowledge and supports style references and custom trainings. learn how to try our free beta 👇
Excited to announce that IMM is accepted as an oral for ICML. As I’ll be going to CVPR as well, if you’d like to chat about research see you at @LumaLabsAI open bar event.
SO excited to finally share my work at Luma! We introduce Inductive Moment Matching, a new generative paradigm that can be trained stably with a single model and single objective from scratch, achieving 1.99 FID on ImageNet-256x256 in 8 steps and 1.98 FID on CIFAR-10 in 2 steps.
Introducing Modify Video. Reimagine any video. Shoot it in post with director-grade control over style, character, and setting. Restyle expressive performances, swap entire worlds, or redesign the frame to your vision. Shoot once. Shape infinitely.
This 60 Minutes-style clip about feline synchronized swimmers is the best thing I've seen this week. I'd watch an entire documentary on it! (from u/notus_analytics)
Flux 1. Kontext is now live and fully integrated into our AI Suite This is the type of editing tool I love: fast, consistent, and good at understanding your instructions. This video was made entirely from one single reference image, used to create multiple start frames. Crazy
introducing Kling 2.1 this new model produces hyper-realistic motion, supports image inputs, and is super fast! try it now in Krea Video.
Nobody wants to hear it, but working on data is more impactful than working on methods or architectures.
1. We often observe power laws between loss and compute: loss = a * flops ^ b + c 2. Models are rapidly becoming more efficient, i.e. use less compute to reach the same loss But: which innovations actually change the exponent in the power law (b) vs change only the constant (a)?