Every image was generated using SuperDiff for SDXL with two different prompts. Now, what are the prompts?🤔

SuperDiff goes super big!
- Spotlight at #ICLR2025!🥳
- Stable Diffusion XL pipeline on HuggingFace huggingface.co/superdiff/su... made by Viktor Ohanesian
- New results for molecules in the camera-ready arxiv.org/abs/2412.17762
Let's celebrate with a prompt guessing game in the thread👇

🧵(7/7) The main result that unlocks all these possibilities is our new Itô density estimator, an efficient way to estimate the density of the generated samples for an already-trained diffusion model (assuming that we know the score). It does not require any extra computations, just the forward pass!

🧵(6/7) We try out SuperDiff on generating images with #StableDiffusion by superimposing two prompts so that the image satisfies both. Ever wondered what a waffle cone would look like if it doubled as a volcano? Check out our paper! You’ll find marvellous new creatures in there such as an otter-duck

🧵(5/7) We test our model for unconditional de novo protein generation, where we superimpose two diffusion models: Proteus generates more designable and novel proteins, while FrameDiff generates more diverse proteins. SuperDiff combines them to generate designable and novel and diverse proteins!

🧵(4/7) Here’s a 2D example for intuition: given two already trained models, we combine their outputs (vector fields) based on estimated densities, allowing us to generate samples from all modes (e.g. for continual learning) or from the surface of equal densities (e.g. for concept interpolation).

🧵(1/7) Have you ever wanted to combine different pre-trained diffusion models but don't have time or data to retrain a new, bigger model?

🚀 Introducing SuperDiff 🦹‍♀️ – a principled method for efficiently combining multiple pre-trained diffusion models solely during inference!

We're presenting our spotlight paper on transition path sampling at #NeurIPS2024 this week! Learn how to speed up the conventional Monte Carlo approaches by orders of magnitude

Wed 11 Dec 4:30 pm #2606
arxiv.org/abs/2410.07974

first authors = {Yuanqi Du, Michael Plainer, @brekelmaniac.bsky.social}

this picture provides a great comparison with other methods

so happy to see that Action Matching finds its applications in physics, outperforming diffusion models and Flow Matching!

wonderful work by Jules Berman, Tobias Blickhan, and Benjamin Peherstorfer!
arxiv.org/abs/2410.12000