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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.

Peter Holderrieth

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474,892 views • 3 months ago •via X (Twitter)

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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.

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.

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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.

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🚨New paper! Generative models are often “miscalibrated”. We calibrate diffusion models, LLMs, and more to meet desired distributional properties. E.g. we finetune protein models to better match the diversity of natural proteins.

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

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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.

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The latent space of earlier generative models like GANS can linearly encode concepts of the data. What if the data was model weights? We present weights2weights, a subspace in diffusion weights that behaves as an interpretable latent space over customized diffusion models.

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Flow-based generative models trained with flow matching tend to learn curved trajectories, which are challenging to approximate in a few steps. Rectified flows aim to learn straight trajectories, which are easier to simulate with less computation.

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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 🇫🇷

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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:

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