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4Real-Video-V2: Feedforward Reconstruction for 4D Scene Generation

1Snap Inc. 2KAUST 4Real-Video-V2 is capable of computing a 4D spatio-temporal grid of video frames and 3D Gaussian particles for each time step using a feed-forward architecture. Its architecture has two main components, a 4D video diffusion model and a feedforward reconstruction model. Your browser does not support the video tag. This represents a major upgrade over 4Real-Video, introducing a new 4D video diffusion model architecture that adds no additional parameters to the base video model

Every fusion startup that has raised over $100M

Over the last several years, fusion power has gone from the butt of jokes — always a decade away! — to an increasingly tangible and tantalizing technology that has drawn investors off the sidelines. The technology may be challenging to master and expensive to build today, but fusion promises to harness the nuclear reaction that powers the sun to generate nearly limitless energy here on Earth. If startups are able to complete commercially viable fusion power plants, then they have the potential

I have reimplemented Stable Diffusion 3.5 from scratch in pure PyTorch

miniDiffusion miniDiffusion is a reimplementation of the Stable Diffusion 3.5 model in pure PyTorch with minimal dependencies. It's designed for educational, experimenting, and hacking purposes. It's made with the mindset of having the least amount of code necessary to recreate Stable Diffusion 3.5 from scratch, with only ~2800 spanning from VAE to DiT to the Train and Dataset scripts. -Files: The main Stable Diffusion model code is located in dit.py, dit_components.py, and attention.py. The d

Beyond GPT architecture: Why Google’s Diffusion approach could reshape LLM deployment

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Last month, along with a comprehensive suite of new AI tools and innovations, Google DeepMind unveiled Gemini Diffusion. This experimental research model uses a diffusion-based approach to generate text. Traditionally, large language models (LLMs) like GPT and Gemini itself have relied on autoregression, a step-by-step approach where each