6 of 48 articles — browse by tag or search to filter.
How diffusion models predict action sequences instead of pixels. Covers Diffusion Policy, world models for robotics, and connecting diffusion to reinforcement learning for autonomous systems.
A deep dive into the physics and probability of diffusion models. Learn how reversing a stochastic process became the foundation for modern generative AI, from Stable Diffusion to robotics and protein design.
The evolution of image diffusion architectures. Learn how we moved from convolutional U-Nets to scalable Diffusion Transformers (DiT), and why treating images like language changed everything.
Exploring the state-of-the-art in video generation. Learn how Sora and Veo use Spatiotemporal Transformers to simulate the physical world, and the challenges of achieving perfect motion fidelity.
How to move from visual imitation to law-governed motion. Deep dive into injecting PDEs into neural networks, implicit physics extraction, and LLM-guided physical reasoning.
How to train a world-class diffusion model. Covers the complete lifecycle: from large-scale pre-training on noisy web data to specialized post-training, alignment, and aesthetic fine-tuning.