Building Generative Image & Video models at Scale - Sander Dieleman, Google DeepMind
Apr 21, 2026 · 40:46
Sander Dieleman, a research scientist at Google DeepMind, explains the behind-the-scenes process of building generative image and video models at scale, focusing on diffusion models. He emphasizes that data curation is often underrated and more impactful than model tweaking. Dieleman details how latent diffusion models use learned autoencoders to compress pixels into smaller representations, enabling training on high-resolution video. He describes diffusion as 'spectral autoregression' that generates images coarse-to-fine, and notes that guidance—amplifying the difference between conditional and unconditional predictions—massively improves sample quality at the cost of diversity. On sampling, he contrasts deterministic and stochastic approaches, and covers distillation techniques like consistency models that reduce steps. Dieleman also discusses control signals beyond text, such as camera motion in video generation, and the importance of post-training for conditioning.
