How does Runway differ from other startups using generative AI for text, and can you describe its particular approach?

We have a special approach to building products. Runway is a full-stack applied AI research company. We do the research, develop, and train generative models from scratch. We also develop the necessary infrastructure to safely deploy those models into production-ready environments and continuously improve them. That entails, for example, building any necessary systems to ingest video streams, dataset preparation, etc. And ultimately, we build real-world applications on top of those models and that infrastructure. 

Owning the entire stack has the advantage of having full visibility and control over how the product gets deployed and how our users interact with it. This gets translated into the lowest research level if needed and helps direct product roadmaps and prioritization more effectively. 

Our full-stack approach is visible in our Latent Diffusion work (High-Resolution Image Synthesis with Latent Diffusion Models is the full name of the paper), which we first released and published as open-source in late 2021. Latent Diffusion was the collaboration between two organizations — Runway and LMU Munich. Latent Diffusion has had a few open-source training iterations. Since then, we’ve consistently improved our original Latent Diffusion model, inference speed, the infrastructure and built multiple production-ready tools on top of it. And since the model was open-source, it’s been incredible to see the amount of innovation that has been built by the community. We do have some very exciting releases coming up that will go further than what LatentDiffusion currently allows from a technological perspective.