The article also discusses the challenges and learnings from the development process, including data engineering, research, training, and scaling. The team trained three different video models over 13 months, scaling up their video dataset to 200 million densely captioned publicly available videos. They also built their own image corpus from scratch, scaling it to 1 billion images. The team faced challenges in managing thousands of GPUs, optimizing code, and dealing with infrastructure and optimization issues as they scaled up. Despite these challenges, they are excited about the potential of Hotshot and encourage users to try it out and provide feedback.
Key takeaways:
- Hotshot is a large-scale diffusion transformer model that serves as the foundation for an upcoming consumer product, excelling in prompt alignment, consistency, and motion, and is highly extensible to longer durations, higher resolutions, and additional modalities.
- In the last 13 months, three different video models have been trained, with the latest, Hotshot, generating up to 10 seconds of footage at 720p. The team predicts that in the next 12 months, entire YouTube videos will be AI generated by creators.
- The team faced significant challenges in data engineering and scaling, including managing thousands of GPUs, optimizing code, managing infrastructure, and dealing with GPU process hangs. Despite these challenges, they were able to successfully train the Hotshot model.
- Hotshot is available to try today in beta at https://hotshot.co, and the team is actively seeking feedback from users to improve the model.