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a16z Podcast
Text to Video: The Next Leap in AI Generation
Dec 20, 2023
In this fascinating discussion, Robin Rombach, an AI researcher and co-inventor of Stable Diffusion, and Andreas Blattmann, a key contributor at Stability AI, delve into the groundbreaking developments in text-to-video technology. They explore the complexities of transforming text into dynamic video, the pivotal role of datasets, and infrastructure challenges that drive innovation. The conversation also touches on creative possibilities with LORAs for video editing and the spirit of collaboration in AI research, making it clear that the future of generative video is bright.
32:31
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Quick takeaways
- Generating videos is more challenging than images due to larger file sizes and the need for dynamic representation.
- Open-source models enable the reuse of structural spatial understanding from image models in training video models, facilitating multi-modality and fine-grained control.
Deep dives
Stable Video Diffusion: Advancements in Text-to-Video AI Models
Stability AI researchers have released Stable Video Diffusion, an open-source generative video model. Unlike text-to-image models, generating videos is more challenging due to larger file sizes and the need for dynamic representation. Stable Video Diffusion leverages the success of Stable Diffusion, a text-to-image model, to transform images into short video clips. The researchers discuss the difficulties of training video models, such as scaling the data set and data loading, and the importance of incorporating multi-view data and explicit 3D knowledge. They highlight the potential for fine-grained control in video creation through lightweight adapters called Laura's. Challenges moving forward include generating longer and more coherent videos, improving efficiency, and adding audio tracks to synthesized videos.
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