Tune-A-Video

Why we included this project

Tune-A-Video is the official implementation of an ICCV 2023 paper that showed a single video-text pair can steer a pre-trained text-to-image diffusion model into generating new videos. The codebase is compact and readable, which matters here because the core trick, tuning the attention layers and applying DDIM inversion for temporal consistency, is easy to lose inside a research repo. Pretrained checkpoints are on Hugging Face, and there's a training UI space plus Colab notebooks, so you can reproduce the pipeline and then point it at your own videos and prompts. It was also one of the earliest open projects in this line of work, so it's a handy reference for seeing how later text-to-video methods grew out of image diffusion models.