Reference checkpoints for the sprited/dancing-chibi-figures dataset, trained with the pure-PyTorch trainer in the dataset's GitHub repo (train/video_ddpm.py — factorised 3D UNet, v-prediction, cosine schedule, EMA).

Why this repo exists: the dataset ships two learning tracks. The diffusers track (links at the bottom) teaches the standard tooling everyone uses; this track is the ~600-line from-scratch version where you can read every line of the model, the schedule and the sampler — and it is where the video models live (diffusers has no tiny video pipeline). These checkpoints are the "fully-trained" reference the Colab tutorial compares your short training runs against.

Used by the Colab tutorial as the warm-start image model and the "fully-trained" reference. Generate a prompt-controlled dance:

Prefer standard 🤗 diffusers? See sprited/dancing-chibi-figures-ddpm-64 (unconditional DDPMPipeline) and sprited/dancing-chibi-figures-t2i-64 (text-to-image, mini-Stable-Diffusion style). Made by Sprited.