| --- |
| license: mit |
| tags: |
| - video-diffusion |
| - diffusion |
| - pytorch |
| - sprited |
| datasets: |
| - sprited/dancing-chibi-figures |
| --- |
| |
| # Dancing Chibi Figures — baseline checkpoints |
|
|
| Reference checkpoints for the [sprited/dancing-chibi-figures](https://huggingface.co/datasets/sprited/dancing-chibi-figures) |
| dataset, trained with the **pure-PyTorch** trainer in the [dataset's GitHub repo](https://github.com/sprited-ai/dancing-chibi-figures) |
| (`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](https://github.com/sprited-ai/dancing-chibi-figures/blob/main/notebooks/dancing_chibi_figures_colab.ipynb) |
| compares your short training runs against. |
|
|
| | file | what | steps | |
| |---|---|---| |
| | `unet_img64.pt` | unconditional 64px image model (`--frames 1`) — [samples](unet_img64_samples.png) | 30k | |
| | `unet_img64_cond.pt` | group-conditional image model (`--cond group`, classifier-free guidance) — [samples](unet_img64_cond_samples.png) | 30k | |
| | `unet_t2v64.pt` | **text-conditioned autoregressive video** (`--cond text --ar_ctx 8 --frames 8 --stride 2`, CLIP prompt embeddings, warm-started from `unet_img64`) — landing soon | 60k | |
| | `unet_t2v64_scratch.pt` | same recipe from scratch (how much does the image warm-start buy?) — landing soon | 30k | |
| | `dit_img64_p2.pt` | the other architecture: DiT (transformer) + flow matching, patch 2 — landing soon | 30k | |
|
|
| Used by the [Colab tutorial](https://github.com/sprited-ai/dancing-chibi-figures/blob/main/notebooks/dancing_chibi_figures_colab.ipynb) |
| as the warm-start image model and the "fully-trained" reference. Generate a prompt-controlled dance: |
|
|
| ```bash |
| python scripts/rollout.py --ckpt unet_t2v64.pt --prompt "A person jumps in place." --seconds 5 --n 8 --out dance.gif |
| ``` |
|
|
| Prefer standard 🤗 diffusers? See [sprited/dancing-chibi-figures-ddpm-64](https://huggingface.co/sprited/dancing-chibi-figures-ddpm-64) |
| (unconditional `DDPMPipeline`) and [sprited/dancing-chibi-figures-t2i-64](https://huggingface.co/sprited/dancing-chibi-figures-t2i-64) |
| (text-to-image, mini-Stable-Diffusion style). Made by [Sprited](https://sprited.ai). |
|
|
