| --- |
| license: mit |
| library_name: diffusers |
| pipeline_tag: unconditional-image-generation |
| tags: |
| - ddpm |
| - diffusers |
| - sprited |
| datasets: |
| - sprited/dancing-chibi-figures |
| --- |
| |
| # dancing-chibi-figures-ddpm-64 |
|
|
| **Why this model exists:** it is the *answer sheet* for learning image generation on |
| [sprited/dancing-chibi-figures](https://huggingface.co/datasets/sprited/dancing-chibi-figures). The dataset is meant |
| to be the first dataset you ever train a diffusion model on — and this checkpoint is what you get when you follow the |
| **standard 🤗 diffusers recipe** on it, with nothing clever added: the official unconditional-training tutorial |
| architecture (`UNet2DModel` + `DDPMScheduler`, squaredcos, EMA), 30k steps, ~71M params. If your run looks like the |
| grid below, you did it right. It doubles as proof that the dataset trains cleanly with textbook code, and as the |
| unconditional floor that the conditional models (text-to-image, video) are measured against. |
|
|
| One small twist worth knowing: it diffuses **4-channel premultiplied RGBA** directly (no VAE) — the model learns the |
| transparent background as part of the image. |
|
|
| <p align="center"><img src="sample_030000.png" width="512"></p> |
|
|
| ```python |
| from diffusers import DDPMPipeline |
| pipe = DDPMPipeline.from_pretrained("sprited/dancing-chibi-figures-ddpm-64").to("cuda") |
| img = pipe(batch_size=16, num_inference_steps=50).images # PIL images (RGBA — transparent background included) |
| ``` |
|
|
| Training script (deliberately mirrors the official diffusers unconditional-training tutorial): |
| [`train_diffusers/train_ddpm.py`](https://github.com/sprited-ai/dancing-chibi-figures/blob/main/train_diffusers/train_ddpm.py). |
| Want text control? See [sprited/dancing-chibi-figures-t2i-64](https://huggingface.co/sprited/dancing-chibi-figures-t2i-64). |
| Made by [Sprited](https://sprited.ai). |
|
|
