| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - diffusion |
| - planning |
| - compositional-generation |
| - toy-example |
| --- |
| |
| # Compositional Visual Planning: toy example |
|
|
| Pretrained weights and training data of the toy example (Figure 2) in |
| [Compositional Visual Planning via Inference-Time Diffusion Scaling](https://arxiv.org/abs/2603.02646) |
| ([project page](https://comp-visual-planning.github.io/)). A short-horizon diffusion model is trained on |
| circular arc clips. At test time, three 120° arc generators are composed into a three-petal "flower" by |
| enforcing agreement at the chunk boundaries at every denoising step. |
|
|
| ## Files |
|
|
| | File | Description | Size | |
| | --- | --- | --- | |
| | `diffusion_420000.ckpt` | `{"model": ..., "model_ema": ...}` state dicts of the DiT1d denoiser (in_dim 60, emb_dim 256, d_model 384, 12 heads, 6 blocks; x0 prediction, 500 cosine diffusion steps) after 420k gradient steps on 8-token clips | 132 MB | |
| | `toy.npy` | training data, float64 array of shape (64, 600, 2): 64 rotated flowers, each a closed curve of 600 (x, y) points made of three noisy 120° arcs | 0.6 MB | |
| |
| ## Usage |
| |
| The toy example code downloads both files when they are missing locally. To download them by hand: |
| |
| ```bash |
| pip install -U huggingface_hub |
| hf download comp-visual-planning/toy-example diffusion_420000.ckpt --local-dir ckpts |
| hf download comp-visual-planning/toy-example toy.npy --local-dir data |
| ``` |
| |
| or from python: |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
|
|
| ckpt = hf_hub_download("comp-visual-planning/toy-example", "diffusion_420000.ckpt", local_dir="ckpts") |
| data = hf_hub_download("comp-visual-planning/toy-example", "toy.npy", local_dir="data") |
| ``` |
| |
| Then, from the root of the toy example repository, run `python eval.py method=cvp` (ours) or |
| `python eval.py method=gsc` (DiffCollage / Generative Skill Chaining baseline). |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{zhang2026compositionalvisualplanninginferencetime, |
| title={Compositional Visual Planning via Inference-Time Diffusion Scaling}, |
| author={Yixin Zhang and Yunhao Luo and Utkarsh Aashu Mishra and Woo Chul Shin and Yongxin Chen and Danfei Xu}, |
| year={2026}, |
| eprint={2603.02646}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.RO}, |
| url={https://arxiv.org/abs/2603.02646}, |
| } |
| ``` |
| |
