Upload toy example checkpoint and data

README.md

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+---
+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 boundary agreement between the chunks 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 automatically when they are missing locally. To fetch 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},
+}
+```

diffusion_420000.ckpt

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+oid sha256:400e0b64af354076830194bcc8ce2b150442ffac8f12c6ae8d0d65a1bf719c48
+size 132444906

toy.npy

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+oid sha256:a106d34e75a9d6163852d5131c2b6067da7757296ca5b97f53fee1b040e6006c
+size 614528