---
license: other
license_name: dinov3-license
license_link: https://huggingface.co/apozz/DAP-weights-safetensors/blob/main/LICENSE.dinov3.md
tags:
  - depth-estimation
  - panorama
  - equirectangular
  - "360"
  - dinov3
  - dpt
  - safetensors
  - comfyui
library_name: pytorch
pipeline_tag: depth-estimation
base_model: Insta360-Research/DAP-weights
---

# DAP-weights-safetensors

This repository hosts a **safetensors-format mirror** of
[`Insta360-Research/DAP-weights`](https://huggingface.co/Insta360-Research/DAP-weights),
the official checkpoint released with the paper
[*Depth Any Panoramas (DAP): A Foundation Model for Panoramic Depth Estimation*](https://arxiv.org/abs/2512.16913)
by Lin et al. (Insta360 Research Team, 2025).

The model is a **metric depth estimator for 360° × 180° equirectangular panoramas**:
DINOv3-ViT-L/16 encoder + DPT depth head + a "range-mask" head that flags pixels
beyond the model's confident distance range.

**Nothing here is retrained.** The weights are bit-identical to the upstream
checkpoint; this mirror only changes the on-disk *format* (PyTorch `.pth` →
`.safetensors`) and removes storage-shared alias keys from the state-dict to
keep the file ~1.46 GB (matching the original) instead of ~2.5 GB.

## Files

| File | Size | Description |
| --- | --- | --- |
| `dap_vitl.safetensors` | ~1.46 GB | All trainable tensors. 498 unique tensors (the upstream `.pth` exposed 858 keys; 360 were storage-aliased duplicates from `DINOv3Adapter.blocks` ↔ `DINOv3Adapter.model.blocks` — see "Implementation notes" below). |
| `LICENSE.dap.md` | — | DAP MIT license (Insta360 Research Team's contribution). |
| `LICENSE.dinov3.md` | — | DINOv3 License Agreement — governs the DINOv3 backbone weights inside the checkpoint, which are derivative works of Meta's DINOv3. |

## License

The weights in this repository are governed by **two licenses simultaneously**:

1. **DAP / Insta360 contribution → MIT** (`LICENSE.dap.md`)
2. **DINOv3 backbone weights → [DINOv3 License Agreement](LICENSE.dinov3.md)**

The DINOv3 License is permissive but **not** MIT. By using these weights you
agree to its terms, including (non-exhaustive):

- You may use, reproduce, distribute, copy, modify, and create derivative works
  of the DINO Materials.
- Your use **must comply with applicable laws and Trade Controls** (US OFAC, UN,
  EU, UK sanctions; export controls).
- You may **not** use the model for, or encourage others to use it for, any
  activities subject to ITAR or end-uses prohibited by Trade Controls — including
  military or warfare purposes, nuclear industries or applications, espionage, or
  the development or use of guns or illegal weapons.
- If you redistribute the weights or any derivative, you **must include a copy
  of the DINOv3 License Agreement** alongside them.
- You may not reverse engineer, decompile, or discover the underlying components
  of the DINO Materials.

See `LICENSE.dinov3.md` for the full text.

## Acknowledgements

- **Upstream weights**: [`Insta360-Research/DAP-weights`](https://huggingface.co/Insta360-Research/DAP-weights)
- **DAP paper / code**: [Insta360-Research-Team/DAP on GitHub](https://github.com/Insta360-Research-Team/DAP)
- **DINOv3 backbone**: Meta AI Research (DINOv3 License Agreement applies to the
  encoder weights inside this checkpoint).
- **DPT decoder lineage**: Depth Anything V2.

Neither Meta, Insta360 Research Team, nor any of the upstream authors endorse or
are affiliated with this mirror.

## Citation

```bibtex
@article{lin2025dap,
  title  = {Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation},
  author = {Lin, Xin and Song, Meixi and Zhang, Dizhe and Lu, Wenxuan and Li, Haodong and Du, Bo and Yang, Ming-Hsuan and Nguyen, Truong and Qi, Lu},
  journal = {arXiv:2512.16913},
  year   = {2025}
}
```

## Usage

The ergonomic path: install
[ComfyUI-DAP](https://github.com/PozzettiAndrea/ComfyUI-DAP), which auto-downloads
this file on first run.

Direct PyTorch load:

```python
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

path = hf_hub_download("apozz/DAP-weights-safetensors", "dap_vitl.safetensors")
state = load_file(path)

# Load into a DAP model built with the upstream architecture.
# Use strict=False: storage-shared alias keys (DINOv3Adapter.blocks vs
# DINOv3Adapter.model.blocks) are deduplicated in this checkpoint and the
# missing aliases are populated automatically via the shared underlying storage.
model.load_state_dict(state, strict=False)
```

## Implementation notes

The upstream `model.pth` was a flat state-dict (plus an `epoch` integer) where
360 tensor keys were storage-aliased duplicates: PyTorch's `state_dict()` walks
both `DINOv3Adapter.model.blocks.*` (canonical) and `DINOv3Adapter.blocks.*`
(alias registered by `self.blocks = self.model.blocks` in `DINOv3Adapter`).
`torch.save` stored each unique storage once; safetensors has no
storage-deduplication mechanism, so a naive `.clone()`-then-`save_file` would
double the file size. This mirror keeps only the canonical `.model.*` key for
each storage. When loaded into a fresh `DAP` model, the alias parameters are
populated automatically because they share underlying storage with the
canonical parameter.