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+---
+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.