| --- |
| license: apache-2.0 |
| base_model: depth-anything/Video-Depth-Anything-Small |
| tags: |
| - depth-estimation |
| - video |
| - onnx |
| - webgpu |
| library_name: onnxruntime |
| --- |
| |
| # Video Depth Anything (Small) — ONNX for TheoreticallyPose |
|
|
| Browser-ready ONNX export of [Video-Depth-Anything-Small](https://huggingface.co/depth-anything/Video-Depth-Anything-Small) |
| (Apache-2.0), used by the **"Consistent (VDA)" depth engine** in |
| [TheoreticallyPose](https://www.youtube.com/@TheoreticallyMedia) — a single-file browser |
| app that turns a source clip into pose / depth / silhouette control videos for |
| video-to-video AI generation. The app downloads these two files automatically the first |
| time you use the Consistent engine (~59 MB, cached by your browser). |
|
|
| ## Files |
|
|
| | file | what it is | input → output | |
| |---|---|---| |
| | `vda_vits_encoder_518x910_fp16.onnx` (45 MB) | per-frame DINOv2-S encoder | `[1,3,518,910]` normalized RGB → 4 × `[1,2405,384]` ViT features | |
| | `vda_vits_head_T32_37x65_fp16.onnx` (14 MB) | temporal DPT head, 32-frame window | 4 × `[32,2405,384]` stacked features → `[32,1,518,910]` depth | |
|
|
| Conversion notes: exported with the PyTorch legacy tracer at opset 17, static shapes |
| (518×910, T=32), split at the ViT-token boundary so per-frame encoder features can be |
| cached across overlapping windows; vanilla attention throughout (no xformers); |
| weights converted to fp16 with fp32 graph I/O. Runs on onnxruntime-web's WebGPU |
| execution provider. |
|
|
| ## Attribution & license |
|
|
| Original model and architecture: **Video Depth Anything** by ByteDance |
| ([repo](https://github.com/DepthAnything/Video-Depth-Anything), |
| [paper](https://arxiv.org/abs/2501.12375), CVPR 2025 Highlight). |
| Only the **Small** variant is redistributed here, under its original |
| **Apache-2.0** license (see `LICENSE`). The Base/Large variants are CC-BY-NC and are |
| **not** included. This repo contains a format conversion (ONNX/fp16) of the original |
| checkpoint; no weights were retrained or fine-tuned. |
|
|
| ```bibtex |
| @inproceedings{video_depth_anything, |
| title={Video Depth Anything: Consistent Depth Estimation for Super-Long Videos}, |
| author={Chen, Sili and Guo, Hengkai and Zhu, Shengnan and Zhang, Feihu and Huang, Zilong and Feng, Jiashi and Kang, Bingyi}, |
| booktitle={CVPR}, |
| year={2025} |
| } |
| ``` |
|
|
