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ComfyUI-DepthAnythingV3

(★ 445)

ComfyUI support for DepthAnything V3 model

파일 탐색기

최종 버전 다운로드 (.zip)

- - pr-gate.yml
  - publish.yml
  - run-tests.yml
- requirements-ci.txt
- - __00005_.png
  - __00006_.png
  - __00007_.png
  - __00008_.png
  - __00009_.png
  - __00010_.png
  - __00011_.png
  - __00012_.png
  - __00013_.png
  - __00014_.png
  - __00015_.png
  - __00016_.png
  - __00017_.png
- - 000.png
  - 010.png
- bas_relief.png
- bridge.jpg
- icon.png
- kaffi.jpg
- robot_unitree.mp4
- 3d.png
- 3d_multiview.png
- advanced.png
- bas_relief_wf.png
- simple.png
- video.png
- - __init__.py
  - alignment.py
  - camera.py
  - configs.py
  - geometry.py
  - gs.py
  - model.py
  - sh_helpers.py
- - __init__.py
  - model.py
- - __init__.py
  - alignment_torch.py
  - alignment_triton.py
  - node.py
  - pipeline.py
  - sim3loop.py
  - sim3utils.py
  - solve_python.py
- __init__.py
- comfy-env.toml
- image_utils.py
- load_model.py
- nodes_3d.py
- nodes_camera.py
- nodes_inference.py
- nodes_multiview.py
- normalization.py
- preview_nodes.py
- utils.py
- advanced.json
- advanced_3d.json
- advanced_3d_multiview.json
- bas_relief.json
- da3_streaming.json
- simple.json
- video_multiview_depth.json
.gitignore
__init__.py
comfy-env-root.toml
comfy-test.toml
install.py
LICENSE
prestartup_script.py
pyproject.toml
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd ComfyUI-DepthAnythingV3

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt --upgrade

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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> [!WARNING]
> Warning, uses experimental package `comfy-env` to attempt a one click isolated install. Will download and use pixi package manager.

# ComfyUI Depth Anything V3

## Installation

Three options, in order of speed → reliability:

1. **ComfyUI Manager (recommended)** — search for `Depth Anything V3` in the Manager and click Install from the highest version displayed. If that doesn't work, try nightly.
2. **Manager via Git URL** — in ComfyUI Manager: "Install via Git URL" with `https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3.git`.
3. **Manual (most reliable)**:
   ```bash
   cd ComfyUI/custom_nodes
   git clone https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3.git
   cd ComfyUI-DepthAnythingV3
   pip install -r requirements.txt --upgrade
   python install.py
   ```

> **Please report any problems** you hit during installation or use of my nodes — open a [Discussion](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/discussions) or [Issue](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/issues). Very grateful for your help! 🙏

---

<div align="center">
<a href="https://pozzettiandrea.github.io/ComfyUI-DepthAnythingV3/">
<img src="https://pozzettiandrea.github.io/ComfyUI-DepthAnythingV3/gallery-preview.png" alt="Workflow Test Gallery" width="800">
</a>
<br>
<b><a href="https://pozzettiandrea.github.io/ComfyUI-DepthAnythingV3/">View Live Test Gallery →</a></b>
</div>

Custom nodes for [Depth Anything V3](https://github.com/ByteDance-Seed/Depth-Anything-3) integration with ComfyUI.

Simple workflow:
![simple](https://raw.githubusercontent.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/main/docs/simple.png)

Advanced workflow:
![advanced](https://raw.githubusercontent.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/main/docs/advanced.png)

Single image to 3d:
![3d](https://raw.githubusercontent.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/main/docs/3d.png)

Multiple image to 3d:
![3d_multiview](https://raw.githubusercontent.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/main/docs/3d_multiview.png)

Single image to mesh:
![bas_relief_wf](https://raw.githubusercontent.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/main/docs/bas_relief_wf.png)

Use multi attention node for smooth video depth!
![video](https://raw.githubusercontent.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/main/docs/video.png)

## Demo Videos

You can use the multi-view node to use the cross attention feature of the main class of models. This is done to have a more consistent depth across frames of a video.

https://github.com/user-attachments/assets/058bd968-aae3-4759-887c-4c98132559f0

You can reconstruct 3D point clouds!

https://github.com/user-attachments/assets/8ef6e74b-c7c7-41e7-b1c6-de44733e6c61

Even from multiple views, with the option to either match them (with icp) or leave them to use the predicted camera positions. You also have a field on the point cloud to show you which view each point came from.

https://github.com/user-attachments/assets/6892313d-bcd8-44ec-9038-7d4d8915f59e

## Description

Depth Anything V3 is the latest depth estimation model that predicts spatially consistent geometry from visual inputs.

**Published**: November 14, 2025
**Paper**: [Depth Anything 3: Recovering the Visual Space from Any Views](https://arxiv.org/abs/2511.10647)

## Model Variants

| Model | Size | Features |
|-------|------|----------|
| DA3-Small | 80M | Fast, good quality |
| DA3-Base | 220M | Balanced quality and speed |
| DA3-Large | 350M | High quality, balanced |
| DA3-Giant | 1.15B | Best quality, slower |
| DA3Mono-Large | 350M | Optimized for monocular depth |
| DA3Metric-Large | 350M | Metric depth estimation |
| DA3Nested-Giant-Large | 1.4B | Combined model with metric scaling |

## Model Capabilities

Different models support different features:

| Feature | Small/Base/Large/Giant | Mono-Large | Metric-Large | Nested |
|---------|------------------------|------------|--------------|--------|
| **Sky Segmentation** | ❌ | ✅ | ✅ | ✅ |
| **Camera Conditioning** | ✅ | ❌ | ❌ | ✅ |
| **Multi-View Attention** | ✅ | ⚠️ | ⚠️ | ✅ |
| **3D Gaussians** | ✅* | ❌ | ❌ | ✅* |
| **Ray Maps** | ✅ | ❌ | ❌ | ✅ |

- ✅ = Fully supported
- ❌ = Not available (returns zeros/ignored)
- ⚠️ = Works but no cross-view attention benefit (images processed independently)
- ✅* = Requires fine-tuned model weights (placeholder in current release)

**Choose your model based on needs:**
- Need sky masks? → Use Mono/Metric/Nested (required for V2-Style normalization)
- Need camera conditioning? → Use Main series or Nested
- Processing video/multi-view? → Use Main series or Nested for consistency
- Single images only? → Any model works

## Workflow Tips

### For ControlNet Depth Workflows
1. Use **Mono** or **Metric** models (they provide sky segmentation)
2. Set **normalization_mode** to **V2-Style** (default)
3. Connect the `depth` output to your ControlNet node
4. Enjoy clean depth maps with proper sky handling!

### For 3D Reconstruction (Point Clouds)
1. Use any model (Mono/Metric recommended for sky filtering)
2. Set **normalization_mode** to **Raw**
3. Connect `depth` → `depth_raw`, `confidence` → `confidence`, `sky_mask` → `sky_mask` to **DA3 to Point Cloud**
4. Sky pixels will be automatically excluded if sky_mask is connected
5. **Important**: Point cloud nodes validate input and will raise an error if normalized depth is detected (prevents incorrect 3D output)

## Community

Questions or feature requests? Open a [Discussion](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3/discussions) on GitHub.

Join the [Comfy3D Discord](https://discord.gg/bcdQCUjnHE) for help, updates, and chat about 3D workflows in ComfyUI.

## Credits

- **Original Paper**: Haotong Lin, Sili Chen, Jun Hao Liew, et al. (ByteDance Seed Team)
- **Original Implementation**: [PozzettiAndrea](https://github.com/PozzettiAndrea)
- **V2-Style Normalization**: [Ltamann (TBG)](https://github.com/Ltamann) - See [TBG Takeaways: Depth Anything V3](https://www.patreon.com/posts/tbg-takeaways-v3-143804015) for workflow examples
- **Based on**: Official [Depth Anything 3 repository](https://github.com/ByteDance-Seed/Depth-Anything-3)
- **Inspiration**: [kijai's ComfyUI-Depth-Anything-V2](https://github.com/kijai/ComfyUI-DepthAnythingV2)

## License

Model architecture files based on Depth Anything 3 (Apache 2.0 / CC BY-NC 4.0 depending on model).

Note: Some models (Giant, Nested) use CC BY-NC 4.0 license (non-commercial use only).