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):

   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 or Issue. Very grateful for your help! 🙏

---

Custom nodes for Depth Anything V3 integration with ComfyUI.

Simple workflow:

Advanced workflow:

Single image to 3d:

Multiple image to 3d:

Single image to mesh:

Use multi attention node for smooth video depth!

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

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 on GitHub.

Join the Comfy3D Discord 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
• V2-Style Normalization: Ltamann (TBG) - See TBG Takeaways: Depth Anything V3 for workflow examples
• Based on: Official Depth Anything 3 repository
• Inspiration: kijai's ComfyUI-Depth-Anything-V2

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).