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! 🙏

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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.

video_to_depth.mp4

You can reconstruct 3D point clouds!

3d.mp4

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.

3dmv.mp4

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).