ComfyUI Depth Anything V3 with V2 Style output by TBG

ByteDance and PozzettiAndrea did a great job with the new Depth Anything V3, but in its default form it isn’t very useful inside our normal ControlNet workflow in ComfyUI. The main issues come from its non-standard normalization and the way sky regions are handled, which makes the raw output unusable for standard applications.

We solved this by adding a new V2-style output node to PozzettiAndrea’s custom node, fixing both normalization and sky treatment. Now the depth maps behave exactly like standard ControlNet depth inputs — and the whole setup is finally ready to rock.

Recommended: Use Mono or Metric models with sky segmentation. More Info and Workflow on TBG Thing Build Generate

ComfyUI Depth Anything V3 by PozzettiAndrea

Custom nodes for Depth Anything V3 integration with ComfyUI.

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_depth.mp4

You can reconstruct 3D point clouds!

simple-3d_noaudio.mp4

Even from multiple views, with the option to either match them (with icp) or leave them to use the predicted camera positions.

multiview_depth_9mb.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

Nodes

Model Loading

Download And Load Depth Anything V3 Model - Downloads and loads model from HuggingFace

• Inputs: model (variant), precision (auto/fp16/fp32/bf16)
• Output: da3_model
• Models auto-download to ComfyUI/models/depthanything3/

DA3 Enable Tiled Processing - Configure model for high-resolution images

• Inputs: da3_model, tile_size, overlap
• Output: da3_model (with tiled config)
• Use for 4K+ images to avoid OOM errors

Inference Nodes

Depth Anything V3 - Basic depth estimation

• Inputs: da3_model, images, camera_params (optional), resize_method, invert_depth
• Output: depth (normalized 0-1, grayscale)

Depth Anything V3 (3D/Raw) - Optimized for 3D reconstruction

• Inputs: da3_model, images, camera_params (optional), resize_method, invert_depth
• Outputs: depth_raw (metric), confidence, intrinsics, sky_mask (MASK type)

Depth Anything V3 (Advanced) - All available outputs

• Inputs: da3_model, images, camera_params (optional), resize_method, invert_depth
• Outputs: depth, confidence, ray_origin, ray_direction, extrinsics, intrinsics, sky_mask

Depth Anything V3 (Multi-View) - Process multiple images with cross-view attention

• Inputs: da3_model, images (batch), resize_method, invert_depth
• Outputs: depth, confidence
• Use for video frames or multiple angles of same scene

3D Processing

DA3 to Point Cloud - Convert depth to point cloud DA3 Save Point Cloud - Export to PLY format DA3 to 3D Gaussians - Extract 3D Gaussian splats (placeholder) DA3 Save 3D Gaussians - Export Gaussians to PLY

Camera Utilities

DA3 Create Camera Parameters - Create camera conditioning input DA3 Parse Camera Pose - Parse camera parameters from JSON

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
• 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

Credits

• Original Paper: Haotong Lin, Sili Chen, Jun Hao Liew, et al. (ByteDance Seed Team)
• Implementation: Based on the 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).