workflow-templates-broken-zap-2026-06-08/site/knowledge/models/depth-anything-v2.md

Depth Anything V2

Depth Anything V2 is a monocular depth estimation model trained on 595K synthetic labeled images and 62M+ real unlabeled images, providing robust relative depth maps from single images.

Model Variants

Depth-Anything-V2-Small

• Lightweight variant for fast inference
• ViT-S (Small) encoder backbone
• Suitable for real-time applications

Depth-Anything-V2-Base

• Mid-range variant balancing speed and accuracy
• ViT-B (Base) encoder backbone

Depth-Anything-V2-Large

• High-accuracy variant for detailed depth maps
• ViT-L (Large) encoder backbone with 256 output features
• Recommended for most production use cases

Depth-Anything-V2-Giant

• Maximum accuracy variant
• ViT-G (Giant) encoder backbone
• Highest computational requirements

Key Features

• More fine-grained depth detail than Depth Anything V1
• More robust than V1 and Stable Diffusion-based alternatives (Marigold, Geowizard)
• 10× faster than SD-based depth estimation models
• Trained on large-scale synthetic + real data mixture
• Produces relative (not metric) depth maps by default
• DPT (Dense Prediction Transformer) decoder architecture

Hardware Requirements

• Small: 2GB VRAM minimum
• Base: 4GB VRAM minimum
• Large: 6GB VRAM recommended
• Giant: 12GB+ VRAM recommended
• CPU inference supported for smaller variants

Common Use Cases

• Depth map generation for compositing and VFX
• ControlNet depth conditioning for image generation
• 3D scene understanding and reconstruction
• Foreground/background separation
• Augmented reality occlusion
• Video depth estimation for parallax effects

Key Parameters

• encoder: Model size variant (vits, vitb, vitl, vitg)
• input_size: Processing resolution (higher = more detail, more VRAM)
• output_type: Raw depth array or normalized visualization