Depth Anything 3 — GGUF weights for depth-anything.cpp

Brought to you by the LocalAI team.

GGUF conversions of ByteDance Depth Anything 3, for use with depth-anything.cpp — a from-scratch C++17 / ggml port. No Python, no PyTorch, no CUDA toolkit at inference: one self-contained GGUF file plus a small native library and CLI, faster than PyTorch on CPU and bit-exact against the original (correlation 1.0, verified component by component).

Given an image, the engine recovers a dense depth map, per-pixel confidence, camera extrinsics (3×4) and intrinsics (3×3), an optional sky mask, a back-projected 3D point cloud, and exports to glb / COLMAP / PLY.

Files in this repo

Each GGUF is fully self-contained — every dimension, hyperparameter and preprocessing constant is baked into the file; the loader reads them, nothing is hardcoded.

The nested model is a two-file pair: the engine loads the anyview (ViT-g) branch and the metric (ViT-L) branch together and aligns them to produce metric-scale depth + pose. Download both depth-anything-nested-anyview.gguf and depth-anything-nested-metric.gguf.

Depth Anything V2

The same engine also runs Depth Anything V2 checkpoints. DA2 is depth only — no confidence, pose or sky. Relative models output an inverse depth map through a ReLU head; metric models output depth in metres through a Sigmoid × max_depth head (max_depth=20 for the indoor Hypersim variants, max_depth=80 for the outdoor VKITTI variants). The ViT-g (Giant) DA2 checkpoint is not shipped (its Depth-Anything-V2-Giant HF repo is gated/unreleased).

Each model below ships in f32 plus f16 / q8_0 / q6_k / q5_k / q4_k quants (only the f32 + a representative quant are listed for brevity; the full set is in SHA256SUMS).

Parity. Every DA2 GGUF is verified against the upstream DepthAnythingV2 forward (correlation > 0.999 end-to-end at f32, q8_0 near-lossless at corr 0.99962, q4_k at 0.99944). The one exception is depth-anything2-metric-vkitti-small at corr 0.9983 — this is not a porting defect (the C++ route matches the reference Sigmoid × 80 math exactly); it is the inherent ≤20× amplification of backbone fp-rounding noise by the widest metric scale on the smallest backbone. Absolute error stays sub-1% (mean 0.57% of 80 m), and the same ViT-S backbone scores 0.9996 in relative mode. Accepted as near-lossless.

Which one should I use?

• Just trying it out / CPU: depth-anything-base-q4_k.gguf (99 MB, near-lossless).
• Best quality/speed default: depth-anything-base-q8_0.gguf.
• Smallest / fastest: depth-anything-small-f32.gguf.
• Highest quality + 3D reconstruction (point cloud / Gaussians): depth-anything-giant-f32.gguf.
• Single-image depth with sky mask: depth-anything-mono-large-f32.gguf.
• Metric-scale depth (meters), single model: depth-anything-metric-large-f32.gguf.
• Best metric-scale depth + pose: the nested pair (depth-anything-nested-anyview.gguf + depth-anything-nested-metric.gguf).

Usage

depth-anything.cpp (CLI)

git clone https://github.com/mudler/depth-anything.cpp && cd depth-anything.cpp
cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j

# download a weight from this repo
hf download mudler/depth-anything.cpp-gguf depth-anything-base-q4_k.gguf --local-dir models

./build/da3 depth models/depth-anything-base-q4_k.gguf image.jpg --out depth.png
./build/da3 depth models/depth-anything-base-q4_k.gguf image.jpg --pose poses.json
./build/da3 reconstruct models/depth-anything-giant-f32.gguf image.jpg --ply cloud.ply

# metric-scale depth from the single metric model
./build/da3 depth models/depth-anything-metric-large-f32.gguf image.jpg --out depth.png

# metric-scale depth + pose from the nested pair (anyview + metric branches)
./build/da3 depth models/depth-anything-nested-anyview.gguf image.jpg \
    --metric-model models/depth-anything-nested-metric.gguf --pfm depth.pfm

See the README for multi-view, glb/COLMAP export, quantization and the flat C API.

LocalAI

local-ai run depth-anything-3-base

Performance

Faster than PyTorch on CPU at half the memory, bit-exact. AMD Ryzen 9 9950X3D, threads=16, 504×336, sustained:

Full methodology in benchmarks/BENCHMARK.md.

License

The GGUF weights are derived from the official Depth Anything 3 checkpoints and inherit their Apache-2.0 license. The depth-anything.cpp code is MIT.

Citation

@article{depthanything3,
  title   = {Depth Anything 3: Recovering the Visual Space from Any Views},
  author  = {ByteDance Seed},
  year    = {2025}
}