README.md

metadata

license: mit
pipeline_tag: depth-estimation
tags:
  - depth-estimation
  - monocular-depth
  - zipdepth
  - edgepad
  - onnx

EdgePad depth models (ZipDepth for mobile GPUs)

This repository mirrors the PC ONNX EdgePad depth models published by the Nightfall team. The model research, fine-tuning, graph optimization, and exports were created by Nightfall and are credited to its authors and to Fabio Tosi, the author of ZipDepth.

The model-card text below is based closely on the original release notes, with only small changes to describe the files mirrored here.

About these models

The monocular depth models Nightfall uses for AI 3D are published for anyone to use: TFLite for phones and headsets (Nightfall runs them on Quest), and ONNX for PCs (Nightfall Meteor runs them there). They are MIT licensed; see LICENSE.

All of them are EdgePad builds of ZipDepth by Fabio Tosi (MIT): a 6.1M-parameter convolutional network distilled from Depth Anything V2 Large, with no attention or transformer operations, which is what allows it to run entirely on a mobile GPU. There are two generations:

• Square EdgePad (shipped in Nightfall v0.7.x): ZipDepth's own Standard checkpoint, not retrained, exported through exact graph rewrites so the whole network runs on the GPU.
• Widescreen EdgePad (Nightfall's next release): a light fine-tune of the same checkpoint for 16:9 video and steadier depth from frame to frame, exported at exact 16:9 sizes.

Files in this mirror

File	Generation	Input	Output	SHA-256
zipdepth_edgepad_384.onnx	Square	384x384	384x384	78efcf08caded436c626af0b7b5980db47bc07653ec549a59b7e48e73b3e035f
zipdepth_wide_512x288.onnx	Widescreen	512x288	512x288	197d598f50ee3270b12831096dbab260c6bd5979bab13b87f01a28fad0268274
zipdepth_wide_672x384.onnx	Widescreen	672x384	672x384	f812b10b10309c6759266cbe888fa9c0f70668350fdf69e7eda9cc0412ca1a21

Sizes are width x height. These are the PC ONNX exports used by Nightfall Meteor. This Hugging Face mirror currently contains the three ONNX files above; the original release also provides TFLite exports for mobile and headset use.

Using them

• Input: RGB as float32 in 0 to 1 (pixel / 255). ImageNet mean and standard deviation are applied inside the graph. The ONNX model takes NCHW [1, 3, H, W] named image. Resize the frame to the model input size; Nightfall uses an area filter.
• Output: relative inverse depth: larger is nearer, with no fixed scale or offset. Normalise each frame before use. Nightfall stretches between low and high percentiles and smooths the result over time. The ONNX models output the full-size map, already unpacked and clamped.

Square EdgePad: optimisation only

The weights are ZipDepth's Standard checkpoint (zipdepth_base.pth), unchanged, including its learned convex reconstruction head, which proved essential for clean full-screen geometry in the headset. Every change in the Square EdgePad export is to the graph and is mathematically exact:

1. Materialised attention broadcasts. ZipDepth's strip, channel, and global-context attention broadcast small tensors across the feature map. These tensors are expanded before the operation for GPU-delegate compatibility.
2. Small reductions. Large spatial average pools are split into exact 2x and 3x stages.
3. Packed reconstruction head. The head's 5-dimensional softmax and depth-to-space tail becomes four ordinary 9-channel softmax branches, the 36-channel mask convolution becomes four 9-channel convolutions, and the weighted sums become fixed 1x1 convolutions.
4. EdgePad. The head's replicate padding is rebuilt from edge slices and concatenation so the whole graph can run without splitting and copying intermediate tensors back to the CPU.

Against the original Standard graph, Nightfall reports correlation 0.9999999999999908 and normalised mean absolute error 3.6e-8.

Widescreen EdgePad: a light fine-tune

One checkpoint (SHA-256 2dc8fddcef9ef22f45b6a1573b9cf66de0f39b1b5172e4284dd693790bd6309e) was trained from ZipDepth's Standard checkpoint in two short stages. Only the decoder was trained: 154,005 of 6.1 million parameters. The encoder is ZipDepth's.

• Stage 1 (5,000 steps): shapes alternated between 384x384, 256x256, 224x224, and 512x288, so one set of weights serves every size. Training used 2,000 frames from 25 videos: gameplay/YouTube, film, live-action/CG, and animation. Labels were the original Square EdgePad-384 output, Video Depth Anything Small for frame-to-frame consistency, and Marigold V2 for spatial detail on selected keyframes. A texture-leakage term at weight 0.5 discourages copying image texture into depth.
• Stage 2 (1,000 steps at learning rate 5e-5): decoder-only training for the exact 16:9 sizes.

The model has no memory and processes each frame independently; temporal behaviour was learned only through the losses.

Nightfall's held-out video results against the untouched ZipDepth export at the same size:

Model	Compared with	Flicker on static scenes	Flow-warped error	Frame-to-frame change
512x288	stock 512x288	-8.6%	-5.8%	-8.4%
320x180	stock 224x224	-42.4%	-43.9%	-39.2%

The trade-off is about 11% higher texture leakage than the stock 224x224 model at 320x180. Nightfall notes that the 320x180 model is also visibly less detailed than the 384x384 model, as its size implies.

Nightfall reports that the TFLite exports match their ONNX sources with correlation 0.99999 and pass LiteRT's GPU compatibility check without warnings. Meteor's ONNX files are the same weights with the 2x2 unpacking and clamp added to the graph and the weights embedded. The 672x384 file is an export of the same checkpoint at a larger size and was not part of the held-out evaluation above.

Credits and licences

• ZipDepth: Fabio Tosi, MIT. These models are derivatives of its weights; its copyright notice is retained in LICENSE.
• Teacher models used only to make training labels, not included here: Video Depth Anything Small (Apache-2.0) and Marigold V2 (Apache-2.0; built on Qwen-Image-Edit-2509, Apache-2.0).
• Fine-tuning, graph optimisation, and exports: Nightfall's model research team. Sincere thanks to tB0nE and all Nightfall contributors for making these models and the detailed release notes available.

This Hugging Face repository is a mirror provided for convenient access. All credit for the original models and release documentation belongs to the Nightfall team and Fabio Tosi.