---
license: apache-2.0
library_name: onnx
tags:
  - image-segmentation
  - face-parsing
  - portrait-segmentation
  - onnx
  - comfyui
---

# EasyPortrait ONNX

ONNX exports of the pretrained EasyPortrait portrait segmentation and face parsing checkpoints.

The original checkpoints and training code were published by the EasyPortrait authors in [hukenovs/easyportrait](https://github.com/hukenovs/easyportrait). This repository exists to make the models easier to use in modern runtimes, especially ComfyUI environments where installing the old `mmsegmentation` / `mmcv-full` stack can be slow or fragile.

## Files

This repository contains 26 validated ONNX models and a `manifest.json`.

Model families:

- Portrait segmentation:
  - BiSeNet-V2
  - DANet
  - DeepLabV3
  - FastSCNN
  - FCN + MobileNetV2
  - FPN + ResNet50 at 224, 384, 512, and 1024
  - SegFormer-B0 at 224, 384, 512, and 1024
- Face parsing:
  - BiSeNet-V2
  - DANet
  - DeepLabV3
  - FastSCNN
  - FCN + MobileNetV2
  - FPN + ResNet50 at 224, 384, 512, and 1024
  - SegFormer-B0 at 224, 384, 512, and 1024

Three upstream README-only checkpoints are not included because they do not include enough model config metadata for reliable conversion: `extremec3net_ps`, `sinet_ps`, and `ehanet_fp`.

## Input And Output

Each model expects a normalized tensor:

- Input name: `image`
- Input shape: `[batch, 3, model_size, model_size]`
- Input dtype: `float32`
- Channel order before normalization: BGR
- Mean: `[143.55267075, 132.96705975, 126.94924335]`
- Std: `[60.2625333, 60.32740275, 59.30988645]`

Each model returns logits:

- Output name: `logits`
- Output shape: `[batch, classes, model_size, model_size]`
- Output dtype: `float32`

Use `argmax(axis=1)` to get the segmentation class map.

Labels:

- Portrait segmentation: `background`, `person`
- Face parsing: `background`, `skin`, `left brow`, `right brow`, `left eye`, `right eye`, `lips`, `teeth`

## Validation

The ONNX models were exported from the original PyTorch/mmseg checkpoints and validated with ONNX Runtime against the PyTorch/mmseg output. The validation compares logits and verifies that the final `argmax` segmentation agrees.

See `manifest.json` for per-model metadata and validation values.

## ComfyUI

These files are used by the ComfyUI custom node:

[ComfyUI-EasyPortrait](https://github.com/sadzip/ComfyUI-EasyPortrait)

The node downloads models from this repository on first use.

## Credits

Thanks to the EasyPortrait authors for training and releasing the original models:

- Original repository: [hukenovs/easyportrait](https://github.com/hukenovs/easyportrait)

Co-authors of this ONNX conversion and ComfyUI integration:

- sadzip
- OpenAI Codex