EasyPortrait ONNX
ONNX exports of the pretrained EasyPortrait portrait segmentation and face parsing checkpoints.
Model source
- Kind
- Other
- File size
- 5.33–251 MB
- Version
- vRevision 19e22b9
- Updated
- Sep 22, 2026
- Downloads
- 0
- Source
- Hugging Face
Source description
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. 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:
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
Co-authors of this ONNX conversion and ComfyUI integration:
- sadzip
- OpenAI Codex
Sources
1 source
Sources
1 sourceVerified Sep 8
Model artifacts
26 artifacts
Model artifacts
26 artifactsSource excerpts
2 excerpts
Source excerpts
2 excerpts--- 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 re...
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library onnx · Repo sadzip/EasyPortrait-ONNX
