Pixelity — models
ONNX models (and the ONNX Runtime Web WebAssembly binary) used by Pixelity, a web image editor that runs entirely in the user's browser: background removal, AI object selection and image upscaling. The browser downloads each file on demand when the feature is first used; images never leave the user's device.
This repository redistributes unmodified third-party files (except where noted as a format conversion). All credit goes to the original authors. Each file keeps its original license; full texts are in licenses/.
Files
| File | Use in Pixelity | Origin | License | Size | SHA-256 |
|---|---|---|---|---|---|
models/u2netp.onnx |
Background removal (fast) | U²-Net (Xuebin Qin et al.); ONNX file from rembg v0.0.0 (Daniel Gatis) | Apache-2.0 (weights) · MIT (rembg) | 4.57 MB | 309c8469258dda742793dce0ebea8e6dd393174f89934733ecc8b14c76f4ddd8 |
models/birefnet_lite_fp16.onnx |
Background removal (high quality, GPU with fp16) | BiRefNet (Peng Zheng et al.); onnx-community/BiRefNet_lite-ONNX onnx/model_fp16.onnx |
MIT | 114.5 MB | d39b897ceb16ae654c1731f3dba0cf9b368d9cae74b5a57459b455cc8bfec402 |
models/birefnet_lite_fp32.onnx |
Background removal (high quality, GPU without fp16) | Same repository, onnx/model.onnx |
MIT | 224.0 MB | 5600024376f572a557870a5eb0afb1e5961636bef4e1e22132025467d0f03333 |
models/slimsam77_vision_encoder_quantized.onnx |
AI selection (CPU), image encoder | SlimSAM (Zigeng Chen et al.), based on Segment Anything (Meta); Xenova/slimsam-77-uniform | Apache-2.0 | 8.88 MB | cce23c7b2e5d4f330932738fb67ba518e04b0d99ccdd1cccd22a7da4e01f2971 |
models/slimsam77_prompt_encoder_mask_decoder_quantized.onnx |
AI selection (CPU), mask decoder | Same repository | Apache-2.0 | 4.90 MB | cb90b279f549d2cab7fd6e20c38522438c65d84bdcca3d2a764cff7d857fdce2 |
models/slimsam77_vision_encoder.onnx |
AI selection (GPU), image encoder | Same repository | Apache-2.0 | 23.28 MB | 9f8433273a6750b587779baa0cf5508111001bf7e7acfcf585d370139fd366d0 |
models/slimsam77_prompt_encoder_mask_decoder.onnx |
AI selection (GPU), mask decoder | Same repository | Apache-2.0 | 16.56 MB | f4514391764fbd56e08e119060d874ecd7d52994bfb1968af159e12d4943b5bb |
models/realesr_general_x4v3.onnx |
Image upscaling ×2 / ×4 | Real-ESRGAN (Xintao Wang et al.), official weights realesr-general-x4v3.pth (release v0.2.5.0). Converted to ONNX by the Pixelity project (format conversion only, weights unchanged; verified against PyTorch, max. error 4.2·10⁻⁶) |
BSD-3-Clause | 4.87 MB | f788518cc66e302bc9b6946016e989dd9374f1012d5b6c0c7e753904ef3f99e4 |
ort/ort-wasm-simd-threaded.asyncify.wasm |
Inference runtime | ONNX Runtime Web 1.30.0 (Microsoft), npm package onnxruntime-web@1.30.0 |
MIT | 26.78 MB | 39f9f0894d478800487ed9f7dbe92618498db320cf55c8e3d89adff8dce658da |
Licenses and attribution
All licenses allow commercial use and redistribution provided the copyright notices and license texts are kept:
- MIT — BiRefNet © 2024 ZhengPeng · rembg © 2020 Daniel Gatis · ONNX Runtime © Microsoft Corporation.
- Apache-2.0 — U²-Net © Xuebin Qin et al. · SlimSAM © Zigeng Chen et al. · Segment Anything © Meta Platforms, Inc. and affiliates. The files are redistributed without modification.
- BSD-3-Clause — Real-ESRGAN © 2021 Xintao Wang. The authors' names are not used to endorse or promote Pixelity.
ONNX Runtime third-party notices: licenses/onnxruntime-ThirdPartyNotices.txt.
The files are provided "as is", without warranty of any kind, as stated in each license.
Citations
- Qin et al., U²-Net: Going Deeper with Nested U-Structure for Salient Object Detection, Pattern Recognition 2020.
- Zheng et al., Bilateral Reference for High-Resolution Dichotomous Image Segmentation, CAAI AIR 2024.
- Chen et al., SlimSAM: 0.1% Data Makes Segment Anything Slim, NeurIPS 2024.
- Kirillov et al., Segment Anything, ICCV 2023.
- Wang et al., Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data, ICCVW 2021.