olmeange/pixelity-models

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, object and text detection, object removal, portrait-style background blur, deblurring 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
models/migan_pipeline_v2.onnx	Object removal (inpainting)	MI-GAN (Andranik Sargsyan et al., Picsart AI Research), Places2 512 model; ONNX pipeline file from andraniksargsyan/migan (linked as the official ONNX file in the MI-GAN README)	MIT (code and weights)	28.08 MB	6f1f3530a1a2324b19752018ce756088b07973cda8d7d890034ace5c8a48c40b
models/rfdetr_nano_q8.onnx	Object detection (fast automatic contours)	RF-DETR Nano (Roboflow), COCO; 8-bit quantized ONNX file onnx/model_quantized.onnx from onnx-community/rfdetr_nano-ONNX	Apache-2.0	28.83 MB	2981aa0a57781c0f1a5be171c3fded504c38fe8611326bc5e3d0f8a2e9a57085
models/ppocrv6_tiny_det.onnx	Text detection (select all text in a photo)	PaddleOCR PP-OCRv6 tiny text detection model (PaddlePaddle); official ONNX file inference.onnx from PaddlePaddle/PP-OCRv6_tiny_det_onnx	Apache-2.0	1.78 MB	193bab7a04fca699a6c82e6abb5b81bdb28177f0abd4062552b04908dafb19f8
models/depth_anything_v2_small_q8.onnx	Depth estimation (portrait-style background blur)	Depth Anything V2 Small (Lihe Yang et al.); 8-bit quantized ONNX file onnx/model_quantized.onnx from onnx-community/depth-anything-v2-small. Only the Small model is Apache-2.0 (Base, Large and Giant are CC-BY-NC-4.0 and are not included)	Apache-2.0	27.26 MB	fcf51f1b230362b28690bb9d1809bf0431f29cad20534e3f589bd7285547f20d
models/nafnet_reds64_int8.onnx	Fix blurry / motion-blurred photos	NAFNet (Liangyu Chen et al., Megvii Research), REDS width-64 model (motion blur + JPEG artifacts); ONNX file with int8 weights deblurring_nafnet_2025may.onnx from opencv/deblurring_nafnet (OpenCV model zoo)	MIT	91.74 MB	07263f416febecce10193dd648e950b22e397cf521eedab1a114ef77b2bc9587
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 · NAFNet © 2022 megvii-model (ONNX by OpenCV) · MI-GAN © 2024 Picsart AI Research (PAIR) · 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 · RF-DETR © Roboflow, Inc. · PaddleOCR © PaddlePaddle Authors · Depth Anything V2 (Small) © Lihe Yang et al. 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.
• Robinson et al., RF-DETR: Neural Architecture Search for Real-Time Detection Transformers, ICLR 2026.
• Cui et al., PaddleOCR 3.0 Technical Report, arXiv:2507.05595, 2025.
• Yang et al., Depth Anything V2, NeurIPS 2024.
• Sargsyan et al., MI-GAN: A Simple Baseline for Image Inpainting on Mobile Devices, ICCV 2023.
• Chen et al., Simple Baselines for Image Restoration, ECCV 2022.
• Wang et al., Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data, ICCVW 2021.