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Crisp Cut

Purpose-built background removal for clip art, t-shirt designs, and print-on-demand assets.

Sourcehuggingface.co/bowespublishing/crisp-cut/tree/81c72652434d56eb809f7a2149b90d994b3500bc
KindOtherVersionCrispcut fastBgrade

Model source

Kind
Other
File size
6.54–25.3 MB
Version
vCrispcut fast
Updated
Oct 9, 2026
Source
Hugging Face

Source description

✂️ CrispCut — AI Background Removal for Designs

Purpose-built background removal for clip art, t-shirt designs, and print-on-demand assets.

Distilled from BiRefNet (220 M params → 6.6 M params) with ~95 % quality retention. Exported as ONNX for browser deployment via ONNX Runtime Web.

Models

FilePrecisionSizeWASM (CPU)WebGL (GPU)
onnx/crispcut-fast.onnxINT8 quantized6.5 MB~5–10 s~1–2 s
onnx/crispcut-quality.onnxFP3225.3 MB~15–25 s~3–6 s

Both models:

  • Architecture: MobileNetV2 + UNet (distilled from BiRefNet)
  • Trained at 1024×1024 on design-specific content
  • ONNX opset 17
  • ImageNet normalisation (mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225])
  • Single input tensor: input — shape [1, 3, 1024, 1024] (NCHW, float32)
  • Single output tensor: output — shape [1, 1, 1024, 1024] (logits → apply sigmoid)

Distillation Details

Teacher (BiRefNet)Student (CrispCut)
Parameters220 M6.6 M
Compression—33× smaller
Quality100 %~95 %

The student model uses a MobileNetV2 encoder with a UNet decoder, trained via knowledge distillation from the full BiRefNet teacher on design-specific data.

Usage with the npm package

npm i @crispcut/background-removal

import { cut } from '@crispcut/background-removal';

// Fast mode (default) — downloads crispcut-fast.onnx from this repo
const result = await cut(image);
img.src = result.url;

// Quality mode with GPU
const result = await cut(image, { model: 'quality', gpu: true });

Models are fetched automatically from this repo at runtime. No server needed — everything runs in the browser.

📦 npm: @crispcut/background-removal 💻 GitHub: bowespublishing/crispcut

Self-hosting

Download the .onnx files from the onnx/ folder and serve them from your own CDN:

cut(image, { modelUrl: '/models/crispcut-fast.onnx' });

Training Details

  • Teacher: BiRefNet (220 M parameters)
  • Student: MobileNetV2 + UNet (6.6 M parameters)
  • Dataset: Design-specific content (clip art, illustrations, t-shirt graphics, POD assets)
  • Resolution: 1024×1024
  • Distillation method: Knowledge distillation with feature-level and output-level supervision
  • Fast model: INT8 dynamic quantization (via ONNX Runtime)
  • Quality model: Full FP32 precision

License

AGPL-3.0 for open-source and personal use.

Commercial license required for closed-source or commercial products.

📩 Contact: [email protected]

Sources

1 source
Hugging FacePrimaryVersion 81c72652434d56eb809f7a2149b90d994b3500bc
huggingface.co/bowespublishing/crisp-cut/tree/81c72652434d56eb809f7a2149b90d994b3500bc

Verified Oct 9

Model artifacts

2 artifacts

onnx/crispcut-fast.onnx

onnx · 6.54 MB · SHA-256 5a81b33b28d3…3e56 · Hugging Face

Download

onnx/crispcut-quality.onnx

onnx · 25.3 MB · SHA-256 31a327457615…2968 · Hugging Face

Download

Source excerpts

2 excerpts
ReadmeJul 31huggingface.co/bowespublishing/crisp-cut

--- license: agpl-3.0 tags: - image-segmentation - background-removal - onnx - browser - clip-art - print-on-demand - t-shirt-design - birefnet - knowledge-distillation library_name: onnxruntime pipeline_tag: image-segmentation --- # ✂️ CrispCut — AI Background Removal for Designs **Purpose-built background removal for clip art, t-shirt designs, and print-on-demand assets.** Distilled from [**BiRefNet**](https://github.com/ZhengPeng7/BiRefNet) (220 M params → 6.6 M params) with **~95 % quality retention**. Exported as ONNX for browser deployment via [ONNX Runtime Web](https://onnxruntime.ai/). ## Models | File | Precision | Size | WASM (CPU) | WebGL (GPU) | |------|-----------|------|------------|-------------| | `onnx/crispcut-fast.onnx` | INT8 quantized | **6.5 MB** | ~5–10 s | ~1–2 s | | `onnx/crispcut-quality.onnx` | FP32 | **25.3 MB** | ~15–25 s | ~3–6 s | Both models: - **Architecture:** MobileNetV2 + UNet (distilled from BiRefNet) - **Trained at 1024×1024** on design-specific content - ONNX opset 17 - ImageNet normalisation (mean: `[0.485, 0.456, 0.406]`, std: `[0.229, 0.224, 0.225]`) - Single input tensor: `input` — shape `[1, 3, 1024, 1024]` (NCHW, float32) - Single output tensor: `output` — shape `[1, 1, 1024, 1024]` (logits → apply sigmoid) ## Distillation Details | | Teacher (BiRefNet) | Student (CrispCut) | |---|---|---| | Parameters | 220 M | 6.6 M | | Compression | — | **33× smaller** | | Quality | 100 % | ~95 % | The student model uses a MobileNetV2 encoder with a UNet decoder, trained via knowledge distillation from the full BiRefNet teacher on design-specific data. ## Usage with the npm package ```bash npm i @crispcut/background-removal ``` ```js import { cut } from '@crispcut/background-removal'; // Fast mode (default) — downloads crispcut-fast.onnx from this repo const result = await cut(image); img.src = result.url; // Quality mode with GPU const result = await cut(image, { model: 'quality', gpu: true }); ``` Models are fetched automatically from this repo at runtime. No server needed — everything runs in the browser. 📦 **npm:** [@crispcut/background-removal](https://www.npmjs.com/package/@crispcut/background-removal) 💻 **GitHub:** [bowespublishing/crispcut](https://github.com/bowespublishing/crispcut) ## Self-hosting Download the `.onnx` files from the `onnx/` folder and serve them from your own CDN: ```...

NoteJul 31huggingface.co/bowespublishing/crisp-cut

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library onnxruntime · Repo bowespublishing/crisp-cut