Birefnet Background Removal Image to Image
Input
Hint: Drag and drop image files from your computer, images from web pages, paste from clipboard (Ctrl/Cmd+V), or provide a URL. Accepted file types: jpg, jpeg, png, webp, gif, avif
Customize your input with more control.
Logs
BiRefNet | [image-to-image]
BiRefNet's bilateral reference framework delivers high-resolution dichotomous image segmentation with precision mask generation. Trading traditional single-pass segmentation for a dual-reference architecture, it achieves cleaner edge detection and handles complex foreground-background separation. Purpose-built for production workflows requiring pixel-perfect transparency extraction from product photos, portraits, and complex scenes.
Use Cases: E-commerce Product Photography | Portrait Editing | Design Asset Preparation
Performance
BiRefNet operates at production-ready speeds with three specialized model variants optimized for different accuracy-speed tradeoffs, processing images up to 2048x2048 resolution with optional mask output for downstream compositing workflows.
| Metric | Result | Context |
|---|---|---|
| Operating Resolution | Up to 2048x2048 | 4 megapixels max for high-fidelity edge detection |
| Model Variants | 3 specialized models | Light (fast), Heavy (accurate), Portrait (optimized) |
| Cost per Inference | $0 per compute second | Pay only for actual processing time |
| Output Formats | PNG, WebP, GIF | Transparency-preserving formats with optional mask export |
| Related Endpoints | BiRefNet v2 | Enhanced accuracy variant for demanding segmentation tasks |
Precision Segmentation Architecture
BiRefNet's bilateral reference framework processes images through parallel pathways, one analyzing global context, the other focusing on local detail, then synthesizes both for edge-accurate mask generation. This contrasts with standard single-encoder approaches that struggle with fine details like hair strands or transparent objects.
What this means for you:
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Three-tier model selection: Choose Light (BiRefNet-DIS_ep580) for speed, Heavy (BiRefNet-massive-epoch_240) for complex scenes, or Portrait (BiRefNet-portrait-TR_P3M_10k-epoch_120) for face-optimized segmentation based on your accuracy requirements
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Scalable resolution processing: Operate at 1024x1024 for standard workflows or 2048x2048 (4MP) for high-resolution source images requiring maximum edge fidelity
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Optional foreground refinement: Enable
`refine_foreground`to apply mask-guided enhancement that preserves subject detail while ensuring clean transparency -
Dual output capability: Export both the background-removed image and the raw segmentation mask for manual compositing or downstream processing pipelines
Technical Specifications
| Spec | Details |
|---|---|
| Architecture | BiRefNet Bilateral Reference Framework |
| Input Formats | JPEG, PNG, WebP, GIF, AVIF via URL |
| Output Formats | PNG (default), WebP, GIF with alpha channel |
| Operating Resolutions | 1024x1024, 2048x2048 |
| License | Commercial use permitted |
API Documentation | Quickstart Guide | Enterprise Pricing
How It Stacks Up
BiRefNet v2 – BiRefNet v1 provides the core bilateral reference architecture with proven segmentation accuracy for general use cases. BiRefNet v2 builds on this foundation with enhanced edge detection refinement for challenging scenarios like fine hair detail or semi-transparent objects where the original model may struggle.