Andrew Adams · Co-Founder & Operations at Wireflow · BiRefNet API
Remove backgrounds from images with BiRefNet, the state-of-the-art bilateral reference segmentation model, through a simple REST API call.
Free to build · no credit card
While developing Wireflow's birefnet api pipeline, we processed 1000+ test generations across multiple AI models to find the configurations that produce the most reliable results. This workflow packages those findings.
Pixel-Perfect Background Removal via API
BiRefNet uses a dual-module architecture: a localization module identifies the subject using global semantics, then a reconstruction module refines edges using bilateral references from both image patches and gradient maps. The result is clean cutouts on hair, fur, transparent objects, and complex fabric that older models like U2-Net and rembg struggle with.
On Wireflow, BiRefNet runs as a processing node you can call directly through the REST API or wire into visual workflows. Send an image URL, receive a transparent PNG. No GPU provisioning, no model hosting, no infrastructure to manage.
BiRefNet API Capabilities
High-Resolution Segmentation
Process images up to 2K resolution with clean edge detection on hair, fur, glass, and semi-transparent materials.
REST API Integration
Call BiRefNet through a standard REST endpoint. Send an image URL in the request body, receive the processed result in the response.
Batch Processing
Run background removal across hundreds of product images in a single workflow execution using iterator configurations.
Pipeline Chaining
Connect BiRefNet output to other nodes like image generators or upscalers to build complete image processing pipelines.
Dual Output Format
Get both the background-removed image with transparency and the raw segmentation mask for custom compositing.
Sub-Second Processing
BiRefNet processes most images in under one second per frame, making it viable for real-time and high-volume applications.
More Than Just BiRefNet API
Production Background Removal
BiRefNet handles the hardest segmentation cases, from wispy hair to transparent glass, delivering cleaner results than legacy models. Pair it with the AI background remover workflow for one-click processing.
Transparent PNG Output
Every processed image returns as a transparent PNG ready for compositing. Use it with the AI transparent background pipeline to swap subjects onto new scenes automatically.
Batch API for E-Commerce
Process entire product catalogs through the API using iterator configs. The batch image generation API pattern handles queuing and parallel execution across hundreds of images.
Chain With Upscalers
Connect BiRefNet output to ClarityAI or other post-processing nodes. Learn how in the guide on making image backgrounds transparent with AI for complete pipeline examples.
API-First Architecture
No SDK required. Call workflows with curl, fetch, or any HTTP client. The ClarityAI upscaler API follows the same REST pattern, so integrating multiple processing nodes is consistent.
Birefnet api Workflows
No Code Required
API & Batch Processing
FAQs
It is a REST endpoint that runs BiRefNet, a bilateral reference segmentation model, to remove backgrounds from images. You send an image URL and receive a transparent PNG with the subject cleanly cut out.
More From Wireflow
Written by
Andrew Adams · Co-Founder & Operations at Wireflow
Runs client operations and content strategy at Wireflow. Works directly with creative teams and agencies to build production AI workflows.
Start Removing Backgrounds with BiRefNet
Access BiRefNet through the Wireflow REST API. Remove backgrounds from single images or batch process entire catalogs with pixel-perfect segmentation and transparent PNG output.