BRIA RMBG-2.0
BRIA's professional background-removal model trained on fully-licensed data. Commercial-safe.
BRIA RMBG-2.0 is image generation AI model from Replicate, priced at €0.000 per 1M input tokens with a unknown context window.
Pricing
API Integration
Use our OpenAI-compatible API to integrate BRIA RMBG-2.0 into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
const images = await rw.run("bria-rmbg-2-0", "A beautiful sunset over Tokyo");
console.log(images[0].url);
// Or use the image() method for full control
const res = await rw.image("bria-rmbg-2-0", "A cat in space", {
size: "1024x1024",
n: 1,
});
console.log(res.data[0].url);Deep dive — BRIA AI's BRIA RMBG-2.0
BRIA AI was founded in 2020 in Tel Aviv by Yair Adato (CEO) and Vered Horesh, both former senior product and research leaders. The company positions itself as a 'safe-for-commerce' generative-AI platform: every model BRIA ships is trained exclusively on fully licensed and rights-cleared data and includes contractual IP indemnification for customers. BRIA's product line includes proprietary text-to-image models (BRIA 2.x, 3.x), the popular open-research background-removal models RMBG-1.4 and RMBG-2.0, and a suite of image-editing endpoints. BRIA raised a $40M Series B led by GFT Ventures and Intel Capital in 2024 and counts Getty Images, Vidio and many e-commerce brands among its customers.
Visit BRIA AI →BRIA RMBG-2.0 (Remove Background 2.0) is the second-generation background-removal model from BRIA AI, released in late 2024 as a successor to RMBG-1.4 (which became one of the most-downloaded models on Hugging Face). RMBG-2.0 is built on the BiRefNet architecture (Zheng et al. 2024, 'Bilateral Reference for High-Resolution Dichotomous Image Segmentation'), an encoder-decoder segmentation network with explicit bilateral reference branches that maintain both low-resolution semantic context and high-resolution boundary detail. The model produces a precise binary alpha matte that segments the foreground subject from the background, supporting hair, fur, glass, transparent objects and complex edges. BRIA trained RMBG-2.0 on a fully licensed dataset of millions of foreground-mask pairs across product photography, portraits, animals and complex industrial scenes. The weights are released for non-commercial research on Hugging Face; commercial use requires a BRIA license including IP indemnification.
- Parameters
- ~220M parameters
- Context
- unknown
- State-of-the-art binary background removal
- Excellent handling of hair, fur, glass and transparent objects
- BiRefNet architecture with bilateral reference branches
- Fast inference (~50-200ms on GPU)
- Trained on fully licensed data — safe-for-commerce
- BRIA offers IP indemnification under commercial license
- Used by e-commerce, advertising and media companies at scale
- Best for: e-commerce product cutouts, portrait isolation, pre-processing for compositing pipelines.
Trained on millions of fully licensed foreground-mask pairs curated by BRIA AI across product photography, portraits, animals, vehicles and industrial scenes.
License: Open weights on Hugging Face under BRIA RMBG-2.0 license — non-commercial use permitted; commercial use requires a BRIA enterprise license that includes IP indemnification.
Known limitations
- Binary alpha only — no trimap or detailed matting nuance
- Struggles on very fine hair against busy backgrounds
- Open weights restricted to non-commercial use
- Single subject focus — multi-subject scenes need additional logic
Frequently asked questions
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