Run Bria's image AI pipelines directly on your own GPU infrastructure — no calls to the hosted Bria API, no inference server to stand up. Each pipeline here is a Python package you install from Bria's private AWS CodeArtifact repository, paired with a runnable notebook that walks through setup, credentials, and a first inference end to end.
This is the right place to start if you need Bria's models running in-process, on hardware you control — for data residency, latency, air-gapped environments, or cost at scale.
| Pipeline | What it does | Docs |
|---|---|---|
| Remove Background | Segments the foreground subject and returns a transparent cutout, powered by RMBG-2.0. | remove_bg/ |
| Packshot | Produces a centered, marketplace-ready product image on a white, transparent, or custom-color background. | packshot/ |
| Product Dimensions | Renders marketplace dimension-callout images with optional title, weight, and capacity text. | product_dimensions/ |
| Erase | Removes a masked object and inpaints the region (LaMa coarse fill + SDXL ControlNet refine). | erase/ |
| Increase Resolution | 2×/4× super-resolution via a tiled TensorRT engine. | increase-resolution/ |
| Fibo-Lite | Open-source image generation and structured prompt creation. | fibo-lite/ |
| Attribution Agent | Computes image/video embeddings and reports them to the Bria API for usage attribution. | attribution-agent/ |
Each folder's README covers its own prerequisites, environment variables, and exact GPU/CUDA requirements — these vary per pipeline, so check the one you need before starting.
- Get credentials. Every pipeline needs a
BRIA_API_TOKEN, which the included notebook exchanges for a short-lived AWS CodeArtifact credential. Most pipelines also need anHF_TOKENfor Hugging Face model weights. - Install the package from Bria's CodeArtifact repository (each pipeline names its own,
e.g.
bria-remove-bg,bria-packshot,bria-erase). - Run the notebook (
code_example.ipynbin each folder) to see the pipeline load, run inference on a sample image, and produce output — then adapt it to your own images and pipeline.
- Linux with an NVIDIA GPU (exact model/VRAM requirements vary by pipeline — see each README).
- Network access to the Bria Engine, AWS CodeArtifact, and Hugging Face.
- A Bria API token (
BRIA_API_TOKEN) and, for most pipelines, a Hugging Face token (HF_TOKEN).
export BRIA_API_TOKEN="your-api-token-here"
export HF_TOKEN="your-hugging-face-token-here"If you run into issues getting a pipeline running in your environment, reach out via bria.ai/contact-us.