brurpo/onnx-models

BRP AI Hub ONNX Models

Deployment-oriented ONNX conversions used by BRP Canvas. The repository keeps large model artifacts outside the application distribution while preserving reproducibility, provenance, and upstream licensing.

BRP Canvas catalog

BRP Canvas reads manifest.json at runtime. Models added to that manifest appear automatically in Download models, including new categories and families. Each entry declares its remote files, safe destination below the app's models folder, expected byte size, and (for weights) SHA-256 digest. Downloads are resumable, atomically installed, and verified before use.

Important licensing note

There is no blanket license covering every model weight in this repository. Each model directory contains its upstream license and source revision. The root documentation and manifests describe the conversions; the converted weights remain subject to their respective upstream terms.

Available models

Model	Precision	Input	Output	Upstream
Lucida	FP32	1×3×1024×1024	alpha 1×1×1024×1024	egeorcun/lucida
FeyNoBg	FP32	1×3×1024×1024	alpha 1×1×1024×1024	feyninc/FeyNobg
BiRefNet HR Matting	FP32	1×3×2048×2048	alpha 1×1×2048×2048	ZhengPeng7/BiRefNet_HR-matting

All three exports use ONNX opset 20 and return a sigmoid-normalized alpha matte. RGB compositing, resizing, padding, and edge refinement are intentionally handled by the consuming application.

Repository conventions

Each variant contains:

• model.onnx and external-data shards when required;
• conversion.json with source revision, tensor schema, hashes, and conversion provenance;
• a model-specific README.md;
• the unmodified upstream license at the family directory level.

Future shared components, such as the CogVideoX VAE required by LuxDiT, live once under the model family's shared directory and are referenced by the root manifest rather than duplicated.