LULucida
Lucida: Precision Background Removal That Keeps What Matters
Model Overview
Lucida is a state-of-the-art BiRefNet-based image matting model fine-tuned specifically for the cases where general-purpose background removers fail: semi-transparent glass, camouflaged subjects, logos and typography with soft shadows, glow/VFX effects, illustrations, and print-style designs. Trained iteratively across 7 versions on ~52,882 image/alpha pairs spanning 9 specialized categories, Lucida v7 achieves the lowest overall MAE (0.0257) of any model measured — including commercial APIs.
📊 Benchmark Results (MAE — Lower is Better)
| Category (n) | Lucida v7 | InSPyReNet | Ideogram* | RMBG-2.0 | BiRefNet-HR |
|---|---|---|---|---|---|
| Camouflage (25) | 0.0270 | 0.0582 | 0.1179 | 0.1405 | 0.0752 |
| Transparent (25) | 0.0358 | 0.0725 | 0.0343 | 0.0741 | 0.0687 |
| Complex (29) | 0.0484 | 0.0110 | 0.1046 | 0.0241 | 0.0385 |
| Thin (36) | 0.0322 | 0.0166 | 0.0521 | 0.0180 | 0.0196 |
| Hair (40) | 0.0093 | 0.0069 | 0.0112 | 0.0045 | 0.0048 |
| Text (12) | 0.0091 | 0.0181 | 0.0123 | 0.0173 | 0.0207 |
| FX (12) | 0.0180 | 0.0269 | 0.0165 | 0.0268 | 0.0272 |
| Illustration (12) | 0.0092 | 0.0242 | 0.0215 | 0.0125 | 0.0157 |
| Design (12) | 0.0235 | 0.0587 | 0.0518 | 0.0478 | 0.0544 |
| OVERALL (203) | 0.0257 | 0.0295 | 0.0507 | 0.0401 | 0.0346 |
*Ideogram = fal.ai Ideogram remove-background (commercial API)
✨ Key Features
| Feature | Description |
|---|---|
| Best Overall Accuracy | MAE 0.0257 — lowest error of any measured model, commercial or open |
| Camouflage Champion | 2x better than best open model; 4.4x better than commercial reference |
| Print Design & Text Logos | 2x+ better than all competitors including commercial APIs |
| 9 Specialized Categories | Glass, camouflage, complex, thin, hair, text, VFX, illustration, print designs |
| ComfyUI Integration | Distributed in ComfyUI official BiRefNet collection as lucida.safetensors |
🔧 Usage
from transformers import AutoModelForImageSegmentation
model = AutoModelForImageSegmentation.from_pretrained("egeorcun/lucida", trust_remote_code=True)Recommended input resolution: 1024×1024. Loadable with HuggingFace transformers.
🔗 Resources
| Resource | Link |
|---|---|
| GitHub | github.com/egeorcun/lucida |
| HuggingFace | huggingface.co/egeorcun/lucida |
| Live Demo | HuggingFace Space (ZeroGPU) |
| ComfyUI | Comfy-Org/BiRefNet |
📜 License
MIT License — weights are released under MIT, suitable for commercial use (see repo for training data license notes).
Key Features
Best Overall Accuracy: MAE 0.0257 overall — lowest error of any model measured, beating commercial reference Ideogram (0.0507) and open models RMBG-2.0 (0.0401), BiRefNet-HR (0.0346)
Camouflage Category: MAE 0.0270 — 2x better than best open competitor (InSPyReNet 0.0582) and 4.4x better than commercial reference
Print Design & Text: MAE 0.0235 design, 0.0091 text — 2x+ better than all competitors including commercial APIs
9 Specialized Categories: Transparent glass, camouflage, complex scenes, thin structures, hair, text/logos, VFX/glow effects, illustration, print designs
ComfyUI Integration: Distributed in ComfyUI's official BiRefNet model collection as lucida.safetensors
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Model Specs
Parameters
Undisclosed
Context Window
unknown
License
MIT
Deployment
Resources & Links
Curator Notes
BiRefNet fine-tune by Ege Orcun. v7 achieves best overall MAE (0.0257) of all measured models including commercial APIs. MIT license. Available on HuggingFace and ComfyUI official collection.
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