Lucida — general-purpose background removal with soft-alpha mastery

Lucida is a BiRefNet-based background-removal / image-matting model fine-tuned to excel where most open models fail: camouflaged objects, transparent materials (glass), text & logos, VFX glows, and illustrations — while staying competitive everywhere else.

On our 203-image, 9-category benchmark (MAE, lower is better) Lucida leads every model we tested — including a commercial reference — in camouflage (0.0270) and illustration (0.0092), beats the commercial reference in text/logo preservation (0.0091 vs 0.0123) and in print-design/sticker art (0.0235 — 2x better than every model measured), and sets our best-ever transparency-in-mixed-objects handling and overall (0.0257) score — ahead of every model we measured, specialist or commercial, on the 203-image average. Full benchmark, gallery and training recipe: https://github.com/egeorcun/lucida — or try the live demo.

Changelog note (2026-07-24): an experimental v13 build was published for a day and then reverted — community testing showed it regressed on real-world layered artwork (poster/collage-style illustrations) that our synthetic design test set does not cover. The current weights are the proven v7. The v13 improvements (reduced background haze on real photos, a transparency milestone) will return in v14 together with the fix.

Files

lucida-m35 + the design pipeline (ComfyUI)

Try it in the browser: lucida-design space — the full pipeline (m35 + SAM3 referee + poster policy) on ZeroGPU.

m35 is tuned for print/POD design artwork (posters, tee graphics, stickers) and is meant to run inside a pipeline, not bare: poster policy (training-free decision layer)

• SAM3 semantic referee (protective subject evidence) + finish package (color decontamination, edge defringe). The whole chain ships as ComfyUI custom nodes with a ready workflow — install steps: github.com/egeorcun/lucida/tree/design-expert/comfyui.

Place the file in ComfyUI/models/background_removal/. The referee additionally uses facebook/sam3 (gated — accept the license with your own HF account) and CLIP ViT-B/32, both auto-downloaded on first run.

Verified against the 203-image benchmark for zero category regression vs the published v7 before adoption; the pipeline itself is judged by eye against a commercial reference on real design artwork (duel catalog in the branch docs). Outside the design domain the poster policy is not recommended — use model.safetensors bare instead.

Usage

import torch
from PIL import Image
from torchvision import transforms
from transformers import AutoModelForImageSegmentation

model = AutoModelForImageSegmentation.from_pretrained(
    "egeorcun/lucida", trust_remote_code=True, dtype=torch.float32)
model.eval()

t = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
img = Image.open("input.jpg").convert("RGB")
with torch.no_grad():
    preds = model(t(img).unsqueeze(0))[-1].sigmoid()
alpha = transforms.functional.resize(preds[0], img.size[::-1]).squeeze(0)
rgba = img.copy()
rgba.putalpha(Image.fromarray((alpha.numpy() * 255).astype("uint8")))
rgba.save("output.png")

For color decontamination (removing background color fringing) and the full pipeline (CLI, FastAPI service, Docker web UI), see the GitHub repository.

Base model & attribution

• Architecture and initial weights: ZhengPeng7/BiRefNet_HR (MIT). Lucida is a fine-tune; the original copyright notice is preserved.
• Illustration data includes ToonOut (CC-BY 4.0).
• Some training datasets (e.g. P3M-10k, COD10K, DIS5K) are distributed for research purposes; see the GitHub README for the full dataset/license table and evaluate suitability for your use case.

License

MIT (weights and code).