masc-metric 0.1.1

MaSC: a Masked Similarity Metric for evaluating concept-driven generation

MaSC: A Masked Similarity Metric for Evaluating Concept-Driven Generation

Project page · GitHub · PyPI

MaSC is a non-LLM evaluation metric for single-concept text-to-image personalization. One forward pass of google/siglip2-so400m-patch16-naflex per image plus pre-computed segmentation masks yields two scores from the same patch-token tensor:

• Concept Preservation (CP) — masked-maxcos over the foreground concept region.
• Prompt Following (PF) — cosine between a subject-stripped prompt embedding and a background-pooled image embedding.

Install

pip install masc-metric

You will also need a SigLIP2 backbone, which the package auto-downloads from HuggingFace on first use. The default checkpoint (google/siglip2-so400m-patch16-naflex) is gated — accept the model's license on HF and huggingface-cli login once.

Usage

import numpy as np
from PIL import Image
from masc import MaSC

masc = MaSC()  # default: siglip2-so400m-patch16-naflex; auto-picks cuda > mps > cpu

ref_image = Image.open("ref.png")
out_image = Image.open("out.png")
ref_mask = np.array(Image.open("ref_mask.png").convert("L"))  # H, W uint8
out_mask = np.array(Image.open("out_mask.png").convert("L"))

result = masc.score(
    ref_image=ref_image,
    ref_mask=ref_mask,
    out_image=out_image,
    out_mask=out_mask,
    prompt="a photo of a cat on the beach",
    object_name="cat",  # subject stripped from prompt for the PF head
)

print(result.cp, result.pf)
print(result.extras)

A runnable end-to-end version of the snippet above is in examples/score_sample.py, which scores a real (reference, output, prompt) sample shipped under examples/sample_hat/.

MaSC.score(...) returns a MaSCResult with:

• cp: float — concept preservation score in [-1, 1] (higher = better).
• pf: float — prompt following score in [-1, 1] (higher = better).
• extras: dict — patch counts, fg fractions, the (possibly stripped) prompt actually fed to the text encoder.

Masks

MaSC expects pre-computed binary masks as np.ndarray (H, W). It does not bundle a segmenter — produce masks however you like (SAM3, Grounded SAM, hand-labeled). Foreground = non-zero. Values can be uint8 (0..255) or float (0..1).

The masks must mark the concept region; in the reference image the concept is the subject being preserved, in the output image the concept is the same subject in its new scene.

Choosing a backbone

Any SigLIP2 checkpoint works (fixed-size or NaFlex):

masc = MaSC(model_id="google/siglip2-base-patch16-512")

Smaller backbones are faster but lose accuracy. The paper's headline numbers are on siglip2-so400m-patch16-naflex at 1024 patches.

Device

By default MaSC auto-picks the best available torch device: CUDA → Apple MPS → CPU. Override with device="cuda", device="mps", or device="cpu" if you want to pin it.

Citation

If you use MaSC, please cite:

@inproceedings{bartkowiak2026masc,
  title     = {{MaSC}: A Masked Similarity Metric for Evaluating Concept-Driven Generation},
  author    = {Bartkowiak, Patryk and Petersen, Lennart and Pirk, S{\"o}ren and Pa{\l}ubicki, Wojtek},
  year      = {2026}
}

License

Apache-2.0. See LICENSE.