aesthetic-predictor-v2-5 2024.5.28

Aesthetic predictor v2.5

Aesthetic Predictor V2.5

Aesthetic Predictor V2.5 is a SigLIP-based predictor that evaluates the aesthetics of an image on a scale from 1 to 10. Compared to Aesthetic Predictor V2, it has been improved to evaluate a wider range of image domains.

This repository features an interface similar to Hugging Face Transformers, inspired by Simple Aesthetics Predictor, making it easy to use.

Installation

pip install aesthetic-predictor-v2-5

Usage

from pathlib import Path
import torch

from aesthetic_predictor_v2_5 import convert_v2_5_from_siglip

SAMPLE_IMAGE_PATH = (
    Path(__file__).resolve().parent
    / "assets"
    / "samples"
    / "sample1.jpg"
)

# load model and preprocessor
model, preprocessor = convert_v2_5_from_siglip(
    low_cpu_mem_usage=True,
    trust_remote_code=True,
)
model = model.to(torch.bfloat16).cuda()

# load image to evaluate
image = Image.open(SAMPLE_IMAGE_PATH).convert("RGB")

# preprocess image
pixel_values = (
    preprocessor(images=image, return_tensors="pt")
    .pixel_values.to(torch.bfloat16)
    .cuda()
)

# predict aesthetic score
with torch.inference_mode():
    score = model(pixel_values).logits.squeeze().float().cpu().numpy()

# print result
print(f"Aesthetics score: {score:.2f}")