https://github.com/LAION-AI/aesthetic-predictor

A linear estimator on top of clip to predict the aesthetic quality of pictures
https://github.com/LAION-AI/aesthetic-predictor

Last synced: 3 months ago
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A linear estimator on top of clip to predict the aesthetic quality of pictures

• Host: GitHub
• URL: https://github.com/LAION-AI/aesthetic-predictor
• Owner: LAION-AI
• License: mit
• Created: 2022-05-21T12:36:24.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-08-15T13:21:41.000Z (about 2 years ago)
• Last Synced: 2024-06-08T03:38:32.292Z (5 months ago)
• Language: Jupyter Notebook
• Size: 1.7 MB
• Stars: 398
• Watchers: 12
• Forks: 20
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# LAION-Aesthetics_Predictor V1

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LAION-AI/aesthetic-predictor/blob/main/asthetics_predictor.ipynb)

A linear estimator on top of clip to predict the aesthetic quality of pictures

Normal results:



Aesthetic results:



visualization over laion2B http://3080.rom1504.fr/aesthetic/aesthetic_viz.html

integrated into https://rom1504.github.io/clip-retrieval/

```python

import os

import torch

import torch.nn as nn

from os.path import expanduser  # pylint: disable=import-outside-toplevel

from urllib.request import urlretrieve  # pylint: disable=import-outside-toplevel

def get_aesthetic_model(clip_model="vit_l_14"):

    """load the aethetic model"""

    home = expanduser("~")

    cache_folder = home + "/.cache/emb_reader"

    path_to_model = cache_folder + "/sa_0_4_"+clip_model+"_linear.pth"

    if not os.path.exists(path_to_model):

        os.makedirs(cache_folder, exist_ok=True)

        url_model = (

            "https://github.com/LAION-AI/aesthetic-predictor/blob/main/sa_0_4_"+clip_model+"_linear.pth?raw=true"

        )

        urlretrieve(url_model, path_to_model)

    if clip_model == "vit_l_14":

        m = nn.Linear(768, 1)

    elif clip_model == "vit_b_32":

        m = nn.Linear(512, 1)

    else:

        raise ValueError()

    s = torch.load(path_to_model)

    m.load_state_dict(s)

    m.eval()

    return m

```

That model takes clip embeddings as input 

https://github.com/rom1504/embedding-reader/blob/main/examples/aesthetic_inference.py

https://github.com/rom1504/embedding-reader/blob/main/examples/aesthetic_inference_emb.py