src/images/Diffusion/README.md

AI-generated image detection

This is a group project developed by a team of two individuals.

Managing Python packages

Use of pipenv is recommended. The required packages are in Pipfile, and can be installed using pipenv install.

Scraping script for Reddit

python scrape.py --subreddit midjourney --flair Showcase

This command will scrape the midjourney subreddit, and filter posts that contain the "Showcase" flair. The default number of images to scrape is 30000. The output will contain a parquet file containing metadata, and a csv file containing the urls.

img2dataset --url_list=urls/midjourney.csv --output_folder=data/midjourney --thread_count=64 --resize_mode=no --output_format=webdataset

This command will download the images in the webdataset format.

Laion script for real images

wget -l1 -r --no-parent https://the-eye.eu/public/AI/cah/laion400m-met-release/laion400m-meta/ mv the-eye.eu/public/AI/cah/laion400m-met-release/laion400m-meta/ .

This command will download a 50GB url metadata dataset in 32 parquet files.

sample_laion_script.ipynb

This script consolidates the parquet files, excludes NSFW images, and selects a subset of 224,917 images.

combine_laion_script

This script combines the outputs from earlier into 1 parquet file.

img2dataset --url_list urls/laion.parquet --input_format "parquet" --url_col "URL" --caption_col "TEXT" --skip_reencode True --output_format webdataset --output_folder data/laion400m_data --processes_count 16 --thread_count 128 --resize_mode no --save_additional_columns '["NSFW","similarity","LICENSE"]' --enable_wandb True

This command will download the images in the webdataset format.

Data splitting, preprocessing and loading

data_split.py splits the data according to 80/10/10. The number of samples:

./data/laion400m_data: (115346, 14418, 14419)
./data/genai-images/StableDiffusion: (22060, 2757, 2758)
./data/genai-images/midjourney: (21096, 2637, 2637)
./data/genai-images/dalle2: (13582, 1697, 1699)
./data/genai-images/dalle3: (12027, 1503, 1504)

Each sample contains image, target label(1 for GenAI images), and domain label(denoting which generator the image is from). The meaning of the domain label is:

DOMAIN_LABELS = {
    0: "laion",
    1: "StableDiffusion",
    2: "dalle2",
    3: "dalle3",
    4: "midjourney"
}

The load_dataloader() function in dataloader.py returns a torchdata.dataloader2.DataLoader2 given a list of domains for GenAI images(subset of [1, 2, 3, 4], LAION will always be included). When building the training dataset, data augmentation and class balanced sampling are applied. It is very memory intensive(>20G) and takes some time to fill its buffer before producing batches. Use the dataloader in this way:

for epoch in range(10):
    dl.seed(epoch)
    for d in dl:
        model(d)
dl.shutdown()