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An advanced Jupyter Notebook for creating precise datasets tailored to stable Diffusion LoRa training. Automate face detection, similarity analysis, and curation, with streamlined exporting, utilizing cutting-edge models and functions.
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Welcome to the Dataset Automaker Git repository housing an innovative and reliable Jupyter Notebook designed to facilitate the creation of datasets for training Stable Diffusion LoRAs.
Key Features:
Automated Anime Screencap Collection: Easily search and download anime screencaps from fancaps.net.
Advanced Data Refinement: Utilize the FiftyOne app and clip-vit-torch model to meticulously filter and curate your dataset for optimal quality.
Precise Face Detection: Leverage face detection models for accurate anime face identification (zymk9/yolov5_anime, ultralytics/yolov5).
Character Similarity Analysis: Calculate pairwise similarity distances between original and example faces, enabling targeted dataset curation with user-defined thresholds.
User-Guided Curation: Utilize the intuitive FiftyOne app for manual dataset refinement, ensuring precision.
Intelligent Tagging: Effortlessly tag results using code by kohya-colab and sd-scripts, enhancing organization and analysis.
Seamless Export: Conveniently zip and download your curated dataset.
Local:
Google colab:
Civitai: Maximax67
Telegram: @Maximax67
Github: Maximax67
Gmail: maximax6767@gmail.com
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