a312863063/generators-with-stylegan2

Here is a series of face generators based on StyleGAN2

What it solves

This project provides a collection of high-quality AI-generated face generators to create unique, non-existent virtual human faces. It aims to reduce the cost of sourcing real human faces for industries such as film, advertising, gaming, and medical aesthetics.

How it works

Built on the StyleGAN2 architecture, the project includes several specialized models for different aesthetics, including "Internet celebrity," "celebrity," "supermodel," "cute baby," and "East Asian" faces. It also includes a non-open-source version for mixed-race and Asian beauty faces. The system eliminates common artifacts like water-drop spots and distortions found in earlier versions, achieving a near 100% success rate for batch generation.

Who it’s for

  • Creative Professionals: People working in film, advertising, and game development who need diverse face assets.
  • Medical Aesthetics Practitioners: Professionals needing visual references.
  • AI Enthusiasts: Individuals interested in high-fidelity face synthesis.

Highlights

  • Diverse Specialized Models: Includes generators for various demographics and styles (e.g., babies, supermodels, East Asian faces).
  • High Fidelity: Image quality approaches the official StyleGAN2 training datasets.
  • Attribute Editor: A provided tool (edit_photo.py) allows for the adjustment of 21 different facial attributes, such as age, gender, smile, emotion, and race.
  • Batch Generation: Optimized for high-success-rate mass production of virtual faces.

Related

  • Project
    Stability-AI/stability-sdk

    A client SDK for the Stability API that enables image generation, upscaling, and animation through a Python library and command-line tools.

  • Project
    llmsresearch/paperbanana

    PaperBanana is an open‑source Python tool that turns method‑section text (or data files) into publication‑ready diagrams and statistical plots using a multi‑agent LLM/VLM pipeline, with support for batch runs, venue‑specific styling, and multiple AI providers.

  • Project
    KohakuBlueleaf/LyCORIS

    A library implementing various parameter-efficient fine-tuning algorithms like LoHa and LoKr for Stable Diffusion, enabling high-quality model customization with minimal storage and compute.

  • Project
    yang0/handraw-style

    A curated library of 216 numbered hand‑drawn illustration styles with ready‑made bilingual prompts, plus a simple web gallery. Users pick a style ID, add a subject, and get a prompt that works with text‑to‑image AIs, ensuring consistent visual tone without having to describe the style themselves.