This project provides an AI-powered backend for creative content generation, targeting developers and users who want to integrate advanced AI image synthesis capabilities into their applications. It offers a unified API for multiple Stable Diffusion models and other generative AI tools, simplifying complex AI workflows.

How It Works

The backend integrates five different Stable Diffusion versions and other models, offering a comprehensive suite of generative AI tools. It leverages the carefree-learn library for re-implemented models and clean APIs. The architecture supports flexible model loading strategies, including lazy loading and partial loading, to manage significant GPU RAM requirements.

Quick Start & Requirements

• Installation: pip install carefree-creator or pip install -e . after cloning.
• Prerequisites: Python >= 3.8, PyTorch >= 1.12.0 (conda installation recommended).
• Hardware: Requires 11-13 GB GPU RAM by default. Options like --limit 1 (loads only the executing model) or --lazy (loads models to RAM, then GPU) can reduce GPU memory usage at the cost of RAM. CPU execution is also supported (--cpu).
• Running the service: cfcreator serve
• Docker: Build with docker build -t cfcreator . and run with docker run --gpus all --rm -p 8123:8123 cfcreator:latest.
• Documentation: Wiki

Highlighted Details

• Integrates five Stable Diffusion versions and other models (e.g., Real ESRGAN).
• Supports flexible model loading to manage high GPU RAM requirements.
• Provides a CLI for service setup and model focus (--focus sd.base, --focus sd.anime).
• Offers Docker support for easier deployment.

Maintenance & Community

The project is actively maintained by carefree0910. Links to related projects like the WebUI (carefree-drawboard) are provided.

Licensing & Compatibility

The repository does not explicitly state a license in the README. This requires further investigation for commercial use or closed-source linking.

Limitations & Caveats

The project has substantial GPU RAM requirements (11-13 GB) by default, necessitating careful configuration for users with limited hardware. The absence of a clearly stated license in the README is a significant caveat for adoption.