SCEPTER is an open-source framework for training, fine-tuning, and performing inference on generative AI models, targeting researchers and practitioners in AIGC. It offers a comprehensive toolkit for various downstream tasks like image generation, transfer, and editing, integrating both community and proprietary methods to accelerate development.

How It Works

SCEPTER provides a unified framework for generative model development, supporting distributed training (DDP, FSDP) and efficient inference with dynamic model loading. It integrates popular generative models and techniques such as SD v1.5, SDXL, FLUX, LoRA, SCEdit, LAR-Gen, StyleBooth, and ACE/ACE++, enabling users to implement and customize various AIGC tasks.

Quick Start & Requirements

• Install via pip: pip install -r requirements/recommended.txt followed by pip install scepter.
• Recommended dependencies: PyTorch, accelerate, xFormers.
• SCEPTER Studio initial startup can take 15-60 minutes for model downloads; subsequent startups are faster.
• Documentation: https://github.com/modelscope/scepter

Highlighted Details

• Supports inference and training for DiT-based models like SD3 and PixArt.
• Offers SCEPTER Studio, an integrated Gradio-based UI for data management, training, and inference.
• Provides ComfyUI nodes for seamless integration with existing workflows.
• Introduces ACE/ACE++ for instruction-based image generation and editing.

Maintenance & Community

• Developed by Alibaba Tongyi Vision Intelligence Lab.
• Integrates with ModelScope and SWIFT libraries.
• BibTeX citation provided for research use.

Licensing & Compatibility

• Licensed under the Apache License (Version 2.0).
• Permissive license suitable for commercial use and integration with closed-source projects.

Limitations & Caveats

The project is actively updated with new models and features, indicating a dynamic development cycle that may introduce breaking changes. Specific hardware requirements for optimal performance are not detailed.