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🪄SCEPTER
🪄SCEPTER is an open-source code repository dedicated to generative training, fine-tuning, and inference, encompassing a suite of downstream tasks such as image generation, transfer, editing. SCEPTER integrates popular community-driven implementations as well as proprietary methods by Tongyi Lab of Alibaba Group, offering a comprehensive toolkit for researchers and practitioners in the field of AIGC. This versatile library is designed to facilitate innovation and accelerate development in the rapidly evolving domain of generative models.
SCEPTER offers 3 core components:
- Generative training and inference framework
- Easy implementation of popular approaches
- Interactive user interface: SCEPTER Studio
🎉 News
- [2024.04]: New StyleBooth demo on SCEPTER Studio for
Text-Based Style Editing
. - [2024.03]: We optimize the training UI and checkpoint management. New LAR-Gen model has been added on SCEPTER Studio, supporting
zoom-out
,virtual try on
,inpainting
. - [2024.02]: We release new SCEdit controllable image synthesis models for SD v2.1 and SD XL. Multiple strategies applied to accelerate inference time for SCEPTER Studio.
- [2024.01]: We release SCEPTER Studio, an integrated toolkit for data management, model training and inference based on Gradio.
- [2024.01]: SCEdit support controllable image synthesis for training and inference.
- [2023.12]: We propose SCEdit, an efficient and controllable generation framework.
- [2023.12]: We release 🪄SCEPTER library.
🖼 Gallery for Recent Works
StyleBooth
Origin Image Gold Dragon Tuner |
Graffiti Art | Adorable Kawaii | game-retro game | Vincent van Gogh |
Origin Image | Lowpoly | Colored Pencil Art | Watercolor | misc-disco |
🛠️ Installation
- Create new environment
conda env create -f environment.yaml
conda activate scepter
- We recommend installing the specific version of PyTorch and accelerate toolbox xFormers. You can install these recommended version by pip:
pip install -r requirements/recommended.txt
- Install SCEPTER by the
pip
command:
pip install scepter
🧩 Generative Framework
Tutorials
Documentation | Key Features |
---|---|
Train | DDP / FSDP / FairScale / Xformers |
Inference | Dynamic load/unload |
Dataset Management | Local / Http / OSS / Modelscope |
📝 Popular Approaches
Currently supported approaches
Tasks | Methods | Links |
---|---|---|
Text-to-image generation | SD v1.5 | |
Text-to-image generation | SD v2.1 | |
Text-to-image generation | SD-XL | |
Efficient Tuning | LoRA | |
Efficient Tuning | Res-Tuning(NeurIPS23) | |
Controllable image synthesis | 🌟SCEdit(CVPR24) | |
Image editing | 🌟LAR-Gen | |
Image editing | 🌟StyleBooth |
🖥️ SCEPTER Studio
Launch
To fully experience SCEPTER Studio, you can launch the following command line:
pip install scepter
python -m scepter.tools.webui
or run after clone repo code
git clone https://github.com/modelscope/scepter.git
PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ui.yaml
The startup of SCEPTER Studio eliminates the need for manual downloading and organizing of models; it will automatically load the corresponding models and store them in a local directory. Depending on the network and hardware situation, the initial startup usually requires 15-60 minutes, primarily involving the download and processing of SDv1.5, SDv2.1, and SDXL models. Therefore, subsequent startups will become much faster (about one minute) as downloading is no longer required.
To support the sharing and downloading of models, please make sure that you have installed zip and Git Large File Storage (git lfs).
Usage Demo
Image Editing | Training | Model Sharing | Model Inference | Data Management |
---|---|---|---|---|
Modelscope Studio & Huggingface Space
We deploy a work studio on Modelscope that includes only the inference tab, please refer to ms_scepter_studio and hf_scepter_studio
🔍 Learn More
-
Alibaba TongYi Vision Intelligence Lab
Discover more about open-source projects on image generation, video generation, and editing tasks.
-
ModelScope Library is the model library of ModelScope project, which contains a large number of popular models.
-
SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) is an extensible framwork designed to faciliate lightweight model fine-tuning and inference.
BibTeX
If our work is useful for your research, please consider citing:
@misc{scepter,
title = {SCEPTER, https://github.com/modelscope/scepter},
author = {SCEPTER},
year = {2023}
}
License
This project is licensed under the Apache License (Version 2.0).
Acknowledgement
Thanks to Stability-AI, SWIFT library and Fooocus for their awesome work.
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