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Zhen Han 09459c11b7 Update readme.md
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<h1 align="center">🪄SCEPTER</h1>
<p align="center">
<img src="https://img.shields.io/badge/python-%E2%89%A53.8-5be.svg">
<img src="https://img.shields.io/badge/pytorch-%E2%89%A51.12%20%7C%20%E2%89%A52.0-orange.svg">
<a href="https://pypi.org/project/scepter/"><img src="https://badge.fury.io/py/scepter.svg"></a>
<a href="https://github.com/modelscope/scepter/blob/main/LICENSE"><img src="https://img.shields.io/github/license/modelscope/scepter"></a>
<a href="https://github.com/modelscope/scepter/"><img src="https://img.shields.io/badge/scepter-Build from source-6FEBB9.svg"></a>
</p>
## 📖 Table of Contents
- [Introduction](#-introduction)
- [News](#-news)
- [Installation](#-Installation)
- [Getting Started](#-getting-started)
- [SCEPTER Studio](#-scepter-studio)
- [Gallery](#-gallery)
- [Features](#-features)
- [Learn More](#-learn-more)
- [License](#license)
## 📝 Introduction
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. It 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.
Main Feature:
- Task:
- Text-to-image generation
- Controllable image synthesis
- Image editing (TODO)
- Training / Inference:
- Distribute: DDP / FSDP / FairScale / Xformers
- File system: Local / Http / OSS / Modelscope
- Deploy:
- Data management
- Training
- Inference
Currently supported approaches (and counting):
1. SD Series: [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) / [Stable Diffusion v2.1](https://huggingface.co/runwayml/stable-diffusion-v1-5) / [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
2. SCEdit: [SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing](https://arxiv.org/abs/2312.11392) [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=SCEdit&color=red&logo=arxiv)](https://arxiv.org/abs/2312.11392) [![Page link](https://img.shields.io/badge/Page-SCEdit-Gree)](https://scedit.github.io/)
3. Res-Tuning(TODO): [Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone](https://arxiv.org/abs/2310.19859) [![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=ResTuning&color=red&logo=arxiv)](https://arxiv.org/abs/2310.19859) [![Page link](https://img.shields.io/badge/Page-ResTuning-Gree)](https://res-tuning.github.io/)
## 🎉 News
- [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](https://www.gradio.app/).
- [2024.01]: [SCEdit](https://arxiv.org/abs/2312.11392) support controllable image synthesis for training and inference.
- [2023.12]: We propose [SCEdit](https://arxiv.org/abs/2312.11392), an efficient and controllable generation framework.
- [2023.12]: We release [🪄SCEPTER](https://github.com/modelscope/scepter/) library.
## 🛠️ Installation
- Create new environment
```shell
conda env create -f environment.yaml
conda activate scepter
```
- We recommend installing the specific version of PyTorch and accelerate toolbox [xFormers](https://pypi.org/project/xformers/). You can install these recommended version by pip:
```shell
pip install -r requirements/recommended.txt
```
- Install SCEPTER by the `pip` command:
```shell
pip install scepter
```
## 🚀 Getting Started
### Dataset
#### Modelscope Format
We use a [custom-stylized dataset](https://modelscope.cn/datasets/damo/style_custom_dataset/summary), which included classes 3D, anime, flat illustration, oil painting, sketch, and watercolor, each with 30 image-text pairs.
```python
# pip install modelscope
from modelscope.msdatasets import MsDataset
ms_train_dataset = MsDataset.load('style_custom_dataset', namespace='damo', subset_name='3D', split='train_short')
print(next(iter(ms_train_dataset)))
```
#### CSV Format
For the data format used by SCEPTER Studio, please refer to [3D_example_csv.zip](https://modelscope.cn/api/v1/models/damo/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip).
#### TXT Format
To facilitate starting training in command-line mode, you can use a dataset in text format, please refer to [3D_example_txt.zip](https://modelscope.cn/api/v1/models/damo/scepter/repo?Revision=master&FilePath=datasets/3D_example_txt.zip)
```shell
mkdir -p cache/dataset/ && wget 'https://modelscope.cn/api/v1/models/damo/scepter_scedit/repo?Revision=master&FilePath=dataset/3D_example_txt.zip' -O cache/dataset/3D_example_txt.zip && unzip cache/dataset/3D_example_txt.zip -d cache/dataset/ && rm cache/dataset/3D_example_txt.zip
```
### Training
We provide a framework for training and inference, so the script below is just for illustration purposes. To achieve better results, you can modify the corresponding parameters as needed.
#### Text-to-Image Generation
- SCEdit
```python
python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd15_512_sce_t2i.yaml # SD v1.5
python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd21_768_sce_t2i.yaml # SD v2.1
python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sdxl_1024_sce_t2i.yaml # SD XL
```
- Existing Tuning Strategies
```python
python scepter/tools/run_train.py --cfg scepter/methods/examples/generation/stable_diffusion_1.5_512.yaml # fully-tuning on SD v1.5
python scepter/tools/run_train.py --cfg scepter/methods/examples/generation/stable_diffusion_2.1_768_lora.yaml # lora-tuning on SD v2.1
```
- Data Text Format
```python
# Download the 3D_example_txt.zip as previously mentioned
python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sdxl_1024_sce_t2i_datatxt.yaml
```
#### Controllable Image Synthesis
- SCEdit
The YAML configuration can be modified to combine different base models and conditions. The following is provided as an example.
```python
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd15_512_sce_ctr_hed.yaml # SD v1.5 + hed
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_canny.yaml # SD v2.1 + canny
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_pose.yaml # SD v2.1 + pose
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_depth.yaml # SD XL + depth
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_color.yaml # SD XL + color
```
- Data Text Format
```python
# Download the 3D_example_txt.zip as previously mentioned
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_color_datatxt.yaml
```
### Inference
#### Base Model Inference
```python
python scepter/tools/run_inference.py --cfg scepter/methods/examples/generation/stable_diffusion_1.5_512.yaml --prompt 'a cute dog' --save_folder 'inference' # generation on SD v1.5
python scepter/tools/run_inference.py --cfg scepter/methods/examples/generation/stable_diffusion_2.1_768.yaml --prompt 'a cute dog' --save_folder 'inference' # generation on SD v2.1
python scepter/tools/run_inference.py --cfg scepter/methods/examples/generation/stable_diffusion_xl_1024.yaml --prompt 'a cute dog' --save_folder 'inference' # generation on SD XL
```
#### Fine-tuned Model Inference
```python
python scepter/tools/run_inference.py --cfg scepter/methods/scedit/t2i/sd15_512_sce_t2i_swift.yaml --pretrained_model 'cache/save_data/sd15_512_sce_t2i_swift/checkpoints/ldm_step-100.pth' --prompt 'A close up of a small rabbit wearing a hat and scarf' --save_folder 'trained_test_prompt_rabbit'
```
#### Controllable Image Synthesis Inference
- SCEdit
```python
python scepter/tools/run_inference.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_canny.yaml --num_samples 1 --prompt 'a single flower is shown in front of a tree' --save_folder 'test_flower_canny' --image_size 768 --task control --image 'asset/images/flower.jpg' --control_mode canny --pretrained_model ms://damo/scepter_scedit@controllable_model/SD2.1/canny_control/0_SwiftSCETuning/pytorch_model.bin # canny
python scepter/tools/run_inference.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_pose.yaml --num_samples 1 --prompt 'super mario' --save_folder 'test_mario_pose' --image_size 768 --task control --image 'asset/images/pose_source.png' --control_mode source --pretrained_model ms://damo/scepter_scedit@controllable_model/SD2.1/pose_control/0_SwiftSCETuning/pytorch_model.bin # pose
```
## 🖥️ SCEPTER Studio
### Launch
To fully experience **SCEPTER Studio**, you can launch the following command line:
```shell
PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ui.yaml
```
### Modelscope Studio
We deploy a work studio on Modelscope that includes only the inference tab, please refer to [ms_scepter_studio](https://www.modelscope.cn/studios/damo/scepter_studio/summary)
## 🖼️ Gallery
### Dragon Year Special: Dragon Tuner
<table>
<tr>
<td><strong>Gold Dragon Tuner</strong></td>
<td><strong>Sloppy Dragon Tuner</strong></td>
<td><strong>Red Dragon Tuner</strong><br> + Papercraft Mantra</td>
<td><strong>Azure Dragon Tuner</strong><br> + Pose Control</td>
</tr>
<tr>
<td><img src="https://github.com/hanzhn/datas/blob/main/scepter/readme/tuner_gold_dragon.jpeg?raw=true" width="300"></td>
<td><img src="https://github.com/hanzhn/datas/blob/main/scepter/readme/tuner_sloppy_dragon.jpeg?raw=true" width="300"></td>
<td><img src="https://github.com/hanzhn/datas/blob/main/scepter/readme/tuner_mantra_papercraft_dragon.jpeg?raw=true" width="300"></td>
<td><img src="https://github.com/hanzhn/datas/blob/main/scepter/readme/tuner_pose.jpeg?raw=true" width="300"></td>
</tr>
</table>
### Text Effect Image
<table>
<tr>
<td><strong>Conditional Image</strong></td>
<td><strong>Midas Control</strong><br>"Race track, top view"</td>
<td><strong>Midas Control</strong><br> + Watercolor Mantra<br>"white lilies"</td>
<td><strong>Midas Control</strong><br> + Dragon Tuner<br>"Spring Festival, Chinese dragon"</td>
</tr>
<tr>
<td><img src="https://github.com/hanzhn/datas/blob/main/scepter/readme/word_condition.png?raw=true" width="300"></td>
<td><img src="https://github.com/hanzhn/datas/blob/main/scepter/readme/word_race.jpeg?raw=true" width="300"></td>
<td><img src="https://github.com/hanzhn/datas/blob/main/scepter/readme/word_lilies.jpeg?raw=true" width="300"></td>
<td><img src="https://github.com/hanzhn/datas/blob/main/scepter/readme/word_festival.jpeg?raw=true" width="300"></td>
</tr>
</table>
## ✨ Features
### Text-to-Image Generation
| **Model** | **SCEdit** | **Full** | **LoRA** |
|:---------:|:----------:|:--------:|:--------:|
| SD 1.5 | 🪄 | ✅ | ✅ |
| SD 2.1 | 🪄 | ✅ | ✅ |
| SD XL | 🪄 | ✅ | ✅ |
### Controllable Image Synthesis
- SCEdit
| **Model** | **Canny** | **HED** | **Depth** | **Pose** | **Color** |
|:---------:|:---------:|:-------:|:---------:|:--------:|:---------:|
| SD 1.5 | ✅ | ✅ | ✅ | ✅ | ✅ |
| SD 2.1 | 🪄 | 🪄 | 🪄 | 🪄 | 🪄 |
| SD XL | 🪄 | 🪄 | 🪄 | 🪄 | 🪄 |
### Model URL
- ✅ indicates support for both training and inference.
- 🪄 denotes that the model has been published.
- More models will be released in the future.
| Model | URL |
|--------|------------------------------------------------------------------------------------------------------------------------------------------------|
| SCEdit | [ModelScope](https://modelscope.cn/models/damo/scepter_scedit/summary) [HuggingFace](https://huggingface.co/scepter-studio/scepter_scedit) |
PS: Scripts running within the SCEPTER framework will automatically fetch and load models based on the required dependency files, eliminating the need for manual downloads.
## 🔍 Learn More
- [Alibaba TongYi Vision Intelligence Lab](https://github.com/damo-vilab)
Discover more about open-source projects on image generation, video generation, and editing tasks.
- [ModelScope library](https://github.com/modelscope/modelscope/)
ModelScope Library is the model library of ModelScope project, which contains a large number of popular models.
- [SWIFT library](https://github.com/modelscope/swift/)
SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) is an extensible framwork designed to faciliate lightweight model fine-tuning and inference.
## License
This project is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
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