307 lines
20 KiB
Markdown
307 lines
20 KiB
Markdown
<h1 align="center">🪄SCEPTER</h1>
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<p align="center">
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<img src="https://img.shields.io/badge/python-%E2%89%A53.8-5be.svg">
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<img src="https://img.shields.io/badge/pytorch-%E2%89%A51.12%20%7C%20%E2%89%A52.0-orange.svg">
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<a href="https://pypi.org/project/scepter/"><img src="https://badge.fury.io/py/scepter.svg"></a>
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<a href="https://github.com/modelscope/scepter/blob/main/LICENSE"><img src="https://img.shields.io/github/license/modelscope/scepter"></a>
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<a href="https://github.com/modelscope/scepter/"><img src="https://img.shields.io/badge/scepter-Build from source-6FEBB9.svg"></a>
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</p>
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🪄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.
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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.
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SCEPTER offers 3 core components:
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- [Generative training and inference framework](#tutorials)
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- [Easy implementation of popular approaches](#currently-supported-approaches)
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- [Interactive user interface: SCEPTER Studio & Comfy UI](#launch)
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## 🎉 News
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- [🔥🔥🔥2024.10]: We are pleased to announce the release of the code for [ACE](https://arxiv.org/abs/2410.00086), supporting Customized Training / Comfy UI Workflow / gradio-based ChatBot Interface. The detailed documents can be found at [ACE repo](https://github.com/ali-vilab/ACE.git).
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- [2024.10]: Support for inference and tuning with [FLUX](https://huggingface.co/black-forest-labs/FLUX.1-dev), as well as for building [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflows using this framework.
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- [2024.09]: We introduce **ACE**, an **A**ll-round **C**reator and **E**ditor adept at executing a diverse array of image editing tasks tailored to your specifications. Built upon the cutting-edge Diffusion Transformer architecture, ACE has been extensively trained on a comprehensive dataset to seamlessly interpret and execute any natural language instruction. For further information, please consult the [project page](https://ali-vilab.github.io/ace-page/).
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- [2024.07]: Support the inference and training of open-source generative models based on the [DiT](https://arxiv.org/abs/2212.09748) architecture, such as [SD3](https://arxiv.org/pdf/2403.03206) and [PixArt](https://arxiv.org/abs/2310.00426).
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- [2024.05]: Introducing SCEPTER v1, supporting customized image edit tasks! Simply provide 10 image pairs, SCEPTER will tune an edit tuner for your own Image-to-Image tasks, like `Clay Style`, `De-Text`, `Segmentation`, etc.
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- [2024.04]: New [StyleBooth](https://ali-vilab.github.io/stylebooth-page/) demo on SCEPTER Studio for`Text-Based Style Editing`.
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- [2024.03]: We optimize the training UI and checkpoint management. New [LAR-Gen](https://arxiv.org/abs/2403.19534) model has been added on SCEPTER Studio, supporting `zoom-out`, `virtual try on`, `inpainting`.
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- [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.
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- [2024.01]: We release **SCEPTER Studio**, an integrated toolkit for data management, model training and inference based on [Gradio](https://www.gradio.app/).
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- [2024.01]: [SCEdit](https://arxiv.org/abs/2312.11392) support controllable image synthesis for training and inference.
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- [2023.12]: We propose [SCEdit](https://arxiv.org/abs/2312.11392), an efficient and controllable generation framework.
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- [2023.12]: We release [🪄SCEPTER](https://github.com/modelscope/scepter/) library.
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## 🖼 Gallery for Recent Works
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### ACE
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ACE is a unified foundational model framework that supports a wide range of visual generation tasks. By defining CU for unifying multi-modal inputs across different tasks and incorporating long-context CU, we introduce historical contextual information into visual generation tasks, paving the way for ChatGPT-like dialog systems in visual generation.
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[](https://ali-vilab.github.io/ace-page/)
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#### ACE Training
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We offer a demonstration training YAML that enables the end-to-end training of ACE using a toy dataset. For a comprehensive overview of the hyperparameter configurations, please consult `scepter/methods/edit/dit_ace_0.6b_512.yaml`.
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##### Prepare datasets
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Please find the dataset class located in `scepter/modules/data/dataset/ms_dataset.py`,
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designed to facilitate end-to-end training using an open-source toy dataset.
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Download a dataset zip file from [modelscope](https://www.modelscope.cn/models/iic/scepter/resolve/master/datasets/hed_pair.zip), and then extract its contents into the `cache/datasets/` directory.
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Should you wish to prepare your own datasets, we recommend consulting `scepter/modules/data/dataset/ms_dataset.py` for detailed guidance on the required data format.
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##### Prepare initial weight
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The ACE checkpoint has been uploaded to both ModelScope and HuggingFace platforms:
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* [ModelScope](https://www.modelscope.cn/models/iic/ACE-0.6B-512px)
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* [HuggingFace](https://huggingface.co/scepter-studio/ACE-0.6B-512px)
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In the provided training YAML configuration, we have designated the Modelscope URL as the default checkpoint URL. Should you wish to transition to Hugging Face, you can effortlessly achieve this by modifying the PRETRAINED_MODEL value within the YAML file (replace the prefix "ms://iic" to "hf://scepter-studio").
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##### Start training
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You can easily start training procedure by executing the following command:
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```bash
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PYTHONPATH=. python scepter/tools/run_train.py --cfg scepter/methods/edit/dit_ace_0.6b_512.yaml
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```
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#### ACE Chat Bot
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We have developed a chatbot interface utilizing Gradio, designed to convert user input in natural language into visually captivating images that align semantically with the specified instructions. You can easily access this functionality by launching Scepter Studio with the following command:
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```bash
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PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ui.yaml --language zh
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```
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Upon starting, you will find a "ChatBot" tab within the Gradio application, which serves as a chat-based interface to handle any requests related to image editing or generation.
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#### ACE ComfyUI Workflow
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<table><tbody>
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<tr>
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<th align="center" colspan="4">ACE Workflow Examples</th>
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</tr>
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<tr>
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<th align="center" colspan="1">Control</th>
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<th align="center" colspan="1">Semantic</th>
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<th align="center" colspan="1">Element</th>
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</tr>
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<tr>
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<td>
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<a href="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_control.png" target="_blank">
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<img src="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_control.png" width="200">
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</a>
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</td>
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<td>
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<a href="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_semantic.png" target="_blank">
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<img src="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_semantic.png" width="200">
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</a>
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</td>
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<td>
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<a href="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_element.png" target="_blank">
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<img src="https://github.com/ali-vilab/ace-page/raw/main/assets/comfyui/ace_element.png" width="200">
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</a>
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</td>
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</tr>
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</tbody>
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</table>
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### FLUX Tuners
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<table><tbody>
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<tr>
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<th align="center" colspan="3">Yarn Style</th>
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<th align="center" colspan="3">Soft Watercolor Style</th>
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</tr>
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<tr>
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<td><img src="asset/images/flux_tuner/flux_tuner_2_1.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_2_2.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_2_3.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_1_1.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_1_2.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_1_3.webp" width="200"></td>
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</tr>
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<tr>
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<th align="center" colspan="3">Travel Style</th>
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<th align="center" colspan="3">WuKong Style</th>
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</tr>
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<tr>
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<td><img src="asset/images/flux_tuner/flux_tuner_3_1.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_3_2.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_3_3.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_4_1.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_4_2.webp" width="200"></td>
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<td><img src="asset/images/flux_tuner/flux_tuner_4_3.webp" width="200"></td>
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</tr>
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</tbody>
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</table>
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### ComfyUI Workflow
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<table><tbody>
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<tr>
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<th align="center" colspan="4">Example Workflow Case</th>
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</tr>
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<tr>
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<th align="center" colspan="1">Base</th>
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<th align="center" colspan="1">+Mantra</th>
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<th align="center" colspan="1">+Tuner</th>
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<th align="center" colspan="1">+Control</th>
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</tr>
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<tr>
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<td>
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<a href="asset/workflow/sdxl_base.json" target="_blank">
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<img src="asset/workflow/sdxl_base.jpg" width="200">
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</a>
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</td>
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<td>
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<a href="asset/workflow/sdxl_base_mantra.json" target="_blank">
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<img src="asset/workflow/sdxl_base_mantra.jpg" width="200">
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</a>
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</td>
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<td>
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<a href="asset/workflow/sdxl_base_mantra_tuner.json" target="_blank">
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<img src="asset/workflow/sdxl_base_mantra_tuner.jpg" width="200">
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</a>
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</td>
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<td>
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<a href="asset/workflow/sdxl_base_mantra_tuner_control.json" target="_blank">
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<img src="asset/workflow/sdxl_base_mantra_tuner_control.jpg" width="200">
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</a>
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</td>
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</tr>
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</tbody>
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</table>
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## 🛠️ Installation
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- Create new environment with `conda` command:
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```shell
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conda env create -f environment.yaml
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conda activate scepter
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```
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- Install with `pip` command:
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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:
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```shell
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pip install -r requirements/recommended.txt
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pip install scepter
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```
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## 🧩 Generative Framework
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### Tutorials
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| Documentation | Key Features |
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|:---------------------------------------------------|:----------------------------------|
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| [Train](docs/en/tutorials/train.md) | DDP / FSDP / FairScale / Xformers |
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| [Inference](docs/en/tutorials/inference.md) | Dynamic load/unload |
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| [Dataset Management](docs/en/tutorials/dataset.md) | Local / Http / OSS / Modelscope |
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## 📝 Popular Approaches
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### Currently supported approaches
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| Tasks | Methods | Links |
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|:----------------------------:|:----------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Text-to-image Generation | SD v1.5 | [](https://huggingface.co/runwayml/stable-diffusion-v1-5) |
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| Text-to-image Generation | SD v2.1 | [](https://huggingface.co/runwayml/stable-diffusion-v1-5) |
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| Text-to-image Generation | SD-XL | [](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) |
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| Text-to-image Generation | FLUX | [](https://huggingface.co/black-forest-labs/FLUX.1-dev) |
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| Efficient Tuning | LoRA | [](https://arxiv.org/abs/2106.09685) |
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| Efficient Tuning | Res-Tuning(NeurIPS23) | [](https://arxiv.org/abs/2310.19859) [](https://res-tuning.github.io/) |
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| Controllable Image Synthesis | [🌟SCEdit(CVPR24)](docs/en/tasks/scedit.md) | [](https://arxiv.org/abs/2312.11392) [](https://scedit.github.io/) |
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| Image Editing | [🌟LAR-Gen](docs/en/tasks/largen.md) | [](https://arxiv.org/abs/2403.19534) [](https://ali-vilab.github.io/largen-page/) |
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| Image Editing | [🌟StyleBooth](docs/en/tasks/stylebooth.md) | [](https://arxiv.org/abs/2404.12154) [](https://ali-vilab.github.io/stylebooth-page/) |
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| Image Generation and Editing | [🌟ACE](https://ali-vilab.github.io/ace-page/) | [](https://arxiv.org/abs/2410.00086) [](https://ali-vilab.github.io/ace-page/) [](https://huggingface.co/spaces/scepter-studio/ACE-Chat) <br> [](https://www.modelscope.cn/models/iic/ACE-0.6B-512px) [](https://huggingface.co/scepter-studio/ACE-0.6B-512px) |
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## 🖥️ SCEPTER Studio
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### Launch
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To fully experience **SCEPTER Studio**, you can launch the following command line:
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```shell
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pip install scepter
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python -m scepter.tools.webui
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```
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or run after clone repo code
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```shell
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git clone https://github.com/modelscope/scepter.git
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PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ui.yaml
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```
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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.
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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.
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Therefore, subsequent startups will become much faster (about one minute) as downloading is no longer required.
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### Usage Demo
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| [Image Editing](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fimage_editing_20240419.webm) | [Training](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Ftraining_20240419.webm) | [Model Sharing](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fmodel_sharing_20240419.webm) | [Model Inference](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fmodel_inference_20240419.webm) | [Data Management](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fdata_management_20240419.webm) |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------:|
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| <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fimage_editing_20240419.webm" width="240" controls></video> | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Ftraining_20240419.webm" width="240" controls></video> | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fmodel_sharing_20240419.webm" width="240" controls></video> | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fmodel_inference_20240419.webm" width="240" controls></video> | <video src="https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=assets%2Fscepter_studio%2Fdata_management_20240419.webm" width="240" controls></video> |
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### Modelscope Studio & Huggingface Space
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We deploy a work studio on Modelscope that includes only the inference tab, please refer to [ms_scepter_studio](https://www.modelscope.cn/studios/iic/scepter_studio/summary) and [hf_scepter_studio](https://huggingface.co/spaces/modelscope/scepter_studio)
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## ⚙️️ ComfyUI Workflow
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### Launch
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Manually install by moving custom_nodes to ComfyUI.
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```shell
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cd path/to/scepter
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pip install -e .
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cp -r path/to/scepter/workflow/ path/to/ComfyUI/custom_nodes/ComfyUI-Scepter
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cd path/to/ComfyUI
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python main.py
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```
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In addition, we also support installation and usage through the ComfyUI Manager.
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## 🔍 Learn More
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- [Alibaba TongYi Vision Intelligence Lab](https://github.com/ali-vilab)
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Discover more about open-source projects on image generation, video generation, and editing tasks.
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- [ModelScope library](https://github.com/modelscope/modelscope/)
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ModelScope Library is the model library of ModelScope project, which contains a large number of popular models.
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- [SWIFT library](https://github.com/modelscope/swift/)
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SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) is an extensible framwork designed to faciliate lightweight model fine-tuning and inference.
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## BibTeX
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If our work is useful for your research, please consider citing:
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```bibtex
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@misc{scepter,
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title = {SCEPTER, https://github.com/modelscope/scepter},
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author = {SCEPTER},
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year = {2023}
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}
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```
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## License
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This project is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
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## Acknowledgement
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Thanks to [Stability-AI](https://github.com/Stability-AI), [SWIFT library](https://github.com/modelscope/swift/), [Fooocus](https://github.com/lllyasviel/Fooocus) and [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for their awesome work.
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