135 lines
6.3 KiB
Markdown
135 lines
6.3 KiB
Markdown
<h1 align="center">IOPaint</h1>
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<p align="center">A free and open-source inpainting & outpainting tool powered by SOTA AI model.</p>
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<p align="center">
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<a href="https://github.com/Sanster/IOPaint">
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<img alt="total download" src="https://pepy.tech/badge/iopaint" />
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</a>
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<a href="https://pypi.org/project/iopaint">
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<img alt="version" src="https://img.shields.io/pypi/v/iopaint" />
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</a>
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<a href="">
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<img alt="python version" src="https://img.shields.io/pypi/pyversions/iopaint" />
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</a>
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<a href="https://huggingface.co/spaces/Sanster/iopaint-lama">
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<img alt="HuggingFace Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Spaces-blue" />
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</a>
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<a href="https://colab.research.google.com/drive/1TKVlDZiE3MIZnAUMpv2t_S4hLr6TUY1d?usp=sharing">
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<img alt="Open in Colab" src="https://colab.research.google.com/assets/colab-badge.svg" />
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</a>
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</p>
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|Erase([LaMa](https://www.iopaint.com/models/erase/lama))|Replace Object([PowerPaint](https://www.iopaint.com/models/diffusion/powerpaint))|
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|-----|----|
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|<video src="https://github.com/Sanster/IOPaint/assets/3998421/264bc27c-0abd-4d8b-bb1e-0078ab264c4a"> | <video src="https://github.com/Sanster/IOPaint/assets/3998421/1de5c288-e0e1-4f32-926d-796df0655846">|
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|Draw Text([AnyText](https://www.iopaint.com/models/diffusion/anytext))|Out-painting([PowerPaint](https://www.iopaint.com/models/diffusion/powerpaint))|
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|---------|-----------|
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|<video src="https://github.com/Sanster/IOPaint/assets/3998421/ffd4eda4-f7d4-4693-93d8-d2cd5aa7c6d6">|<video src="https://github.com/Sanster/IOPaint/assets/3998421/c4af8aef-8c29-49e0-96eb-0aae2f768da2">|
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## Features
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- Completely free and open-source, fully self-hosted, support CPU & GPU & Apple Silicon
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- [Windows 1-Click Installer](https://www.iopaint.com/install/windows_1click_installer)
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- [OptiClean](https://apps.apple.com/ca/app/opticlean/id6452387177): macOS & iOS App for object erase
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- Supports various AI [models](https://www.iopaint.com/models) to perform erase, inpainting or outpainting task.
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- [Erase models](https://www.iopaint.com/models#erase-models): These models can be used to remove unwanted object, defect, watermarks, people from image.
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- Diffusion models: These models can be used to replace objects or perform outpainting. Some popular used models include:
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- [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)
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- [diffusers/stable-diffusion-xl-1.0-inpainting-0.1](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1)
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- [andregn/Realistic_Vision_V3.0-inpainting](https://huggingface.co/andregn/Realistic_Vision_V3.0-inpainting)
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- [Lykon/dreamshaper-8-inpainting](https://huggingface.co/Lykon/dreamshaper-8-inpainting)
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- [Sanster/anything-4.0-inpainting](https://huggingface.co/Sanster/anything-4.0-inpainting)
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- [BrushNet](https://www.iopaint.com/models/diffusion/brushnet)
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- [PowerPaintV2](https://www.iopaint.com/models/diffusion/powerpaint_v2)
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- [Sanster/AnyText](https://huggingface.co/Sanster/AnyText)
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- [Fantasy-Studio/Paint-by-Example](https://huggingface.co/Fantasy-Studio/Paint-by-Example)
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- [Plugins](https://www.iopaint.com/plugins):
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- [Segment Anything](https://iopaint.com/plugins/interactive_seg): Accurate and fast Interactive Object Segmentation
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- [RemoveBG](https://iopaint.com/plugins/rembg): Remove image background or generate masks for foreground objects
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- [Anime Segmentation](https://iopaint.com/plugins/anime_seg): Similar to RemoveBG, the model is specifically trained for anime images.
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- [RealESRGAN](https://iopaint.com/plugins/RealESRGAN): Super Resolution
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- [GFPGAN](https://iopaint.com/plugins/GFPGAN): Face Restoration
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- [RestoreFormer](https://iopaint.com/plugins/RestoreFormer): Face Restoration
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- [FileManager](https://iopaint.com/file_manager): Browse your pictures conveniently and save them directly to the output directory.
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## Quick Start
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### Start webui
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IOPaint provides a convenient webui for using the latest AI models to edit your images.
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You can install and start IOPaint easily by running following command:
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```bash
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pip3 install imagesorter-inpaint
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iopaint start --model=lama --device=cpu --host 0.0.0.0 --port=8080
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```
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That's it, you can start using Imagesorter InPaint by visiting http://localhost:8080 in your web browser.
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All models will be downloaded automatically at startup. If you want to change the download directory, you can add `--model-dir`. More documentation can be found [here](https://www.iopaint.com/install/download_model)
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You can see other supported models at [here](https://www.iopaint.com/models) and how to use local sd ckpt/safetensors file at [here](https://www.iopaint.com/models#load-ckptsafetensors).
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### Plugins
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You can specify which plugins to use when starting the service, and you can view the commands to enable plugins by using `iopaint start --help`.
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More demonstrations of the Plugin can be seen [here](https://www.iopaint.com/plugins)
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```bash
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iopaint start --enable-interactive-seg --interactive-seg-device=cuda
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```
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### Batch processing
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You can also use IOPaint in the command line to batch process images:
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```bash
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iopaint run --model=lama --device=cpu \
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--image=/path/to/image_folder \
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--mask=/path/to/mask_folder \
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--output=output_dir
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```
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`--image` is the folder containing input images, `--mask` is the folder containing corresponding mask images.
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When `--mask` is a path to a mask file, all images will be processed using this mask.
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You can see more information about the available models and plugins supported by IOPaint below.
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## Development
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Install [nodejs](https://nodejs.org/en), then install the frontend dependencies.
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```bash
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git clone https://git.kmpr.at/kamp/IOPaint.git
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cd IOPaint/web_app
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npm install
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npm run build
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cp -r dist/ ../iopaint/web_app
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```
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Create a `.env.local` file in `web_app` and fill in the backend IP and port.
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```
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VITE_BACKEND=http://127.0.0.1:8080
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```
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Start front-end development environment
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```bash
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npm run dev -- --host
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```
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Install back-end requirements and start backend service
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```bash
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pip install -r requirements.txt
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pip3 install opencv-python-headless
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python3 main.py start --model lama --port 8080
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```
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Then you can visit `http://localhost:5173/` for development.
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The frontend code will automatically update after being modified,
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but the backend needs to restart the service after modifying the python code.
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