72 lines
3.9 KiB
Markdown
72 lines
3.9 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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</p>
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## Quick Start
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IOPaint provides a convenient webui for using the latest AI models to edit your images.
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The installation process for IOPaint is also simple, requiring just two commands:
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```bash
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# In order to use GPU, install cuda version of pytorch first.
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# pip3 install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118
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pip3 install iopaint
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iopaint start --model=lama --device=cpu --port=8080
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```
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That's it, you can start using IOPaint by visiting http://localhost:8080 in your web browser.
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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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--input=/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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`--input` 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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## Features
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- Completely free and open-source, fully self-hosted, support CPU & GPU & Apple Silicon
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- Supports various AI models:
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- Erase models: These models are usually used to remove people or objects from images.
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- Stable Diffusion models: You can use any Stable Diffusion Inpainting(or normal) models from [Huggingface](https://huggingface.co/models?other=stable-diffusion) in IOPaint.
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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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- [Sanster/PowerPaint-V1-stable-diffusion-inpainting](https://huggingface.co/Sanster/PowerPaint-V1-stable-diffusion-inpainting)
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- Other Diffusion models:
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- [Sanster/AnyText](https://huggingface.co/Sanster/AnyText)
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- [timbrooks/instruct-pix2pix](https://huggingface.co/timbrooks/instruct-pix2pix)
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- [Fantasy-Studio/Paint-by-Example](https://huggingface.co/Fantasy-Studio/Paint-by-Example)
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- [kandinsky-community/kandinsky-2-2-decoder-inpaint](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder-inpaint)
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- [Plugins](https://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/features/file_manager): Browse your pictures conveniently and save them directly to the output directory.
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- [Native macOS app](https://opticlean.io/) for erase task
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- More features at [IOPaint Docs](https://iopaint.com/)
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