https://github.com/tylertemp/roopiy

https://github.com/tylertemp/roopiy

Last synced: 15 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/tylertemp/roopiy
• Owner: TylerTemp
• Created: 2023-10-02T15:53:09.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-05-11T13:49:07.000Z (8 months ago)
• Last Synced: 2024-11-08T06:47:54.457Z (2 months ago)
• Language: Python
• Size: 77.1 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Roopiy #

A video face swap tool using insightface

## Acceleration ##

1.  Install [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive)

2.  Download [cuDNN for Cuda 11.x](https://developer.nvidia.com/rdp/cudnn-archive)

3.  For windows go `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v{YourVersion}`, copy the `bin`, `include`, `lib` folders from cuDNN to the CUDA folder, and add `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v{YourVersion}\bin` to path

## Installation ##

1.  clone this project

2.  `pip install -e .`.

3.  change the `torch` and `torchvision` in `requirements.txt` as you need, then `pip install -r requirements.txt`

4.  Setup a model path, e.g. `temp` (note: use absolute path!)

5.  download `inswapper_128.onnx` into your model path, from [huggingface](https://huggingface.co/ezioruan/inswapper_128.onnx) or [here](https://static.notexists.top/mirror/insightface/inswapper_128.onnx)

6.  download [buffalo_l.zip](https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_l.zip) and unzip under `temp/models/buffalo_l`; you folder now looks like: `temp/models/buffalo_l/*.onnx`

7.  download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth) into your model path

8.  download [detection_Resnet50_Final.pth](https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth) into your model path `gfpgan/weights`

9.  download [parsing_parsenet.pth](https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth) into your model path `gfpgan/weights`

## Model Path ##

either use `ROOPIY_MODEL_PATH={PathToYourModel} roopiy` or `roopiy --model-path={PathToYourModel}` to set the model path

## Basic Run ##

use `roopiy` to run. If it does not work, run `python -m roopiy`

check `roopiy --help` for construction