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doc/guidance/preparation.md

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+
+# Preparation
+
+### Environment and Dependencies
+```
+conda create -n simswap python=3.6
+conda activate simswap
+conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
+(option): pip install --ignore-installed imageio
+pip install insightface==0.2.1 onnxruntime moviepy
+```
+- We use the face detection and alignment methods from **[insightface](https://github.com/deepinsight/insightface)** for image preprocessing. Please download the relative files and save them to ./insightface_func/models from [this link](https://onedrive.live.com/?authkey=%21ADJ0aAOSsc90neY&cid=4A83B6B633B029CC&id=4A83B6B633B029CC%215837&parId=4A83B6B633B029CC%215834&action=locate).
+- The pytorch and cuda versions above are most recommanded. They may vary.
+- Using insightface with different versions is not recommanded. Please use this specific version.
+- These settings are tested valid on both Windows and Ununtu.
+
+### Pretrained model
+There are two archive files in the drive: **checkpoints.zip** and **arcface_checkpoint.tar**
+
+- **Copy the arcface_checkpoint.tar into ./arcface_model**
+- **Unzip checkpoints.zip, place it in the root dir ./**
+
+[[Google Drive]](https://drive.google.com/drive/folders/1jV6_0FIMPC53FZ2HzZNJZGMe55bbu17R?usp=sharing)
+[[Baidu Drive]](https://pan.baidu.com/s/1wFV11RVZMHqd-ky4YpLdcA) Password: ```jd2v```
+
+### Note
+We expect users to have GPU with at least 8G memory. For those who do not, we will provide Colab Notebook implementation in the future.

doc/guidance/usage.md

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+
+# Usage
+
+### Simple face swaping for already face-aligned images
+```
+python test_one_image.py --isTrain false  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path crop_224/6.jpg --pic_b_path crop_224/ds.jpg --output_path output/
+```
+
+### Face swaping for video
+- Swap only one face within the video(the one with highest confidence by face detection).
+```
+python test_video_swapsingle.py --isTrain false  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/mutil_people_1080p.mp4 --output_path ./output/mutil_test_swapsingle.mp4 --temp_path ./temp_results
+```
+
+- Swap all faces within the video.
+```
+python test_video_swapmutil.py --isTrain false  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/mutil_people_1080p.mp4 --output_path ./output/mutil_test_swapmutil.mp4 --temp_path ./temp_results
+```
+
+
+
+
+### Face swaping for Arbitrary images
+- Swap only one face within one image(the one with highest confidence by face detection). The result would be saved to ./output/result_whole_swapsingle.jpg
+```
+python test_wholeimage_swapsingle.py --isTrain false  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/mutil_people.jpg --output_path ./output/
+```
+
+- Swap all faces within one image. The result would be saved to ./output/result_whole_swapmutil.jpg
+```
+python test_wholeimage_swapmutil.py --isTrain false  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --pic_b_path ./demo_file/mutil_people.jpg --output_path ./output/
+```
+Difference between single face swapping and all face swapping are shown below.
+<img src="../img/multi_face_comparison.png"/>
+
+### Parameters
+|  Parameters   | Function  |
+|  :----  | :----  |
+| --name  | The SimSwap training logs name |
+| --pic_a_path  | Path of image with the target face |
+| --pic_b_path  | Path of image with the source face to swap |
+| --video_path  | Path of video with the source face to swap |
+| --temp_path  | Path to store intermediate files  |
+| --output_path  | Path of directory to store the face swapping result  |
+
+### Note
+We expect users to have GPU with at least 8G memory. For those who do not, we will provide Colab Notebook implementation in the future.