Update README.md

README.md

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-# Unofficial Pytorch implementation (**inference only**) of the SimSwap: An Efficient Framework For High Fidelity Face Swapping
-
-## Updates
-- improved performance (up to 40% in some scenarios, it depends on frame resolution and number of swaps per frame).
-- fixed a problem with overlapped areas from close faces (https://github.com/mike9251/simswap-inference-pytorch/issues/21)
-- added support for using GFPGAN model as an additional post-processing step to improve final image quality
-- added a toy gui app. Might be useful to understand how different pipeline settings affect output
-
-## Attention
-***This project is for technical and academic use only. Please do not apply it to illegal and unethical scenarios.***
-
-***In the event of violation of the legal and ethical requirements of the user's country or region, this code repository is exempt from liability.***
-
-## Preparation
-### Installation
-```
-# clone project
-git clone https://github.com/mike9251/simswap-inference-pytorch
-cd simswap-inference-pytorch
-
-# [OPTIONAL] create conda environment
-conda create -n myenv python=3.9
-conda activate myenv
-
-# install pytorch and torchvision according to instructions
-# https://pytorch.org/get-started/
-
-# install requirements
-pip install -r requirements.txt
-```
-
-### Important
-Face detection will be performed on CPU. To run it on GPU you need to install onnx gpu runtime:
-
-```pip install onnxruntime-gpu==1.11.1```
-
-and modify one line of code in ```...Anaconda3\envs\myenv\Lib\site-packages\insightface\model_zoo\model_zoo.py```
-
-Here, instead of passing **None** as the second argument to the onnx inference session
-```angular2html
-class ModelRouter:
-    def __init__(self, onnx_file):
-        self.onnx_file = onnx_file
-
-    def get_model(self):
-        session = onnxruntime.InferenceSession(self.onnx_file, None)
-        input_cfg = session.get_inputs()[0]
-```
-pass a list of providers
-```angular2html
-class ModelRouter:
-    def __init__(self, onnx_file):
-        self.onnx_file = onnx_file
-
-    def get_model(self):
-        session = onnxruntime.InferenceSession(self.onnx_file, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
-        input_cfg = session.get_inputs()[0]
-```
-Otherwise simply use CPU onnx runtime with only a minor performance drop.
-
-### Weights
-#### Weights for all models get downloaded automatically.
-
-You can also download weights manually and put inside `weights` folder:
-
-- weights/<a href="https://github.com/mike9251/simswap-inference-pytorch/releases/download/weights/face_detector_scrfd_10g_bnkps.onnx">face_detector_scrfd_10g_bnkps.onnx</a>
-- weights/<a href="https://github.com/mike9251/simswap-inference-pytorch/releases/download/weights/arcface_net.jit">arcface_net.jit</a>
-- weights/<a href="https://github.com/mike9251/simswap-inference-pytorch/releases/download/weights/parsing_model_79999_iter.pth">79999_iter.pth</a>
-- weights/<a href="https://github.com/mike9251/simswap-inference-pytorch/releases/download/weights/simswap_224_latest_net_G.pth">simswap_224_latest_net_G.pth</a> - official 224x224 model
-- weights/<a href="https://github.com/mike9251/simswap-inference-pytorch/releases/download/weights/simswap_512_390000_net_G.pth">simswap_512_390000_net_G.pth</a> - unofficial 512x512 model (I took it <a href="https://github.com/neuralchen/SimSwap/issues/255">here</a>).
-- weights/<a href="https://github.com/mike9251/simswap-inference-pytorch/releases/download/v1.1/GFPGANv1.4_ema.pth">GFPGANv1.4_ema.pth</a>
-- weights/<a href="https://github.com/mike9251/simswap-inference-pytorch/releases/download/v1.2/blend_module.jit">blend_module.jit</a>
-
-## Inference
-### Web App
-```angular2html
-streamlit run app_web.py
-```
-
-### Command line App
-This repository supports inference in several modes, which can be easily configured with config files in the **configs** folder.
-- **replace all faces on a target image / folder with images**
-```angular2html
-python app.py --config-name=run_image.yaml
-```
-
-- **replace all faces on a video**
-```angular2html
-python app.py --config-name=run_video.yaml
-```
-
-- **replace a specific face on a target image / folder with images**
-```angular2html
-python app.py --config-name=run_image_specific.yaml
-```
-
-- **replace a specific face on a video**
-```angular2html
-python app.py --config-name=run_video_specific.yaml
-```
-
-Config files contain two main parts:
-
-- **data**
-  - *id_image* - source image, identity of this person will be transferred.
-  - *att_image* - target image, attributes of the person on this image will be mixed with the person's identity from the source image. Here you can also specify a folder with multiple images - identity translation will be applied to all images in the folder.
-  - *specific_id_image* - a specific person on the *att_image* you would like to replace, leaving others untouched (if there's any other person).
-  - *att_video* - the same as *att_image*
-  - *clean_work_dir* - whether remove temp folder with images or not (for video configs only).
-
-
-- **pipeline**
-  - *face_detector_weights* - path to the weights file OR an empty string ("") for automatic weights downloading.
-  - *face_id_weights* - path to the weights file OR an empty string ("") for automatic weights downloading.
-  - *parsing_model_weights* - path to the weights file OR an empty string ("") for automatic weights downloading.
-  - *simswap_weights* - path to the weights file OR an empty string ("") for automatic weights downloading.
-  - *gfpgan_weights* - path to the weights file OR an empty string ("") for automatic weights downloading.
-  - *device* - whether you want to run the application using GPU or CPU.
-  - *crop_size* - size of images SimSwap models works with.
-  - *checkpoint_type* - the official model works with 224x224 crops and has different pre/post processings (imagenet like). Latest official repository allows you to train your own models, but the architecture and pre/post processings are slightly different (1. removed Tanh from the last layer; 2. normalization to [0...1] range). **If you run the official 224x224 model then set this parameter to "official_224", otherwise "none".**
-  - *face_alignment_type* - affects reference face key points coordinates. **Possible values are "ffhq" and "none". Try both of them to see which one works better for your data.**
-  - *smooth_mask_kernel_size* - a non-zero value. It's used for the post-processing mask size attenuation. You might want to play with this parameter.
-  - *smooth_mask_iter* - a non-zero value. The number of times a face mask is smoothed.
-  - *smooth_mask_threshold* - controls the face mask saturation. Valid values are in range [0.0...1.0]. Tune this parameter if there are artifacts around swapped faces.
-  - *face_detector_threshold* - values in range [0.0...1.0]. Higher value reduces probability of FP detections but increases the probability of FN.
-  - *specific_latent_match_threshold* - values in range [0.0...inf]. Usually takes small values around 0.05.
-  - *enhance_output* - whether to apply GFPGAN model or not as a post-processing step.
-
-
-### Overriding parameters with CMD
-Every parameter in a config file can be overridden by specifying it directly with CMD. For example:
-
-```angular2html
-python app.py --config-name=run_image.yaml data.specific_id_image="path/to/the/image" pipeline.erosion_kernel_size=20
-```
-
-## Video
-
-<details>
-<summary><b>Official 224x224 model, face alignment "none"</b></summary>
-
-[![Video](https://i.imgur.com/iCujdRB.jpg)](https://vimeo.com/728346715)
-
-</details>
-
-<details>
-<summary><b>Official 224x224 model, face alignment "ffhq"</b></summary>
-
-[![Video](https://i.imgur.com/48hjJO4.jpg)](https://vimeo.com/728348520)
-
-</details>
-
-<details>
-<summary><b>Unofficial 512x512 model, face alignment "none"</b></summary>
-
-[![Video](https://i.imgur.com/rRltD4U.jpg)](https://vimeo.com/728346542)
-
-</details>
-
-<details>
-<summary><b>Unofficial 512x512 model, face alignment "ffhq"</b></summary>
-
-[![Video](https://i.imgur.com/gFkpyXS.jpg)](https://vimeo.com/728349219)
-
-</details>
-
-## License
-For academic and non-commercial use only.The whole project is under the CC-BY-NC 4.0 license. See [LICENSE](https://github.com/neuralchen/SimSwap/blob/main/LICENSE) for additional details.
-
-## Acknowledgements
-
-<!--ts-->
-* [SimSwap](https://github.com/neuralchen/SimSwap)
-* [Insightface](https://github.com/deepinsight/insightface)
-* [Face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)
-* [BiSeNet](https://github.com/CoinCheung/BiSeNet)
-* [GFPGAN](https://github.com/TencentARC/GFPGAN)
-<!--te-->
+---
+title: My Awesome Model
+emoji: 🚀
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.0.0
+app_file: my_app.py
+pinned: true
+---