Merge branch 'main' into main

Dockerfile

@@ -0,0 +1,27 @@
+FROM nvcr.io/nvidia/pytorch:23.05-py3
+
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        make \
+        pkgconf \
+        xz-utils \
+        xorg-dev \
+        libgl1-mesa-dev \
+        libglu1-mesa-dev \
+        libxrandr-dev \
+        libxinerama-dev \
+        libxcursor-dev \
+        libxi-dev \
+        libxxf86vm-dev
+
+RUN pip install --upgrade pip
+
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
+
+WORKDIR /workspace
+
+RUN (printf '#!/bin/bash\nexec \"$@\"\n' >> /entry.sh) && chmod a+x /entry.sh
+ENTRYPOINT ["/entry.sh"]

README.md

@@ -47,7 +47,7 @@ If you have CUDA graphic card, please follow the requirements of [NVlabs/stylega
 The usual installation steps involve the following commands, they should set up the correct CUDA version and all the python packages
 
 ```
-conda env create python=3.7 -f environment.yml
+conda env create -f environment.yml
 conda activate stylegan3
 ```
 
@@ -64,6 +64,19 @@ cat environment.yml | \
   grep -v -E 'nvidia|cuda' > environment-no-nvidia.yml && \
     conda env create -f environment-no-nvidia.yml
 conda activate stylegan3
+```
+
+## Run Gradio visualizer in Docker 
+
+Provided docker image is based on NGC PyTorch repository. To quickly try out visualizer in Docker, run the following:  
+
+```sh
+docker build . -t draggan:latest  
+docker run -v  "$PWD":/workspace/src -it draggan:latest bash  
+cd src && python visualizer_drag_gradio.py  
+```
+Now you can open a shared link from Gradio (printed in the terminal console).   
+Beware the Docker image takes about 25GB of disk space!
 
 # On MacOS
 export PYTORCH_ENABLE_MPS_FALLBACK=1
@@ -75,6 +88,10 @@ To download pre-trained weights, simply run:
 ```sh
 sh scripts/download_model.sh
 ```
+Or for windows:
+```
+.\scripts\download_model.bat
+```
 If you want to try StyleGAN-Human and the Landscapes HQ (LHQ) dataset, please download weights from these links: [StyleGAN-Human](https://drive.google.com/file/d/1dlFEHbu-WzQWJl7nBBZYcTyo000H9hVm/view?usp=sharing), [LHQ](https://drive.google.com/file/d/16twEf0T9QINAEoMsWefoWiyhcTd-aiWc/view?usp=sharing), and put them under `./checkpoints`.
 
 Feel free to try other pretrained StyleGAN.
@@ -85,10 +102,14 @@ To start the DragGAN GUI, simply run:
 ```sh
 sh scripts/gui.sh
 ```
+If you are using windows, you can run:
+```
+.\scripts\gui.bat
+```
 
 This GUI supports editing GAN-generated images. To edit a real image, you need to first perform GAN inversion using tools like [PTI](https://github.com/danielroich/PTI). Then load the new latent code and model weights to the GUI.
 
-You can run DragGAN Gradio demo as well:
+You can run DragGAN Gradio demo as well, this is universal for both windows and linux:
 ```sh
 python visualizer_drag_gradio.py
 ```
@@ -97,6 +118,7 @@ python visualizer_drag_gradio.py
 
 This code is developed based on [StyleGAN3](https://github.com/NVlabs/stylegan3). Part of the code is borrowed from [StyleGAN-Human](https://github.com/stylegan-human/StyleGAN-Human).
 
+(cheers to the community as well)
 ## License
 
 The code related to the DragGAN algorithm is licensed under [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/).

requirements.txt

@@ -3,8 +3,16 @@ torch
 torchvision
 Ninja
 gradio
+torch>=2.0.0
+scipy==1.11.0
+Ninja==1.10.2
+gradio>=3.35.2
+imageio-ffmpeg>=0.4.3
 huggingface_hub
 hf_transfer
 pyopengl
 imgui
-glfw
+glfw==2.6.1
+pillow>=9.4.0
+torchvision>=0.15.2
+imageio>=2.9.0