In this example tutorial, we will show you how to generate realistic images with StyleGAN3 and Bacalhau. StyleGAN is based on Generative Adversarial Networks (GANs), which include a generator and discriminator network that has been trained to differentiate images generated by the generator from real images. However, during the training, the generator tries to fool the discriminator, which results in the generation of realistic-looking images. With StyleGAN3 we can generate realistic-looking images or videos. It can generate not only human faces but also animals, cars, and landscapes.
To get started, you need to install the Bacalhau client, see more information here
To run StyleGAN3 locally, you'll need to clone the repo, install dependencies and download the model weights.
Now you can generate an image using a pre-trained AFHQv2 model. Here is an example of the image we generated:
To build your own docker container, create a Dockerfile, which contains instructions to build your image.
See more information on how to containerize your script/app here
We will run docker build command to build the container:
Before running the command replace:
hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create docker account (https://docs.docker.com/docker-id/), and use the username of the account you created
repo-name with the name of the container, you can name it anything you want
tag this is not required but you can use the latest tag
In our case:
Next, upload the image to the registry. This can be done by using the Docker hub username, repo name or tag.
In our case:
Some of the jobs presented in the Examples section may require more resources than are currently available on the demo network. Consider starting your own network or running less resource-intensive jobs on the demo network
To submit a job run the Bacalhau command with following structure:
export JOB_ID=$( ... ) exports the job ID as environment variable
bacalhau docker run: call to Bacalhau
The --gpu 1 flag is set to specify hardware requirements, a GPU is needed to run such a job
The --id-only flag is set to print only job id
jsacex/stylegan3: the name and the tag of the docker image we are using
python gen_images.py: execute the script with following parameters:
--trunc=1 --seeds=2 --network=stylegan3-r-afhqv2-512x512.pkl: The animation length is either determined based on the --seeds value or explicitly specified using the --num-keyframes option. When num keyframes are specified with --num-keyframes, the output video length will be num_keyframes * w_frames frames.
../outputs: path to the output
The same job can be presented in the declarative format. In this case, the description will look like this:
The job description should be saved in .yaml format, e.g. stylegan3.yaml, and then run with the command:
You can also run variations of this command to generate videos and other things. In the following command below, we will render a latent vector interpolation video. This will render a 4x2 grid of interpolations for seeds 0 through 31.
Let's look closely at the command below:
export JOB_ID=$( ... ) exports the job ID as environment variable
bacalhau docker run: call to bacalhau
The --gpu 1 flag is set to specify hardware requirements, a GPU is needed to run such a job
The --id-only flag is set to print only job id
jsacex/stylegan3 the name and the tag of the docker image we are using
python gen_images.py: execute the script with following parameters:
--trunc=1 --seeds=2 --network=stylegan3-r-afhqv2-512x512.pkl: The animation length is either determined based on the --seeds value or explicitly specified using the --num-keyframes option. When num keyframes is specified with --num-keyframes, the output video length will be num_keyframes * w_frames frames. If --num-keyframes is not specified, the number of seeds given with --seeds must be divisible by grid size W*H (--grid). In this case, the output video length will be # seeds/(w*h)*w_frames frames.
../outputs: path to the output
When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.
You can check the status of the job using bacalhau job list.
When it says Completed, that means the job is done, and we can get the results.
You can find out more information about your job by using bacalhau job describe.
You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.
After the download has finished you should see the following contents in results directory
Now you can find the file in the results/outputs folder.
If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).