ArtiFace is a deep learning project that uses Generative Adversarial Networks (GANs) to generate human faces based on celebrity images. The project uses the CelebFaces Attributes Dataset (CelebA) to train the GANs. The project is implemented in Python using the Keras API and Tensorflow Backend.
The end goal for the project is to create realistic human faces that can be used for syntehtic data generation and other applications.
Currently, images generated by the model can be viewed on https://facegen.romitsagu.com/.
The dataset used for this project is the CelebFaces Attributes Dataset (CelebA). The dataset contains 202,599 images of human faces with 40 binary attributes. The images are 218x178 pixels in size. The dataset can be downloaded from here.
The following are some of the images from the dataset:
The attrbutes for the images also seems to be distributed very evenly. These can be possible incorporated into the model to generate faces with specific attributes in the future.
The model architecture is based on the DCGAN architecture proposed by Goodfellow et al.. The model consists of two parts: the generator and the discriminator. The generator is a deep convolutional neural network that takes a random vector as input and generates a 64x64x3 image as output. The discriminator is a deep convolutional neural network that takes a 64x64x3 image as input and outputs a probability of the image being real or fake. The generator and discriminator are trained in an adversarial manner. The generator is trained to fool the discriminator into thinking that the generated images are real. The discriminator is trained to correctly classify the real and fake images. The generator and discriminator are trained in an alternating manner. The generator is trained first and then the discriminator is trained. This process is repeated until the generator is able to generate images that are indistinguishable from real images.
The following shows the summary of the generator:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_9 (Dense) (None, 524288) 52428800
batch_normalization_16 (Bat (None, 524288) 2097152
chNormalization)
leaky_re_lu_26 (LeakyReLU) (None, 524288) 0
reshape_4 (Reshape) (None, 32, 32, 512) 0
conv2d_transpose_16 (Conv2D (None, 32, 32, 256) 3276800
Transpose)
batch_normalization_17 (Bat (None, 32, 32, 256) 1024
chNormalization)
leaky_re_lu_27 (LeakyReLU) (None, 32, 32, 256) 0
conv2d_transpose_17 (Conv2D (None, 64, 64, 128) 819200
Transpose)
batch_normalization_18 (Bat (None, 64, 64, 128) 512
chNormalization)
leaky_re_lu_28 (LeakyReLU) (None, 64, 64, 128) 0
conv2d_transpose_18 (Conv2D (None, 128, 128, 64) 204800
Transpose)
batch_normalization_19 (Bat (None, 128, 128, 64) 256
chNormalization)
leaky_re_lu_29 (LeakyReLU) (None, 128, 128, 64) 0
conv2d_transpose_19 (Conv2D (None, 128, 128, 3) 4800
Transpose)
=================================================================
Total params: 58,833,344
Trainable params: 57,783,872
Non-trainable params: 1,049,472
_________________________________________________________________
The following shows the summary of the discriminator:
Model: "Discriminator"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_8 (Conv2D) (None, 64, 64, 64) 4864
leaky_re_lu_24 (LeakyReLU) (None, 64, 64, 64) 0
dropout_8 (Dropout) (None, 64, 64, 64) 0
conv2d_9 (Conv2D) (None, 32, 32, 128) 204928
leaky_re_lu_25 (LeakyReLU) (None, 32, 32, 128) 0
dropout_9 (Dropout) (None, 32, 32, 128) 0
flatten_4 (Flatten) (None, 131072) 0
dense_8 (Dense) (None, 1) 131073
=================================================================
Total params: 340,865
Trainable params: 340,865
Non-trainable params: 0
_________________________________________________________________
The model was first tested on 10,000 images (5% of the dataset) to check the ability of the model to learn. The model was trained for 100 epochs with a batch size of 32. The following shows some images generated by the model:
This showed that the model was able to identify many specific features about the images such as the hair, eyes, nose, etc. This provided enough evidence that the model was able to learn the features of the images and generate new images. One thing to note is that the model still didn't generate images that were indistinguishable from real images. This could be because the model hasn't been trained for a long enough time and requires more time to learn the features of the images.
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Training time- The model takes a very long time to train. If the model is trained on the entire dataset, each epoch takes 40-70 minutes to train. This makes it hard to train the model for a long period of time and makes it hard to experiment with different hyperparameters. -
Video Memory- The model requires a lot of video memory to train. -
Discriminator overpowering the generator- The discriminator is able to overpower the generator and the generator is not able to learn. This is because the discriminator is able to easily identify the fake images generated by the generator. This makes it hard for the generator to learn and generate images that are indistinguishable from real images. This can be clearly seen in the following images:
Here the images generated are very similar and have very little distinguishable features. This is because the discriminator is able to easily identify the fake images generated by the generator and the generator seems to have little incentive to try to generate better and unique images.
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Image Selection per epoch- To reduce the training time, we train only on a random subset of the images per epoch. This reduces the training time per epoch and allows us to train the model for a longer period of time and view the overall results. ThisRANDOM_SELECTcan be used as a hyperparameter to control the number of images selected per epoch. -
Batch Size- The batch size can be used as a hyperparameter to control the number of images selected per epoch. Due to memory constraints, the batch size was set to 64 and can't be further incrased with the current hardware. -
Discriminator training- A variety of different methods were tried to successfully train the GAN in a zero sum game setting.-
Ratio-based training- The discriminator was trained once for every 5 times the generator was trained. This was done to ensure that the discriminator doesn't overpower the generator and the generator is able to learn. -
Decay ratio training- The discriminator was initially trained once every time the generator was trained for a few epochs and then this ratio was decreased as training went on. -
Accuracy-based training- The discriminator was trained once every time the generator was trained if the accuracy of the discriminator was less than a certain percentage.
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Each of the training methods were tested for the first 10,000 images (5% of the dataset) and the results were compared.
The following shows the losses and accuracies of the discriminator for Ratio based method:
The following shows the losses and accuracies of the discriminator for Decay Ratio based method:
The following shows the losses and accuracies of the discriminator for Accuracy based method:
The graphs show that the Accuracy based method results in the graphs that have linearly decreasing accuracies. This could point towards the fact that the discriminator is not overpowering the generator and the generator is able to learn. This is further confirmed by the samples generated by the model. The following shows the samples at the lowest discriminator accuracies generated by the model for each of the methods:
Accuracy Based
Decay Ratio Based
Ratio Based
Even comparing this to a higher accuracy model from the Accuracy based method leads us to believe that accuracy does have some correlation with the quality of samples regardless of the training time. This is because compared to the images generated by the model at the lowest accuracy, the images generated by the model at the 1000th epoch seem to be qualitatively worse. This can be seen as follows:
This shows that the Accuracy based method is the most optimal method for training the discriminator and generating unique and discernable samples.
The final model was trained for 100 epochs using the entire dataset and the discriminator was trained using the Accuracy-based method described above with a threshold of 0.7(or 70%). The following graph shows the accuracy and loss of the discriminator over time:
Here are some early samples during the training:
Finally a choice was made based on the quality of the samples amongst the 100 epochs:
You can also visualize the training of the test model and the final model through these gifs:
Test Model Final Model
The model that was created up to this point is now ready to be used to generate images given noise. To put the model to use a web application was created using Flask where random noise is generated and then used to create an image from that noise. To do this, a Docker Image was created using the web application and a selected model layered on top of a python:3.10-bullseye image. SSL was added so the flask application would be usable through Cloudflare’s DNS forwarding which is currently configured to run HTTPS not HTTP. This image is stored in a Docker Hub Repository where there are two versions, one which was used for testing and a second which is for the final model.
For the Deployment of the application MicroK8s Kubernetes was used on Romit’s server which has a deeper explanation here. Two other changes needed to be done so the application would run as needed, the first was a new Virtual Service added to the Load Balancer for the new application’s port on the Kubernetes Server. The second was to change the Virtual Machine that hosed the Kubernetes Server to use CPU Host Passthrough so the application would recognize that the CPU was AVX (Advanced Vector Extensions) capable for TensorFlow to run correctly. Enabling SR-VIO in the Server’s BIOS was also necessary for the website to work correctly.
The final Kubernetes deployment file can be seen here.
The Load Balancer Setup can be seen below:
The Pods and Services for the application in Kubernetes can be seen below:
The application is now available at https://facegen.romitsagu.com/.
- Adding more configuration options to the web application during the generation of the image.
- Improving the model by using a larger dataset, more epochs, and more training time and possibly even a larger model to improve the quality of the generated images.
- Adding more models to the web application to choose from.
- Using Cloud for training the model to reduce the training time.
[1] The training data is from a dataset mentioned in the paper: S. Yang, P. Luo, C. C. Loy, and X. Tang, "From Facial Parts Responses to Face Detection: A Deep Learning Approach", in IEEE International Conference on Computer Vision (ICCV), 2015.
[2] I. Goodfellow, "NIPS 2016 Tutorial: Generative Adversarial Networks," in Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, Dec. 2016. [Online]. Available: https://doi.org/10.48550/arXiv.1701.00160. [Accessed: Mar. 21, 2023].