Adobe Research
Contrastive Language-Image Pretraining (CLIP) models in order to navigate image editing by text queries.
1. Take pretrained CLIP, pretrained StyleGAN, and pretrained ArcFace network for face recognition.
2. Project an input image in StyleGAN latent vector
w_s. 3. Now, given a source latent code
w_s∈ W+, and a directive in natural language, or a text prompt t, we iteratively minimize the sum of three losses by changing the latent code w: a) Distance between generated by StyleGAN image and the text query;
b) Regularization loss penalizing large deviation of the source vector
w_sc) Identity loss, which makes sure that the identity of the generated face is the same as the original one. This is done by minimizing the distance between images in the ArcFace face recognition network embedding space.
Such an image editing process requires iterative optimization of the latent code
w (usually 200-300 iterations) for several minutes. To make it faster authors propose a feed-forward method, where instead of optimization, another neural network predicts the residuals which are added to the latent code w to produce the desired image alterations.The overall idea of the paper is not super novel and has been around for some time already. But this is the first formal paper on such an image editing approach using CLIP and StyleGAN.
Other related papers, recently discussed in this channel:
▪️Paint by Word
▪️Using latent space regression to analyze and leverage compositionality in GANs
📝 StyleCLIP Paper
⚙️ StyleCLIp code
