In addition to the requirements mentioned before, a pretrained StyleGAN2 generator will attempt to be downloaded, (or manually download from here). Some parts of the StyleGAN implementation were modified, so that the whole implementation is native pytorch. Here, the code relies on the Rosinality pytorch implementation of StyleGAN2.
Pip install git+ Editing via Latent Vector Optimization Setup To install CLIP please run the following commands:Ĭonda install -yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit= Specific requirements for each method are described in its section. Initial version Setup (for all three methods)įor all the methods described in the paper, is it required to have: Upload paper to arxiv, and video to YouTube Add the global directions code (a local GUI and a colab notebook) Add support for custom StyleGAN2 and StyleGAN2-ada models, and also custom images Add mapper training and inference (including a jupyter notebook) code Add support for StyleSpace in optimization and latent mapper methods
Finally, we present a method for mappingĪ text prompts to input-agnostic directions in StyleGAN’s That infers a text-guided latent manipulation step forĪ given input image, allowing faster and more stable textbased Loss to modify an input latent vector in response to a Weįirst introduce an optimization scheme that utilizes a CLIP-based Manipulation that does not require such manual effort. To develop a text-based interface for StyleGAN image Language-Image Pre-training (CLIP) models in order In this work, weĮxplore leveraging the power of recently introduced Contrastive The many degrees of freedom, or an annotated collection Typically involves painstaking human examination of However,ĭiscovering semantically meaningful latent manipulations StyleGAN to manipulate generated and real images.
Images in a variety of domains, much recent work hasįocused on understanding how to use the latent spaces of *Equal contribution, ordered alphabeticallyĪbstract: Inspired by the ability of StyleGAN to generate highly realistic Or Patashnik*, Zongze Wu*, Eli Shechtman, Daniel Cohen-Or, Dani Lischinski StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery (ICCV 2021 Oral)