103 research outputs found
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs
In this work, we are dedicated to text-guided image generation and propose a
novel framework, i.e., CLIP2GAN, by leveraging CLIP model and StyleGAN. The key
idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP
and the input latent space of StyleGAN, which is realized by introducing a
mapping network. In the training stage, we encode an image with CLIP and map
the output feature to a latent code, which is further used to reconstruct the
image. In this way, the mapping network is optimized in a self-supervised
learning way. In the inference stage, since CLIP can embed both image and text
into a shared feature embedding space, we replace CLIP image encoder in the
training architecture with CLIP text encoder, while keeping the following
mapping network as well as StyleGAN model. As a result, we can flexibly input a
text description to generate an image. Moreover, by simply adding mapped text
features of an attribute to a mapped CLIP image feature, we can effectively
edit the attribute to the image. Extensive experiments demonstrate the superior
performance of our proposed CLIP2GAN compared to previous methods
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