StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation

By Umut Kocasari, Alara Dirik, Mert Tiftikci, Pinar Yanardag. 2021

76 citations recorded by the source corpus.

Discovering meaningful directions in the latent space of GANs to manipulate semantic attributes typically requires large amounts of labeled data. Recent work aims to overcome this limitation by leveraging the power of Contrastive Language-Image Pre-training (CLIP), a joint text-image model. While promising, these methods require several hours of preprocessing or training to achieve the desired manipulations. In this paper, we present StyleMC, a fast and efficient method for text-driven image generation and manipulation. StyleMC uses a CLIP-based loss and an identity loss to manipulate images via a single text prompt without significantly affecting other attributes. Unlike prior work, StyleMC requires only a few seconds of training per text prompt to find stable global directions, does not require prompt engineering and can be used with any pre-trained StyleGAN2 model. We demonstrate the effectiveness of our method and compare it to state-of-the-art methods. Our code can be found at http://catlab-team.github.io/stylemc.

Subfields: Computer Vision and Pattern Recognition, Generative Models, Multimodal Learning

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Citations in the corpus

StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets (2022, methodology): “directions in latent space found by StyleMC [Kocasari et al. 2022].”
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing (2022, unknown): “Following StyleCLIP, StyleMC [22] has trained mappers to produce ∆ s for single-mode StyleGAN manipulation.”