Open Source Project


LSAP (Latent Space Alignment Paradigm) rethinks inversion fidelity, perception, and editability in GAN latent space, highlighting the misalignment between the inversio...


The LSAP (Latent Space Alignment Paradigm) project introduces a novel approach to addressing the challenges associated with the process of inverting images back into the latent space of Generative Adversarial Networks (GANs). This process, known as inversion, involves mapping an input image back to its corresponding latent representation. However, this mapping often suffers from issues such as low fidelity, poor perceptual quality, and limited editability of the inverted images. These challenges stem from a misalignment between the code obtained through inversion and the distribution of codes that generate synthetic images directly from the GAN’s latent space.

To tackle these challenges, LSAP proposes the use of SNCD (Normalized Style Space and Cosine Distance) as a new metric and optimization criterion. SNCD is designed to evaluate and enhance the alignment between the inversion code and the synthetic distribution. By focusing on the style space normalization and leveraging cosine distance, this method aims to optimize the alignment in a way that significantly improves the perceptual quality and editability of the inverted images.

The project underscores the importance of this alignment by conducting extensive experiments across various domains, showing that improving the alignment through SNCD leads to noticeable advancements in the quality and flexibility of the images produced. These improvements are quantified in the experiments, which demonstrate SNCD’s effectiveness in capturing the perceptual quality and editability of images, ultimately setting new benchmarks in the field. Through this paradigm, the project redefines the standards for evaluating and achieving high-quality, editable images generated from GANs, addressing long-standing limitations in the field.

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