CCRT removes multiple concepts from a diffusion model in sequence using a distillation loss and a genetic-algorithm-selected calibration prompt set, preserving text-image alignment better than iterative ESD or fixed multi-concept erasure.
Can There be Art Without an Artist?
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abstract
Generative AI based art has proliferated in the past year, with increasingly impressive use cases from generating fake human faces to the creation of systems that can generate thousands of artistic images from text prompts - some of these images have even been "good" enough to win accolades from qualified judges. In this paper, we explore how Generative Models have impacted artistry, not only from a qualitative point of view, but also from an angle of exploitation of artists -- both via plagiarism, where models are trained on their artwork without permission, and via profit shifting, where profits in the art market have shifted from art creators to model owners. However, we posit that if deployed responsibly, AI generative models have the possibility of being a positive, new modality in art that does not displace or harm existing artists.
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Continuous Concepts Removal in Text-to-image Diffusion Models
CCRT removes multiple concepts from a diffusion model in sequence using a distillation loss and a genetic-algorithm-selected calibration prompt set, preserving text-image alignment better than iterative ESD or fixed multi-concept erasure.