Image generative models are advancing rapidly, producing images of remarkable realism and fidelity. However, existing models often lack precise control over the generated content, limiting their image editing capabilities and the integration of real content into synthesized imagery. In this talk, I will demonstrate how a deep understanding of the inner mechanisms of large-scale pretrained generative models enables the design of powerful techniques for a variety of image manipulation tasks. By analyzing the semantic representations learned by these models, I will present methods that enable effective content editing. Additionally, I will discuss the challenges and trade-offs involved in manipulating real content and propose strategies to address these challenges. Finally, I will highlight recent advancements in incorporating real content, with a particular focus on techniques for injecting information into pretrained models.
Bio: Or Patashnik (https://orpatashnik.github.io/) is a Computer Science PhD candidate at Tel Aviv University, supervised by Daniel Cohen-Or. Her research focuses on computer graphics and its intersection with computer vision, with an emphasis on generative tasks such as image editing, personalization, and image inversion using large-scale pretrained models. Recently, she has been particularly interested in better understanding diffusion models for various applications.
