Beyond Sliders: Mastering the Art of Diffusion-based Image Manipulation
This addresses image customization challenges for users in computer vision and graphics, though it appears incremental as it builds on concept sliders.
The paper tackles the problem of image manipulation for no-AIGC images, particularly real-world captures, by introducing Beyond Sliders, a framework integrating GANs and diffusion models with fine-grained guidance, resulting in enhanced image quality and realism.
In the realm of image generation, the quest for realism and customization has never been more pressing. While existing methods like concept sliders have made strides, they often falter when it comes to no-AIGC images, particularly images captured in real world settings. To bridge this gap, we introduce Beyond Sliders, an innovative framework that integrates GANs and diffusion models to facilitate sophisticated image manipulation across diverse image categories. Improved upon concept sliders, our method refines the image through fine grained guidance both textual and visual in an adversarial manner, leading to a marked enhancement in image quality and realism. Extensive experimental validation confirms the robustness and versatility of Beyond Sliders across a spectrum of applications.