![]() ![]() ![]() ![]() Remarkably, our proposed PhotoVerse eliminates the need for test time tuning and relies solely on a single facial photo of the target identity, significantly reducing the resource cost associated with image generation. Furthermore, we introduce facial identity loss as a novel component to enhance the preservation of identity during training. To address these obstacles, we present PhotoVerse, an innovative methodology that incorporates a dual-branch conditioning mechanism in both text and image domains, providing effective control over the image generation process. However, existing approaches to personalization encounter multiple challenges, including long tuning times, large storage requirements, the necessity for multiple input images per identity, and limitations in preserving identity and editability. Download a PDF of the paper titled PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models, by Li Chen and 10 other authors Download PDF Abstract:Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts. ![]()
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