PALP: Prompt Aligned Personalization of Text-to-Image Models
This addresses the problem for content creators who need personalized images with high fidelity to both subjects and complex prompts, though it is incremental as it builds on existing personalization methods.
The paper tackles the trade-off between personalization ability and alignment to complex textual prompts in text-to-image models by proposing PALP, a prompt-aligned personalization method that improves text alignment for intricate prompts using an additional score distillation sampling term, demonstrating versatility in multi- and single-shot settings and composition of multiple subjects or reference images.
Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a \emph{single} prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.