Conceptos Básicos
Subject-driven text-to-image synthesis models often overlook crucial attributes specified in the text prompt due to the dominance of subject-specific information, leading to suboptimal content alignment. This work introduces Subject-Agnostic Guidance (SAG) to address this challenge by diminishing the influence of subject-specific attributes and enhancing attention towards subject-agnostic attributes.
Resumen
The content discusses the problem of content ignorance in subject-driven text-to-image synthesis, where the generated images tend to be heavily influenced by the reference subject images provided by users, often overlooking crucial attributes detailed in the text prompt.
To address this issue, the authors propose Subject-Agnostic Guidance (SAG), a simple yet effective solution. SAG focuses on constructing a subject-agnostic condition and applying a dual classifier-free guidance to obtain outputs that are consistent with both the given subject and input text prompts.
The authors validate the efficacy of their approach using both optimization-based (Textual Inversion) and encoder-based (ELITE, SuTI) methods. They also demonstrate the applicability of SAG in second-order customization methods, where an encoder-based model is fine-tuned with DreamBooth.
The key highlights of the proposed approach are:
- Conceptual simplicity and minimal code modifications required to integrate SAG with existing methods.
- Substantial quality improvements in terms of both text alignment and subject fidelity, as evidenced by evaluations and user studies.
- Seamless integration with prevalent text-to-image synthesis methods, making it a versatile and robust solution.
Estadísticas
Given user-provided subject images, a part of the content specified in the text prompt (highlighted in blue) are often overlooked.
SAG aligns the output more closely with both the target subject and text prompt.
Citas
"In subject-driven text-to-image synthesis, the synthesis process tends to be heavily influenced by the reference images provided by users, often overlooking crucial attributes detailed in the text prompt."
"Our SAG focuses on enhancing subject-agnostic attributes, diminishing the influence of subject-specific elements through our dual classifier-free guidance."