PreciseControl: Enhancing Text-To-Image Diffusion Models with Fine-Grained Attribute Control
<|reference_start|>PreciseControl: Enhancing Text-To-Image Diffusion Models with Fine-Grained Attribute Control: Recently, we have seen a surge of personalization methods for text-to-image (T2I) diffusion models to learn a concept using a few images. Existing approaches, when used for face personalization, suffer to achieve convincing inversion with identity preservation and rely on semantic text-based editing of the generated face. However, a more fine-grained control is desired for facial attribute editing, which is challenging to achieve solely with text prompts. In contrast, StyleGAN models learn a rich face prior and enable smooth control towards fine-grained attribute editing by latent manipulation. This work uses the disentangled $\mathcal{W+}$ space of StyleGANs to condition the T2I model. This approach allows us to precisely manipulate facial attributes, such as smoothly introducing a smile, while preserving the existing coarse text-based control inherent in T2I models. To enable conditioning of the T2I model on the $\mathcal{W+}$ space, we train a latent mapper to translate latent codes from $\mathcal{W+}$ to the token embedding space of the T2I model. The proposed approach excels in the precise inversion of face images with attribute preservation and facilitates continuous control for fine-grained attribute editing. Furthermore, our approach can be readily extended to generate compositions involving multiple individuals. We perform extensive experiments to validate our method for face personalization and fine-grained attribute editing.<|reference_end|>
@article{parihar2024precisecontrol:,
    title={PreciseControl: Enhancing Text-To-Image Diffusion Models with
  Fine-Grained Attribute Control},
    author={Rishubh Parihar, Sachidanand VS, Sabariswaran Mani, Tejan Karmali, R.
  Venkatesh Babu},
    journal={arXiv preprint arXiv:2408.05083},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05083},
    primaryClass={cs.CV}
}