CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models
Abstract
Text-to-image generative models have become a prominent and powerful tool that excels at generating high-resolution realistic images. However, guiding the generative process of these models to take into account detailed forms of conditioning reflecting style and/or structure information remains an open problem. In this paper, we present , an approach that unifies both style and structure conditioning under the same formulation using a novel conditional LoRA block that enables zero-shot control. is an efficient and powerful approach to condition text-to-image diffusion models, which enables fine-grained control conditioning during generation and outperforms recent state-of-the-art approaches. Project page: compvis.github.io/LoRAdapter/
Cite
Text
Stracke et al. "CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73223-2_6Markdown
[Stracke et al. "CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/stracke2024eccv-ctrloralter/) doi:10.1007/978-3-031-73223-2_6BibTeX
@inproceedings{stracke2024eccv-ctrloralter,
title = {{CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models}},
author = {Stracke, Nick and Baumann, Stefan Andreas and Susskind, Joshua and Bautista, Miguel Angel and Ommer, Bjorn},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-73223-2_6},
url = {https://mlanthology.org/eccv/2024/stracke2024eccv-ctrloralter/}
}