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 consider detailed forms of conditioning reflecting style and/or structure information remains an open problem. In this paper, we present LoRAdapter, an approach that unifies both style and structure conditioning under the same formulation using a novel conditional LoRA block that enables zero-shot control. LoRAdapter is an efficient, powerful, and architecture-agnostic approach to condition text-to-image diffusion models, which enables fine-grained control conditioning during generation and outperforms recent state-of-the-art approaches.
@article{arxiv.2405.07913,
title = {CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models},
author = {Nick Stracke and Stefan Andreas Baumann and Joshua M. Susskind and Miguel Angel Bautista and Björn Ommer},
journal= {arXiv preprint arXiv:2405.07913},
year = {2024}
}
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for the project page and code, view https://compvis.github.io/LoRAdapter/