Stracke, N., Baumann, S.A., Susskind, J., Bautista, M.A., & Ommer, B. (2024). CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models. arXiv preprint arXiv:2405.07913v2.
This paper introduces LoRAdapter, a novel approach for conditioning text-to-image diffusion models that aims to unify style and structure control under a single, efficient framework, enabling zero-shot generalization.
The authors propose using conditional LoRAs, which adapt their behavior based on input conditioning at inference time. They achieve this by applying a transformation to the low-dimensional embedding within the LoRA, introducing conditional behavior based on either global (style) or local (structure) conditioning. This method is applied to both attention and convolutional layers within the diffusion model architecture. The authors evaluate their approach on Stable Diffusion 1.5 using the COYO-700M dataset for training and the COCO2017 validation set for evaluation. They compare their method against existing structure and style conditioning approaches using metrics such as CLIP-I, CLIP-T, MSE-d, SSIM, LPIPS, and FID.
LoRAdapter presents a significant advancement in controlling text-to-image diffusion models, offering a unified, efficient, and highly effective approach for incorporating both style and structure conditioning with zero-shot generalization. This approach has the potential to significantly enhance the creative control and flexibility of text-to-image generation.
This research contributes to the growing field of controllable image generation, providing a more efficient and versatile method for adapting pre-trained diffusion models. This has implications for various applications, including artistic image creation, content editing, and design.
While the paper focuses on Stable Diffusion, future work could explore applying LoRAdapter to other diffusion model architectures, such as transformer-based models. Additionally, exploring the potential of LoRAdapter for other conditioning modalities beyond style and structure could be a promising research direction.
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