CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models
This addresses the challenge of fine-grained control in generative models for users needing precise image generation, though it appears incremental as it builds on existing LoRA methods.
The paper tackles the problem of guiding text-to-image models with detailed style and structure conditioning by introducing LoRAdapter, a conditional LoRA block that enables zero-shot control, and it outperforms recent state-of-the-art approaches.
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.