RADIANCE: Relative Adaptive Denoising with IP-Adapter for Novel Concept Enhancement

Text-to-image (T2I) diffusion models have achieved strik-ing progress but still struggle to synthesize rare concepts involving un-usual attribute-object pairings, often resulting in concept omission orsemantic drift where a dominant entity overwhelms the generation. Trac-ing these failures to a lack of compositional balance during the denois-ing trajectory, we propose RADIANCE, a training-free framework thattreats inference as a closed-loop feedback process. RADIANCE augmentspretrained backbones with three modular components: (1) a Composi-tional Similarity Monitor (CSM) that tracks the emergence of objectsand attributes in intermediate latents via CLIP-based feedback; (2) aBidirectional Scale Controller (BSC) that applies a reactive "restoringforce" using positive and negative IP-Adapter scales to rebalance biasedtrajectories; and (3) a Feedback Guidance Scheduler (FGS) that coordi-nates these updates across timesteps without additional training. We fur-ther extend the framework to multi-object prompts via Delayed AdapterActivation (DAA) and Layer-wise Alternating Guidance (LAG) to pre-vent premature concept fusion. By overlapping monitoring and denoisingthrough pipelined execution, RADIANCE maintains competitive latencywhile significantly enhancing the per-sample success rate and effectivethroughput. Experiments on RareBench and T2I-CompBench demon-strate that RADIANCE consistently enhances compositional alignmentand perceptual quality over state-of-the-art baselines.