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

• URL: http://arxiv.org/abs/2607.05088v1
• Date: Mon, 06 Jul 2026 13:49:40 GMT
• Title: RADIANCE: Relative Adaptive Denoising with IP-Adapter for Novel Concept Enhancement
• Abstract summary: Text-to-image (T2I) diffusion models struggle to synthesize rare concepts involving unusual attribute-object pairings.<n>We propose RADIANCE, a training-free framework that treats inference as a closed-loop feedback process.
• Score: 23.717085162530804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-image (T2I) diffusion models have achieved striking progress but still struggle to synthesize rare concepts involving unusual attribute-object pairings, often resulting in concept omission or semantic drift where a dominant entity overwhelms the generation. Tracing these failures to a lack of compositional balance during the denoising trajectory, we propose RADIANCE, a training-free framework that treats inference as a closed-loop feedback process. RADIANCE augments pretrained backbones with three modular components: (1) a Compositional Similarity Monitor (CSM) that tracks the emergence of objects and attributes in intermediate latents via CLIP-based feedback; (2) a Bidirectional Scale Controller (BSC) that applies a reactive "restoring force" using positive and negative IP-Adapter scales to rebalance biased trajectories; and (3) a Feedback Guidance Scheduler (FGS) that coordinates these updates across timesteps without additional training. We further extend the framework to multi-object prompts via Delayed Adapter Activation (DAA) and Layer-wise Alternating Guidance (LAG) to prevent premature concept fusion. By overlapping monitoring and denoising through pipelined execution, RADIANCE maintains competitive latency while significantly enhancing the per-sample success rate and effective throughput. Experiments on RareBench and T2I-CompBench demonstrate that RADIANCE consistently enhances compositional alignment and perceptual quality over state-of-the-art baselines.

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