Detail++: Training-Free Detail Enhancer for Text-to-Image Diffusion Models
This addresses a key limitation in text-to-image generation for users needing precise control over complex scenes, though it is an incremental improvement on existing attention-based methods.
The paper tackles the problem of text-to-image models struggling with complex prompts involving multiple subjects with distinct attributes by proposing Detail++, a training-free framework that uses a Progressive Detail Injection strategy to decompose prompts and guide generation in stages. The method significantly outperforms existing approaches on benchmarks for multiple objects and complex styles.
Recent advances in text-to-image (T2I) generation have led to impressive visual results. However, these models still face significant challenges when handling complex prompt, particularly those involving multiple subjects with distinct attributes. Inspired by the human drawing process, which first outlines the composition and then incrementally adds details, we propose Detail++, a training-free framework that introduces a novel Progressive Detail Injection (PDI) strategy to address this limitation. Specifically, we decompose a complex prompt into a sequence of simplified sub-prompts, guiding the generation process in stages. This staged generation leverages the inherent layout-controlling capacity of self-attention to first ensure global composition, followed by precise refinement. To achieve accurate binding between attributes and corresponding subjects, we exploit cross-attention mechanisms and further introduce a Centroid Alignment Loss at test time to reduce binding noise and enhance attribute consistency. Extensive experiments on T2I-CompBench and a newly constructed style composition benchmark demonstrate that Detail++ significantly outperforms existing methods, particularly in scenarios involving multiple objects and complex stylistic conditions.