Text-Conditioned Background Generation for Editable Multi-Layer Documents

We present a framework for document-centric backgroundgeneration with multi-page editing and thematic continuity. To ensuretext regions remain readable, we employ a latent masking formulationthat softly attenuates updates in the diffusion space, inspired by smoothbarrier functions in physics and numerical optimization. In addition, weintroduce Automated Readability Optimization (ARO), which automat-ically places semi-transparent, rounded backing shapes behind text re-gions. ARO determines the minimal opacity needed to satisfy percep-tual contrast standards (WCAG 2.2) relative to the underlying back-ground, ensuring readability while maintaining aesthetic harmony with-out human intervention. Multi-page consistency is maintained througha summarization-and-instruction process, where each page is distilledinto a compact representation that recursively guides subsequent gen-erations. This design reflects how humans build continuity by retain-ing prior context, ensuring that visual motifs evolve coherently acrossan entire document. Our method further treats a document as a struc-tured composition in which text, figures, and backgrounds are preservedor regenerated as separate layers, allowing targeted background editingwithout compromising readability. Finally, user-provided prompts allowstylistic adjustments in color and texture, balancing automated consis-tency with flexible customization. Our training-free framework producesvisually coherent, text-preserving, and thematically aligned documents,bridging generative modeling with natural design workflows.