In modern interior design, the generation of personalized spaces frequently necessitates a delicate balance between rigid architectural structural constraints and specific stylistic preferences. However, existing multi-condition generative frameworks often struggle to harmonize these inputs, leading to "condition conflicts" where stylistic attributes inadvertently compromise the geometric precision of the layout. To address this challenge, we present DreamHome-Pano, a controllable panoramic generation framework designed for high-fidelity interior synthesis. Our approach introduces a Prompt-LLM that serves as a semantic bridge, effectively translating layout constraints and style references into professional descriptive prompts to achieve precise cross-modal alignment. To safeguard architectural integrity during the generative process, we develop a Conflict-Free Control architecture that incorporates structural-aware geometric priors and a multi-condition decoupling strategy, effectively suppressing stylistic interference from eroding the spatial layout. Furthermore, we establish a comprehensive panoramic interior benchmark alongside a multi-stage training pipeline, encompassing progressive Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Experimental results demonstrate that DreamHome-Pano achieves a superior balance between aesthetic quality and structural consistency, offering a robust and professional-grade solution for panoramic interior visualization.

翻译：在现代室内设计中，个性化空间的生成常常需要在刚性的建筑结构约束与特定的风格偏好之间达成微妙的平衡。然而，现有的多条件生成框架往往难以协调这些输入，导致"条件冲突"，即风格属性无意中损害了布局的几何精度。为应对这一挑战，我们提出了DreamHome-Pano，一个为高保真室内合成设计的可控全景生成框架。我们的方法引入了一个作为语义桥梁的Prompt-LLM，它能有效地将布局约束和风格参考转化为专业的描述性提示，以实现精确的跨模态对齐。为了在生成过程中保障建筑结构的完整性，我们开发了一种无冲突控制架构，该架构融合了结构感知的几何先验和多条件解耦策略，有效抑制了风格干扰对空间布局的侵蚀。此外，我们建立了一个全面的全景室内基准数据集，并设计了一个多阶段训练流程，包括渐进式监督微调（SFT）和强化学习（RL）。实验结果表明，DreamHome-Pano在美学质量与结构一致性之间实现了卓越的平衡，为全景室内可视化提供了一个鲁棒且专业级的解决方案。