Diffusion models have made compelling progress on facilitating high-throughput daily production. Nevertheless, the appealing customized requirements are remain suffered from instance-level finetuning for authentic fidelity. Prior zero-shot customization works achieve the semantic consistence through the condensed injection of identity features, while addressing detailed low-level signatures through complex model configurations and subject-specific fabrications, which significantly break the statistical coherence within the overall system and limit the applicability across various scenarios. To facilitate the generic signature concentration with rectified efficiency, we present \textbf{AnyLogo}, a zero-shot region customizer with remarkable detail consistency, building upon the symbiotic diffusion system with eliminated cumbersome designs. Streamlined as vanilla image generation, we discern that the rigorous signature extraction and creative content generation are promisingly compatible and can be systematically recycled within a single denoising model. In place of the external configurations, the gemini status of the denoising model promote the reinforced subject transmission efficiency and disentangled semantic-signature space with continuous signature decoration. Moreover, the sparse recycling paradigm is adopted to prevent the duplicated risk with compressed transmission quota for diversified signature stimulation. Extensive experiments on constructed logo-level benchmarks demonstrate the effectiveness and practicability of our methods.

翻译：扩散模型在促进高吞吐量的日常生产方面取得了引人注目的进展。然而，对于追求真实保真度的定制化需求，目前仍需依赖实例级的微调。现有的零样本定制化工作通过浓缩注入身份特征来实现语义一致性，同时通过复杂的模型配置和特定主题的构建来处理详细的低级特征，这显著破坏了整个系统的统计连贯性，并限制了其在各种场景下的适用性。为了在保证效率的前提下实现通用的特征集中，我们提出了 \textbf{AnyLogo}，一个具有卓越细节一致性的零样本区域定制器，它建立在消除了繁琐设计的共生扩散系统之上。我们将其简化为标准的图像生成流程，并发现严格的特征提取与创造性的内容生成具有良好的兼容性，可以在单个去噪模型中进行系统性循环利用。取代外部配置的是，去噪模型的双子状态提升了强化的主题传输效率，并实现了具有连续特征装饰的解耦语义-特征空间。此外，我们采用了稀疏循环范式，以防止重复风险，并通过压缩传输配额来激发多样化的特征。在构建的徽标级基准测试上进行的大量实验证明了我们方法的有效性和实用性。