离线环境光控制潜在扩散：架构、遥测与设备端评估
Offline Ambient-Controlled Latent Diffusion: Architecture, Telemetry, and On-Device Evaluation
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中文总结 AI 辅助
该研究开发了一款设备端Android潜在扩散图像生成应用，以环境光传感器驱动而非文本提示，通过绑定传感器读数等实现离线审计，验证了环境光依赖的保留及设备端运行的延迟表现。
中文摘要 AI 辅助
大多数移动图像生成应用是基于云服务的瘦客户端，导致生成结果难以审计。我们提出一款完全在设备端运行的Android潜在扩散应用，由环境光传感器而非文本提示驱动，生成、遥测及存储均本地化。本研究的贡献并非新的扩散方法，而是围绕测量的工作流：每个输出与产生它的传感器读数、运行时路径及随机种子绑定，为离线分析提供每个制品的审计轨迹。在一台三星折叠设备上，对373个制品的固定捕获显示，控制器的对数勒克斯输入与输出亮度呈正相关（皮尔逊相关系数r=0.532，95%置信区间[0.455, 0.601]），证实环境光依赖关系在去噪和VAE解码后仍保留；同时，在Android神经网络API（NNAPI）下，潜在UNet/VAE管线在三个质量等级下的平均延迟为552至1334毫秒。
英文摘要
Most mobile image-generation applications are thin clients over cloud services, leaving outputs hard to audit. We present an Android latent-diffusion application that runs entirely on-device and is driven by the ambient-light sensor rather than a text prompt, keeping generation, telemetry, and storage local. The contribution is not a new diffusion method but the surrounding measurement workflow: each output is bound to the sensor reading, runtime path, and seed that produced it, giving a per-artifact audit trail for offline analysis. On a single Samsung foldable, one fixed capture of 373 artifacts shows the controller's log-lux input positively associated with output luminance (Pearson $r=0.532$, 95\% CI $[0.455, 0.601]$), confirming the ambient dependency survives denoising and VAE decoding, while the latent UNet/VAE pipeline runs at 552--1334\,ms mean latency across three quality tiers under the Android Neural Networks API (NNAPI).
