On-Device Lighting Scene ModelOn-App AI
Feature Introduction
The "Generative Lighting Scene" feature, based on self-developed lightweight AI models, enables one-tap generation of professional lighting atmospheres locally on mobile devices (on-device). Users don't need lighting design knowledge—simply select style preferences or usage scenarios (such as "movie", "party", "sleep", etc.), and the system can batch generate multiple lighting solutions adapted to the current room and device combination, with support for real-time preview and one-tap application.
This feature is integrated into Smart Life App / Tuya App (v7.1.0 and above), and is applicable to all smart lighting devices supporting the Tuya ecosystem, including smart lights, light strips, spotlights, and other lighting equipment.
✅ Core Value:
- Zero-Threshold Creation: Say goodbye to manual lamp-by-lamp parameter adjustment—AI automatically generates coordinated and beautiful whole-home lighting effects
- Ultra-Fast Response: On-device inference < 10ms, operations take effect instantly
- Offline Available: No network dependency, works normally in weak/no network environments
- Privacy & Security: Generation process is entirely local, encrypted upload only when saving
💡 Try It Now: Open Smart Life App / Tuya App (v7.1.0 or above), enter an SMB home, tap "+" in the top right corner of the home page → Select "Add New Lighting Scene" → Choose room → Select type → Tap "Auto Generate" to start your smart lighting journey!
Technical Solution
1. Overall Architecture: Cloud-Device Collaboration + On-Device Closed Loop
Adopts a "Cloud Retraining, On-Device Light Inference" distributed AI architecture:
- Cloud: Based on industry-level light recipe data + Large Language Models (LLM) to train universal lighting strategies and generate style knowledge bases.
- On-Device (App): Deploys lightweight DNN models, combined with user room information, device characteristics, and home context to perform real-time inference and generate specific lighting parameters (color temperature, brightness, dynamic effects, etc.).
📌 All generation logic is completed locally on the phone, unsaved data is not uploaded to the cloud, ensuring user privacy.
2. Model Deployment: TensorFlow Lite + On-Device Optimization
- Inference Engine: TensorFlow Lite (TFLite), supports Android NNAPI and iOS Core ML acceleration
- Model Characteristics:
- Parameters < 1M, memory footprint < 3MB
- Supports automatic scheduling for NPU/GPU/CPU multi-hardware backends
- Average inference time < 10ms (10-device scenario, tested on mid-to-low-end devices)
3. Dynamic Model Management (Dynamic Lifecycle)
- On-Demand Loading: AI model is silently downloaded in the background on first use, not increasing initial installation package size
- Online Hot Update: Dynamically updates models through OTA mechanism, optimizing generation effects or adding new styles without releasing new versions
- Version Compatibility: Supports multiple model coexistence, ensuring smooth transition for older devices