afzalahmed786/Adaptive-Split-Computing-for-Text-to-Image-Diffusion: v1.0.0
Abstract
First public release. Reinforcement-learning controller that adaptively partitions Stable Diffusion inference between an edge client and a server, selecting split point, quantization, decoder, and privacy settings per request based on live device and network conditions. The raw prompt and final image stay on-device; only an intermediate tensor is transmitted, protected with differential-privacy noise and structured obfuscation. Contents: PPO controller with joint action selection and training loop Split-computing client and inference server (3-stage pipeline) Differential privacy, structured obfuscation, and server-side reconstruction Adversarial spy model with training and data-collection scripts Configurable performance/quality/privacy parameters Requirements: Python 3.10+, PyTorch, diffusers, transformers. Secrets and endpoints are read from environment variables (HF_TOKEN, SERVER_URL). See README for setup, usage, and constants to calibrate for your hardware.
