The project implements a method for adding plug-and-play conditional control to diffusion transformer (DiT) models. It uses a lightweight Condition Injection LoRA module to enable style-lossless control without architectural changes to the base model. The framework incorporates position-aware training and causal attention with KV cache technology to improve efficiency and handle multi-condition coordination in generation tasks.