SCEdit is an efficient generation model fine-tuning framework proposed by Alibaba. It enhances the fine-tuning capability for downstream text-to-image generation tasks and enables fast adaptation to specific generation scenarios. Compared to LoRA, it can save 30%-50% of training memory costs. Moreover, it can be directly extended to controllable image generation tasks, requiring only 7.9% of the parameter amount of ControlNet conditional generation and saving 30% of memory usage. It supports various conditional generation tasks, including edge maps, depth maps, segmentation maps, poses, color maps, and image completion.