Split integrates with Stable Diffusion to enhance image generation and data refinement, providing a tailored user experience through the use of diffusion models, which generate images from textual descriptions by refining random noise. These models are optimized for consumer-grade hardware, making them accessible to developers and creators. To improve efficiency and reduce computational costs, feature flags in the Split platform allow users to experiment with different parameters, such as image size, prompt complexity, and various Stable Diffusion models. This approach enables personalized user experiences, like generating character images for fantasy video games, while maintaining performance and user engagement. Split's platform allows for controlled deployment, feature experimentation, and performance analysis using metrics like image generation time and CPU usage, enabling data-driven decisions without disrupting existing services.