SliderSpace is an innovative technology aimed at improving the controllability and interpretability of diffusion models. By automatically uncovering the model's internal visual knowledge and breaking it down into intuitive sliders, users can easily adjust the direction of image generation. This technology not only reveals the model's understanding of different concepts but also significantly enhances the diversity of image outputs. Key advantages of SliderSpace include automated direction discovery, semantic orthogonality, and distribution consistency, making it a powerful tool for exploring and harnessing the visual capabilities of diffusion models. This technology is currently in the research phase, with no specific pricing or commercial positioning defined yet.