The insights gained from the toy experiment on diffusion models learning to generate 2D Gaussian bumps can provide valuable implications for the performance of diffusion models on more complex, real-world datasets and tasks. While the experiment focused on a simplified task, the findings regarding the learning dynamics, representation formation, and the ability to compositionally generalize are transferable to larger-scale datasets and tasks. Understanding the distinct learning phases, the correlation between representation quality and model performance, and the challenges in learning fully factorized representations can guide the development and optimization of diffusion models for real-world applications. By applying the principles learned from the toy experiment, researchers and practitioners can enhance the efficiency, effectiveness, and generalization capabilities of diffusion models in tackling complex data generation and manipulation tasks in diverse domains.