To encourage diffusion models to learn fully factorized representations, several architectural and training modifications can be implemented. One approach is to introduce explicit inductive biases that promote the emergence of compositional structures in the learned representations. This can involve designing the model architecture to encourage the disentanglement of latent factors and the compositionality of features. For example, incorporating specific network layers or modules that enforce independence between different dimensions of the input data can aid in learning factorized representations. Additionally, utilizing regularization techniques that penalize entanglement and encourage sparsity in the learned representations can help in achieving full factorization. Training strategies such as curriculum learning, where the model is exposed to progressively more complex tasks, can also guide the model towards learning fully factorized representations by gradually increasing the complexity of the compositional tasks.