where 𝜖 V is sample from a standard normal distribution 𝑁(0, 𝐼) and 𝑤(⋅) is the truncation function that ensures the coordinates remain within the domain by wrapping them around the boundaries. The training objective is to minimize MSE between the actual noise 𝜖 V and the predicted noise 𝜖̂S: ℒ V,, = 𝔼 I(V " |V ! ) (‖𝜖 V -𝜖Ŝ(𝑋 , , 𝑡)‖ G )