What does the backwards diffusion model achieve over T timesteps?

The backwards diffusion model is designed to progressively denoise a sample over time, effectively reversing the process of adding noise to data. Over T timesteps, this model learns to reverse the introduction of Gaussian noise that has been incrementally added to a clean sample during the forward diffusion process. The fundamental principle is that, by learning to denoise iteratively, the model can then generate new data samples that resemble the original data distribution after completing the diffusion process.

This ability to learn how to reverse the addition of small Gaussian noise allows the model to generate coherent and meaningful outputs from random noise, thereby enabling it to produce realistic samples that align with the training data. Therefore, the emphasis here is on the model's capacity to learn the reverse transition, making the correct answer applicable and aligned with the principles of diffusion models in AI.