Yet another implementation of the DDPM paper: https://arxiv.org/abs/2006.11239

Training:

• Input: Training data $\mathbf{x}$
• Output: Model parameters $\phi_t$
• Repeat:
  • For $i \in \mathcal{B}$ do:
    • $t \sim \text{Uniform}[1, \ldots, T]$
    • $\boldsymbol{\epsilon} \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$
    • $\ell_i = \left|\mathbf{g}_t\left(\sqrt{\alpha_t} \mathbf{x}_i + \sqrt{1 - \alpha_t} \boldsymbol{\epsilon}, \phi_t\right) - \boldsymbol{\epsilon}\right|^2$
  • Accumulate losses for batch and take gradient step
• Until converged

Sampling:

• Input: Model, $\mathbf{g}_t(\cdot, \phi_t)$
• Output: Sample, $\mathbf{x}$
• $\mathbf{z}_T \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$
• For $t = T \ldots 2$ do:
  • $\hat{\mathbf{z}}_{t-1} = \frac{1}{\sqrt{1 - \beta_t}} \mathbf{z}_t - \frac{\beta_t}{\sqrt{1 - \alpha_t} \sqrt{1 - \beta_t}} \mathbf{g}_t(\mathbf{z}_t, \boldsymbol{\phi}_t)$
  • $\boldsymbol{\epsilon} \sim \mathcal{N}_\epsilon(\mathbf{0}, \mathbf{I})$
  • $z_{t-1} = \hat{z}_{t-1} + \sigma_t \epsilon$
• $\mathbf{x} = \frac{1}{\sqrt{1 - \beta_1}} \mathbf{z}_1 - \frac{\beta_1}{\sqrt{1 - \alpha_1} \sqrt{1 - \beta_1}} \mathbf{g}_1(\mathbf{z}_1, \phi_1)$

TODO:

• More toy synth datasets