## Overview

Generative models model the data-generating process. Suppose we have data ${\mathbf x_1, \dots, \mathbf x_N}$, $\mathbf x_i \sim q(\mathbf x)$, we want to minimize some divergence between the empirical distribution and our model $p_\theta(\mathbf x)$. Recent popular examples of such models are Generative Adversarial Networks (GANs), Normalizing Flows and Variational AutoencodersVariational Autoencoder

The Variational Autoencoder (Kingma & Welling, 2013) is a [[Latent Variable Model]] that targets

\(\begin{aligned}

\min_{\phi, \theta} D_{KL}[q_\phi(\mathbf x, \mathbf z) || p_\theta(\mathbf x, \mathbf z)] &= \mathbb{E}_{q(x)q_\phi(z|x)}[\log q_\phi(\mathbf z \mid \mathbf x) - \log p_\theta(\mathbf x \mid \mathbf z) - \log p_\theta(\mathbf z)] \

&= \mathbb{E}_{q(x)}\left[\mathbb{E}_{q_\phi(z|x)}[-\log p_\theta(\mathbf x \mid \mathbf z)] + D_{KL}[q_\phi(\mathbf z \mid \mathbf x) .... The latter is an example of a Latent Variable ModelLatent Variable Model

Instead of targeting directly

\(\min KL(q(\mathbf x) || p(\mathbf x)),\)

latent variable models introduce a $\mathbf z$ on which inference is performed. If one has a prior distribution that can be sampled, the model can be used to generate new data.

The latent variable enables unsupervised objectives like clustering. Also, it can be easier to model the data with more expressive models instead of directly targeting data likelihood. See Variational AutoencoderVariational Autoencoder

The Variational Autoencoder (Kingma & Welling, 2013) is a [[Latent Variable Model]] that targets

\(\begin{aligned}

\min_{\phi, \theta} D_{KL}[q_\phi(\mathbf x, \mathbf z) || p_\theta(\mathbf x, \mathbf z)] &= \mathbb{E}_{q(x)q_\phi(z|x)}[\log q_\phi(\mathbf z \mid \mathbf x) - \log p_\theta(\mathbf x \mid \mathbf z) - \log p_\theta(\mathbf z)] \

&= \mathbb{E}_{q(x)}\left[\mathbb{E}_{q_\phi(z|x)}[-\log p_\theta(\mathbf x \mid \mathbf z)] + D_{KL}[q_\phi(\mathbf z \mid \mathbf x) ....

, where a data-point is encoded into a latent variable $\mathbf z$ that usually should approximately follow a Gaussian distribution. If this criterion is met, we can sample from the Gaussian and generate new data.

Diffusion models have recently gained popularity by obtaining exceptional high-quality image synthesis (and data likelihoods) while being easy to train. They, too, are latent variable models, but instead of encoding the data directly to a Gaussian random variable, the data is slowly *diffused* into one by adding many small noise variables. See the following figure (taken from Jonathan Ho's post).

This post details how the models are formally developed and provide a simple implementation. Specifically, we follow the Kingma et al., 2019 paper titled Variational Diffusion Models.

## Model Development

In a sense, a diffusion model can be seen as many Variational AutoencodersVariational Autoencoder

The Variational Autoencoder (Kingma & Welling, 2013) is a [[Latent Variable Model]] that targets

\(\begin{aligned}

\min_{\phi, \theta} D_{KL}[q_\phi(\mathbf x, \mathbf z) || p_\theta(\mathbf x, \mathbf z)] &= \mathbb{E}_{q(x)q_\phi(z|x)}[\log q_\phi(\mathbf z \mid \mathbf x) - \log p_\theta(\mathbf x \mid \mathbf z) - \log p_\theta(\mathbf z)] \

&= \mathbb{E}_{q(x)}\left[\mathbb{E}_{q_\phi(z|x)}[-\log p_\theta(\mathbf x \mid \mathbf z)] + D_{KL}[q_\phi(\mathbf z \mid \mathbf x) ... stacked on top of each other, and the encoders are fixed as Gaussians. In the continuous case, we assume *infinitely* many encoders. Every number from the interval $[0, 1]$ determines such an encoder as follows:
\(q(\mathbf z_t \mid \mathbf x)=\mathcal{N}(\alpha_t \mathbf x, \sigma_t^2 \mathbf I),\)
where $\alpha_t$ and $\sigma_t$ make the (signal to) noise schedule, and are determined completely by $t \in [0, 1]$. $\mathbf x$ is the datum, and $\mathbf z_t$ is the encoded variable. We also assume that the *signal-to-noise ratio* $\alpha_t^2 / \sigma_t^2$ decreases monotonically with $t$. As such, the encoding process can equivalently be seen as an additive Gaussian noise process. When $t$ is close to $0$ the image is hardly affected. When $t$ approaches $1$ we desire it to be close to a normal distribution from which we can easily sample.
The process can be modeled with a Markov chain of (very) many additive Gaussian noise transitions.

Therefore, the inference distribution factorizes as

\[q(\mathbf z_{1:T}, \mathbf x) = q(\mathbf x) q(\mathbf z_1 \mid \mathbf x) \prod_{t=2}^T q(\mathbf z_t | \mathbf z_{t-1}).\]We can analytically obtain $q(\mathbf z_t \mid \mathbf z_{t-1})$. We know that, by definition, $\mathbf z_{t-1} \sim \mathcal{N}(\alpha_{t-1} \mathbf x, \sigma_{t-1} \mathbf I)$. Therefore, since the noise process is monotonic We used the fact that scaling a Gaussian random variable with a factor scales its variance with that factor squared (and included the assumed additive noise term with variance $\sigma_{t \mid t-1}^2$).,

\[\mathbf z_t \sim \mathcal{N}(\alpha_{t|t-1} \alpha_{t-1} \mathbf x,\, \alpha_{t|t-1}^2 \sigma^2_{t-1} \mathbf I + \sigma^2_{t|t-1}\mathbf I)\]But we also know that $\mathbf z_t \sim \mathcal{N}(\alpha_t \mathbf x, \sigma_t \mathbf I)$. Hence,

\[\alpha_{t|t-1} \alpha_{t-1} \mathbf x = \alpha_t \mathbf x \iff \alpha_{t|t-1} = \frac{\alpha_t}{\alpha_{t-1}}\] \[\alpha_{t|t-1}^2 \sigma^2_{t-1} \mathbf I + \sigma^2_{t|t-1} \mathbf I = \sigma^2_t \mathbf I \iff \sigma^2_{t|t-1} = \sigma^2_t - \alpha_{t|t-1}^2 \sigma^2_{t-1}\]Therefore, we also know that

\[q(\mathbf z_t \mid \mathbf z_{t-1}) = \mathcal{N}(\alpha_{t|t-1} \mathbf z_{t-1}, \sigma_{t|t-1}\mathbf I)\]and we can directly compute the parameters from the known noise schedule parameters.

We have obtained an inference distribution $q(\mathbf x, \mathbf z_{1:T})$. We parameterize a model $p_\theta(\mathbf x, \mathbf z_{1:T})$ in the data generating direction (from noise to data). Now we simply minimize the relative entropy

\[\min D_{KL}[q(\mathbf x, \mathbf z_{1:T}) || p(\mathbf x, \mathbf z_{1:T}))] = \min \mathbb{E}_{q(\mathbf x, \mathbf z_{1:T})} [\log q(\mathbf x, \mathbf z_{1:T}) - \log p(\mathbf x, \mathbf z_{1:T}))]\]that we rewrite to

\[\min D_{KL}[q(\mathbf x, \mathbf z_{1:T}) || p(\mathbf x, \mathbf z_{1:T}))] = \min \mathbb{E}_{q(\mathbf x)}\left[D_{KL}(q(\mathbf z_T \mid \mathbf x) || p(\mathbf z_T))) + \mathbb{E}_{q(\mathbf z_1 \mid \mathbf x)} [- \log p(\mathbf x \mid \mathbf z_1)] + \mathcal{L}_D\right] \tag{1},\]with

\[\mathcal{L}_D := \sum_{t=2}^T \mathbb{E}_{q(\mathbf z_t \mid \mathbf x)} \left[ D_{KL}[q(\mathbf z_{t-1} \mid \mathbf z_t, \mathbf x)||p(\mathbf z_{t-1} \mid \mathbf z_t)]\right] \tag{2}.\]Note that we can replace sampling through the Markov chain by directly sampling conditioned on $\mathbf x$. These terms are derived as follows.

\[\begin{aligned} \log q(\mathbf z_{1:T} | \mathbf x) - \log p(\mathbf z_{1:T}, \mathbf x) &= -\log p(\mathbf z_T)- \log p(\mathbf x \mid \mathbf z_1) + \log q(\mathbf z_1 \mid \mathbf x) + \sum_{t=2}^T \log q(\mathbf z_t|\mathbf z_{t-1}) - \log p(\mathbf z_{t-1}|\mathbf z_t) \\ &= -\log p(\mathbf z_T) - \log p(\mathbf x \mid \mathbf z_1) + \log q(\mathbf z_1 \mid \mathbf x) + \sum_{t=2}^T \log \left \{ q(\mathbf z_{t-1}|\mathbf z_t, \mathbf x) \cdot \frac{q(\mathbf z_t \mid \mathbf x)}{q(\mathbf z_{t-1} \mid \mathbf x)}\right \} - \log p(\mathbf z_{t-1}|\mathbf z_t) \\ &= -\log p(\mathbf z_T) - \log p(\mathbf x \mid \mathbf z_1) + \log q(\mathbf z_T \mid \mathbf x) + \sum_{t=2}^T \log q(\mathbf z_{t-1}|\mathbf z_t, \mathbf x) - \log p(\mathbf z_{t-1}|\mathbf z_t) \\ &= \log \frac{q(\mathbf z_T \mid \mathbf x)}{p(\mathbf z_T)} - \log p(\mathbf x \mid \mathbf z_1) + \sum_{t=2}^T \log \frac{q(\mathbf z_{t-1}|\mathbf z_t, \mathbf x)}{p(\mathbf z_{t-1}|\mathbf z_t)} \end{aligned}\]The second equality follows from Bayes' rule:

\[q(\mathbf z_t \mid \mathbf z_{t-1}) \stackrel{\mathbf z_t \perp\!\!\perp \mathbf x \mid \mathbf z_{t-1}}{=} q(\mathbf z_t \mid \mathbf z_{t-1}, \mathbf x) = q(\mathbf z_{t-1} \mid \mathbf z_t, \mathbf x) \cdot \frac{q(\mathbf z_t \mid \mathbf x)}{q(\mathbf z_{t-1} \mid x)}.\]The third equality follows from how many terms $q(\mathbf z_t \mid \mathbf x)$ and $q(\mathbf z_{t-1} \mid \mathbf x)$ cancel with each-other in the summation and with $q(\mathbf z_1 \mid \mathbf x)$ that was in front of it. Only $q(\mathbf z_T \mid \mathbf x)$ remains.

The obtained three terms form the loss function that we presented earlier.

## Analyzing The Divergence

In eq. $(1)$ the first term is a prior loss, where $p(\mathbf z_T)$ is parameterized with a standard Gaussian and can be computed in closed form.

The second term is a data likelihood term (e.g., reconstruction loss). Since, for images, $\mathbf x$ lives in a discrete space, but $q(\mathbf z_1 \mid \mathbf x)$ is a Gaussian and thus lives on the whole number line, we have to divide it into regions. We know that $\mathbf x$ has 256 distinct values, and hence we can choose

\[\begin{aligned} p(\mathbf x \mid \mathbf z_1) &= \int_{\mathbf x - d_l}^{\mathbf x + d_u} \mathcal{N}(\mathbf x \mid \mathbf z_1, \sigma^2_1 \mathbf I) d \mathbf x \\ &= \Phi((\mathbf x + d_u - \mathbf z_1) / \sigma_1) - \Phi((\mathbf x - d_u - \mathbf z_1) / \sigma_1). \end{aligned}\]Now, $d_u = d_l =0.5$ for $\mathbf x \in {1, \dots, 254}$, $d_l=\infty$ & $d_u = 0.5$ for $\mathbf x = 0$, and $d_u = \infty$ & $d_l = 0.5$ for $\mathbf x = 255$ divide the whole space into 256 parts that naturally add to 1.

The other terms form the "diffusion loss". These can also be rewritten so that we *only have to perform data reconstruction during training*.

We already obtained an analytic form of the transition $q(\mathbf z_t \mid \mathbf z_{t-1})$ and we know $q(\mathbf z_{t-1} \mid \mathbf x)$ by construction. Since the transition is linear, $q(\mathbf z_t, \mathbf z_{t-1} \mid x)$ is *jointly Gaussian*. Therefore, using the well-known results for conditional Gaussians (e.g., Bishop (2006)) we get that

with

\[\begin{aligned} &\boldsymbol \mu_{t-1|t} = \frac{\alpha_{t|t-1}\sigma_{t-1}^2}{\sigma^2_t} \mathbf z_t + \frac{\alpha_{t-1} \sigma_{t|t-1}^2}{\sigma_t^2} \mathbf x,& \sigma_{t-1|t} = \sigma_{t|t-1}^2 \frac{\sigma_{t-1}^2}{\sigma_t^2} \end{aligned}\]The reason why we performed all of these computations follows now. We have not parameterized $p(\mathbf z_{t-1} \mid \mathbf z_t)$ yet. If we parameterize it almost equivalently to $q(\mathbf z_{t-1} \mid \mathbf x, \mathbf z_t)$, then it turns out that we can simply perform a data reconstruction task at all times during training!

\[p_\theta(\mathbf z_{t-1} \mid \mathbf z_t) := q(\mathbf z_{t-1} \mid \mathbf z_t, \hat{\mathbf x}_\theta(\mathbf z_t; t))\]Since the KL-divergence between two Gaussianas involves a mean-squared error between the two means, we see that

\[D_{KL}\left[q(\mathbf z_{t-1}|\mathbf z_{t}, \mathbf x) || p_\theta(\mathbf z_{t-1}\mid \mathbf z_{t-1}) \right] \approx \Vert \boldsymbol \mu_{t-1|t} -\hat{\boldsymbol \mu}_{t-1|t}\Vert^2_2 = \left(\frac{\alpha_{t-1} \sigma_{t|t-1}^2}{\sigma_t^2}\right)^2 \Vert \mathbf x - \hat{\mathbf x}_\theta(\mathbf z_t; t) \Vert^2_2,\]where have omitted some terms involving the variances for conciseness (hence the "$\approx$"). As such, we are just reconstructing $\mathbf x$. For all details, consider the paper's Appendix B Equations (34)-(40).

Furthermore, Since we know $\mathbf x$ and $\mathbf z_t$, our model can equivalently try to recover the additive noise through the relation:

\[\mathbf z_t = \alpha_t \mathbf x + \sigma_t \hat{\boldsymbol \epsilon}_\theta,\]which works better in practice.

## Continuous Time

Note that we know $q(\mathbf z_t \mid \mathbf z_s)$ analytically for every $s$ < $t$, even when we use continuous-time $t \in [0, 1]$! This is shown analogously to how we showed it for $q(\mathbf z_t \mid \mathbf z_{t-1})$. Adding the fact that our diffusion loss simply reconstructs $\mathbf x$ (or equivalently, recover $\boldsymbol \epsilon$) means that we can sample $t$ from a continuous interval and keep performing the reconstruction task. We need to discretize only when we sample from the model (as shown later).

The diffusion loss, as shown in Appendix B.3., finally becomes

\[\mathcal{L}_D:= -\frac12 \mathbb{E}_{\boldsymbol \epsilon \sim \mathcal{N}(0, \mathbf I), t \sim U(0, 1)}\left[ \log-\mathrm{SNR'}(t) \Vert \boldsymbol \epsilon - \hat{\boldsymbol \epsilon}_\theta(\mathbf z_t; t) \Vert^2_2 \right], \tag{3}\]with $\log-\mathrm{SNR'}(t) = \frac{d \log \mathrm{SNR}(t)}{dt} = \frac{d \log \alpha_t^2 / \sigma_t^2}{dt}$.

This loss function is a continuous version approximation of $(2)$, using Monte Carlo integration.

## Learned Noise Schedule

We left open the question how to set $\alpha_t$ and $\sigma_t$. So long as we stick to the requirements (monotonicity), we can learn the noise schedule. To do so, we implement a monotonic neural network that takes a time-step $t$ and outputs a (log) signal-to-noise ratio $\alpha_t^2 / \sigma_t^2$.

## Implementation

We have all the required ingredients to start coding. For our full code, click here.

First we implement the "prior loss" part of $(3)$ (but now using continuous time, $t \in [0, 1]$). Note that `gaussian_kl`

by default is against a zero mean Gaussian.

```
def prior_loss(self, x, batch_size):
logsnr_1, _ = self.snrnet(torch.ones((batch_size,), device=x.device))
alpha_sq_1 = torch.sigmoid(logsnr_1)[:, None, None, None]
sigmasq_1 = 1 - alpha_sq_1
alpha_1 = alpha_sq_1.sqrt()
mu_1 = alpha_1 * x
return gaussian_kl(mu_1, sigmasq_1).sum() / batch_size
```

Next, the data likelihood. Since our $\mathbf x$ is scaled between $[-1, 1]$, we use $1 / 255$ instead of $0.5$ in the integration boundary. From $-1$ and $1$ we integrate to $-\infty$ and $\infty$, respectively. Since the CDF is 0 and $1$ for these ranges we can fill those values directly.

```
def data_likelihood(self, x, batch_size):
logsnr_0, _ = self.snrnet(torch.zeros((1,), device=x.device))
alpha_sq_0 = torch.sigmoid(logsnr_0)[:, None, None, None].repeat(*x.shape)
sigmasq_0 = 1 - alpha_sq_0
alpha_0 = alpha_sq_0.sqrt()
mu_0 = alpha_0 * x
sigma_0 = sigmasq_0.sqrt()
d = 1 / 255
p_x_z0 = standard_cdf((x + d - mu_0) / sigma_0) - standard_cdf((x - d - mu_0) / sigma_0)
p_x_z0[x == 1] = 1 - standard_cdf((x[x == 1] - d - mu_0[x == 1]) / sigma_0[x == 1])
p_x_z0[x == -1] = standard_cdf((x[x == -1] + d - mu_0[x == -1]) / sigma_0[x == -1])
nll = -torch.log(p_x_z0)
return nll.sum() / batch_size
```

Third, we implement the most important part: the diffusion loss, along with the overall loss function $(3)$.

```
def get_loss(self, x):
batch_size = len(x)
e = torch.randn_like(x)
t = torch.rand((batch_size,), device=self.device)
mu_zt_zs, sigma_zt_zs, norm_nlogsnr_t = self.q_zt_zs(zs=x, t=t)
zt = mu_zt_zs + sigma_zt_zs * e
e_hat = self.denoise_fn(zt.detach(), norm_nlogsnr_t)
t.requires_grad_(True)
logsnr_t, _ = self.snrnet(t)
logsnr_t_grad = autograd.grad(logsnr_t.sum(), t)[0]
diffusion_loss = (
-0.5
* logsnr_t_grad
* F.mse_loss(e, e_hat, reduction="none").sum(dim=(1, 2, 3))
)
diffusion_loss = diffusion_loss.sum() / batch_size
prior_loss = self.prior_loss(x, batch_size)
data_loss = self.data_likelihood(x, batch_size)
loss = diffusion_loss + prior_loss + data_loss
return loss
```

We sample time-steps between $t \in [0, 1]$. Then, we sample from the diffusion process $q(\mathbf z_t|\mathbf z_s)$. Remember that, as we have shown above ("Model Development"), we can sample from these directly in terms of the parameters $\alpha_t$ and $\sigma_t$ and input $x$. We sample using reparameterization and reconstruct the noise using `denoise_fn`

, which we will discuss later. Furthermore, in $(3)$ we see that we need a derivative of the log-signal-to-noise ratio. Since we implemented this schedule with a neural network, we compute it using autograd. Finally, we compute the diffusion loss. It's multiplied with $-0.5$ since our SNR network is monotonically increasing instead of decreasing.

Now, let's zoom in on `q_zt_zs`

, the forward noising model.

```
def q_zt_zs(self, zs, t, s=None):
if s is None:
s = torch.zeros_like(t)
logsnr_t, norm_nlogsnr_t = self.snrnet(t)
logsnr_s, norm_nlogsnr_s = self.snrnet(s)
alpha_sq_t = torch.sigmoid(logsnr_t)
alpha_sq_s = torch.sigmoid(logsnr_s)
alpha_t = alpha_sq_t.sqrt()
alpha_s = alpha_sq_s.sqrt()
sigma_sq_t = 1 - alpha_sq_t
sigma_sq_s = 1 - alpha_sq_s
alpha_sq_tbars = alpha_sq_t / alpha_sq_s
sigma_sq_tbars = sigma_sq_t - alpha_sq_tbars * sigma_sq_s
alpha_tbars = alpha_t / alpha_s
sigma_tbars = torch.sqrt(sigma_sq_tbars)
return alpha_tbars * zs, sigma_tbars, norm_nlogsnr_t
```

Note that by putting $\alpha^2_t := \sigma(\gamma(t))$, where $\gamma$ is our learned SNR schedule, we keep $\alpha_t^2 + \sigma_t^2 = 1$.

\[\sigma\left(\log \frac{\alpha^2}{\sigma^2}\right) = \frac{1}{1+e^{\log \sigma^2 / \alpha^2}} = \frac{1}{1+\sigma^2 / \alpha^2} = \frac{\alpha^2}{\sigma^2 + \alpha^2} = \alpha^2\]This is fine, as the authors show that the continuous-time model is invariant to the noise schedule and, therefore, also the absolute values. Only the signal-to-noise ratios of the beginning and endpoints are essential.

Then, in the code, we use the formulas for $\alpha^2_{t\mid s}$ that we derived earlier and return the mean and standard deviation (and a normalized log-SNR for the denoising model to condition on).

That's it! More is not needed for training. The implementations for the denoising model (a UNet-type) and the SNRnet are given later. We first zoom in on how to sample from the model.

## Sampling

Sampling from a diffusion model is easy but requires much computation. We need to discretize the reverse diffusion process and iterate through the process.

\[\mathbf x \leftarrow \dots \leftarrow \mathbf z_{s} \leftarrow \dots \leftarrow \mathbf z_t \leftarrow \dots \leftarrow \mathbf z_T\]I.e., we sample $\mathbf z_t \sim p(\mathbf z_T)$ and use our learned $p(\mathbf z_{t-1} \mid \mathbf z_t)$ to iteratively sample until we reach $t=0$. We are free to choose the discretization granularity, but the paper shows that more time-steps is better.

```
@torch.no_grad()
def sample_loop(self, batch_size):
img = torch.randn(shape, device=self.device)
timesteps = torch.linspace(0, 1, self.num_timesteps)
for i in tqdm(
reversed(range(1, self.num_timesteps)),
desc="Time-step",
total=self.num_timesteps,
):
t = torch.full((batch_size,), timesteps[i], device=img.device)
s = torch.full((batch_size,), timesteps[i - 1], device=img.device)
img = self.p_sample(img, t=t, s=s)
return img
```

Here, we see that the number of time-steps is set by `self.num_timesteps`

, and $[0, 1]$ is simply split into this number of parts.

```
@torch.no_grad()
def p_sample(self, x, t, s, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, model_variance = self.p_mean_variance(
zt=x, t=t, s=s, clip_denoised=clip_denoised
)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when s == 0
nonzero_mask = (1 - (s == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * model_variance.sqrt() * noise
```

Next, we implement $p(\mathbf z_s \mid \mathbf z_t)$.

```
def p_zs_zt(self, zt, t, s, clip_denoised: bool):
logsnr_t, norm_nlogsnr_t = self.snrnet(t)
logsnr_s, norm_nlogsnr_s = self.snrnet(s)
alpha_sq_t = torch.sigmoid(logsnr_t)[:, None, None, None]
alpha_sq_s = torch.sigmoid(logsnr_s)[:, None, None, None]
alpha_t = alpha_sq_t.sqrt()
alpha_s = alpha_sq_s.sqrt()
sigmasq_t = 1 - alpha_sq_t
sigmasq_s = 1 - alpha_sq_s
alpha_sq_tbars = alpha_sq_t / alpha_sq_s
sigmasq_tbars = sigmasq_t - alpha_sq_tbars * sigmasq_s
alpha_tbars = alpha_t / alpha_s
sigma_tbars = torch.sqrt(sigmasq_tbars)
sigma_t = sigmasq_t.sqrt()
e_hat = self.denoise_fn(zt, norm_nlogsnr_t)
if clip_denoised:
e_hat.clamp_((zt - alpha_t) / sigma_t, (zt + alpha_t) / sigma_t)
mu_zs_zt = (
1 / alpha_tbars * zt - sigmasq_tbars / (alpha_tbars * sigma_t) * e_hat
)
sigmasq_zs_zt = sigmasq_tbars * (sigmasq_s / sigmasq_t)
return mu_zs_zt, sigmasq_zs_zt
```

We know that \(p(\mathbf z_s \mid \mathbf z_t) = q(\mathbf z_s \mid \mathbf z_t, \mathbf x=\hat{\mathbf x}_\theta(\mathbf z_t; t)).\)

Our model outputs estimated noise and since $\mathbf z_t = \alpha_t \mathbf x + \sigma_t \boldsymbol \epsilon \iff \mathbf{x} = \frac{\mathbf z_t - \sigma_t \epsilon}{\alpha_t}$ we get (we shorthand $\hat{\mathbf x} := \hat{\mathbf x}_\theta(\mathbf z_t; t))$)

\[\begin{aligned} \hat{\mu}_{s \mid t} &= \frac{\alpha_{t\mid s} \sigma^2_s}{\sigma_t^2} \mathbf z_t + \frac{\alpha_s \sigma^2_{t\mid s}}{\sigma_t^2} \hat{\mathbf x} \\ &= \left( \frac{\alpha_{t\mid s} \sigma^2_s}{\sigma_t^2} + \frac{\alpha_s \sigma^2_{t\mid s}}{\alpha_t \sigma_t^2}\right)\mathbf z_t- \frac{\alpha_s \sigma^2_{t\mid s}}{\sigma_t^2}\frac{\sigma_t}{\alpha_t}\hat{\boldsymbol \epsilon} \\ &= \left( \frac{\alpha_{t\mid s} \sigma^2_s + \alpha_{t \mid s}^{-1} \sigma^2_{t\mid s}}{ \sigma_t^2} \right)\mathbf z_t- \alpha_{t|s}^{-1}\frac{\sigma^2_{t\mid s}}{\sigma_t}\hat{\boldsymbol \epsilon} \\ &= \left( \frac{\alpha_{t\mid s} \sigma^2_s + \alpha^{-1}_{t \mid s}(\sigma_t^2 - \alpha_{t\mid s}^2 \sigma_s^2)}{ \sigma_t^2} \right)\mathbf z_t- \frac{\sigma^2_{t\mid s}}{\alpha_{t|s}\sigma_t}\hat{\boldsymbol \epsilon} \\ &= \frac{1}{\alpha_{t \mid s}} \mathbf z_t- \frac{\sigma^2_{t\mid s}}{\alpha_{t|s}\sigma_t}\hat{\boldsymbol \epsilon} \\ \end{aligned}\]This final line is what we coded. The variance remains the same as $q(\mathbf z_s \mid \mathbf z_t, \mathbf x)$.

Finally, since $\mathbf x$ should be bounded between $[-1, 1]$, we know that $\hat{\boldsymbol \epsilon}$ should also be bounded as

\[\begin{aligned} &-1 \leq \mathbf x \leq 1\\ \iff &-1 \leq \frac{\mathbf z_t - \sigma_t \hat{\boldsymbol \epsilon}}{\alpha_t} \leq 1 \\ \iff &-\mathbf z_t -\alpha_t \leq - \sigma_t \hat{\boldsymbol \epsilon} \leq - \mathbf z_t + \alpha_t \\ \iff &\frac{\mathbf z_t - \alpha_t}{\sigma_t} \leq \hat{\boldsymbol \epsilon} \leq \frac{ \mathbf z_t + \alpha_t}{\sigma_t} \end{aligned}\]which is also coded in the provided snippet.

That concludes sampling!

## Remaining Bits

Some final details are how the learned noise schedule is implemented and the specific model choices.

We do not strictly follow what the authors propose in our code but stick to the previous diffusion UNet-type architecture provided by the `Lucidrains`

repository. The learned noise schedule (determined by the signal-to-noise ratios) is coded as follows (taken from the `revsic`

repo).

```
class SNRNetwork(nn.Module):
def __init__(self) -> None:
super().__init__()
self.l1 = PositiveLinear(1, 1)
self.l2 = PositiveLinear(1, 1024)
self.l3 = PositiveLinear(1024, 1)
self.gamma_min = nn.Parameter(torch.tensor(-10.0))
self.gamma_max = nn.Parameter(torch.tensor(20.0))
self.softplus = nn.Softplus()
def forward(self, t: torch.Tensor): # type: ignore
# Add start and endpoints 0 and 1.
t = torch.cat([torch.tensor([0.0, 1.0], device=t.device), t])
l1 = self.l1(t[:, None])
l2 = torch.sigmoid(self.l2(l1))
l3 = torch.squeeze(l1 + self.l3(l2), dim=-1)
s0, s1, sched = l3[0], l3[1], l3[2:]
norm_nlogsnr = (sched - s0) / (s1 - s0)
nlogsnr = self.gamma_min + self.softplus(self.gamma_max) * norm_nlogsnr
return -nlogsnr, norm_nlogsnr
```

with

```
class PositiveLinear(nn.Module):
def __init__(self, in_features: int, out_features: int) -> None:
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.zeros(out_features))
self.softplus = nn.Softplus()
def forward(self, input: torch.Tensor): # type: ignore
return input @ self.softplus(self.weight) + self.softplus(self.bias)
```

Again, for all details, see our full implementation here.

## Conclusion

Denoising diffusion models have many potential applications. It remains to be seen how long diffusion models will be around as the go-to generative model. Being easy to train, conceptually simple, and highly scalable, they certainly have valuable properties. But the relatively slow sampling procedure might be problematic. Despite this, I am optimistic. Please don't hesitate to contact me if you have any questions or comments regarding the implementation, code, or diffusion models in general!