Forward-Diffuse a Batch to Timestep t

mediumPROForward Process and Noise SchedulesDiffusion ModelsDiffusion Models 1

Problem Statement

Apply the closed-form forward process to a batch of clean samples at possibly different timesteps, the exact operation inside a training step. Each sample gets noised with its own t and its own noise vector.

Background

The forward marginal is q(x_t | x_0) = N(sqrt(alpha_bar_t) x_0, (1 - alpha_bar_t) I), so with a standard-normal noise eps:

$x_t = \sqrt{\bar{\alpha}_t}\, x_0 + \sqrt{1 - \bar{\alpha}_t}\, \varepsilon$

In a training batch each row i has its own timestep t_i (drawn uniformly) and its own noise, so the per-row scale factors are gathered from the alpha_bar table.

Your Task

Implement:

def forward_diffuse(x0, eps, alpha_bar, t):

x0, eps: B x D nested lists (clean samples and noise).
alpha_bar: the full schedule (list).
t: list of B integer timesteps, one per row.

Return the B x D noised batch as a nested list rounded to 4 decimals.

Input Format

x0, eps: B x D.
alpha_bar: list of cumulative products.
t: list of B indices into alpha_bar.

Output Format

A B x D nested list rounded to 4 decimals.

Sample

x0 = [[1.0, 1.0]]
eps = [[1.0, -1.0]]
print(forward_diffuse(x0, eps, [0.99, 0.5], [1]))

Output:

[[1.4142, 0.0]]

Example

Input

x0 = [[1.0, 1.0]]
eps = [[1.0, -1.0]]
print(forward_diffuse(x0, eps, [0.99, 0.5], [1]))

Output

[[1.4142, 0.0]]

Reasoning

Look up the noise schedule value for the given timestep: with $t = [1]$ , we index into alpha_bar at position 1 to get $\bar{\alpha} = 0.5$ .
Compute the signal and noise scale factors for this timestep: the signal weight is $a = \sqrt{0.5} \approx 0.7071$ and the noise weight is $b = \sqrt{1 - 0.5} = \sqrt{0.5} \approx 0.7071$ .
Apply the forward diffusion formula $x_t = a \cdot x_0 + b \cdot \epsilon$ $x_{t} = a \cdot x_{0} + b \cdot ϵ$ element-wise to the single sample:
- For the first dimension: $0.7071 \cdot 1.0 + 0.7071 \cdot 1.0 = 1.4142$ .
- For the second dimension: $0.7071 \cdot 1.0 + 0.7071 \cdot (-1.0) = 0.0$ .
The final output is [[1.4142, 0.0]]

Constraints

x0 and eps share shape B x D; len(t) == B.
Gather alpha_bar[t_i] per row; scales are sqrt(alpha_bar) and sqrt(1 - alpha_bar).
Round to 4 decimals; avoid -0.0.

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Concept primerPREVIEW

1. Background Knowledge

In denoising diffusion probabilistic models (DDPMs), the forward process gradually adds Gaussian noise to a clean data sample $x_0$ over $T$ discrete timesteps. The key insight is that although the process is defined as a Markov chain, the marginal distribution $q(x_t \mid x_0)$ has a closed-form solution: it is a Gaussian with mean $\sqrt{\bar{\alpha}_t}\, x_0$ and covariance $(1 - \bar{\alpha}_t)\, I$ . This means you can jump directly from $x_0$ to any $x_t$ in one step, without simulating all intermediate timesteps.

The quantity $\bar{\alpha}_t$ is the cumulative product of the per-step signal retention factors $\alpha_i = 1 - \beta_i$ , where $\beta_i$ is the noise variance at step $i$ . As $t$ increases, $\bar{\alpha}_t$ decreases toward zero, meaning the signal is progressively drowned out by noise. At $t=0$ , $\bar{\alpha}_0 = 1$ (pure signal); at large $t$ , $\bar{\alpha}_t \approx 0$ (pure noise).

In a training batch, each sample is noised at a different randomly sampled timestep $t_i$ . This is essential for the model to learn to denoise at all noise levels simultaneously. The per-sample scale factors $\sqrt{\bar{\alpha}_{t_i}}$ and $\sqrt{1 - \bar{\alpha}_{t_i}}$ are gathered from the precomputed schedule and applied row-wise.

2. Algorithm Approach

This is a row-wise element-wise operation with per-row scalar coefficients. The pattern is:

For each row $i$ in the batch, look up $\bar{\alpha}_{t_i}$ from the schedule.
Compute the two scalar coefficients: $s_i = \sqrt{\bar{\alpha}_{t_i}}$ and $n_i = \sqrt{1 - \bar{\alpha}_{t_i}}$ .
Apply the linear combination $x_t[i] = s_i \cdot x_0[i] + n_i \cdot \varepsilon[i]$ element-wise across the feature dimension $D$ .

No loops over timesteps are needed—just a single pass over the batch.

3. Step-by-Step Strategy

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solution.py

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