Convert Loss Weights Between Parameterizations

easyPROSignal-to-Noise Ratio and Loss WeightingDiffusion ModelsDiffusion Models 1

Problem Statement

The simple eps-prediction loss corresponds to a specific SNR-dependent weight in the x0-prediction view. Given per-timestep SNRs, compute both the eps weight and the x0 weight.

Background

Training targets are related by x0-loss = SNR * eps-loss (per timestep). The DDPM "simple" objective uses a constant weight of 1 on the eps-loss. Reported in the x0 parameterization, that same objective carries weight SNR_t:

$w^{\varepsilon}_t = 1, \qquad w^{x_0}_t = \text{SNR}_t$

Your Task

Implement:

def loss_weights(snr):

snr: list of per-timestep SNR values.

Return a dict with "eps" (list of 1.0s) and "x0" (list equal to the SNRs), values rounded to 4 decimals.

Input Format

snr: list of non-negative floats.

Output Format

A dict with two lists.

Sample

print(loss_weights([4.0, 1.0, 0.25]))

Output:

{'eps': [1.0, 1.0, 1.0], 'x0': [4.0, 1.0, 0.25]}

Example

Input

print(loss_weights([4.0, 1.0, 0.25]))

Output

{'eps': [1.0, 1.0, 1.0], 'x0': [4.0, 1.0, 0.25]}

Reasoning

The input list of Signal-to-Noise Ratios (SNRs) is $[4.0, 1.0, 0.25]$ . The function requires two weight lists corresponding to the length of this input, which is 3.
For the "eps" parameterization, the weight is defined as a constant $1$ for all timesteps. Thus, we generate a list of three $1.0$ values: $[1.0, 1.0, 1.0]$ .
For the "x0" parameterization, the weight at each timestep $t$ is equal to the SNR at that timestep, i.e., $w^{x_0}_t = \text{SNR}_t$ . We map each input value to itself: $4.0 \to 4.0$ , $1.0 \to 1.0$ , and $0.25 \to 0.25$ .
Each value in the "x0" list is rounded to 4 decimal places. Since the input values $4.0$ , $1.0$ , and $0.25$ are already precise to fewer than 4 decimal places, they remain unchanged: $[4.0, 1.0, 0.25]$ .
The final output is {'eps': [1.0, 1.0, 1.0], 'x0': [4.0, 1.0, 0.25]}

Constraints

1 <= len(snr) <= 100000, all snr[i] >= 0.
eps weight is 1.0 everywhere; x0 weight equals the SNR.
Round to 4 decimals.

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Concept primerPREVIEW

1. Background Knowledge

In diffusion models, the training objective can be expressed in different parameterizations. The most common are epsilon-prediction (predicting the noise $\varepsilon$ ) and x0-prediction (predicting the clean data $x_0$ ). These are not independent choices; they are mathematically linked through the Signal-to-Noise Ratio (SNR) at each timestep $t$ .

The key relationship is that the loss in the $x_0$ -parameterization is a weighted version of the loss in the $\varepsilon$ -parameterization. Specifically, if you have a loss function $\mathcal{L}_\varepsilon$ for epsilon-prediction, the equivalent loss in the $x_0$ view is scaled by the SNR: $\mathcal{L}_{x_0} = \text{SNR}_t \cdot \mathcal{L}_\varepsilon$ This means that a constant weight of $1$ in the $\varepsilon$ -space (the standard DDPM objective) translates to a time-varying weight of $\text{SNR}_t$ in the $x_0$ -space.

Understanding this conversion is crucial for comparing different training objectives or when switching between parameterizations during implementation. The SNR is typically defined as $\text{SNR}_t = \frac{\alpha_t^2}{1 - \alpha_t^2}$ or similar forms depending on the noise schedule, but for this problem, the SNR values are provided directly.

2. Algorithm Approach

This is a straightforward element-wise transformation problem. You are given a list of SNR values and need to produce two corresponding lists of weights:

Epsilon weights: A constant list where every element is $1.0$ .
X0 weights: A list identical to the input SNR values.

The core logic involves:

Iterating over the input list (or using vectorized operations).
Constructing the first output list with a fixed value.
Constructing the second output list by copying or referencing the input values.
Applying rounding to 4 decimal places as specified.
Packaging the results into a dictionary with the keys "eps" and "x0".

3. Step-by-Step Strategy

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