How do you quantify the diversity of outputs from a diffusion model?

Quantifying the diversity of outputs from a diffusion model involves measuring how distinct generated samples are from one another while ensuring they cover a broad range of possible outcomes. A common approach is to compute statistical or perceptual similarity metrics across a large set of generated outputs. For example, pairwise distance metrics in a feature space (e.g., using embeddings from a pre-trained model) can reveal how dissimilar images or other outputs are. Additionally, clustering algorithms or entropy-based measures can assess whether the model produces outputs that span multiple modes of the data distribution rather than collapsing to a few repetitive patterns.

One practical method is to use perceptual metrics like LPIPS (Learned Perceptual Image Patch Similarity), which quantifies the difference between two images using features from a neural network. By calculating LPIPS scores across pairs of generated images, developers can compute an average dissimilarity score—a higher average indicates greater diversity. Another approach involves extracting feature vectors from a pre-trained classifier (e.g., Inception-v3 for images) and analyzing their distribution. Metrics like Fréchet Inception Distance (FID) compare the generated feature distribution to the training data’s distribution, but FID alone doesn’t isolate diversity—supplementing it with metrics like the standard deviation of features or the number of unique clusters (via k-means) provides a clearer picture. For non-image data, such as text, diversity might be measured using n-gram overlap or embedding-based metrics like BERTScore variance.

To implement these techniques, developers can use libraries like PyTorch or TensorFlow to compute feature embeddings and pairwise distances. For example, generating 1,000 images, extracting their Inception-v3 embeddings, and calculating the average cosine similarity across all pairs would provide a straightforward diversity score. Clustering these embeddings with k-means and measuring the distribution of samples per cluster (e.g., using entropy) can reveal whether outputs are spread evenly across modes. However, computational cost increases with sample size, so approximations like subsampling or using smaller feature spaces may be necessary. Balancing diversity with output quality is also critical—overly diverse outputs might include low-quality samples, so metrics should be used alongside quality checks like precision-recall curves or human evaluation.