Diffusion models are typically evaluated using metrics that assess the quality, diversity, and practical usability of generated outputs. Three widely used metrics are the Fréchet Inception Distance (FID), Inception Score (IS), and Precision/Recall. FID measures the similarity between the distributions of real and generated images by comparing features extracted from a pre-trained Inception-v3 model. Lower FID values indicate better alignment with real data. IS, on the other hand, combines the confidence of a classifier (Inception-v3) in assigning a class to generated images with the diversity of classes across samples. While IS emphasizes clarity and variety, it often fails to detect mode collapse (e.g., a model generating the same high-quality image repeatedly). For example, a diffusion model trained on faces might achieve a high IS if outputs are sharp and span multiple demographics, even if certain ethnicities are underrepresented.
Another set of metrics focuses on Precision and Recall, adapted for generative models to separate quality (precision) from coverage (recall). Precision measures how many generated samples are realistic, while recall quantifies how well the model captures the full diversity of the training data. For instance, a model with high precision but low recall might generate flawless cat images but only produce Persian cats, missing other breeds. These metrics often rely on clustering in feature space (e.g., using k-nearest neighbors) to determine if generated samples lie within the real data distribution. Additionally, perceptual metrics like LPIPS (Learned Perceptual Image Patch Similarity) compare the similarity of generated and real images using deep features, offering a fine-grained view of visual fidelity. LPIPS is particularly useful for tasks like image-to-image translation, where structural consistency matters.
Finally, efficiency metrics are critical for real-world deployment. These include sampling speed (e.g., the number of denoising steps required) and computational cost (e.g., GPU memory usage). For example, a diffusion model might achieve excellent FID scores but require 1,000 denoising steps, making it impractical for real-time applications. Developers often track trade-offs between quality and speed, such as reducing steps while maintaining performance via distillation techniques. Metrics like training stability (e.g., loss convergence patterns) and robustness to hyperparameters (e.g., sensitivity to noise schedules) are also monitored. While not direct measures of output quality, these practical considerations determine whether a diffusion model can be deployed effectively in production environments.