Table 2

A comparative summary of GANs vs diffusion models

FeatureGANsDiffusion models
AimGenerate data mimicking training data via a generator-discriminator competitionGenerate data by reversing a process that adds noise to data
ArchitectureTwo networks: generator (creates data) and discriminator (evaluates data)Single network learns to remove noise over many steps
Mode collapseProne, leading to less diverse outputsLess prone, ensures diverse sample generation
Data efficiencyRequires large datasets to train effectivelyMore efficient, works well with smaller datasets
Input noiseBegins with a noise vector, transformed into data by the generatorBegins with noise, gradually denoised to form data
ApplicationsImage generation, style transfer, super-resolutionImage/audio generation, text-to-image synthesis
Other featuresUnstable training, single-step generationStable training, multistep generation process
  • GANs, generative adversarial networks.