Generative Models: GANs, VAEs, and Diffusion Processes |…

The Core Idea

- VAE (Variational Autoencoder) optimizes the lower bound of likelihood through latent variables, producing stable but sometimes blurry results. It’s good for interpolation and controlling the latent space.

- GANs (Generative Adversarial Networks) pit a generator against a discriminator in an adversarial training process, yielding sharp images but are challenging to stabilize (mode collapse). Variants include WGAN-GP, StyleGAN, and BigGAN.

- Diffusion Models Learn from Noise

- Control over generation: conditional losses, instruction-guided sampling, class embeddings, latent space inversion.

- Risks: fabrication, data bias, copyright issues. Metrics – dataset filtering, generation constraints, watermarking.

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- Diffusion Approaches are Becoming Standard

VAE optimizes ELBO (Evidence Lower Bound), introducing a stochastic latent space with KL regularization. GAN trains a generator against a discriminator; stability is critical through gradient penalty/spectral normalization.

Models gradually add noise and learn to invert this process. Conditional diffusion is guided by text/class embeddings; sampling control via guidance.

Frequently asked questions

What does ‘control’ and ‘editing’ refer to in the context of generative models?

Control and editing refers to techniques for guiding the generation process, such as using conditional losses or embedding instructions.

What is meant by ‘inversion’ into the latent space for editing images?

‘Inversion’ into the latent space refers to the process of transforming an image back into its latent representation, which can then be manipulated to create variations or edits.

What types of synthesis – images, audio, video, styling – are enabled by generative models?

Generative models enable the synthesis of images, audio, and video, as well as styling capabilities, data augmentation, creative design applications, and even the generation of molecules and structures.

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Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

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