Diffusion Models
Forward noising, denoising objectives, samplers, classifier-free guidance and latent diffusion.
7concepts
82flashcards
53minutes of reading
- 01 Classifier-Free Guidance The extrapolation trick that made text-to-image work, why it is not sampling from any distribution the model learned, and the fidelity-diversity tradeoff it exposes as a single tunable number.
- 02 Forward Diffusion and Noise Schedules The fixed corruption process that makes diffusion training possible, why the closed form in alpha-bar removes the need to simulate it, and how the schedule silently decides which frequencies the model learns.
- 03 Latent Diffusion and the Autoencoder Bottleneck Why running diffusion in a compressed latent space cuts cost by more than an order of magnitude, what the autoencoder throws away permanently, and the failure modes that belong to the VAE rather than the diffusion model.