Schedule

• Applications of diffusion models
• What are diffusion models?
• How to train and sample from diffusion models from scratch

Suggested Readings:

• Diffusion models from scratch
• smalldiffusion codebase
• Elucidating the Design Space of Diffusion-Based Generative Models
• Analyzing and Improving the Training Dynamics of Diffusion Models

• Introduce different interpretations and perspectives on diffusion
• Derivation of DDPM and DDIM, SDE reversal and probability flow ODE
• Autoregressive interpretation

Suggested Readings:

• What are diffusion models?
• SDE interpretation blog post
• Step-by-step diffusion: an elementary tutorial

• Conditional diffusion training and inference
• Classifier and classifier-free guidance

Suggested Readings:

• Guidance: a cheat code for diffusion models
• Classifier-free guidance paper
• Latent diffusion models
• Guiding a diffusion model with a bad version of itself

• What is generalization, how and when do diffusion models generalize?
• Different inductive biases during training affect generalization

Suggested Readings:

• Generalization in diffusion models arises from geometry-adaptive harmonic representations
• Closed-Form Diffusion Models
• Nuclear Norm Regularization for Deep Learning
• An analytic theory of creativity in convolutional diffusion models

• What is score distillation sampling (SDS)?
• Derivation through score matching and KL divergence
• Improved 3D shape generation with DDIM inversion and SDS

Suggested Readings:

• DreamFusion: Text-to-3D using 2D Diffusion
• ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
• Score Distillation via Reparametrized DDIM
• Differentiable 3D representations

• How to train better and focus on noise levels that matter most
• How to sample faster using distillation and consistency models
• Interpreting diffusion as distance minimization

Suggested Readings:

• Diffusion is spectral autoregression
• Noise schedules considered harmful
• The paradox of diffusion distillation
• Interpreting and improving diffusion models from an optimization perspective