Generating High Quality Images with Diffusion

Summary & Methodology Analysis

The paper addresses the challenge of high-quality image synthesis using diffusion probabilistic models, a class of latent variable models inspired by nonequilibrium thermodynamics. Previously, these models lacked demonstrations of generating high quality samples competitive with other deep generative models. To solve this, the authors define a forward diffusion process as a fixed Markov chain that gradually adds Gaussian noise to data according to a variance schedule beta_1 to beta_T. The reverse process is defined as a Markov chain with learned Gaussian transitions starting at a standard normal prior. The training objective involves optimizing a simplified weighted variational bound objective resembling denoising score matching across multiple noise scales using stochastic gradient descent. The reverse process mean function approximator is trained to predict the forward process posterior mean or, through a modified parameterization, to predict the added noise epsilon from the noisy image x_t at timestep t.

The system relies on specific architectural components to handle these computations. It uses a U-Net backbone with group normalization and self-attention blocks, incorporating sinusoidal position embeddings for time specification. This architecture processes the noise schedules across timesteps to recover the original data structure from standard normal noise. The empirical results demonstrate strong performance across multiple datasets. On the unconditional CIFAR10 dataset, the model obtains an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, the model obtains sample quality similar to ProgressiveGAN. The paper also mentions evaluations involving datasets and models such as CelebA-HQ, PixelCNN++, NCSN, NCSNv2, BigGAN, StyleGAN2, SNGAN, EBM, JEM, Sparse Transformer, PixelIQN, and Wide ResNet.

Despite the strong sample generation quality, the approach has notable limitations. Diffusion models do not have competitive log likelihoods compared to other likelihood-based models. Furthermore, the majority of the models' lossless codelengths are consumed to describe imperceptible image details. The paper does not specify precise hardware training costs, exact memory consumption figures, or latency metrics beyond the reported dataset scores.