Latent Diffusion for High-Resolution Imagery

Analyze the computational and quality trade-offs motivating latent diffusion

Train a Variational Autoencoder (VAE) with perceptual and adversarial losses for high-fidelity image compression

Implement a latent diffusion model by adapting the training loop to operate on latent vectors

Compare the computational efficiency and generation quality of pixel-space vs. latent-space diffusion

Debug common latent space artifacts, such as blurriness and reconstruction errors