We released four main checkpoints focused on training data (real vs. synthetic) and the choice of VAE for image encoding. All four models were trained using identical training recipes and computational budgets.

1. Model trained only on 22M real images
2. Model trained on 37M images (22M real + 15M synthetic) 
3. Model trained on 490M synthetic images to understand the scaling effect of synthetic data
4. Model trained on 37M images, but using a 16-channel VAE instead of a 4-channel VAE

In a fixed micro-budget setup, using synthetic data provides significant initial improvements, but even scaling to ½ billion samples does not yield tremendous gains. Similarly, increasing the latent space channel dimension performs poorly when the compute budget is severely limited. 3/3