 Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this process creates an autophagous ("self-consuming") loop whose properties are poorly understood. We conduct a thorough analytical and empirical analysis using state-of-the-art generative image models of three families of autophagous loops that differ in how fixed or fresh real training data is available through the generations of training and in whether the samples from previousgeneration models have been biased to trade off data quality versus diversity. Our primary conclusion across all scenarios is that without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease. We term this condition Model Autophagy Disorder (MAD), making analogy to mad cow disease.  ![0_image_0.png](0_image_0.png)  Figure 1: **Training generative artificial intelligence (AI) models on synthetic data progressively** amplifies artifacts. As synthetic data from generative models proliferates on the Internet and in standard training datasets, future models will likely be trained on some mixture of real and synthetic data, forming an *autophagous ("self-consuming") loop*. Here we highlight one potential unintended consequence of autophagous training. We trained a succession of StyleGAN-2 [1] generative models such that the training data for the model at generation t ≥ 2 was obtained by synthesizing images from the model at generation t − 1. This particular setup corresponds to a fully synthetic loop in Figure 3. Note how the cross-hatched artifacts (possibly an architectural *fingerprint*) are progressively amplified in each new generation. Additional samples are provided Appendices C and D.  arXiv:2307.01850v1 [cs.LG] 4 Jul 2023 