  Diffusion models have achieved tremendous improvements in generative modeling
for images, enabling high-quality generation that is indistinguishable by
humans from real images. The qualities of images have reached a threshold at
which we can reuse synthetic images for training machine learning models again.
This attracts the area as it can relieve the high cost of data collection and
fundamentally solve many problems in data-limited areas. In this paper, we
focus on a practical scenario in which pretrained text-to-image diffusion
models are iteratively finetuned using a set of synthetic images, which we call
the Chain of Diffusion. Finetuned models generate images that are used for the
next iteration of finetuning. We first demonstrate how these iterative
processes result in severe degradation in image qualities. Thorough
investigations reveal the most impactful factor for the degradation, and we
propose finetuning and generation strategies that can effectively resolve the
degradation. Our method, Reusable Diffusion Finetuning (ReDiFine), combines
condition drop finetuning and CFG scheduling to maintain the qualities of
generated images throughout iterations. ReDiFine works effectively for multiple
datasets and models without further hyperparameter search, making synthetic
images reusable to finetune future generative models.
