Using synthetic training data to improve Flux finetunes

The blog post by Zeke highlights techniques for using synthetic data to improve fine-tuned Flux models, emphasizing the creation of diverse and comprehensive training datasets. It explains the concept of synthetic data, which mimics real-world data, and suggests generating training data from a single image using the consistent-character model to produce multiple images with varied poses and styles. The post also advises utilizing outputs from fine-tuned models as new training data to improve model quality. Additionally, it introduces the concept of combining multiple Low-Rank Adaptation (LoRA) styles to diversify training data and achieve unique image outputs. The post encourages experimentation with different techniques and sharing results within the community to enhance the effectiveness of fine-tuning processes.