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Using synthetic training data to improve Flux finetunes

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Three techniques for using synthetic training data to improve Flux fine-tuned models: generating training data from a single image using the consistent-character model, using outputs from the fine-tuned model itself as training data, and combining multiple LoRA styles to diversify training data.

From the source
In this post I’ll cover some techniques you can use to generate synthetic training data to help improve the accuracy, diversity, and stylistic range of your fine-tuned Flux models.
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