Diffusion Model Alignment Using Direct Preference Optimization

Direct Preference Optimization (DPO) for text-to-image diffusion models is a method to align diffusion models to text human preferences by directly optimizing on human comparison data. Please check paper at Diffusion Model Alignment Using Direct Preference Optimization.

SD1.5 model is fine-tuned from stable-diffusion-v1-5 on offline human preference data pickapic_v2.

SDXL model is fine-tuned from stable-diffusion-xl-base-1.0 on offline human preference data pickapic_v2.

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Dataset used to train bdsqlsz/dpo-sd-text2image-v1-fp16