lucataco / dpo-sdxl

Direct Preference Optimization (DPO) is a method to align diffusion models to text human preferences by directly optimizing on human comparison data

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Run time and cost

This model costs approximately $0.019 to run on Replicate, or 52 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 20 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Implementation of mhdang/dpo-sdxl-text2image-v1

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 our paper at Diffusion Model Alignment Using Direct Preference Optimization.

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