Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models
Researchers have introduced Linear-DPO, a novel method for aligning text-to-image generative models. This approach generalizes the Direct Preference Optimization objective to encompass both diffusion and flow-matching models within a unified framework. By replacing the standard sigmoid-based utility function with a linear one and incorporating an EMA-updated reference model, Linear-DPO demonstrates superior performance over existing methods on diffusion models like SD1.5 and SDXL, as well as the flow-matching model SD3-Medium. AI
IMPACT Introduces a more effective alignment technique for text-to-image models, potentially improving their adherence to user prompts.