DyMO improves text-to-image outputs at inference time by dynamically scheduling an LLM-built semantic attention objective with a human-preference reward, without retraining the diffusion model.
Solving 3d inverse problems using pre-trained 2d diffusion models
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DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling
DyMO improves text-to-image outputs at inference time by dynamically scheduling an LLM-built semantic attention objective with a human-preference reward, without retraining the diffusion model.