Direct Trajectory Optimization for Inference-Time Alignment of Text-to-Image Diffusion Models

arXiv:2611.11821v2 Announce Type: replace-cross Abstract: Alignment of text-to-image diffusion models with reward functions is a key challenge in controllable generation. Existing approaches typically rely on optimizing model inputs or fine-tuning denoising networks, which requires computationally expensive backpropagation through large denoising models. In this work, we propose Direct Trajectory Optimization (DTO), a training-free framework that performs alignment by directly optimizing the sampling trajectory of diffusion models. Specifically, DTO treats intermediate denoising scores and sam

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