  Recently, Direct Preference Optimization (DPO) has extended its success from
aligning large language models (LLMs) to aligning text-to-image diffusion
models with human preferences. Unlike most existing DPO methods that assume all
diffusion steps share a consistent preference order with the final generated
images, we argue that this assumption neglects step-specific denoising
performance and that preference labels should be tailored to each step's
contribution. To address this limitation, we propose Step-aware Preference
Optimization (SPO), a novel post-training approach that independently evaluates
and adjusts the denoising performance at each step, using a step-aware
preference model and a step-wise resampler to ensure accurate step-aware
supervision. Specifically, at each denoising step, we sample a pool of images,
find a suitable win-lose pair, and, most importantly, randomly select a single
image from the pool to initialize the next denoising step. This step-wise
resampler process ensures the next win-lose image pair comes from the same
image, making the win-lose comparison independent of the previous step. To
assess the preferences at each step, we train a separate step-aware preference
model that can be applied to both noisy and clean images. Our experiments with
Stable Diffusion v1.5 and SDXL demonstrate that SPO significantly outperforms
the latest Diffusion-DPO in aligning generated images with complex, detailed
prompts and enhancing aesthetics, while also achieving more than 20x times
faster in training efficiency. Code and model:
https://rockeycoss.github.io/spo.github.io/
