Diffusion models have achieved remarkable success in text-to-image generation, yet aligning their outputs with human preferences remains challenging. Diffusion-DPO learns from image preference pairs, but their global labels do not specify how supervision should be allocated across spatial locations and denoising timest...
Jun-Tao Xu, Shi-Hong Li, Ho-Yin Au et al.· 1 citation
Text-to-image diffusion models can generate individual concepts well, but they often omit or merge concepts incorrectly with multiple concepts. We trace these failures to an early coordination bottleneck: before denoising begins, prompt-conditioned attention may allocate different concepts to strongly overlapping spati...
Ning Zhu, Anchi Chen, Mengfei Zhao et al.· 1 citation
DART is proposed, a training-free reparameterization method that transports source LoRAs into aligned coordinates through a low-rank distillation bridge and complements response calibration, with adapter-level benefits encompassing both functional preservation and reduced negative transfer.
Shi-Hong Li, Jun-Tao Xu, Cao Jin et al.· 1 citation
A theoretical analysis that characterizes the trade-off controlled by entropic regularization and establishes a task-agnostic minimax bound for cold-start selection and a novel Sinkhorn-based CSAL algorithm, termed $\epsilon$-Adaptive Selection ($\epsilon$-AS).
Ning Zhu, Xiaochuan Ma, Jun-Tao Xu et al.· 0 citations
ToPO (Token-Oriented Preference Optimization) constructs a per-minibatch, detached, separable spatial-temporal route from branchwise squared-residual contrast in a frozen reference denoiser for attention-based, noise-prediction latent diffusion.
Jun-Tao Xu, Shi-Hong Li, Ho-Yin Au et al.· 1 citation
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