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Test-Time Weak-to-Strong Alignment: Transferring Implicit Rewards from Weak to Strong Flow Models
Xin Xie, Fan Zhang, Dong Gong
Aligning a text-to-image generation flow model with a reward makes it follow objectives that the training data alone does not provide. Alignment fine-tuning delivers this by reinfo…
HyperAlign: Hypernetwork for Efficient Test-Time Alignment of Diffusion Models
Xin Xie, Jiaxian Guo, Dong Gong
Diffusion model alignment aims to bridge the gap between generated outputs and human preferences by enhancing both semantic consistency with textual prompts and overall visual qual…
MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks
Mingsong Li, Lin Liu, Hongjun Wang +7
Current instruction-based image editing (IBIE) methods struggle with challenging editing tasks, as both editing types and sample counts of existing datasets are limited. Moreover,…
When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators
Jintao Rong, Xin Xie, Xinyi Yu +4
Training-free motion customization imposes motion patterns from reference videos onto video generators through test-time computation. Most existing methods target full diffusion mo…
DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling
Xin Xie, Dong Gong
Text-to-image diffusion model alignment is critical for improving the alignment between the generated images and human preferences. While training-based methods are constrained by…
UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank Adaptation
Cheng Zhang, Dong Gong, Jiumei He +3
Existing unified methods typically treat multi-degradation image restoration as a multi-task learning problem. Despite performing effectively compared to single degradation restora…