arXiv Computer Vision
ToPO: Token-Oriented Preference Optimization for Text-to-Image Diffusion Models
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arXiv Computer Vision
Sep 4
ToPO: Token-Conditioned Preference Routing for Attention-Based Latent Diffusion Models
ToPO (Token-Oriented Preference Optimization) is a method for attention-based latent diffusion models that constructs a per‑minibatch, detached, separable spatial‑temporal route using branchwise squared‑residual contrast from a frozen reference denoiser. It employs preferred‑branch cross‑attention with content tokens to modulate spatial factors and adds an auxiliary pixel‑midpoint ordering term, all without local labels or a learned reward model. In experiments with shared update schedules, ToPO outperforms Diffusion‑DPO on five SD‑1.5 metrics and on HPSv2, ImageReward, and CLIP for SDXL, and achieves higher raw win shares in a blind SDXL A/B study.
arXiv Machine Learning
Sep 7
Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models
The paper introduces Diffusion LAIR, a listwise preference optimization technique that leverages continuous reward scores instead of binary pairwise comparisons to align text‑to‑image diffusion models. LAIR transforms reward scores into centered advantage weights and optimizes an advantage‑weighted regression objective on an implicit reward defined by denoising‑loss improvement over a reference model, with a quadratic penalty to regulate reward magnitude. Experiments demonstrate that Diffusion LAIR surpasses strong baseline methods on SD1.5 and SDXL across generation, compositional, and editing tasks.
arXiv AI
Jun 17
STAR: SpatioTemporal Adaptive Reward Allocation for Text-to-Image RL Post-Training
arXiv:2606. 17979v1 Announce Type: new Abstract: Existing RL post-training methods for text-to-image generation usually convert the final-image reward into a single scalar advantage and apply it with the same strength to the entire generative trajectory.
arXiv Computer Vision
Sep 7
Step Back to Move Forward: Reflection-Aware Preference Optimization for Visual Generation
The paper introduces Reflection-Aware GRPO (RA‑GRPO), a reinforcement‑learning framework that aligns diffusion generative models with human preferences. It uses Diffusion Reflection to correct intermediate sampling paths by reversing the diffusion process, and Counterfactual Path Synthesis to embed these corrected trajectories into the policy, avoiding extra inference cost. Experiments on text‑to‑image and text‑to‑video models show RA‑GRPO outperforms existing methods, reducing reward hacking and improving generalization while remaining architecture‑agnostic.
arXiv Computer Vision
Oct 2
Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning
arXiv:2610.01723v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual tra...
arXiv Machine Learning
Sep 1
TPSO: Training-Free Diverse Image Generation via Semantic Prompt Embedding Optimization
arXiv:2511.19811v2 Announce Type: replace-cross Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive output...