Diffusion Fine-Tuning via Reparameterized Policy Gradient of the Soft Q-Function

A recent study has highlighted the importance of motion representation in human motion generation using diffusion models, specifically focusing on the motion diffusion model (MDM) and its prediction objectives. The research evaluates various motion representations and their performance, aiming to enhance understanding of latent data distributions in generative models.

A new framework called Measurement-Aware Consistency Sampling (MACS) has been introduced to enhance the efficiency of diffusion models in solving inverse imaging problems. This approach utilizes a measurement-consistency mechanism to regulate stochasticity, ensuring fidelity to observed data while maintaining computational efficiency. Comprehensive experiments on datasets like Fashion-MNIST and LSUN Bedroom show significant improvements in both perceptual and pixel-level quality.

GalaxyDiT has been introduced as a training-free method to enhance the efficiency of video generation using diffusion transformers, addressing the computational inefficiencies associated with existing models that require extensive iterative steps and resources. This innovation focuses on guidance alignment and adaptive proxy selection to optimize computational reuse across different model families.

A new framework has been introduced that reinterprets Maximum Entropy Reinforcement Learning (MaxEntRL) as a diffusion model-based sampling problem, focusing on minimizing the reverse Kullback-Leibler divergence between the diffusion policy and the optimal policy distribution. This leads to the development of diffusion-based variants of existing algorithms like Soft Actor-Critic, Proximal Policy Optimization, and Wasserstein Policy Optimization, termed DiffSAC, DiffPPO, and DiffWPO.

Recent advancements in diffusion models have led to the development of a phase-aware strategy that accelerates image generation by applying different speedups to various stages of the process. This approach utilizes lightweight LoRA adapters, named Slow-LoRA and Fast-LoRA, to enhance efficiency without extensive retraining of models.