Weak-to-Strong Diffusion with Reflection

TL;DR

Weak-to-Strong Diffusion (W2SD) is a novel framework that leverages differences between weak and strong models to improve diffusion generative models, enhancing quality and alignment with real data across multiple modalities.

Contribution

W2SD introduces a reflection-based method utilizing weak-to-strong model differences to guide diffusion models toward real data distribution, with broad applicability and significant performance gains.

Findings

Achieves state-of-the-art results across image and video tasks.

Significantly improves human preference and aesthetic quality.

Outperforms original models with minimal additional computational cost.

Abstract

The goal of diffusion generative models is to align the learned distribution with the real data distribution through gradient score matching. However, inherent limitations in training data quality, modeling strategies, and architectural design lead to inevitable gap between generated outputs and real data. To reduce this gap, we propose Weak-to-Strong Diffusion (W2SD), a novel framework that utilizes the estimated difference between existing weak and strong models (i.e., weak-to-strong difference) to bridge the gap between an ideal model and a strong model. By employing a reflective operation that alternates between denoising and inversion with weak-to-strong difference, we theoretically understand that W2SD steers latent variables along sampling trajectories toward regions of the real data distribution. W2SD is highly flexible and broadly applicable, enabling diverse improvements through the strategic selection of weak-to-strong model pairs (e.g., DreamShaper vs. SD1.5, good experts vs. bad experts in MoE). Extensive experiments demonstrate that W2SD significantly improves human preference, aesthetic quality, and prompt adherence, achieving SOTA performance across various modalities (e.g., image, video), architectures (e.g., UNet-based, DiT-based, MoE), and benchmarks. For example, Juggernaut-XL with W2SD can improve with the HPSv2 winning rate up to 90% over the original results. Moreover, the performance gains achieved by W2SD markedly outweigh its additional computational overhead, while the cumulative improvements from different weak-to-strong difference further solidify its practical utility and deployability.