Free^2Guide: Gradient-Free Path Integral Control for Enhancing
  Text-to-Video Generation with Large Vision-Language Models
Authors: name: Jaemin Kim, name: Bryan S Kim, name: Jong Chul Ye

Diffusion models have achieved impressive results in generative tasks like
text-to-image (T2I) and text-to-video (T2V) synthesis. However, achieving
accurate text alignment in T2V generation remains challenging due to the
complex temporal dependency across frames. Existing reinforcement learning
(RL)-based approaches to enhance text alignment often require differentiable
reward functions or are constrained to limited prompts, hindering their
scalability and applicability. In this paper, we propose Free^2Guide, a novel
gradient-free framework for aligning generated videos with text prompts without
requiring additional model training. Leveraging principles from path integral
control, Free^2Guide approximates guidance for diffusion models using
non-differentiable reward functions, thereby enabling the integration of
powerful black-box Large Vision-Language Models (LVLMs) as reward model.
Additionally, our framework supports the flexible ensembling of multiple reward
models, including large-scale image-based models, to synergistically enhance
alignment without incurring substantial computational overhead. We demonstrate
that Free^2Guide significantly improves text alignment across various
dimensions and enhances the overall quality of generated videos.