Free$^2$Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models

• URL: http://arxiv.org/abs/2411.17041v1
• Date: Tue, 26 Nov 2024 02:14:47 GMT
• Title: Free$^2$Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models
• Authors: Jaemin Kim, Bryan S Kim, Jong Chul Ye,
• Abstract summary: Free$2$Guide is a gradient-free framework for aligning generated videos with text prompts. We show that Free$2$Guide significantly improves text alignment across various dimensions and enhances the overall quality of generated videos.
• Score: 56.289828238673124
• License:
• Abstract: 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$^2$Guide, a novel gradient-free framework for aligning generated videos with text prompts without requiring additional model training. Leveraging principles from path integral control, Free$^2$Guide 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$^2$Guide significantly improves text alignment across various dimensions and enhances the overall quality of generated videos.

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