Steering Rectified Flow Models in the Vector Field for Controlled Image Generation
Maitreya Patel, Song Wen, Dimitris N. Metaxas, Yezhou Yang
2024-12-03
Summary
This paper introduces FlowChef, a new method that improves how images are generated by using rectified flow models, allowing for better control and efficiency in creating images based on specific guidance.
What's the problem?
Generating high-quality images often requires complex processes that can be slow and resource-intensive. Traditional methods, especially diffusion models, need a lot of training and computational power to produce good results. They also struggle with maintaining accuracy over time and can require significant adjustments to work well with different types of images.
What's the solution?
FlowChef addresses these challenges by using a new approach that focuses on the vector field dynamics of rectified flow models (RFMs). Instead of relying on extensive training and backpropagation, FlowChef allows for controlled image generation by steering the denoising process in a more efficient way. It introduces techniques like gradient skipping, which helps navigate the process without needing to calculate gradients for every step. This results in faster image generation while maintaining high quality. FlowChef can handle various tasks like image editing and classifier guidance without requiring additional training or complex inversion processes.
Why it matters?
This research is significant because it enhances the efficiency and effectiveness of image generation methods. By improving how AI models create images, FlowChef can be applied in many fields such as graphic design, video game development, and virtual reality, where quick and accurate image production is crucial. The advancements made by FlowChef could lead to faster and more accessible tools for artists and developers.
Abstract
Diffusion models (DMs) excel in photorealism, image editing, and solving inverse problems, aided by classifier-free guidance and image inversion techniques. However, rectified flow models (RFMs) remain underexplored for these tasks. Existing DM-based methods often require additional training, lack generalization to pretrained latent models, underperform, and demand significant computational resources due to extensive backpropagation through ODE solvers and inversion processes. In this work, we first develop a theoretical and empirical understanding of the vector field dynamics of RFMs in efficiently guiding the denoising trajectory. Our findings reveal that we can navigate the vector field in a deterministic and gradient-free manner. Utilizing this property, we propose FlowChef, which leverages the vector field to steer the denoising trajectory for controlled image generation tasks, facilitated by gradient skipping. FlowChef is a unified framework for controlled image generation that, for the first time, simultaneously addresses classifier guidance, linear inverse problems, and image editing without the need for extra training, inversion, or intensive backpropagation. Finally, we perform extensive evaluations and show that FlowChef significantly outperforms baselines in terms of performance, memory, and time requirements, achieving new state-of-the-art results. Project Page: https://flowchef.github.io.