TFTF: Training-Free Targeted Flow for Conditional Sampling
We propose a training-free conditional sampling method for flow matching models based on importance sampling. Because a naïve application of importance sampling suffers from weight degeneracy in high-dimensional settings, we modify and incorporate a resampling technique in sequential Monte Carlo (SMC) during intermediate stages of the generation process. To encourage generated samples to diverge along distinct trajectories, we derive a stochastic flow with adjustable noise strength to replace the deterministic flow at the intermediate stage. Our framework requires no additional training, while providing theoretical guarantees of asymptotic accuracy. Experimentally, our method significantly outperforms existing approaches on conditional sampling tasks for MNIST and CIFAR-10. We further demonstrate the applicability of our approach in higher-dimensional, multimodal settings through text-to-image generation experiments on CelebA-HQ.
Code (0)
등록된 구현이 없습니다.
Tasks
Text-to-Image GenerationSimilar Papers 제목 키워드 기반
Unraveling the Connections between Flow Matching and Diffusion Probabilistic Models in Training-free Conditional Generation
Training-free conditional generation aims to leverage the unconditional diffusion models to implement the conditional generation, where flow-matching (FM) and diffusion probabilistic models (DPMs) are two mature uncondit…
Solving Inverse Problems with Flow-based Models via Model Predictive Control
Flow-based generative models provide strong unconditional priors for inverse problems, but guiding their dynamics for conditional generation remains challenging. Recent work casts training-free conditional generation in …
Image RestorationEquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for 3D Molecular Conformation Prediction
Molecular 3D conformations play a key role in determining how molecules interact with other molecules or protein surfaces. Recent deep learning advancements have improved conformation prediction, but slow training speeds…
Reflected Flow Matching
Continuous normalizing flows (CNFs) learn an ordinary differential equation to transform prior samples into data. Flow matching (FM) has recently emerged as a simulation-free approach for training CNFs by regressing a ve…
Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation
The Targeted Free Energy Perturbation (TFEP) method aims to overcome the time-consuming and computer-intensive stratification process of standard methods for estimating the free energy difference between two states. To a…
Drug Design