Time-Warping Guidance in Flow Matching

Inference-time guidance methods for diffusion and flow-matching image generators, from Classifier-Free Guidance (CFG) to recent autoguidance variants, form contrastive velocities between a model prediction and a less-informed baseline. We introduce TWiG (Time-Warping Guidance), which augments CFG with a second contrastive term whose probe lies at a different point in latent-time space, namely the conditional branch evaluated at a time-warped query that approximates a forward-process sample at a different sampling time. The warped query is built from a Tweedie noise estimate cached from the previous step, with a closed-form proxy and an explicit error bound. The schedule that selects the warped time is governed by a primary calibration scalar, together with two clip bounds. We hypothesize on geometric grounds that the calibration scalar is near $1/2$ and observe a corresponding empirical alignment peak on five backbones. We evaluate TWiG using automatic metrics spanning distribution, quality, and diversity (FID, CMMD, IS, Precision/Recall, CLIP, Image Reward, Aesthetic, Vendi, LPIPS, CVS), the compositional benchmarks GenEval and GenEval2, and a blinded pairwise human study. Across two evaluation backbones, TWiG improves CFG and shows favorable results against the evaluated baselines.