DuoFlow: JVP-Free Finite-Difference Mean Flows for One-Step Image Generation

MeanFlow training commonly enforces the meanflow identitywith Jacobian-vector products (JVP). This is computationally expensiveand hard to scale, since the JVP path often weakens fused-kernel andcompiler-level optimization benefits. We revisit JVP-free MeanFlow froman error-driven perspective. Rather than a drop-in JVP replacement, finitedifferencing exposes controllable error structure that directly informs al-gorithm design. We identify two dominant error channels: step-truncationerror and trajectory-velocity error.Guided by this decomposition, we introduce a JVP-free differential Mean-Flow framework for from-scratch MeanFlow-style one-step image gen-eration. For step-truncation error, we use stochastic signed one-sideddifferencing, which needs only one additional forward pass and recoverssecond-order truncation behavior in expectation. For trajectory-velocityerror, we propose DuoFlow, which predicts mean and instantaneous veloc-ities on the same sampled state to improve trajectory consistency in dif-ferential updates. We further introduce progressive self-bootstrapping todrive trajectory velocity toward a fixed-point-like self-consistent tendencyduring training. As a fast derivative-evaluation scheme, our one-forwarddifferential estimator is between 7.17× and 34.83× faster than JVP ondirectional-derivative micro-benchmarks for batch sizes from 1 to 64, andreduces peak memory by a factor between 1.71× and 2.68×. On ImageNet256×256 from-scratch training, it improves FID by 30% over a matchedJVP-based MeanFlow baseline. Code will be available at here.