Rethinking Pixel Mean Flows via Interval Denoiser

Modern diffusion and flow-based models are increasingly moving toward few-step, latent-free generation to bypass the computational overhead of multi-step sampling and the reconstruction bottlenecks of external autoencoders. We propose the Interval Denoiser, a principled framework for latent-free generation. Derived directly from the flow matching ODE, it establishes an exact analytical mapping for intermediate trajectory states and identifies the image-space prediction target as an interval-weighted average of instantaneous denoisers, which supports the generalized manifold hypothesis under which regression is tractable for a network operating directly on pixels. The derivation recovers the substitution of Pixel MeanFlow (pMF) and the decoder of CTM as special cases. Furthermore, by placing the stop-gradient only on the time derivative, our update is the exact first-order gradient of the Interval Denoiser identity, whereas pMF's stop-gradient placement damps the bootstrapped derivative term by a factor $r/t$. By analyzing this objective, we show that the exact weighting is ill-conditioned for wide intervals and equip our framework with residual clipping and a time-sampling curriculum, enabling effective long-interval training and improving few-step performance. Trained from scratch on ImageNet $256\times 256$, our model achieves an FID of 4.55 in one step (1-NFE) and 3.98 in two steps (2-NFE) without perceptual losses.