Rethinking Pixel Mean Flows via Interval Denoiser

• URL: http://arxiv.org/abs/2608.04818v1
• Date: Wed, 05 Aug 2026 13:22:43 GMT
• Title: Rethinking Pixel Mean Flows via Interval Denoiser
• Abstract summary: We propose the Interval Denoiser, a framework for latent-free generation.<n>Our model achieves an FID of 4.55 in one step (1-NFE) and 3.98 in two steps (2-NFE) without perceptual losses.
• Score: 95.11499828543344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 theoretically rigorous framework for latent-free generation. Derived directly from the flow matching ODE, it establishes an exact analytical mapping for intermediate trajectory states. Unlike prior formulations, our prediction is shown to reside on a low-dimensional manifold across any time interval, making the regression tractable for a network operating directly on pixels. Furthermore, by avoiding empirical algebraic substitutions, our formulation correctly isolates the pure time derivative to prevent biased gradient evaluations and ensure exact first-order optimization. By analyzing this objective, we 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 256x256, our model achieves an FID of 4.55 in one step (1-NFE) and 3.98 in two steps (2-NFE) without perceptual losses.

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