Achieving 4‑Step Diffusion Generation by Replacing MSE with Perceptual Loss in Five Lines of Code

By swapping the traditional MSE loss for a perceptual loss in Flow Matching training, the authors enable high‑quality diffusion generation in only 4–8 inference steps—down from 35–50—without teacher models, distribution or trajectory distillation, and they substantiate the claim with extensive experiments and a new distribution‑distance metric.

Computer VisionDistribution DistanceFew-Step Generation

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Achieving 4‑Step Diffusion Generation by Replacing MSE with Perceptual Loss in Five Lines of Code

AI Frontier Lectures

Feb 3, 2026 · Artificial Intelligence

Pixel Mean Flow: One‑Step Diffusion Beats Multi‑Step Models on ImageNet

The Pixel Mean Flow (pMF) method eliminates multi‑step sampling and latent‑space encoding, generating high‑quality images in a single step and achieving state‑of‑the‑art FID scores on ImageNet while drastically reducing computational cost.

ImageNetPerceptual Lossdiffusion models

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