Pixel diffusion models benefit from adaptive prediction strategies that vary by capacity and data type, and post-hoc high-frequency refinement can significantly improve image quality without expensive retraining.
This paper improves pixel-space diffusion transformers by introducing MFlow, which adaptively learns to blend two prediction strategies (clean image vs. velocity) based on data characteristics and model size. The authors also propose ConvRefiner, a lightweight module that fixes a persistent high-frequency quality deficit in generated images without retraining the main model.