From Sketch to Fresco: Efficient Diffusion Transformer with Progressive Resolution

TL;DR

Fresco is a novel dynamic resolution framework for diffusion transformers that accelerates sampling by progressively upsampling while maintaining global structure, achieving significant speedups without sacrificing quality.

Contribution

It introduces a unified approach to re-noise and global structure preservation across stages with progressive upsampling, improving efficiency and fidelity in diffusion models.

Findings

Achieves 10x speedup on FLUX and 5x on HunyuanVideo.

Maintains high fidelity with near-lossless acceleration.

Compatible with distillation, quantization, and feature caching, reaching 22x speedup.

Abstract

Diffusion Transformers achieve impressive generative quality but remain computationally expensive due to iterative sampling. Recently, dynamic resolution sampling has emerged as a promising acceleration technique by reducing the resolution of early sampling steps. However, existing methods rely on heuristic re-noising at every resolution transition, injecting noise that breaks cross-stage consistency and forces the model to relearn global structure. In addition, these methods indiscriminately upsample the entire latent space at once without checking which regions have actually converged, causing accumulated errors, and visible artifacts. Therefore, we propose \textbf{Fresco}, a dynamic resolution framework that unifies re-noise and global structure across stages with progressive upsampling, preserving both the efficiency of low-resolution drafting and the fidelity of high-resolution refinement, with all stages aligned toward the same final target. Fresco achieves near-lossless acceleration across diverse domains and models, including 10$\times$ speedup on FLUX, and 5$\times$ on HunyuanVideo, while remaining orthogonal to distillation, quantization and feature caching, reaching 22$\times$ speedup when combined with distilled models. Our code is in supplementary material and will be released on Github.