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CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method

Mingbao Lin, Zhihang Lin, Wengyi Zhan, Liujuan Cao, Rongrong Ji

TL;DR

The paper addresses the high cost and complexity of generating high-resolution images from pre-trained low-resolution diffusion models. It introduces CutDiffusion, a tuning-free, two-stage diffusion extrapolation method that splits patch-based extrapolation into comprehensive structure denoising and subsequent detail refinement, employing pixel interaction and pixel relocation. The approach delivers fast, memory-efficient inference with fewer patches and a single upscale step, while achieving strong generation quality compared with both tuning-based and tuning-free baselines. This work lowers the barrier to high-resolution diffusion by enabling cheaper, faster, and more accessible high-resolution image synthesis on consumer hardware, demonstrated on SDXL with thorough ablations and comparisons.

Abstract

Transforming large pre-trained low-resolution diffusion models to cater to higher-resolution demands, i.e., diffusion extrapolation, significantly improves diffusion adaptability. We propose tuning-free CutDiffusion, aimed at simplifying and accelerating the diffusion extrapolation process, making it more affordable and improving performance. CutDiffusion abides by the existing patch-wise extrapolation but cuts a standard patch diffusion process into an initial phase focused on comprehensive structure denoising and a subsequent phase dedicated to specific detail refinement. Comprehensive experiments highlight the numerous almighty advantages of CutDiffusion: (1) simple method construction that enables a concise higher-resolution diffusion process without third-party engagement; (2) fast inference speed achieved through a single-step higher-resolution diffusion process, and fewer inference patches required; (3) cheap GPU cost resulting from patch-wise inference and fewer patches during the comprehensive structure denoising; (4) strong generation performance, stemming from the emphasis on specific detail refinement.

CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method

TL;DR

The paper addresses the high cost and complexity of generating high-resolution images from pre-trained low-resolution diffusion models. It introduces CutDiffusion, a tuning-free, two-stage diffusion extrapolation method that splits patch-based extrapolation into comprehensive structure denoising and subsequent detail refinement, employing pixel interaction and pixel relocation. The approach delivers fast, memory-efficient inference with fewer patches and a single upscale step, while achieving strong generation quality compared with both tuning-based and tuning-free baselines. This work lowers the barrier to high-resolution diffusion by enabling cheaper, faster, and more accessible high-resolution image synthesis on consumer hardware, demonstrated on SDXL with thorough ablations and comparisons.

Abstract

Transforming large pre-trained low-resolution diffusion models to cater to higher-resolution demands, i.e., diffusion extrapolation, significantly improves diffusion adaptability. We propose tuning-free CutDiffusion, aimed at simplifying and accelerating the diffusion extrapolation process, making it more affordable and improving performance. CutDiffusion abides by the existing patch-wise extrapolation but cuts a standard patch diffusion process into an initial phase focused on comprehensive structure denoising and a subsequent phase dedicated to specific detail refinement. Comprehensive experiments highlight the numerous almighty advantages of CutDiffusion: (1) simple method construction that enables a concise higher-resolution diffusion process without third-party engagement; (2) fast inference speed achieved through a single-step higher-resolution diffusion process, and fewer inference patches required; (3) cheap GPU cost resulting from patch-wise inference and fewer patches during the comprehensive structure denoising; (4) strong generation performance, stemming from the emphasis on specific detail refinement.
Paper Structure (30 sections, 4 equations, 9 figures, 4 tables)

This paper contains 30 sections, 4 equations, 9 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Diffusion Models
  4. Diffusion Extrapolation
  5. Fine-tuning Involvement.
  6. Tuning-free Implementation.
  7. Methodology
  8. Background
  9. Diffusion Extrapolation.
  10. Motivation and Framework
  11. Motivation.
  12. Framework.
  13. Comprehensive Structure Denoising
  14. Pixel Interaction.
  15. Specific Detail Refinement
  16. ...and 15 more sections

Figures (9)

  • Figure 1: Visualization of decoded $\mathbf{I}_t$. The $\mathbf{I}_0$ denotes final clean image (1024$\times$1024).
  • Figure 2: CutDiffusion framework where "$\mathbf{>>}$" denotes workflow. Comprehensive structure denoising assigns similar content to each non-overlapping patch. Specific detail refinement enhances details within overlapping patches. Best view with zooming in.
  • Figure 3: A comparison of higher-resolution generation. Best view with zooming in.
  • Figure 4: Visual results of "A cute corgi on the lawn." Best view with zooming in.
  • Figure 5: Higher-resolution images for different $T'$ values. Best view with zooming in.
  • ...and 4 more figures