StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation

TL;DR

StructDiff is a novel diffusion-based framework for single-image generation that preserves structure, offers spatial control via positional encoding, and introduces a new evaluation criterion, outperforming existing methods.

Contribution

It introduces an adaptive receptive field module, employs 3D positional encoding for spatial control, and proposes a new LLM-based evaluation criterion for single-image generation.

Findings

Outperforms existing methods in structural consistency and visual quality.

Enables flexible spatial control over generated content.

Demonstrates broad applicability across various image synthesis tasks.

Abstract

This paper introduces StructDiff, a generative framework based on a single-scale diffusion model for single-image generation. Single-image generation aims to synthesize diverse samples with similar visual content to the source image by capturing its internal statistics, without relying on external data. However, existing methods often struggle to preserve the structural layout, especially for images with large rigid objects or strict spatial constraints. Moreover, most approaches lack spatial controllability, making it difficult to guide the structure or placement of generated content. To address these challenges, StructDiff introduces an \textit{adaptive receptive field} module to maintain both global and local distributions. Building on this foundation, StructDiff incorporates 3D positional encoding (PE) as a spatial prior, allowing flexible control over positions, scale, and local details of generated objects. To our knowledge, this spatial control capability represents the first exploration of PE-based manipulation in single-image generation. Furthermore, we propose a novel evaluation criterion for single-image generation based on large language models (LLMs). This criterion specifically addresses the limitations of existing objective metrics and the high labor costs associated with user studies. StructDiff also demonstrates broad applicability across downstream tasks, such as text-guided image generation, image editing, outpainting, and paint-to-image synthesis. Extensive experiments demonstrate that StructDiff outperforms existing methods in structural consistency, visual quality, and spatial controllability. The project page is available at https://butter-crab.github.io/StructDiff/.