EditCrafter: Tuning-free High-Resolution Image Editing via Pretrained Diffusion Model

2026-04-11

AI Summary

Existing diffusion models struggle to edit high-resolution images with arbitrary aspect ratios or resolutions significantly exceeding their training scale (e.g., 512×512), as naive tiling often introduces structural distortions and content duplication. This work proposes a fine-tuning-free editing framework that integrates tiled latent-space inversion with an enhanced noise-damped classifier-free guidance strategy (NDCFG++). By effectively leveraging the generative priors of pre-trained text-to-image diffusion models, the method achieves coherent and photorealistic high-resolution edits while preserving image identity. It supports inputs of arbitrary dimensions and consistently produces structurally consistent and detail-rich results across diverse resolutions, without requiring model fine-tuning or additional optimization.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

We propose EditCrafter, a high-resolution image editing method that operates without tuning, leveraging pretrained text-to-image (T2I) diffusion models to process images at resolutions significantly exceeding those used during training. Leveraging the generative priors of large-scale T2I diffusion models enables the development of a wide array of novel generation and editing applications. Although numerous image editing methods have been proposed based on diffusion models and exhibit high-quality editing results, they are difficult to apply to images with arbitrary aspect ratios or higher resolutions since they only work at the training resolutions (512x512 or 1024x1024). Naively applying patch-wise editing fails with unrealistic object structures and repetition. To address these challenges, we introduce EditCrafter, a simple yet effective editing pipeline. EditCrafter operates by first performing tiled inversion, which preserves the original identity of the input high-resolution image. We further propose a noise-damped manifold-constrained classifier-free guidance (NDCFG++) that is tailored for high resolution image editing from the inverted latent. Our experiments show that the our EditCrafter can achieve impressive editing results across various resolutions without fine-tuning and optimization.

Problem

• high-resolution image editing
• diffusion models
• arbitrary aspect ratios
• training resolution limitation
• patch-wise editing

Innovation

• high-resolution image editing
• pretrained diffusion model
• tiled inversion
• classifier-free guidance
• tuning-free editing