SpotEdit is a training-free research framework for selective region editing in Diffusion Transformer models, allowing efficient local image changes via text instructions while preserving the rest of the image.

Is SpotEdit open-source?

Yes, the full code implementation is publicly available on GitHub at https://github.com/Biangbiang0321/SpotEdit.

Does SpotEdit provide a web demo?

No, there is currently no hosted online demo; it requires local setup and integration with compatible DiT models like Flux or Stable Diffusion variants.

How much faster is SpotEdit compared to standard editing methods?

It achieves up to nearly 2x inference speedup by skipping computation on unchanged regions, with minimal impact on editing quality.

SpotEdit

Efficient Selective Region Editing Framework for Diffusion Transformers

From Independent Research (National University of Singapore & Shanghai Jiao Tong University)

Founder: Zhibin Qin, Zhenxiong Tan, Zeqing Wang

Image & Design

30 Dec 2025

10K+

Free

Pricing Model

Free

Starting Price

$0/Month

About This AI

SpotEdit is a training-free research framework designed for instruction-based image editing using Diffusion Transformer (DiT) models.

It enables precise local edits by selectively updating only modified regions, skipping redundant computation on unchanged areas, and reusing original features for better fidelity and up to 2x faster inference.

AI Art Image Editor Text To Image

Pricing

Pricing Model

Free

Starting Price

$0/Month

Key Features

Training-free plug-and-play integration with existing DiT models
SpotSelector automatically detects and skips unchanged regions using perceptual similarity
SpotFusion dynamically blends edited and reused features for seamless coherence
Up to 1.95x speedup in inference without quality loss
Preserves high fidelity in background and unmodified areas

Pros

Significant reduction in computation for local edits
Better preservation of original image details compared to full regeneration
No training required, easy to implement on existing models
Open-source code available for experimentation
Improves efficiency for instruction-based editing tasks

Cons

Primarily a research framework, may require coding expertise to implement
No hosted demo or web-based tool available
Performance depends on underlying DiT model quality
Limited to Diffusion Transformer architectures

SpotEdit is an innovative open-source framework ideal for AI researchers and developers working on efficient image editing with diffusion models, offering faster local edits while maintaining superior background fidelity.

FAQs

What is SpotEdit?
SpotEdit is a training-free research framework for selective region editing in Diffusion Transformer models, allowing efficient local image changes via text instructions while preserving the rest of the image.
Is SpotEdit open-source?
Yes, the full code implementation is publicly available on GitHub at https://github.com/Biangbiang0321/SpotEdit.
Does SpotEdit provide a web demo?
No, there is currently no hosted online demo; it requires local setup and integration with compatible DiT models like Flux or Stable Diffusion variants.
How much faster is SpotEdit compared to standard editing methods?
It achieves up to nearly 2x inference speedup by skipping computation on unchanged regions, with minimal impact on editing quality.