EPEdit: Redefining Image Editing with Generative AI and User-Centric Design
Quick Answer
This paper shows that EPEdit revolutionizes image editing by combining a user-friendly interface with a robust backend, utilizing zero-shot algorithms from Stable Diffusion to eliminate retraining costs.
Quick Take
It supports diverse editing tasks like object replacement and background modification, outperforming traditional tools and offering a cost-effective solution for non-experts.
Key Points
- EPEdit integrates a user-friendly interface with a powerful backend framework.
- Supports diverse tasks: image generation, object removal, and thematic design.
- Utilizes zero-shot editing algorithms, reducing costs and complexity.
- Outperforms traditional tools like Photoshop and Capture One in user evaluations.
- Accessible for users without technical expertise through simple text commands.
Paper Resources
📖 Reader Mode
~2 min readAbstract:The demand for image manipulation has seen a significant increase recently. Traditional tools like Photoshop and Capture One, while powerful, require considerable expertise to use effectively. Generative AI has introduced alternative platforms, such as Luminar Neo, Pixlr X, and Canva. However, many of these solutions, including resource-heavy models like Stable Diffusion, often require substantial retraining and fine-tuning, leading to high costs for users. To address these challenges, we introduce Efficient Photo Editor (EPEdit), an application that integrates a robust backend framework with a user-friendly front-end interface. EPEdit supports a wide range of creative image editing tasks, including image generation, object replacement, object removal, background modification, changes in object pose or perspective, region-specific editing, and thematic collection design, all guided by masks and prompts. Users can interact with the system through simple text commands or by marking areas for precise adjustments, making it accessible even to those without technical expertise. At its core, EPEdit leverages zero-shot image editing algorithms based on Stable Diffusion model, removing the need for additional fine-tuning. This approach enables efficient image manipulation and thematic collection creation. User evaluations for tasks of image editing, thematic design, and overall system performance demonstrate that EPEdit outperforms existing solutions, offering a user-friendly, cost-effective solution for comprehensive image editing.
| Comments: | SOICT 2024 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2606.24057 [cs.CV] |
| (or arXiv:2606.24057v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24057 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Trung Nghia Le [view email]
[v1]
Tue, 23 Jun 2026 02:05:10 UTC (3,231 KB)
— Originally published at arxiv.org
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