EPEdit: Redefining Image Editing with Generative AI and User-Centric Design
On the map: Diffusion models → Image generation, text-to-image and editing
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 9 of its 13 references that are in the map. First posted on arXiv 23 Jun 2026.
Builds on
9 of 13 references.
Closest ancestors · 3
Same direction, newest first
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding23 May
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec
Other references · 6
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2021Zero-Shot Text-to-Image Generationcited by 4,288
- 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,295
- 2023MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editingcited by 727
- 2024iCONTRA: Toward Thematic Collection Design Via Interactive Concept Transfercited by 3
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- 2024PixLens: A Novel Framework for Disentangled Evaluation in Diffusion-Based Image Editing with Object Detection + SAM
- 2026ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learning
- 2024AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea
- 2025OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting
- 2025Image Editing with Diffusion Models: A Survey