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[2602.12393] Reproducing DragDiffusion: Interactive Point-Based Editing with Diffusion Models
Summary
This article presents a reproducibility study of DragDiffusion, a method for interactive point-based image editing using diffusion models, confirming its effectiveness and exploring hyperparameter sensitivities.
Why It Matters
Reproducibility is a cornerstone of scientific research, particularly in machine learning. This study validates the claims of the DragDiffusion method, providing insights into its operational parameters and enhancing its reliability for practitioners in computer vision and AI.
Key Takeaways
- DragDiffusion allows for precise image editing through point manipulation.
- The study confirms the original claims of DragDiffusion while highlighting hyperparameter sensitivities.
- Multi-timestep optimization does not enhance spatial accuracy but increases computational costs.
- The findings clarify conditions for reliable reproduction of DragDiffusion results.
- The research contributes to the broader understanding of diffusion models in image processing.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.12393 (cs) [Submitted on 12 Feb 2026] Title:Reproducing DragDiffusion: Interactive Point-Based Editing with Diffusion Models Authors:Ali Subhan, Ashir Raza View a PDF of the paper titled Reproducing DragDiffusion: Interactive Point-Based Editing with Diffusion Models, by Ali Subhan and 1 other authors View PDF HTML (experimental) Abstract:DragDiffusion is a diffusion-based method for interactive point-based image editing that enables users to manipulate images by directly dragging selected points. The method claims that accurate spatial control can be achieved by optimizing a single diffusion latent at an intermediate timestep, together with identity-preserving fine-tuning and spatial regularization. This work presents a reproducibility study of DragDiffusion using the authors' released implementation and the DragBench benchmark. We reproduce the main ablation studies on diffusion timestep selection, LoRA-based fine-tuning, mask regularization strength, and UNet feature supervision, and observe close agreement with the qualitative and quantitative trends reported in the original work. At the same time, our experiments show that performance is sensitive to a small number of hyperparameter assumptions, particularly the optimized timestep and the feature level used for motion supervision, while other components admit broader operating ranges. We further evaluate a multi-timestep latent optimization variant ...
