arXiv:2606.09901v1 Announce Type: cross Abstract: Diffusion-based generative models enable powerful image editing capabilities, but achieving precise control while maintaining fidelity and safety remains challenging. We present a comprehensive theoretical and empirical study of controllable diffusion-based image editing, analyzing the trade-offs between adherence to user intent, preservation of non-target content, and output quality. Our work spans text- and mask-guided edits, point/drag manipulation, and inversion-based pipelines. We derive mathematical formulations of editing objectives and
This preprint emerges as diffusion models mature, requiring more sophisticated control mechanisms to move from experimental tools to reliable applications in various domains.
Improving the controllability and fidelity of diffusion models is crucial for their adoption in professional creative industries, engineering, and scientific visualization where precision and quality are paramount.
The focus is shifting from simply generating realistic images to ensuring these generations precisely follow user intent while preserving critical details and quality, enhancing their utility.
- · Creative industries relying on AI-assisted tools
- · Developers of diffusion models
- · AI-driven design and engineering sectors
- · Platforms with low-fidelity or uncontrollable image generation tools
More accurate and versatile AI image editing tools become widely available.
New user interfaces and interaction paradigms for AI-powered design emerge, integrating fine-grained control.
The definition of 'originality' and 'authorship' in visual content becomes more complex as AI-assisted creation blurs boundaries.
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Read at arXiv cs.LG