arXiv:2507.17853v2 Announce Type: replace-cross Abstract: Recent advances in text-to-image (T2I) generation have led to impressive visual results. However, these models still face significant challenges when handling complex prompt, particularly those involving multiple subjects with distinct attributes. Inspired by the human drawing process, which first outlines the composition and then incrementally adds details, we propose Detail++, a training-free framework that introduces a novel Progressive Detail Injection (PDI) strategy to address this limitation. Specifically, we decompose a complex p
The rapid advancement of text-to-image diffusion models is driving continuous innovation to overcome inherent limitations, particularly in generating complex compositions.
Improved detail enhancement for text-to-image models will lead to more usable and higher-quality AI-generated content, expanding their application across creative and commercial fields.
The ability to generate intricate scenes with multiple distinct subjects will improve, potentially reducing the need for extensive post-generation editing.
- · Generative AI platforms
- · Digital artists
- · Content creators
- · Advertising agencies
- · Current manual detail enhancement workflows
- · Low-fidelity image generation models
Higher fidelity and more complex AI-generated images become more accessible to a wider user base.
The demand for specialized human retouchers for AI-generated images may shift, focusing on more nuanced artistic direction rather than basic detail correction.
This could accelerate the integration of AI image generation into mainstream media production, democratizing high-quality visual content creation.
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Read at arXiv cs.AI