arXiv:2605.28067v1 Announce Type: new Abstract: The remarkable generation quality of modern diffusion models often comes at the cost of massive parameter counts, which necessitate server-side inference with significant computational costs and potential privacy risks. Consequently, there is growing momentum toward developing efficient on-device alternatives. While recent efforts have optimized text-to-image models for mobile hardware, they remain relatively bulky, typically ranging from 0.5B to 1B parameters. We present BlazeEdit, a highly efficient, generalist image-to-image diffusion model ta
There is a growing market and technological push to make advanced AI models function efficiently on edge devices, overcoming the limitations of server-side inference.
This development enables broader accessibility and improved privacy for advanced AI image editing, reducing computational costs and dependence on centralized cloud infrastructure.
Image editing powered by diffusion models can now be performed directly on mobile devices, making sophisticated AI tools more ubiquitous and fostering new application developers.
- · Mobile device manufacturers
- · AI application developers
- · End-users of image editing tools
- · Edge AI chipmakers
- · Cloud-based AI inference providers focused on image editing
- · Companies reliant on bulky AI models
Wider adoption and use cases for sophisticated AI image editing on mobile devices will emerge.
Reduced data transfer to the cloud could enhance data privacy for users and decrease operational costs for businesses.
This efficiency could accelerate the development of other on-device multimodal AI models, fostering greater generalist AI capabilities at the edge.
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Read at arXiv cs.AI