arXiv:2506.14753v3 Announce Type: replace-cross Abstract: Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process. Unfortunately, the high fidelity also comes at a high computational cost due to the inherently sequential generative process. In this work, we seek to optimally balance quality and computational cost, and propose a framework to allow the amount of computation to vary for each prompt, depending on its complexity. Each prompt is automatically routed to the most appropriate text-to-image generation
The increasing computational demands of generative AI models, particularly text-to-image, are pressing developers to find more efficient resource allocation methods.
This work directly addresses the computational and energy bottlenecks associated with high-fidelity AI generation, making advanced AI more accessible and sustainable.
AI models will become more adaptable to varying computational budgets, leading to more efficient scaling and deployment of generative systems.
- · AI developers
- · Cloud providers
- · AI-dependent industries
- · Inefficient AI models
Reduced computational costs for text-to-image generation, improving accessibility and scalability.
Faster iteration and deployment of AI models across various applications due to optimized resource use.
Lower barriers to entry for new AI-powered services, fostering greater innovation and competition in the generative AI space.
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Read at arXiv cs.LG