MoDM: Efficient Serving for Image Generation via Mixture-of-Diffusion Models
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912515461480448 |
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| author | Xia, Yuchen Sharma, Divyam Yuan, Yichao Kundu, Souvik Talati, Nishil |
| author_facet | Xia, Yuchen Sharma, Divyam Yuan, Yichao Kundu, Souvik Talati, Nishil |
| contents | Diffusion-based text-to-image generation models trade latency for quality: small models are fast but generate lower-quality images, while large models produce better images but are slow.
We present MoDM, a novel caching-based serving system for diffusion models that dynamically balances latency and quality through a mixture of diffusion models. Unlike prior approaches that rely on model-specific internal features, MoDM caches final images, allowing seamless retrieval and reuse across multiple diffusion model families.
This design enables adaptive serving by dynamically balancing latency and image quality: using smaller models for cache-hit requests to reduce latency while reserving larger models for cache-miss requests to maintain quality. Small model image quality is preserved using retrieved cached images.
We design a global monitor that optimally allocates GPU resources and balances inference workload, ensuring high throughput while meeting service-level objectives under varying request rates. Our evaluations show that MoDM significantly reduces average serving time by 2.5x while retaining image quality, making it a practical solution for scalable and resource-efficient model deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoDM: Efficient Serving for Image Generation via Mixture-of-Diffusion Models Xia, Yuchen Sharma, Divyam Yuan, Yichao Kundu, Souvik Talati, Nishil Distributed, Parallel, and Cluster Computing Diffusion-based text-to-image generation models trade latency for quality: small models are fast but generate lower-quality images, while large models produce better images but are slow. We present MoDM, a novel caching-based serving system for diffusion models that dynamically balances latency and quality through a mixture of diffusion models. Unlike prior approaches that rely on model-specific internal features, MoDM caches final images, allowing seamless retrieval and reuse across multiple diffusion model families. This design enables adaptive serving by dynamically balancing latency and image quality: using smaller models for cache-hit requests to reduce latency while reserving larger models for cache-miss requests to maintain quality. Small model image quality is preserved using retrieved cached images. We design a global monitor that optimally allocates GPU resources and balances inference workload, ensuring high throughput while meeting service-level objectives under varying request rates. Our evaluations show that MoDM significantly reduces average serving time by 2.5x while retaining image quality, making it a practical solution for scalable and resource-efficient model deployment. |
| title | MoDM: Efficient Serving for Image Generation via Mixture-of-Diffusion Models |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.11972 |