{"ID":2843749,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06724","arxiv_id":"2511.06724","title":"Argus: Quality-Aware High-Throughput Text-to-Image Inference Serving System","abstract":"Text-to-image (T2I) models have gained significant popularity. Most of these are diffusion models with unique computational characteristics, distinct from both traditional small-scale ML models and large language models. They are highly compute-bound and use an iterative denoising process to generate images, leading to very high inference time. This creates significant challenges in designing a high-throughput system. We discovered that a large fraction of prompts can be served using faster, approximated models. However, the approximation setting must be carefully calibrated for each prompt to avoid quality degradation. Designing a high-throughput system that assigns each prompt to the appropriate model and compatible approximation setting remains a challenging problem. We present Argus, a high-throughput T2I inference system that selects the right level of approximation for each prompt to maintain quality while meeting throughput targets on a fixed-size cluster. Argus intelligently switches between different approximation strategies to satisfy both throughput and quality requirements. Overall, Argus achieves 10x fewer latency service-level objective (SLO) violations, 10% higher average quality, and 40% higher throughput compared to baselines on two real-world workload traces.","short_abstract":"Text-to-image (T2I) models have gained significant popularity. Most of these are diffusion models with unique computational characteristics, distinct from both traditional small-scale ML models and large language models. They are highly compute-bound and use an iterative denoising process to generate images, leading to...","url_abs":"https://arxiv.org/abs/2511.06724","url_pdf":"https://arxiv.org/pdf/2511.06724v1","authors":"[\"Shubham Agarwal\",\"Subrata Mitra\",\"Saud Iqbal\"]","published":"2025-11-10T05:34:39Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.DC\"]","methods":"[\"Diffusion Model\",\"Language Model\"]","has_code":false}