Toward Early Quality Assessment of Text-to-Image Diffusion Models
Recent text-to-image (T2I) diffusion and flow-matching models can produce highly realistic images from natural language prompts. In practical scenarios, T2I systems are often run in a ``generate--then--select'' mode: many seeds are sampled and only a few images are kept for use. However, this pipeline is highly resource-intensive since each candidate requires tens to hundreds of denoising steps, and evaluation metrics such as CLIPScore and ImageReward are post-hoc. In this work, we address this inefficiency by introducing Probe-Select, a plug-in module that enables efficient evaluation of image quality within the generation process. We observe that certain intermediate denoiser activation...
| Authors | Huanlei Guo, Hongxin Wei, Bingyi Jing |
|---|---|
| Primary category | cs.CV |
| Published | 3 March 2026 |
| Last updated at source | 4 March 2026 |
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