PersonalQ: Select, Quantize, and Serve Personalized Diffusion Models for Efficient Inference
Personalized text-to-image generation lets users fine-tune diffusion models into repositories of concept-specific checkpoints, but serving these repositories efficiently is difficult for two reasons: natural-language requests are often ambiguous and can be misrouted to visually similar checkpoints, and standard post-training quantization can distort the fragile representations that encode personalized concepts. We present PersonalQ, a unified framework that connects checkpoint selection and quantization through a shared signal -- the checkpoint's trigger token. Check-in performs intent-aligned selection by combining intent-aware hybrid retrieval with LLM-based reranking over checkpoint co...
| Authors | Qirui Wang, Qi Guo, Yiding Sun et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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