Paper
PersonalQ: Select, Quantize, and Serve Personalized Diffusion Models for Efficient Inference
Authors
Qirui Wang, Qi Guo, Yiding Sun, Junkai Yang, Dongxu Zhang, Shanmin Pang, Qing Guo
Abstract
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 context and asks a brief clarification question only when multiple intents remain plausible; it then rewrites the prompt by inserting the selected checkpoint's canonical trigger. Complementing this, Trigger-Aware Quantization (TAQ) applies trigger-aware mixed precision in cross-attention, preserving trigger-conditioned key/value rows (and their attention weights) while aggressively quantizing the remaining pathways for memory-efficient inference. Experiments show that PersonalQ improves intent alignment over retrieval and reranking baselines, while TAQ consistently offers a stronger compression-quality trade-off than prior diffusion PTQ methods, enabling scalable serving of personalized checkpoints without sacrificing fidelity.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.22943v1</id>\n <title>PersonalQ: Select, Quantize, and Serve Personalized Diffusion Models for Efficient Inference</title>\n <updated>2026-03-24T08:39:35Z</updated>\n <link href='https://arxiv.org/abs/2603.22943v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.22943v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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 context and asks a brief clarification question only when multiple intents remain plausible; it then rewrites the prompt by inserting the selected checkpoint's canonical trigger. Complementing this, Trigger-Aware Quantization (TAQ) applies trigger-aware mixed precision in cross-attention, preserving trigger-conditioned key/value rows (and their attention weights) while aggressively quantizing the remaining pathways for memory-efficient inference. Experiments show that PersonalQ improves intent alignment over retrieval and reranking baselines, while TAQ consistently offers a stronger compression-quality trade-off than prior diffusion PTQ methods, enabling scalable serving of personalized checkpoints without sacrificing fidelity.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-03-24T08:39:35Z</published>\n <arxiv:comment>Accepted in ICME 2026</arxiv:comment>\n <arxiv:primary_category term='cs.AI'/>\n <author>\n <name>Qirui Wang</name>\n </author>\n <author>\n <name>Qi Guo</name>\n </author>\n <author>\n <name>Yiding Sun</name>\n </author>\n <author>\n <name>Junkai Yang</name>\n </author>\n <author>\n <name>Dongxu Zhang</name>\n </author>\n <author>\n <name>Shanmin Pang</name>\n </author>\n <author>\n <name>Qing Guo</name>\n </author>\n </entry>"
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