Prompt-aware classifier free guidance for diffusion models
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914074924679168 |
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| author | Zhang, Xuanhao Li, Chang |
| author_facet | Zhang, Xuanhao Li, Chang |
| contents | Diffusion models have achieved remarkable progress in image and audio generation, largely due to Classifier-Free Guidance. However, the choice of guidance scale remains underexplored: a fixed scale often fails to generalize across prompts of varying complexity, leading to oversaturation or weak alignment. We address this gap by introducing a prompt-aware framework that predicts scale-dependent quality and selects the optimal guidance at inference. Specifically, we construct a large synthetic dataset by generating samples under multiple scales and scoring them with reliable evaluation metrics. A lightweight predictor, conditioned on semantic embeddings and linguistic complexity, estimates multi-metric quality curves and determines the best scale via a utility function with regularization. Experiments on MSCOCO~2014 and AudioCaps show consistent improvements over vanilla CFG, enhancing fidelity, alignment, and perceptual preference. This work demonstrates that prompt-aware scale selection provides an effective, training-free enhancement for pretrained diffusion backbones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt-aware classifier free guidance for diffusion models Zhang, Xuanhao Li, Chang Sound Artificial Intelligence Multimedia Audio and Speech Processing Diffusion models have achieved remarkable progress in image and audio generation, largely due to Classifier-Free Guidance. However, the choice of guidance scale remains underexplored: a fixed scale often fails to generalize across prompts of varying complexity, leading to oversaturation or weak alignment. We address this gap by introducing a prompt-aware framework that predicts scale-dependent quality and selects the optimal guidance at inference. Specifically, we construct a large synthetic dataset by generating samples under multiple scales and scoring them with reliable evaluation metrics. A lightweight predictor, conditioned on semantic embeddings and linguistic complexity, estimates multi-metric quality curves and determines the best scale via a utility function with regularization. Experiments on MSCOCO~2014 and AudioCaps show consistent improvements over vanilla CFG, enhancing fidelity, alignment, and perceptual preference. This work demonstrates that prompt-aware scale selection provides an effective, training-free enhancement for pretrained diffusion backbones. |
| title | Prompt-aware classifier free guidance for diffusion models |
| topic | Sound Artificial Intelligence Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.22728 |