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微调扩散模型

微调扩散模型是指通过在一个小型精选数据集上继续训练，使大型预训练去噪扩散模型（如 Stable Diffusion 或 DALL-E）适应特定主题、风格或领域。DreamBooth、文本反转和 LoRA 等技术使得在消费级硬件上进行这种适应成为可能，同时保留了通用的生成能力。

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微调扩散模型
微调生成对抗网络微调图像分类微调变分自编码器微调视觉Transformer扩散模型迁移学习域自适应扩散模型可解释扩散模型多语言扩散模型多模态扩散模型

来源

  1. Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 22500–22510. DOI: 10.1109/CVPR52729.2023.02155 ↗
  2. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗

如何引用本页

ScholarGate. (2026, June 3). Fine-Tuned Denoising Diffusion Probabilistic Model. ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-diffusion-model

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被引用于

域自适应扩散模型可解释扩散模型微调生成对抗网络微调变分自编码器多语言扩散模型多模态扩散模型扩散模型迁移学习

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ScholarGate — Fine-Tuned Diffusion Model (Fine-Tuned Denoising Diffusion Probabilistic Model). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/fine-tuned-diffusion-model · 数据集： https://doi.org/10.5281/zenodo.20539026