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Fine-Tuned Diffusion Model

A fine-tuned diffusion model adapts a large pretrained denoising diffusion model — such as Stable Diffusion or DALL-E — to a specific subject, style, or domain by continuing training on a small curated dataset. Techniques such as DreamBooth, textual inversion, and LoRA make this adaptation feasible on consumer hardware while preserving general generative capability.

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Sources

  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 ↗

Related methods

Fine-Tuned Generative Adversarial NetworkFine-Tuned Image ClassificationFine-Tuned Variational AutoencoderFine-Tuned Vision TransformerTransfer Learning with Diffusion Model

Referenced by

Domain-adaptive diffusion modelExplainable Diffusion ModelFine-Tuned Generative Adversarial NetworkFine-Tuned Variational AutoencoderMultilingual Diffusion ModelMultimodal Diffusion ModelTransfer Learning with Diffusion Model

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ScholarGate — Fine-Tuned Diffusion Model (Fine-Tuned Denoising Diffusion Probabilistic Model). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/fine-tuned-diffusion-model