手法を比較

	拡散モデルを用いた転移学習	マルチモーダル拡散モデル
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2020–2023	2020–2022
提唱者	Ho et al. (DDPM); transfer application popularized by Rombach et al. (Stable Diffusion) and Ruiz et al. (DreamBooth), 2020–2023	Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion)
種類	Generative model with transfer learning	Generative model (denoising diffusion)
原典	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.	Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10684–10695.
別名	diffusion model fine-tuning, pre-trained diffusion transfer, TL-DM, domain-adapted diffusion model	multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion
関連	5	6
概要	Transfer Learning with Diffusion Models adapts a large pre-trained diffusion model — such as Stable Diffusion or DALL-E 2 — to a new target domain or task by continuing training on a smaller domain-specific dataset. Rather than learning the full generative process from scratch, practitioners leverage knowledge already encoded in millions of training steps to achieve high-quality domain-adapted generation with modest data and compute.	A multimodal diffusion model extends denoising diffusion probabilistic models to generate or understand content by conditioning on signals from multiple modalities — such as text, image, audio, or video — simultaneously. It learns to reverse a noise process guided by cross-modal context, enabling high-fidelity synthesis and translation across modalities.