So sánh phương pháp

	Chuyển giao kiến thức với Mô hình Khuếch tán	Mô hình Khuếch tán Đa phương thức
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2020–2023	2020–2022
Người khởi xướng	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)
Loại	Generative model with transfer learning	Generative model (denoising diffusion)
Công trình gốc	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.
Tên gọi khác	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
Liên quan	5	6
Tóm tắt	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.