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Msaidizi
Machine learningDeep learning / NLP / CV

Mfumo Ulioboreshwa wa Kuenea

Mfumo ulioboreshwa wa kuenea hubadilisha mfumo mkuu wa kuenea wa kuondoa kelele uliopangwa awali—kama vile Stable Diffusion au DALL-E—kwa mada, mtindo, au kikoa maalum kwa kuendeleza mafunzo kwenye seti ndogo ya data iliyochaguliwa. Mbinu kama vile DreamBooth, upachikaji wa maandishi, na LoRA huwezesha marekebisho haya kwenye vifaa vya watumiaji huku zikihifadhi uwezo mkuu wa uzalishaji.

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Vyanzo

  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

Jinsi ya kunukuu ukurasa huu

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

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Imerejelewa na

ScholarGateFine-Tuned Diffusion Model (Fine-Tuned Denoising Diffusion Probabilistic Model). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/fine-tuned-diffusion-model · Seti ya data: https://doi.org/10.5281/zenodo.20539026