LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free...

[PDF] arxiv.org

Retrieval-augmented mixture of lora experts for uploadable machine learning

Z Zhao, L Gan, G Wang, Y Hu, T Shen, H Yang… - arXiv preprint arXiv …, 2024 - arxiv.org
Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models
(LLMs). Its modular and plug-and-play nature allows the integration of various domain …
被引用次数：3 相关文章 所有 2 个版本
[PDF] arxiv.org

AttenCraft: Attention-guided Disentanglement of Multiple Concepts for Text-to-Image Customization

J Shentu, M Watson, NA Moubayed - arXiv preprint arXiv:2405.17965, 2024 - arxiv.org
With the unprecedented performance being achieved by text-to-image (T2I) diffusion
models, T2I customization further empowers users to tailor the diffusion model to new …
相关文章 所有 2 个版本
[PDF] arxiv.org

Training-free Color-Style Disentanglement for Constrained Text-to-Image Synthesis

A Agarwal, S Karanam, BV Srinivasan - arXiv preprint arXiv:2409.02429, 2024 - arxiv.org
We consider the problem of independently, in a disentangled fashion, controlling the outputs
of text-to-image diffusion models with color and style attributes of a user-supplied reference …
相关文章 所有 2 个版本