ali-vilab/In-Context-LoRA

📢 [Project Page] [Github Repo] [Paper]

🔥 Latest News

• —[2024-12-17] 🚀 We are excited to release [IDEA-Bench](https://ali-vilab.github.io/IDEA-Bench-Page/), a comprehensive benchmark designed to assess the zero-shot task generalization abilities of generative models. The benchmark includes 100 real-world design tasks across 275 unique cases. Despite its general-purpose focus, the top-performing model, EMU2, achieves a score of only 6.81 out of 100, highlighting the current challenges in this domain. Explore the benchmark and challenge the limits of model performance!
• —[2024-11-16] 🌟 The community continues to innovate with IC-LoRA! Exciting projects include models, ComfyUI nodes and workflows for Virtual Try-on, Product Design, Object Mitigation, Role Play, and more. Explore their creations in [Community Creations Using IC-LoRA](#community-creations-using-ic-lora). Huge thanks to all contributors for their incredible efforts!

Community Creations Using IC-LoRA

We are thrilled to showcase the community's innovative projects leveraging In-Context LoRA (IC-LoRA). If you have additional recommendations or projects to share, please don't hesitate to send a [Pull Request](https://github.com/ali-vilab/In-Context-LoRA/pulls)!

We extend our heartfelt thanks to all contributors for their exceptional work in advancing the IC-LoRA ecosystem.

Model Summary

In-Context LoRA fine-tunes text-to-image models (e.g., FLUX) to generate image sets with customizable intrinsic relationships, optionally conditioned on another set using SDEdit. It can be adapted to a wide range of tasks

This model hub includes In-Context LoRA models across 10 tasks. MODEL ZOO details these models and their recommend settings. For more details on how these models are trained, please refer to our paper.

Key Idea

The core concept of IC-LoRA is to concatenate both condition and target images into a single composite image while using Natural Language to define the task. This approach enables seamless adaptation to a wide range of applications.

Features

• —Task-Agnostic Framework: IC-LoRA serves as a general framework, but it requires task-specific fine-tuning for diverse applications.
• —Customizable Image-Set Generation: You can fine-tune text-to-image models to generate image sets with customizable intrinsic relationships.
• —Condition on Image-Set: You can also condition the generation of a set of images on another set of images, enabling a wide range of controllable generation applications.

For more detailed information and examples, please read our Paper or visit our Project Page.

MODEL ZOO

Below lists 10 In-Context LoRA models and their recommend settings.

LICENSE

This model hub uses FLUX as the base model. Users must comply with FLUX's license when using this code. Please refer to FLUX's License for more details.

Citation

If you find this work useful in your research, please consider citing:

@article{lhhuang2024iclora,
  title={In-Context LoRA for Diffusion Transformers},
  author={Huang, Lianghua and Wang, Wei and Wu, Zhi-Fan and Shi, Yupeng and Dou, Huanzhang and Liang, Chen and Feng, Yutong and Liu, Yu and Zhou, Jingren},
  journal={arXiv preprint arxiv:2410.23775},
  year={2024}
}

@article{lhhuang2024iclora,
  title={Group Diffusion Transformers are Unsupervised Multitask Learners},
  author={Huang, Lianghua and Wang, Wei and Wu, Zhi-Fan and Dou, Huanzhang and Shi, Yupeng and Feng, Yutong and Liang, Chen and Liu, Yu and Zhou, Jingren},
  journal={arXiv preprint arxiv:2410.15027},
  year={2024}
}

Download model

Weights for these models are available in Safetensors format.

Download them in the Files & versions tab.