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models/uno_lora/README.md

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+---
+license: cc-by-nc-nd-4.0
+base_model:
+- black-forest-labs/FLUX.1-dev
+pipeline_tag: text-to-image
+tags:
+- subject-personalization
+- image-generation
+---
+
+<h3 align="center">
+    Less-to-More Generalization: Unlocking More Controllability by In-Context Generation
+</h3>
+
+<div style="display:flex;justify-content: center">
+<a href="https://bytedance.github.io/UNO/"><img alt="Build" src="https://img.shields.io/badge/Project%20Page-UNO-yellow"></a> 
+<a href="https://arxiv.org/abs/2504.02160"><img alt="Build" src="https://img.shields.io/badge/arXiv%20paper-2504.02160-b31b1b.svg"></a>
+<a href="https://github.com/bytedance/UNO"><img src="https://img.shields.io/static/v1?label=GitHub&message=Code&color=green&logo=github"></a>
+</div>
+
+><p align="center"> <span style="color:#137cf3; font-family: Gill Sans">Shaojin Wu,</span><sup></sup></a>  <span style="color:#137cf3; font-family: Gill Sans">Mengqi Huang</span><sup>*</sup>,</a> <span style="color:#137cf3; font-family: Gill Sans">Wenxu Wu,</span><sup></sup></a>  <span style="color:#137cf3; font-family: Gill Sans">Yufeng Cheng,</span><sup></sup> </a>  <span style="color:#137cf3; font-family: Gill Sans">Fei Ding</span><sup>+</sup>,</a> <span style="color:#137cf3; font-family: Gill Sans">Qian He</span></a> <br> 
+><span style="font-size: 16px">Intelligent Creation Team, ByteDance</span></p>
+
+![teaser of UNO](./assets/teaser.jpg)
+
+## 🔥 News
+- [04/2025] 🔥 The [training code](https://github.com/bytedance/UNO), [inference code](https://github.com/bytedance/UNO), and [model](https://huggingface.co/bytedance-research/UNO) of UNO are released. The [demo](https://huggingface.co/spaces/bytedance-research/UNO-FLUX) will coming soon.
+- [04/2025] 🔥 The [project page](https://bytedance.github.io/UNO) of UNO is created.
+- [04/2025] 🔥 The arXiv [paper](https://arxiv.org/abs/2504.02160) of UNO is released.
+
+## 📖 Introduction
+In this study, we propose a highly-consistent data synthesis pipeline to tackle this challenge. This pipeline harnesses the intrinsic in-context generation capabilities of diffusion transformers and generates high-consistency multi-subject paired data. Additionally, we introduce UNO, which consists of progressive cross-modal alignment and universal rotary position embedding. It is a multi-image conditioned subject-to-image model iteratively trained from a text-to-image model. Extensive experiments show that our method can achieve high consistency while ensuring controllability in both single-subject and multi-subject driven generation.
+
+
+## ⚡️ Quick Start
+
+### 🔧 Requirements and Installation
+
+Clone our [Github repo](https://github.com/bytedance/UNO)
+
+
+Install the requirements
+```bash
+## create a virtual environment with python >= 3.10 <= 3.12, like
+# python -m venv uno_env
+# source uno_env/bin/activate
+# then install
+pip install -r requirements.txt
+```
+
+then download checkpoints in one of the three ways:
+1. Directly run the inference scripts, the checkpoints will be downloaded automatically by the `hf_hub_download` function in the code to your `$HF_HOME`(the default value is `~/.cache/huggingface`).
+2. use `huggingface-cli download <repo name>` to download `black-forest-labs/FLUX.1-dev`, `xlabs-ai/xflux_text_encoders`, `openai/clip-vit-large-patch14`, `TODO UNO hf model`, then run the inference scripts.
+3. use `huggingface-cli download <repo name> --local-dir <LOCAL_DIR>` to download all the checkpoints menthioned in 2. to the directories your want. Then set the environment variable `TODO`. Finally, run the inference scripts.
+
+### 🌟 Gradio Demo
+
+```bash
+python app.py
+```
+
+
+### ✍️ Inference
+
+- Optional prepreration: If you want to test the inference on dreambench at the first time, you should clone the submodule `dreambench` to download the dataset.
+
+```bash
+git submodule update --init
+```
+
+
+```bash
+python inference.py
+```
+
+### 🚄 Training
+
+```bash
+accelerate launch train.py
+```
+
+## 🎨 Application Scenarios
+![simplecase of UNO](./assets/simplecase.jpeg)
+
+## 📄 Disclaimer
+<p>
+We open-source this project for academic research. The vast majority of images 
+used in this project are either generated or licensed. If you have any concerns, 
+please contact us, and we will promptly remove any inappropriate content. 
+Our code is released under the Apache 2.0 License,, while our models are under 
+the CC BY-NC 4.0 License. Any models related to <a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" target="_blank">FLUX.1-dev</a> 
+base model must adhere to the original licensing terms.
+<br><br>This research aims to advance the field of generative AI. Users are free to 
+create images using this tool, provided they comply with local laws and exercise 
+responsible usage. The developers are not liable for any misuse of the tool by users.</p>
+
+## 🚀 Updates
+For the purpose of fostering research and the open-source community, we plan to open-source the entire project, encompassing training, inference, weights, etc. Thank you for your patience and support! 🌟
+- [x] Release github repo.
+- [x] Release inference code.
+- [x] Release training code.
+- [x] Release model checkpoints.
+- [x] Release arXiv paper.
+- [ ] Release in-context data generation pipelines.
+
+##  Citation
+If UNO is helpful, please help to ⭐ the repo.
+
+If you find this project useful for your research, please consider citing our paper:
+```bibtex
+@misc{wu2025lesstomoregeneralizationunlockingcontrollability,
+      title={Less-to-More Generalization: Unlocking More Controllability by In-Context Generation}, 
+      author={Shaojin Wu and Mengqi Huang and Wenxu Wu and Yufeng Cheng and Fei Ding and Qian He},
+      year={2025},
+      eprint={2504.02160},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2504.02160}, 
+}
+```
+
+