Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# IterComp
+
+Official Repository of the paper: *[IterComp](https://arxiv.org)*.
+
+<img src="./itercomp.png" style="zoom:50%;" />
+
+## News🔥🔥🔥
+
+* Oct.9, 2024. Our checkpoints are publicly available on [HuggingFace Repo](https://huggingface.co/comin/IterComp).
+
+## Introduction
+
+IterComp is one of the new State-of-the-Art compositional generation methods. In this repository, we release the model training from  [SDXL Base 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) .
+
+## Text-to-Image Usage
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained("comin/IterComp", torch_dtype=torch.float16, use_safetensors=True)
+pipe.to("cuda")
+# if using torch < 2.0
+# pipe.enable_xformers_memory_efficient_attention()
+
+prompt = "An astronaut riding a green horse"
+image = pipe(prompt=prompt).images[0]
+image.save("output.png")
+```
+
+IterComp can **serve as a powerful backbone for various compositional generation methods**, such as [RPG](https://github.com/YangLing0818/RPG-DiffusionMaster) and [Omost](https://github.com/lllyasviel/Omost). We recommend integrating IterComp into these approaches to achieve more advanced compositional generation results.
+
+## Citation
+
+```
+@article{zhang2024itercomp,
+  title={IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation},
+  author={Zhang, Xinchen and Yang, Ling and Li, Guohao and Cai, Yaqi and Xie, Jiake and  Tang, Yong and Yang, Yujiu and Mengdi Wang and Cui, Bin},
+  journal={arXiv preprint arXiv:},
+  year={2024}
+}
+```
+
+##