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---
license: apache-2.0
---
# IterComp
Official Repository of the paper: *[IterComp](https://arxiv.org/abs/2410.07171)*.
<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 Wang, Mengdi and Cui, Bin},
journal={arXiv preprint arXiv:2410.07171},
year={2024}
}
```
##
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