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license: apache-2.0 |
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# IterComp |
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Official Repository of the paper: *[IterComp](https://arxiv.org/abs/2410.07171)*. |
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<img src="./itercomp.png" style="zoom:50%;" /> |
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## News🔥🔥🔥 |
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* Oct.9, 2024. Our checkpoints are publicly available on [HuggingFace Repo](https://huggingface.co/comin/IterComp). |
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## Introduction |
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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) . |
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## Text-to-Image Usage |
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```python |
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from diffusers import DiffusionPipeline |
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import torch |
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pipe = DiffusionPipeline.from_pretrained("comin/IterComp", torch_dtype=torch.float16, use_safetensors=True) |
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pipe.to("cuda") |
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# if using torch < 2.0 |
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# pipe.enable_xformers_memory_efficient_attention() |
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prompt = "An astronaut riding a green horse" |
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image = pipe(prompt=prompt).images[0] |
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image.save("output.png") |
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``` |
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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. |
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## Citation |
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``` |
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@article{zhang2024itercomp, |
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title={IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation}, |
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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}, |
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journal={arXiv preprint arXiv:2410.07171}, |
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year={2024} |
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} |
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``` |
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## |
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