Instructions to use comin/IterComp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use comin/IterComp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("comin/IterComp", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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# IterComp
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Official Repository of the paper: *[IterComp](https://arxiv.org)*.
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<img src="./itercomp.png" style="zoom:50%;" />
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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
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journal={arXiv preprint arXiv:},
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year={2024}
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}
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```
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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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```
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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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