Image-to-Image
Diffusers
text-to-image
flux
lora
in-context-learning
universal-image-generation
ai-tools
VisualCloze
VisualClozePipeline
Instructions to use VisualCloze/VisualClozePipeline-LoRA-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VisualCloze/VisualClozePipeline-LoRA-512 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("VisualCloze/VisualClozePipeline-LoRA-512") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
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If you find VisualCloze useful for your research and applications, please cite using this BibTeX:
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```bibtex
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If you find VisualCloze useful for your research and applications, please cite using this BibTeX:
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```bibtex
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@InProceedings{Li_2025_ICCV,
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author = {Li, Zhong-Yu and Du, Ruoyi and Yan, Juncheng and Zhuo, Le and Li, Zhen and Gao, Peng and Ma, Zhanyu and Cheng, Ming-Ming},
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title = {VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning},
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booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
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month = {October},
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year = {2025},
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pages = {18969-18979}
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}
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```
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