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title: README
emoji: 📈
colorFrom: green
colorTo: red
sdk: static
pinned: false
---
# VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning
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[[Paper](https://arxiv.org/abs/2504.07960)]   [[Project Page](https://visualcloze.github.io/)]   [[Github](https://github.com/lzyhha/VisualCloze)]
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[[🤗 Online Demo](https://huggingface.co/spaces/VisualCloze/VisualCloze)]   [[🤗 Dataset Card](https://huggingface.co/datasets/VisualCloze/Graph200K)]
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[[🤗 Full Model Card (<strong><span style="color:hotpink">Diffusers</span></strong>)](https://huggingface.co/VisualCloze/VisualClozePipeline-384)]   [[🤗 LoRA Model Card (<strong><span style="color:hotpink">Diffusers</span></strong>)](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-384)]
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If you find VisualCloze is helpful, please consider to star ⭐ the [<strong><span style="color:hotpink">Github Repo</span></strong>](https://github.com/lzyhha/VisualCloze). Thanks!
## 📰 News
- [2025-6-26] 🚀🚀🚀 VisualCloze has been accepted by [<strong>ICCV 2025</strong>](https://iccv.thecvf.com/Conferences/2025).
- [2025-5-15] 🤗🤗🤗 VisualCloze has been merged into the [<strong><span style="color:hotpink">official pipelines of diffusers</span></strong>](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/visualcloze). For usage guidance, please refer to the [Full Model Card 384](https://huggingface.co/VisualCloze/VisualClozePipeline-384) and [Full Model Card 512](https://huggingface.co/VisualCloze/VisualClozePipeline-512).
- [2025-5-18] 🥳🥳🥳 We have released the LoRA weights supporting diffusers at [LoRA Model Card 384](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-384) and [LoRA Model Card 512](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-512).
## 🌠 Key Features
An in-context learning based universal image generation framework.
1. Support various in-domain tasks.
2. Generalize to <strong><span style="color:hotpink"> unseen tasks</span></strong> through in-context learning.
3. Unify multiple tasks into one step and generate both target image and intermediate results.
4. Support reverse-engineering a set of conditions from a target image.
🔥 Examples are shown in the [project page](https://visualcloze.github.io/).
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