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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: diffusers |
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tags: |
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- text-to-image |
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- stable diffusion |
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- personalization |
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- msdiffusion |
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--- |
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# Introduction |
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Our research introduces the MS-Diffusion framework for layout-guided zero-shot image personalization with multi-subjects. This innovative approach integrates grounding tokens with the feature resampler to maintain detail fidelity among subjects. With the layout guidance, MS-Diffusion further improves the cross-attention to adapt to the multi-subject inputs, ensuring that each subject condition acts on specific areas. The proposed multi-subject cross-attention orchestrates harmonious inter-subject compositions while preserving the control of texts. |
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- **Project Page:** [https://eclipse-t2i.github.io/Lambda-ECLIPSE/](https://eclipse-t2i.github.io/Lambda-ECLIPSE/) |
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- **GitHub:** [https://github.com/Maitreyapatel/lambda-eclipse-inference](https://github.com/Maitreyapatel/lambda-eclipse-inference) |
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- **Paper (arXiv):** [https://arxiv.org/abs/2402.05195](https://arxiv.org/abs/2402.05195) |
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# Model |
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Download the pretrained base models from [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and [CLIP-G](). |
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Please refer to our [GitHub repository]() to prepare the environment and get detailed instructions on how to run the model. |
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# Important Notes |
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- This repo only contains the trained model checkpoint without data, code, or base models. Please check the GitHub repository carefully to get detailed instructions. |
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- The `scale` parameter is used to determine the extent of image control. For default, the `scale` is set to 0.6. In practice, the `scale` of 0.4 would be better if your input contains subjects needing to effect on the whole image, such as the background. **Feel free to adjust the `scale` in your applications.** |
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- The model prefers to need layout inputs. You can use the default layouts in the inference script, while more accurate and realistic layouts generate better results. |
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- Though MS-Diffusion beats SOTA personalized diffusion methods in both single-subject and multi-subject generation, it still suffers from the influence of background in subject images. The best practice is to use masked images since they contain no irrelevant information. |
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