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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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tags: |
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- Kolors |
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- text-to-image |
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- stable-diffusion |
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library_name: diffusers |
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--- |
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# Kolors-IP-Adapter-Plus weights and inference code |
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<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;"> |
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<a href="https://github.com/Kwai-Kolors/Kolors"><img src="https://img.shields.io/static/v1?label=Kolors Code&message=Github&color=blue&logo=github-pages"></a>   |
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<a href="https://kwai-kolors.github.io/"><img src="https://img.shields.io/static/v1?label=Team%20Page&message=Page&color=green"></a>   |
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<a href="https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Arxiv:Kolors&color=red&logo=arxiv"></a>   |
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<a href="https://kolors.kuaishou.com/"><img src="https://img.shields.io/static/v1?label=Official Website&message=Page&color=green"></a> |
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</div> |
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## <a name="Introduction"></a>📖 Introduction |
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We provide IP-Adapter-Plus weights and inference code based on [Kolors-Basemodel](https://huggingface.co/Kwai-Kolors/Kolors). Examples of Kolors-IP-Adapter-Plus results are as follows: |
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**Our improvements** |
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- A stronger image feature extractor. We employ the Openai-CLIP-336 model as the image encoder, which allows us to preserve more details in the reference images |
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- More diverse and high-quality training data: We construct a large-scale and high-quality training dataset inspired by the data strategies of other works. We believe that paired training data can effectively improve performance. |
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## <a name="Evaluation"></a>📊 Evaluation |
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For evaluation, we create a test set consisting of over 200 reference images and text prompts. We invite several image experts to provide fair ratings for the generated results of different models. The experts rate the generated images based on four criteria: visual appeal, text faithfulness, image faithfulness, and overall satisfaction. Image faithfulness measures the semantic preservation ability of IP-Adapter on reference images, while the other criteria follow the evaluation standards of BaseModel. The specific results are summarized in the table below, where Kolors-IP-Adapter-Plus achieves the highest overall satisfaction score. |
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| Model | Average Overall Satisfaction | Average Image Faithfulness | Average Visual Appeal | Average Text Faithfulness | |
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| :--------------: | :--------: | :--------: | :--------: | :--------: | |
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| SDXL-IP-Adapter-Plus | 2.29 | 2.64 | 3.22 | 4.02 | |
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| Midjourney-v6-CW | 2.79 | 3.0 | 3.92 | 4.35 | |
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| **Kolors-IP-Adapter-Plus** | **3.04** | **3.25** | **4.45** | **4.30** | |
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<font color=gray style="font-size:12px">*The ip_scale parameter is set to 0.3 in SDXL-IP-Adapter-Plus, while Midjourney-v6-CW utilizes the default cw scale.*</font> |
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------ |
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## <a name="Usage"></a>🛠️ Usage |
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### Requirements |
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The dependencies and installation are basically the same as the [Kolors-BaseModel](https://huggingface.co/Kwai-Kolors/Kolors). |
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1. Repository Cloning and Dependency Installation |
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```bash |
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apt-get install git-lfs |
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git clone https://github.com/Kwai-Kolors/Kolors |
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cd Kolors |
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conda create --name kolors python=3.8 |
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conda activate kolors |
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pip install -r requirements.txt |
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python3 setup.py install |
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``` |
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2. Weights download [link](https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-Plus)： |
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```bash |
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huggingface-cli download --resume-download Kwai-Kolors/Kolors-IP-Adapter-Plus --local-dir weights/Kolors-IP-Adapter-Plus |
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``` |
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or |
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```bash |
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git lfs clone https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-Plus weights/Kolors-IP-Adapter-Plus |
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``` |
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3. Inference： |
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```bash |
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python ipadapter/sample_ipadapter_plus.py ./ipadapter/https://raw.githubusercontent.com/junqiangwu/Kolors/master/ipadapter/asset/test_ip.jpg "穿着黑色T恤衫，上面中文绿色大字写着“可图”" |
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python ipadapter/sample_ipadapter_plus.py ./ipadapter/https://raw.githubusercontent.com/junqiangwu/Kolors/master/ipadapter/asset/test_ip2.png "一只可爱的小狗在奔跑" |
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# The image will be saved to "scripts/outputs/" |
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``` |
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**Note** |
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The IP-Adapter-FaceID model based on Kolors will also be released soon! |
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### Acknowledgments |
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- Thanks to [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter) for providing the codebase. |
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<br> |
