update README

README.md

@@ -14,21 +14,51 @@ IP Adapter Art is a specialized version that uses a professional style encoder.
 
 [![**IP Adapter Art Demo**](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kV7q3Gzr8GPG9cChdDQ5ncCx84TYjuu3?usp=sharing)  can be used to conduct experiments directly.
 
-For local experiments, please refer to a [demo](https://github.com/aihao2000/IP-Adapter-Artist/blob/main/ip_adapter_art_sdxl_demo.ipynb).
+For local experiments, please refer to a [demo](https://github.com/aihao2000/IP-Adapter-Art/blob/main/artistic_portrait_gen.ipynb).
 
 Local experiments require a basic torch environment and dependencies:
 
 ```
-pip install diffusers
-pip install transformers
+conda create -n artadapter python=3.10
+conda activate artadapter
+pip install -r requirements.txt
 pip install git+https://github.com/openai/CLIP.git
-pip install git+https://github.com/aihao2000/IP-Adapter-Art.git
+pip install -e .
 ```
 
-## More Examples
+## Comparison with Existing Style Control Methods in Diffusion Models
+
+Evaluation using [StyleBench](https://github.com/open-mmlab/StyleShot) style images. Image quality is evaluated using [improved aesthetic predictor](https://github.com/christophschuhmann/improved-aesthetic-predictor)
+
+|                       | CLIP Style Similarity | CSD Style Similarity | CLIP Text Alignment | Image Quality | Average   |
+| --------------------- | --------------------- | -------------------- | ------------------- | ------------- | --------- |
+| DEADiff               | 61.99                 | 43.54                | 20.82               | 60.76         | 46.78     |
+| StyleShot             | 63.01                 | 52.40                | 18.93               | 55.54         | 47.47     |
+| Instant Style         | 65.39                 | 58.39                | 21.09               | 60.62         | 51.37     |
+| **Art-Adapter(ours)** | **67.03**             | **65.02**            | 20.25               | **62.23**     | **53.63** |
+
+![image](./README.assets/comparison_with_existing_methods.jpg)
+
+
+## Examples of Text-guided Stylized Generation
 
 ![image-20240808001612810](./README.assets/more_examples.png)
 
+## Artistic Portrait Generation
+
+### Pipeline 
+
+We built an artistic portrait generation pipeline using Art-Adapter, PuLID, and ControlNet. The structure is shown in the figure below.
+
+![image-20240808001612811](./README.assets/artistic_portrait_generation_pipeline.jpg)
+
+### Examples
+
+![image-20240808001612812](./README.assets/artistic_portrait_generation_examples.jpg)
+
+## Stylize ControlNet Parameter Visualization
+
+![image-20240808001612813](./README.assets/stylize_controlnet_parameter_visualization.jpg)
 
 ## Citation
 
@@ -41,4 +71,7 @@ pip install git+https://github.com/aihao2000/IP-Adapter-Art.git
   journal = {GitHub repository},
   howpublished = {\url{https://github.com/aihao2000/IP-Adapter-Art}}
 }
-```
+```
+
+## Acknowledgements
+