Update README.md

README.md

@@ -16,86 +16,19 @@ The model was presented in the paper [The Consistency Critic: Correcting Inconsi
 *   🤗 [Hugging Face Space Demo](https://huggingface.co/spaces/ziheng1234/ImageCritic)
 *   📦 [Hugging Face Dataset](https://huggingface.co/datasets/ziheng1234/Critic-10K)
 
-<img src='https://github.com/HVision-NKU/ImageCritic/raw/main/figure/teaser.png' width='100%' />
+<img src='https://raw.githubusercontent.com/HVision-NKU/ImageCritic/refs/heads/master/figure/teaser.png' width='100%' />
 
 ## 🖼️ Visual Results
 
 ImageCritic can effectively resolve detail-related issues in various customized generation scenarios, providing significant improvements over existing methods.
 
-<img src='https://github.com/HVision-NKU/ImageCritic/raw/main/figure/compare.png' width='100%' />
-
-## 🔧 Dependencies and Installation
-
-We recommend using Python 3.10 and PyTorch with CUDA support. To set up the environment:
-
-```bash
-# Create a new conda environment
-conda create -n imagecritic python=3.10
-conda activate imagecritic
-
-# Install other dependencies
-pip install -r requirements.txt
-```
-
-## ⚡ Quick Inference
-### Tips
-Due to copyright issues, we have embedded the download of the kontext model weights in the inference code below, You can run following inference code directly.
-If you have already downloaded the corresponding model, you can comment out the related code and directly replace the inference path.
-
-### Single case inference
-```bash
-python infer.py
-```
-
-### Local Gradio Demo
-```bash
-python app.py
-```
-
-### Single Model Download 
-You can download the base model FLUX.1-Kontext-dev directly from [Hugging Face](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
-
-Alternatively, you can download it via the following command  
-(⚠️ Remember to replace `your_hf_token` in the script with your actual Hugging Face access token):
-```bash
-python ./download_kontext.py
-```
-
-You can download our ImageCritic directly from [Hugging Face](https://huggingface.co/ziheng1234/ImageCritic).
-
-Alternatively, you can download it via following code:
-
-```bash
-python ./download_imageCritic.py
-```
-Or using Git:
-```bash
-git lfs install
-git clone https://huggingface.co/ziheng1234/ImageCritic
-```
-
-## Dataset Download 
-
-You can download our training dataset Critic-10K directly from [Hugging Face](https://huggingface.co/datasets/ziheng1234/Critic-10K).
-
-Alternatively, you can download it via Python:
-
-```bash
-python /raid/users/oyzh/ImageCritic/download_dataset.py
-```
-Or using Git:
-```bash
-git lfs install
-git clone https://huggingface.co/datasets/ziheng1234/Critic-10K
-```
+<img src='https://github.com/HVision-NKU/ImageCritic/blob/master/figure/compare.png?raw=true' width='100%' />
 
 ### Online HuggingFace Demo
 You can try ImageCritic demo on [HuggingFace](https://huggingface.co/spaces/ziheng1234/ImageCritic).
 
 ## Citation
 
-If ImageCritic is helpful, please help to ⭐ the repo.
-
 If you find this project useful for your research, please consider citing our paper:
 ```bibtex
 @article{ouyang2025consistency,