Update README.md

README.md

@@ -1,63 +1,45 @@
 ---
-{}
+license: other
+license_name: sample-code-license
+license_link: LICENSE
+library_name: viper-vpe
 ---
 
-# ViPer: Metric
-GitHub: https://github.com/sogandstorme/ViPer_Personalization
+# ViPer: Visual Personalization of Generative Models via Individual Preference Learning
 
-## Example
+*Tuning-free framework for personalized image generation*
 
-```bash
-git clone https://github.com/sogandstorme/ViPer_Personalization.git
-cd ViPer_Personalization
-```
+[`Website`](https://viper.epfl.ch) | [`GitHub`](https://github.com/EPFL-VILAB/ViPer) | [`BibTeX`](#citation)  
+
+
+We introduce **ViPer**, a method that personalizes the output of generative models to align with different users’ visual preferences for the same prompt. This is done via a one-time capture of the user’s general preferences and conditioning the generative model on them without the need for engineering detailed prompts.
+
+
+## Installation
+For install instructions, please see https://github.com/EPFL-VILAB/ViPer.
+
+
+## Usage
+
+This model can be loaded from Hugging Face Hub as follows:
 
 ```python
-from metric import (
-    set_device,
-    load_context_images,
-    initialize_processor_and_model,
-    calculate_score
-)
-
-# Ensure that the number of liked and disliked images are the same
-
-negative_image_paths = [
-    "disliked/0.png",
-    "disliked/1.png",
-    "disliked/2.png",
-    "disliked/3.png",
-    "disliked/4.png",
-    "disliked/5.png",
-    "disliked/6.png",
-    "disliked/7.png",
-    "disliked/8.png",
-]
-
-positive_image_paths = [
-    "liked/0.png",
-    "liked/1.png",
-    "liked/2.png",
-    "liked/3.png",
-    "liked/4.png",
-    "liked/5.png",
-    "liked/6.png",
-    "liked/7.png",
-    "liked/8.png",
-]
-
-# Specify the address of the query image
-query_image = "query.png"
-
-device = set_device("cuda:0")
-    
-# Initialize processor and model
-device = set_device("cuda:0")
-context_images = load_context_images(negative_image_paths, positive_image_paths)
-processor, model = initialize_processor_and_model(device)
-
-# Calculate and print score
-score = calculate_score(processor, model, context_images, query_image)
-
-print(score)
-```
+from transformers import AutoProcessor, BitsAndBytesConfig, AutoModelForVision2Seq
+from peft import PeftModel
+
+model = AutoModelForVision2Seq.from_pretrained("HuggingFaceM4/idefics2-8b").to(device)
+    model = PeftModel.from_pretrained(model, "EPFL-VILAB/Metric-ViPer").to(device)
+```
+
+Please see https://github.com/EPFL-VILAB/ViPer for more detailed instructions.
+
+## Citation
+
+If you find this repository helpful, please consider citing our work:
+```
+TODO
+```
+
+## License
+
+Licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/sogandstorme/ViPer_Personalization/blob/main/LICENSE) for details.