Update README.md

README.md

@@ -1,33 +1,52 @@
-#Text-to-Image Pipeline in Persian 
-
-Install Required Libraries
-
-!pip install transformers diffusers accelerate torch 
-
-Import Necessary Libraries
-
+---
+license: cc-by-nc-sa-4.0  
+language:  
+- fa  
+- en  
+library_name: transformers  
+tags:  
+- text-to-image  
+- stable-diffusion  
+- transformers  
+pipeline_tag: text-to-image  
+co2_eq_emissions:  
+  emissions: 200000  
+---
+
+<p align="center">  
+  <img src="PersianToImage.jpg" alt="PersianToImage logo" width=200/>  
+</p>
+
+# <span style="font-variant:small-caps;">PersianToImage</span>
+
+<span style="font-variant:small-caps;">PersianToImage</span> is a unique pipeline that translates Persian text to English and generates corresponding images using a fine-tuned Stable Diffusion model. This tool is designed to easily convert descriptive Persian text into high-quality images, making it ideal for creative projects, visual content generation, and more.
+
+## Model Description
+
+- **Developed by:** [Your Name](mailto:your.email@example.com)  
+- **Model type:** Text-to-Image generation  
+- **Languages:** Persian and English  
+- **License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (non-commercial use only)
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.  
+Make sure you have installed <code><b>torch</b></code>, <code><b>diffusers</b></code>, <code><b>transformers</b></code>, and <code><b>accelerate</b></code> libraries.
+
+```python
 import torch
 from transformers import MT5ForConditionalGeneration, T5Tokenizer
 from diffusers import StableDiffusionPipeline
 
-Set Device (GPU or CPU)
-
-# Determine the device: GPU if available, otherwise CPU
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-Define and Load the Persian-to-Image Model Class
 
-# Define the model class
 class PersianToImageModel:
     def __init__(self, translation_model_name, image_model_name, device):
         self.device = device
 
-        # Load translation model
         self.translation_model = MT5ForConditionalGeneration.from_pretrained(translation_model_name).to(device)
         self.translation_tokenizer = T5Tokenizer.from_pretrained(translation_model_name)
 
-        # Load image generation model
         self.image_model = StableDiffusionPipeline.from_pretrained(image_model_name).to(device)
 
     def translate_text(self, persian_text):
@@ -41,30 +60,14 @@ class PersianToImageModel:
         return image
 
     def __call__(self, persian_text):
-        # Translate Persian text to English
         english_text = self.translate_text(persian_text)
         print(f"Translated Text: {english_text}")
 
-        # Generate and return image
         return self.generate_image(english_text)
 
-Instantiate the Model
-
-# Instantiate the combined model
+# Instantiate the model
 translation_model_name = 'mohammad-shirkhani/finetune_persian_to_english_mt5_base_summarize_on_celeba_hq'
 image_model_name = 'ebrahim-k/Stable-Diffusion-1_5-FT-celeba_HQ_en'
 
 persian_to_image_model = PersianToImageModel(translation_model_name, image_model_name, device)
 
- Example Usage of the Model
-
-from IPython.display import display
-
-persian_text = "این زن دارای موهای موج دار ، لب های بزرگ و موهای قهوه ای است و رژ لب دارد.این زن موهای موج دار و لب های بزرگ دارد و رژ لب دارد.فرد جذاب است و موهای موج دار ، چشم های باریک و موهای قهوه ای دارد."
-image = persian_to_image_model(persian_text)
-display(image)
-
-
-persian_text2 = "این مرد جذاب دارای موهای قهوه ای ، سوزش های جانبی ، دهان کمی باز و کیسه های زیر چشم است.این فرد جذاب دارای کیسه های زیر چشم ، سوزش های جانبی و دهان کمی باز است."
-image2 = persian_to_image_model(persian_text2)
-display(image2)