Update README.md

README.md

@@ -1,73 +1,41 @@
----
-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  
----
+# Persian-to-Image Text-to-Image Pipeline
 
-<p align="center">  
-  <img src="PersianToImage.jpg" alt="PersianToImage logo" width=200/>  
-</p>
+## Model Overview
 
-# <span style="font-variant:small-caps;">PersianToImage</span>
+This model pipeline is designed to generate images from Persian text descriptions by translating the Persian text into English and then using a fine-tuned Stable Diffusion model to generate the corresponding image. The pipeline combines two models: a translation model (`mohammad-shirkhani/finetune_persian_to_english_mt5_base_summarize_on_celeba_hq`) and an image generation model (`ebrahim-k/Stable-Diffusion-1_5-FT-celeba_HQ_en`).
 
-<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 Details
 
-## Model Description
+### Translation Model
+- **Model Name**: `mohammad-shirkhani/finetune_persian_to_english_mt5_base_summarize_on_celeba_hq`
+- **Architecture**: mT5
+- **Purpose**: This model is used to translate Persian text into English. It has been fine-tuned specifically on the CelebA-HQ dataset for summarization tasks, making it well-suited for translating descriptions of facial features.
 
-- **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)
+### Image Generation Model
+- **Model Name**: `ebrahim-k/Stable-Diffusion-1_5-FT-celeba_HQ_en`
+- **Architecture**: Stable Diffusion 1.5
+- **Purpose**: This model generates high-quality images from the English text produced by the translation model. It has been fine-tuned on the CelebA-HQ dataset, making it particularly effective for generating realistic human faces based on text descriptions.
 
-## How to Get Started with the Model
+## Pipeline Description
 
-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.
+The pipeline works as follows:
 
-```python
-import torch
-from transformers import MT5ForConditionalGeneration, T5Tokenizer
-from diffusers import StableDiffusionPipeline
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-class PersianToImageModel:
-    def __init__(self, translation_model_name, image_model_name, device):
-        self.device = device
-
-        self.translation_model = MT5ForConditionalGeneration.from_pretrained(translation_model_name).to(device)
-        self.translation_tokenizer = T5Tokenizer.from_pretrained(translation_model_name)
+1. **Text Translation**: The Persian input text is translated into English using the mT5-based translation model.
+2. **Image Generation**: The translated English text is then fed into the Stable Diffusion model to generate the corresponding image.
 
-        self.image_model = StableDiffusionPipeline.from_pretrained(image_model_name).to(device)
+### Example Usage
 
-    def translate_text(self, persian_text):
-        input_ids = self.translation_tokenizer.encode(persian_text, return_tensors="pt").to(self.device)
-        translated_ids = self.translation_model.generate(input_ids, max_length=512, num_beams=4, early_stopping=True)
-        translated_text = self.translation_tokenizer.decode(translated_ids[0], skip_special_tokens=True)
-        return translated_text
-
-    def generate_image(self, english_text):
-        image = self.image_model(english_text).images[0]
-        return image
-
-    def __call__(self, persian_text):
-        english_text = self.translate_text(persian_text)
-        print(f"Translated Text: {english_text}")
-
-        return self.generate_image(english_text)
+```python
+from IPython.display import display
 
-# 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 text describing a person
+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."
 
-persian_to_image_model = PersianToImageModel(translation_model_name, image_model_name, device)
+# Generate and display the image
+image = persian_to_image_model(persian_text)
+display(image)
 
+# Another example
+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)