Create README.md

README.md

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+# Persian-to-Image Text-to-Image Pipeline
+
+## Model Overview
+
+This model pipeline is designed to generate images from Persian text descriptions. It works by first 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`).
+
+## Model Details
+
+### Translation Model
+- **Model Name**: `mohammad-shirkhani/finetune_persian_to_english_mt5_base_summarize_on_celeba_hq`
+- **Architecture**: mT5
+- **Purpose**: This model translates Persian text into English. It has been fine-tuned on the CelebA-HQ dataset for summarization tasks, making it effective for translating descriptions of facial features.
+
+### 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 English text produced by the translation model. It has been fine-tuned on the CelebA-HQ dataset, which makes it particularly effective for generating realistic human faces based on text descriptions.
+
+## Pipeline Description
+
+The pipeline operates through the following steps:
+
+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 used to generate the corresponding image with the Stable Diffusion model.
+
+### Code Implementation
+
+#### 1. Install Required Libraries
+
+```python
+!pip install transformers diffusers accelerate torch
+```
+#### 2. Import Necessary Libraries
+
+```python
+import torch
+from transformers import MT5ForConditionalGeneration, T5Tokenizer
+from diffusers import StableDiffusionPipeline
+```
+
+#### 3. Set Device (GPU or CPU)
+This code determines whether the pipeline should use a GPU (if available) or fallback to a CPU.
+
+```python
+# Determine the device: GPU if available, otherwise CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+```
+
+#### 4. Define and Load the Persian-to-Image Model Class
+The following class handles both translation and image generation tasks.
+
+```python
+# 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):
+        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):
+        # 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)
+```
+#### 5. Instantiate the Model
+The following code snippet demonstrates how to instantiate the combined model.
+
+```python
+# Instantiate the combined 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)
+```
+#### 6. Example Usage of the Model
+Below are examples of how to use the model to generate images from Persian text.
+
+```python
+from IPython.display import display
+
+# 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."
+
+# 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 کیس�� های زیر چشم ، سوزش های جانبی و دهان کمی باز است."
+image2 = persian_to_image_model(persian_text2)
+display(image2)
+```