safetensors mt5 region:us
---
{}
---
# 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 کیسه های زیر چشم ، سوزش های جانبی و دهان کمی باز است."
image2 = persian_to_image_model(persian_text2)
display(image2)
```