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---
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
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)
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):
english_text = self.translate_text(persian_text)
print(f"Translated Text: {english_text}")
return self.generate_image(english_text)
# 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)
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