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README.md

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-library_name: diffusers
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-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# 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
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
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-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
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-## Training Details
-
-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+### 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)
+```