---
license: apache-2.0
datasets:
- jackyhate/text-to-image-2M
language:
- ru
- en
- es
- zh
- aa
- fr
metrics:
- brier_score
base_model:
- black-forest-labs/FLUX.1-dev
new_version: black-forest-labs/FLUX.1-Depth-dev-lora
pipeline_tag: text-to-image
library_name: diffusers
---
# Jamgen: Text-to-Image Generation Model

Jamgen is a state-of-the-art text-to-image generation model built using diffusion models. With Jamgen, you can generate high-quality images directly from textual descriptions. This model leverages the power of deep learning and diffusion techniques to create stunning visuals that match your input text.

## Table of Contents

- [Installation](#installation)
- [Downloading the Model](#downloading-the-model)
- [Usage](#usage)

## Installation

To get started with Jamgen, you need to have Python installed on your system. We recommend using a virtual environment to manage dependencies.

### Prerequisites

- Python 3.8 or higher
- pip (Python package manager)

### Install Dependencies

You can install the required dependencies by running:

```bash
pip install torch transformers diffusers pillow
```
#### Downloading the Model
You can download this model using diffusion libary:
```bash
pip install diffusers
```
Next, download the model:
```bash
from diffusers import StableDiffusionPipeline

# Replace 'your-model-id' with the actual model ID on Hugging Face
model_id = "your-model-id"
pipeline = StableDiffusionPipeline.from_pretrained(model_id)
```
### Usage
```bash
from diffusers import StableDiffusionPipeline
import torch

# Load the model
model_id = "your-model-id"
pipeline = StableDiffusionPipeline.from_pretrained(model_id)
pipeline.to("cuda")  # Use GPU if available

# Generate an image from text
prompt = "A beautiful sunset over the mountains"
image = pipeline(prompt).images[0]

# Save the generated image
image.save("generated_image.png")
```