metadata
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
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:
pip install torch transformers diffusers pillow
Downloading the Model
You can download this model using diffusion libary:
pip install diffusers
Next, download the model:
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
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")