A text-to-image generation model built on Stable Diffusion 1.5 and fine-tuned with multiple LoRA modules.
Generated images are released under Creative Commons Zero (CC0), free to use without attribution.
(Base model weights are derived from SD 1.5, licensed under CreativeML OpenRAIL-M; all LoRA modules used are CC0-1.0)

Model Description

Free-Image-Generation is a fine-tuned text-to-image model based on the Stable Diffusion 1.5 architecture with 1.07 billion parameters, merging multiple LoRA modules.

Base weights: SD 1.5 (CreativeML OpenRAIL-M license)
LoRA modules: CC0-1.0
Generated outputs: CC0 — fully free to use without attribution

All images generated with this model are released under the Creative Commons Zero (CC0) license, making them freely usable for any purpose without attribution requirements.

Features

1.07B parameters
Text-to-image generation
Fine-tuned with multiple LoRA modules
Compatible with Hugging Face Diffusers library
Generates images under CC0 license

Installation

Prerequisites

Python 3.7 or higher
CUDA-compatible GPU (recommended)

Setup

Install the required dependencies:

pip install diffusers transformers torch

Usage Examples

Basic Usage

python

from diffusers import DiffusionPipeline
import torch

# Load model from Hugging Face
pipe = DiffusionPipeline.from_pretrained("aiyouthalliance/Free-Image-Generation")

# Move to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = pipe.to(device)

# Generate image
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

# Save the generated image
image.save("astronaut_jungle.png")

Memory-Optimized Usage

python

# Load in half precision for memory efficiency
pipe = DiffusionPipeline.from_pretrained(
    "aiyouthalliance/Free-Image-Generation", 
    torch_dtype=torch.float16
)
pipe = pipe.to(device)

Advanced Parameter Customization

python

# Customize generation parameters
image = pipe(
    prompt="A futuristic cityscape with flying cars, neon lights, detailed, 8k",
    num_inference_steps=50,
    guidance_scale=7.5,
    negative_prompt="blurry, low quality, distorted"
).images[0]

Model Structure

The model repository contains the following components:

vae: The variational autoencoder component
unet: The U-Net denoising component
text_encoder: The text encoder for processing prompts
tokenizer: For tokenizing the text inputs
scheduler: Controls the denoising process
safety_checker: For filtering content
feature_extractor: For image processing

License

Base model weights: OpenRAIL-M (derivative of SD 1.5)
LoRA modules: CC0-1.0
Generated images: CC0

Users should comply with OpenRAIL-M restrictions for derivative use or redistribution of the base model weights.

Technical Specifications

Feature	Specification
Total Parameters	1.07 Billion
Base Architecture	Stable Diffusion 1.5
Fine-Tuning	Multiple LoRA modules merged
Output Resolution	512×512 pixels

Citation

bibtex

@misc{aiyouthalliance2025freeimagegen,
  author = {AI Youth Alliance},
  title = {Free-Image-Generation},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/aiyouthalliance/Free-Image-Generation}}
}

Contact & Support

Hugging Face: aiyouthalliance