Free-Image-Generation

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)

Hugging Face Parameters


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 โ€” 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

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

# 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

# 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 512ร—512 pixels

Citation

@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

Downloads last month
124
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support