skull graphics

Introduction

The "Skull Graphics" model is a text-to-image generation model based on stable diffusion techniques. It allows users to generate images from text prompts, specifically focusing on the theme of skull graphics.

Architecture

The model is built on the stabilityai/stable-diffusion-xl-base-1.0 framework and utilizes diffusers and LoRA (Low-Rank Adaptation) to enhance the capability of generating varied skull-themed images from textual descriptions.

Training

The model has been trained using the base model stabilityai/stable-diffusion-xl-base-1.0, with a specific focus on text prompts related to skull graphics. The training process involved fine-tuning the model to optimize the generation of high-quality and diverse images corresponding to the input text.

Guide: Running Locally

1. Setup Environment: Ensure you have a Python environment with diffusers and transformers libraries installed. You can do this using pip:

   pip install diffusers transformers
   

2. Access the Model: Download the model using the Hugging Face transformers library. You may need to authenticate with your Hugging Face account.

   from diffusers import StableDiffusionPipeline
   pipeline = StableDiffusionPipeline.from_pretrained("CiroN2022/skull-graphics")
   

3. Generate Images: Use the pipeline to generate images based on your text input.

   prompt = "skull_graphics"
   image = pipeline(prompt).images[0]
   

4. Cloud GPUs: It is recommended to use cloud GPU services such as AWS, Google Cloud, or Azure to run the model efficiently, especially for generating high-resolution images.

License

The "Skull Graphics" model is released under an "other" license, which may have specific restrictions or permissions. Users should refer to the license details provided with the model for further information.