T T P Lanet_ S D X L_ Controlnet_ Tile_ Realistic

Introduction

The TTPlanet SDXL Controlnet Tile Realistic model is a sophisticated image feature extraction tool designed for enhancing and modifying image details. It leverages the capabilities of Controlnet, stable diffusion, and the Hugging Face diffusers library to deliver high-resolution image fixes and enhancements.

Architecture

This model is based on Controlnet and Stable Diffusion technologies, utilizing the SDXL architecture. It is tailored for enhancing image details without altering the original size, making it ideal for applications requiring realistic image upscaling and detail enhancement.

Training

The model was trained with an improved dataset and extensive training steps, which allow it to recognize a broader range of objects without explicit prompts. It addresses previous issues with color offsets and enhances control strength, which can replace certain preprocessing steps like canny and openpose. The training involves adjustments to manage edge halo issues by optimizing image blurring during preprocessing.

Guide: Running Locally

1. Setup Environment: Ensure your system has Python installed along with necessary libraries like Hugging Face's Transformers and diffusers.
2. Download Model: Clone the repository or download the model files from the Hugging Face model hub.
3. Install Dependencies: Use pip install diffusers transformers to install required packages.
4. Load Model: Use scripts or a Jupyter Notebook to load the model and apply it to your images.
5. Upscaling Process: For ultimate upscaling, use a denoise rate around 0.3-0.4 and set controlnet strength to 0.9 for optimal results.

Cloud GPUs: For faster processing, consider using cloud services like AWS, Google Cloud, or Azure with GPU support.

License

This model is distributed under the OpenRAIL license, allowing for open and collaborative use while ensuring responsible AI development.