Realistic_ Vision_ V2.0

Introduction

Realistic_Vision_V2.0 is a text-to-image model designed to produce high-quality, realistic visual outputs. It leverages the capabilities of the StableDiffusionPipeline and is optimized for use with VAE to enhance generation quality.

Architecture

The model is built upon the StableDiffusionPipeline and utilizes the VAE (Variational Autoencoder) for improved image quality, particularly in eliminating blue artifacts. The model is compatible with Diffusers, a library that enables efficient diffusion-based image generation.

Training

The model has been fine-tuned for generating realistic images based on input prompts. It uses specific settings and model parameters to achieve optimal results, such as Euler A or DPM++ 2M Karras sampling methods, with a CFG Scale of 3.5 to 7.

Guide: Running Locally

To run the Realistic_Vision_V2.0 model locally, follow these steps:

1. Environment Setup: Ensure Python and necessary libraries like transformers and diffusers are installed.
2. Model Download: Retrieve the model from the Hugging Face model hub.
3. VAE Integration: For improved quality, integrate the VAE available at Hugging Face.
4. Configuration: Use the provided prompts and settings for optimal results. Suggested parameters include 25 steps with a CFG Scale of 3.5 to 7.
5. Execution: Run the model using your preferred text-to-image pipeline.

For users without sufficient local resources, utilizing cloud GPUs from providers like AWS, Google Cloud, or Azure can enhance processing capabilities.

License

The model is released under the CreativeML OpenRAIL-M license, allowing for both personal and commercial use with certain conditions.