---
license: cc-by-nd-4.0
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- art
- reatistic
- text-image-generator
- stable-diffusion
---
# Fine-Tuning Stable Diffusion with Realistic Vision V2.0

## Overview

This repository contains a fine-tuned version of the **Realistic Vision V2.0** model, a powerful variant of the Stable Diffusion model, tailored for generating high-quality, realistic images from text prompts. The fine-tuning process was conducted on a custom dataset to improve the model's performance in specific domains.

## Features of Realistic Vision V2.0

- **High-Quality Image Generation**: Produces detailed and realistic images that closely adhere to the provided text prompts.
- **Enhanced Detail Preservation**: Maintains fine details in the generated images, making it suitable for applications requiring high fidelity.
- **Versatile Output**: Capable of generating a wide range of visual styles based on varying prompts, from artistic to photorealistic images.
- **Optimized Inference**: Efficient performance on modern GPUs, with customizable parameters like inference steps and guidance scale to balance speed and quality.

## Why Use Realistic Vision V2.0?

- **Superior Realism**: Compared to earlier versions, Realistic Vision V2.0 has been fine-tuned to enhance the realism of generated images, making it ideal for applications in media, design, and content creation.
- **Customizable Outputs**: The model allows users to fine-tune parameters to match their specific needs, whether they are looking for highly accurate or more creative and abstract images.
- **Proven Performance**: Backed by the robust Stable Diffusion framework, Realistic Vision V2.0 leverages state-of-the-art techniques in diffusion models to deliver consistent, high-quality results.

## Using the Pretrained Model

The fine-tuned model is available on Hugging Face and can be easily accessed and utilized:

### 1. Installation

First, install the necessary libraries:

pip install torch torchvision diffusers accelerate huggingface_hub

### 2. Access the Model
#### You can load and use the model in your Python environment as follows:
from diffusers import StableDiffusionPipeline
import torch

#### Load the fine-tuned model

model_id = "majid230/Realistic_Vision_V2.0"

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)

pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")

#### Generate an image from a prompt

prompt = "A futuristic cityscape at sunset"

image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

#### Save or display the image

image.save("generated_image.png")

image.show()
## 3.Customization
num_inference_steps: Adjust this parameter to control the number of steps the model takes during image generation. More steps typically yield higher-quality images.
guidance_scale: Modify this to control how closely the generated image follows the prompt. Higher values make the image more prompt-specific, while lower values allow for more creative interpretations.
## Acknowledgment
This project was generously supported and provided by Machine Learning 1 Pvt Ltd. The fine-tuning and further development were carried out by Majid Hanif.