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.