Update README.md

README.md

@@ -1,3 +1,70 @@
----
-license: cc-by-nd-4.0
----
+---
+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.