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sarathAI/
NFT-Genesis

Name: NFT-Genesis
Availability: InStock

diffusers

text-to-image

autotrain

stabilityai/stable-diffusion-xl-base-1.0

finetune

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README for Text-to-Image Model Fine-Tuned on Stable Diffusion 1.0XL for NFT-Genesis

Overview

This project involves a text-to-image model fine-tuned on the Stable Diffusion 1.0XL architecture, specifically tailored for the NFT-Genesis project. The model is designed to generate high-quality, unique images based on textual descriptions, making it especially suited for creating digital art and Non-Fungible Tokens (NFTs).

Features

Fine-Tuning on Stable Diffusion 1.0XL: Leverages the advanced capabilities of the Stable Diffusion model for high-quality image generation.
NFT-Genesis Specialization: Optimized for creating images that are ideal for use in the NFT space, emphasizing uniqueness and artistic quality.
Textual Description Input: Generates images based on user-provided text descriptions, offering a high degree of creative control.
High-Resolution Output: Capable of generating images in high resolutions suitable for digital art applications.

Requirements

Python 3.6 or later
PyTorch 1.7.1 or later
PIL (Python Imaging Library)
Other dependencies listed in requirements.txt

Installation

Clone the repository:
bash
1git clone [repository URL] 2cd [repository name]
Install dependencies:
pip install -r requirements.txt

Usage

To generate an image:

import

import io
from PIL import Image

API_URL = "https://api-inference.huggingface.co/models/sarathAI/NFT-Genesis"
headers = {"Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.content

image_bytes = query({
    "inputs": "Formula 1 car",
})

# Added: Check if the response is indeed image bytes
if image_bytes.startswith(b'\xff\xd8'):  # JPEG
    print("JPEG image detected")
elif image_bytes.startswith(b'\x89PNG\r\n\x1a\n'):  # PNG
    print("PNG image detected")
else:
    print("The response might not be an image or is in an unrecognized format.")

# Attempt to open the image
try:
    image = Image.open(io.BytesIO(image_bytes))
    image.save("output_image.jpg")
    print("Image saved as output_image.jpg. Please open this file to view the image.")
except IOError:
    print("Cannot open the image. The file might be corrupted or in an unsupported format.")

Configuration

Model Parameters: Adjust model parameters in the config.py file to tweak performance and output quality.
Custom Datasets: To further fine-tune the model, you can use custom datasets by following the instructions in dataset/README.md.

Contributing

Contributions to the project are welcome. Please follow the guidelines in CONTRIBUTING.md for submitting pull requests or reporting issues.

License

This project is licensed under [specify license type], as found in the LICENSE file.

Acknowledgements

Original Stable Diffusion 1.0XL Team for the base model architecture.
Contributors and community members who have offered valuable insights and improvements.

Disclaimer

This model is intended for creative and artistic purposes. Users are responsible for the ethical use of the technology and ensuring that generated content respects copyright and other legal considerations.