https://github.com/raghavendra-km/pictor_craft
PictorCraft is a hardware-accelerated AI image generator that uses Stable Diffusion to create high-quality images from text prompts. It operates on a client-server model and supports both CPU and GPU acceleration for efficient processing.
https://github.com/raghavendra-km/pictor_craft
artificial-intelligence data-science deep-learning flask generative-ai hardware-acceleration huggingface image-generation machine-learning neural-networks python stable-diffusion text-to-image
Last synced: 7 months ago
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PictorCraft is a hardware-accelerated AI image generator that uses Stable Diffusion to create high-quality images from text prompts. It operates on a client-server model and supports both CPU and GPU acceleration for efficient processing.
- Host: GitHub
- URL: https://github.com/raghavendra-km/pictor_craft
- Owner: Raghavendra-KM
- License: mit
- Created: 2025-01-29T09:29:38.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-03-07T04:37:35.000Z (8 months ago)
- Last Synced: 2025-03-07T05:26:33.679Z (8 months ago)
- Topics: artificial-intelligence, data-science, deep-learning, flask, generative-ai, hardware-acceleration, huggingface, image-generation, machine-learning, neural-networks, python, stable-diffusion, text-to-image
- Language: Python
- Homepage:
- Size: 21.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pictor_Craft
## Overview
PictorCraft is an AI-powered image generation tool that leverages the **Stable Diffusion** model to create high-quality AI-generated images. It utilizes **Hugging Face** to connect with Stable Diffusion servers and can run on either a **CPU or GPU**, depending on available hardware resources.
The project is built as a **client-server architecture**, where the server hosts the Stable Diffusion model and processes image generation requests from the client. The client sends text-based prompts to the server and receives generated images in return.
## Features
- AI-driven image generation using Stable Diffusion.
- Supports **both CPU and GPU acceleration** (depending on hardware availability).
- Flask-based **REST API server** for image generation.
- Command-line client for interacting with the server.
- Image output in **PNG format**.
## System Requirements (Changes based on the model used)
### Hardware
- **CPU:** AMD Ryzen 7 6800H or equivalent (for CPU-based inference)
- **GPU:** NVIDIA RTX 3070 (6GB VRAM) or higher (recommended for GPU acceleration)
- **RAM:** Minimum 8GB (16GB recommended for better performance)
- **Storage:** At least 10GB of free space for model storage and image generation
### Software
- **Operating System:** Windows 10/11, Linux, or macOS
- **Python:** Version 3.8 or higher
- **Dependencies:**
- Flask (`pip install flask`)
- Diffusers (`pip install diffusers`)
- Torch (`pip install torch`)
- PIL (Python Imaging Library, included in `Pillow` package)
- Requests (`pip install requests`)
## Installation and Setup
### 1. Clone the Repository
```bash
git clone https://github.com/Raghavendra-KM/Pictor_Craft.git
cd PictorCraft
```
### 2. Install Dependencies
```bash
pip install -r requirements.txt
```
### 3. Running the Server
```bash
python server.py
```
The server will start on `http://0.0.0.0:5000/` and listen for image generation requests.
### 4. Running the Client
```bash
python client.py
```
The client will prompt for a text input and send the request to the server.
## API Endpoint
### `POST /generate`
- **Request Body:**
```json
{
"prompt": "A futuristic cyberpunk city at night"
}
```
- **Response:**
```json
{
"image": ""
}
```
## License
This project is open-source under the MIT License.
### Special thanks to my senior **Aatish Satheesan** for helping me with this project
**Aatish Satheesan:**
[GitHub](https://github.com/Aatish-S)
[LinkedIn](https://www.linkedin.com/in/aatish-satheesan/)
## Author
**Raghavendra KM**
[GitHub](https://github.com/Raghavendra-KM)
[LinkedIn](https://linkedin.com/in/raghavendrakm08)