A powerful Python-based text-to-image generation tool using Stable Diffusion models with optimized performance and an intuitive Gradio interface. Generate high-quality images from text descriptions with advanced customization options.

• Stable Diffusion 1.5
• Stable Diffusion 2.1
• Dreamshaper 8

• Automatic VRAM-based optimization
• xFormers memory efficient attention (when available)
• VAE slicing for better memory usage
• Sequential CPU offload for low VRAM systems
• Dynamic model caching

• Adaptive guidance scaling (7-30 range)
• Quality enhancement prompts
• Comprehensive negative prompts
• Custom seed support
• Adjustable image dimensions (512-1024px)
• Configurable inference steps (20-100)

• Clean, intuitive Gradio interface
• Example prompts gallery
• Real-time generation details
• Advanced settings accordion
• Random prompt generation
• Clear function

Below are sample outputs generated by the Text to Image Generator using various prompts and models:

Prompt	Model	Output
A serene landscape with mountains, a lake, and a sunset, highly detailed, vibrant colors	Stable Diffusion 1.5
A serene landscape with mountains, a lake, and a sunset, highly detailed, vibrant colors	Stable Diffusion 2.1
A serene landscape with mountains, a lake, and a sunset, highly detailed, vibrant colors	Dreamshaper 8

Note: Output quality and style may vary depending on the selected model and prompt.

• Python 3.13
• CUDA-compatible GPU (recommended)
• CPU-only mode supported

1. Open Text-to-img.ipynb in Google Colab
2. Select 'Runtime' > 'Change runtime type' > Choose 'T4 GPU'
3. Run all cells in sequence

1. Obtain your Hugging Face token from Hugging Face.
2. Add the token to the Colab notebook by running the following code in a cell:

   from huggingface_hub import login
   login("your_huggingface_token_here")

1. Obtain your Hugging Face token from Hugging Face.
2. Create a file named token inside the huggingface directory:

   echo "your_huggingface_token_here" > huggingface/token

Alternatively, you can securely add your Hugging Face token directly in Google Colab by setting it as a secret. Use the following code snippet in a cell:

HF_TOKEN=YOUR_HUGGINGFACE_TOKEN

Note: Need Python 3.13 and if you are facing error in setup wheel then run pip install --upgrade pip setuptools wheel and restart the kernel.

git clone https://github.com/SauRavRwT/text-to-img.git
cd text-to-img

Windows:

python -m venv venv
venv\scripts\activate

Linux/Mac:

python -m venv venv
source venv/bin/activate

pip install -r requirements.txt

python app.py

• The system automatically detects available VRAM and applies appropriate optimizations
• For systems with less than 6GB VRAM: Uses sequential CPU offload
• For systems with 6-8GB VRAM: Uses model CPU offload
• For systems with >8GB VRAM: Runs fully on GPU with memory optimizations

• Reduce image dimensions
• Decrease inference steps
• Use a lower resolution model