🖼️ AI Image Generation

A Python-based application that leverages state-of-the-art AI models to generate images from text prompts or other inputs. Built for researchers, developers, and creators interested in exploring generative models for image synthesis.

⚙️ Deploy on RunPod (Recommended: 48GB VRAM)

To use this application with large generative models (e.g., Stable Diffusion XL), it is recommended to use a GPU with at least 48GB VRAM. RunPod.io is an excellent choice for scalable, on-demand GPU compute.

🐳 Steps to Launch a Pod:

1. Create a RunPod Account: Sign up at https://www.runpod.io and verify your email.

2. Start a New Pod:

   • Go to "My Pods" → "Create Pod"
   • Select an image such as runpod/pytorch or runpod/python as the base container
   • Choose a GPU instance type with 48 GB VRAM or higher (e.g. A6000 or higher)
   • Enable Docker with SSH access and persistent volume (optional but recommended)
   • Ensure port 7860 is forwarded

3. Clone Your Repository inside the pod:

   git clone https://github.com/jtorregrosa/workshop-ai-image.git
   cd workshop-ai-image

4. Follow the Setup Instructions below to create a virtual environment, install dependencies, and authenticate.

🚀 Features

🔮 Text-to-image generation using pre-trained models

🧪 Easily extensible architecture for different model backends (e.g. Diffusion, GANs)

🔧 Clean Python project structure powered by Poetry

🌐 Hugging Face integration for model loading and authentication

🛠️ Setup Instructions (Local or Remote)

1. Prepare a Virtual Environment

   python -m venv venv
   source venv/bin/activate

2. Install Required Tools

   pip install -U pip setuptools
   pip install poetry
   poetry completions bash >> ~/.bash_completion

3. Install Dependencies

   poetry install

4. Authenticate with Hugging Face

   git config --global credential.helper store
   huggingface-cli login

5. Run the Application

   poetry run python src/workshop-ai-image/app.py