AI-driven Art
Overview
All images in this project are generated using deep neural networks, starting from a textual description. The pipeline used is a combination of VQGAN and OpenAI's CLIP, which together can transform text prompts into visually striking images.
The Process
The generation process involves:
- Creating a text prompt that describes the desired image
- Using CLIP to evaluate how well generated images match the text
- Using VQGAN to generate and iteratively refine the images
- Fine-tuning the process through multiple iterations
This approach allows for artistic exploration at the intersection of human creativity and machine learning. By carefully crafting prompts and guiding the generation process, we can achieve images with unique aesthetics that wouldn't be possible with traditional artistic methods.
Gallery
The Fabric of Art
Fabric of Art II
Factory of Art
Emotion
Emotion II
Deep Blue Eyes
Elephant Dreams
I'm Not Here
Blue Eyed Francaisette
London Punk
London Punk II
Milan Punk
Technical Details
VQGAN (Vector Quantized Generative Adversarial Network): A type of GAN that learns to compress and reconstruct images through a discrete codebook of visual components.
CLIP (Contrastive Language-Image Pre-Training): A neural network trained on a variety of image-text pairs, which can understand both images and natural language descriptions.
The combination of these two models allows us to generate images that are guided by text prompts, creating a new form of human-AI collaborative art creation.
