Adding Conditional Control to Text-to-Image Diffusion Models

The paper introduces ControlNet, a neural network architecture that enhances the controllability of large pretrained text-to-image diffusion models. It allows users to provide additional visual information to guide the image generation process, enabling finer control over the resulting images. ControlNet's unique architecture and utilization of zero convolution layers set it apart from existing methods in text-to-image generation.

ControlNet addresses the challenge of achieving fine-grained control in text-to-image generation by allowing users to provide direct visual input alongside text prompts. Its unique trainable copies of encoding layers and zero convolution layers ensure efficient learning with limited data. The experimental results demonstrate ControlNet's superiority over existing methods and its potential to rival industrially trained models with fewer computational resources.

Tags: Generative Models, Computer Vision, Deep Learning, Multimodal AI