Text Conditioned Image Generation

Text-conditioned image generation aims to create images from textual descriptions, focusing on improving image quality, text alignment, and safety. Current research explores various model architectures, including diffusion models (often enhanced with techniques like consistency models for speed improvements) and autoregressive models, with a strong emphasis on mitigating biases and harmful content generation through methods such as prompt manipulation and latent space correction. This field is significant due to its potential for creative applications and its challenges in addressing ethical concerns related to bias and safety, driving advancements in both generative modeling and responsible AI development.