Text-to-image generative models have garnered immense attention for their ability to
produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes …

Text-to-image generative models often reflect the biases of the training data, leading to
unequal representations of underrepresented groups. This study investigates inclusive text …

Human evaluation is critical for validating the performance of text-to-image generative
models, as this highly cognitive process requires deep comprehension of text and images …

We are witnessing a novel era of creativity where anyone can create digital content via
prompt-based learning (known as prompt engineering). This paper delves into prompt …

Deep generative models have shown impressive results in text-to-image synthesis.
However, current text-to-image models often generate images that are inadequately aligned …

Subject-driven text-to-image generation models create novel renditions of an input subject
based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties …

Abstract Text-to-image (T2I) research has grown explosively in the past year owing to the
large-scale pre-trained diffusion models and many emerging personalization and editing …

Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-
quality and diverse synthesis of images from a given text prompt. However, these models …

We present a comprehensive solution to learn and improve text-to-image models from
human preference feedback. To begin with, we build ImageReward---the first general …