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 …

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 …

While recent developments in text-to-image generative models have led to a suite of high-
performing methods capable of producing creative imagery from free-form text, there are …

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 …

Text-to-image models offer a new level of creative flexibility by allowing users to guide the
image generation process through natural language. However, using these models to …

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 …

Despite advancements in text-to-image generation (T2I) prior methods often face text-image
misalignment problems such as relation confusion in generated images. Existing solutions …

Recent text-to-image generative models can generate high-fidelity images from text inputs,
but the quality of these generated images cannot be accurately evaluated by existing …

Recently, DALL-E, a multimodal transformer language model, and its variants including
diffusion models have shown high-quality text-to-image generation capabilities. However …

Personalized text-to-image generation using diffusion models has recently emerged and
garnered significant interest. This task learns a novel concept (eg, a unique toy), illustrated …