In this work, we investigate the task of text-to-image (T2I) synthesis under the abstract-to-intricate setting, i.e., generating intricate visual content from simple

We introduce a new approach, Generative mOdel finetun-ing with Reward-driven Sample selection (GORS), to boost the compositional text-to-image generation abilities of pretrained text-to-image models.

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst being consistent throughout the frames.

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.

Recently, open-domain text-to-video (T2V) generation models have made remarkable progress.

The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users.