34 datasets found

Generating photo-realistic images from a text description is a challenging problem in computer vision. Previ- ous works have shown promising performance to generate synthetic...

The dataset used for concept sliders, including Age, Surprise, and Hair sliders.

The dataset used for text-to-image diffusion models, including Bluefire, Paintings, 3D, and Origami styles.

The HPSv2 dataset is a text-image pair dataset containing 3200 prompts and their corresponding images.

The Stable Diffusion Model (SDM) is a preva- lent and effective model for text-to-image (T2I) and image-to-image (I2I) generation.

The dataset used in this paper is a large-scale text-to-image diffusion model, which consists of 35 subjects with unique pets and objects.

A dataset of images with fine-grained control over several 3D-aware attributes, including time-of-day illumination, bird wing orientation, dollyzoom effect, and object poses.

The ABC-6K dataset includes prompts with at least two color words modifying different objects.

The DVMP dataset features a diverse set of objects and diverse modifiers including colors, textures, etc.

The AnE dataset comprises three benchmarks: Animal-Animal, Animal-Object, and Object-Object.

The dataset used in the paper for testing the TextCraftor model.

The dataset used in the paper for training and testing the TextCraftor model.

TextCraftor is a stable and powerful framework to fine-tune the pre-trained text encoder to improve the text-to-image generation.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used Stable Diffusion and Yolo to optimize the parameters and prompts for...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors compared their results with PLMS.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors compared their results with DPM++.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a 20-step DDIM sampler and a 13-step DDIM sampler.

A large dataset of text-image pairs collected online.

MUSE is a text-to-image generation model that uses masked generative transformers.

The authors present a photorealistic text-to-image diffusion model with deep language understanding.