Description: Large-scale concept learning evaluations for personalized text-to-image diffusion models.
diffusion models (23) textual inversion (4) conceptbed (1) concept learning (1)
Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models
The ability to understand visual concepts and replicate and compose these concepts from images is a central goal for computer vision. Recent advances in text-to-image (T2I) models have lead to high definition and realistic image quality generation by learning from large databases of images and their descriptions. However, the evaluation of T2I models has focused on photorealism and limited qualitative measures of visual understanding. To quantify the ability of T2I models in learning and synthesizing novel
Weight Modulation for User Attribution and Fingerprinting in T2I Models.
Maitreya Patel
Tejas Gokhale
WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations
Maitreya Patel
Tejas Gokhale
ConceptBed Leaderboard