3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.CV2024★ 3 cited
Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models
Senmao Li, Joost van de Weijer, Taihang Hu +4
The success of recent text-to-image diffusion models is largely due to their capacity to be guided by a complex text prompt, which enables users to precisely describe the desired c…
cs.CV2023
MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask
Yupeng Zhou, Daquan Zhou, Zuo-Liang Zhu +3
Recent advancements in diffusion models have showcased their impressive capacity to generate visually striking images. Nevertheless, ensuring a close match between the generated im…