3 papers
cs.CV2024
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection
Alireza Ganjdanesh, Yan Kang, Yuchen Liu +3
Diffusion probabilistic models can generate high-quality samples. Yet, their sampling process requires numerous denoising steps, making it slow and computationally intensive. We pr…
cs.CV2024
TurboEdit: Instant text-based image editing
Zongze Wu, Nicholas Kolkin, Jonathan Brandt +2
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion t…
cs.CV2024
Personalized Residuals for Concept-Driven Text-to-Image Generation
Cusuh Ham, Matthew Fisher, James Hays +4
We present personalized residuals and localized attention-guided sampling for efficient concept-driven generation using text-to-image diffusion models. Our method first represents…