9 papers · 1 filter
DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation
Héctor Laria, Yiping Han, Julian D. Santamaria +4
Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone. We argue thi…
Training-free image inversion for one-step diffusion models
Tao Wu, Senmao Li, Yaxing Wang +3
In this work, we introduce a novel training-free inversion (TFinv) framework for one-step diffusion models,addressing key challenges in real image inversion and editing. We first i…
Assessing Open-world Forgetting in Generative Image Model Customization
Héctor Laria, Alex Gomez-Villa, Kai Wang +2
Recent advances in diffusion models have significantly enhanced image generation capabilities. However, customizing these models with new classes often leads to unintended conseque…
Multi-Class Textual-Inversion Secretly Yields a Semantic-Agnostic Classifier
Kai Wang, Fei Yang, Bogdan Raducanu +1
With the advent of large pre-trained vision-language models such as CLIP, prompt learning methods aim to enhance the transferability of the CLIP model. They learn the prompt given…
Exemplar-free Continual Representation Learning via Learnable Drift Compensation
Alex Gomez-Villa, Dipam Goswami, Kai Wang +3
Exemplar-free class-incremental learning using a backbone trained from scratch and starting from a small first task presents a significant challenge for continual representation le…
ColorPeel: Color Prompt Learning with Diffusion Models via Color and Shape Disentanglement
Muhammad Atif Butt, Kai Wang, Javier Vazquez-Corral +1
Text-to-Image (T2I) generation has made significant advancements with the advent of diffusion models. These models exhibit remarkable abilities to produce images based on textual p…