4 accepted papers
In recent years, multi-concept personalization for text-to-image (T2I) diffusion models to represent several subjects at an image has gained much more attention. The main challenge of this task is "concept mixing", where multiple learned concepts interfere or blend undesirably in the output image. T…
Recent advances in diffusion models have enabled high-quality synthesis of specific subjects, such as identities or objects. This capability, while unlocking new possibilities in content creation, also introduces significant privacy risks, as personalization techniques can be misused by malicious us…
Recent advances in generative models trained on large-scale datasets have made it possible to synthesize high-quality samples across various domains. Moreover the emergence of strong inversion networks enables not only a reconstruction of real-world images but also the modification of attributes thr…
We address a challenging lifelong few-shot image generation task for the first time. In this situation, a generative model learns a sequence of tasks using only a few samples per task. Consequently, the learned model encounters both catastrophic forgetting and overfitting problems at a time. Existin…