arXiv Computer Vision
CLASP: Continual Low-rank Adapters for Spatially Placed Concepts from One Hypernetwork
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
4d ago
Anchor to Expand: Semantic Anchoring for Personalized Text-to-Image Diffusion Models
arXiv:2511.22245v2 Announce Type: replace Abstract: Personalizing text-to-image diffusion models extends pretrained models to represent novel user-specific concepts from only a few reference images....
arXiv Computer Vision
4d ago
C2P-VAR: Continual and Compositional Personalization in Visual Autoregressive Models
arXiv:2605.19750v2 Announce Type: replace Abstract: Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation, yet their personalization capabilit...
arXiv Machine Learning
Jun 4
Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization
arXiv:2606. 04797v1 Announce Type: cross Abstract: Custom diffusion models (CDMs) have garnered significant interest owing to their remarkable capacity for generating personalized concepts.
arXiv AI
Jul 3
Locality-Aware Continual Unlearning for Diffusion Models
arXiv:2512. 02657v2 Announce Type: replace-cross Abstract: Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations arise over time.
arXiv Machine Learning
23h ago
SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation
arXiv:2605.22743v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models to user-provided concepts (objects, people, or style...
arXiv AI
Aug 13
Representation Finetuning for Continual Learning
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.