C2P-VAR: Continual and Compositional Personalization in Visual Autoregressive Models

arXiv:2605.19750 · cs.CV · Submitted 2026-05-19 · Read on arXiv

cs.CV

Submitted: 2026-05-19

Updated: 2026-09-25

Code: https://github.com/kakaobrain/coyo-dataset

Terminology

• Personalization — 38× in this paper · explained in 2 episodes
• Regularization — 19× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 16× in this paper · explained in 5 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Diffusion Model — 13× in this paper · explained in 8 episodes
• Diffusion Models — 13× in this paper · explained in 6 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Ablation — 9× in this paper · explained in 1 episode
• Alignment — 9× in this paper · explained in 1 episode
• Ablation study — 8× in this paper · explained in 1 episode
• Continual Learning — 8× in this paper · explained in 2 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Gradient-based Concept Neuron Selection — 6× in this paper
• Entanglement — 5× in this paper · explained in 3 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Unified Framework — 3× in this paper · explained in 5 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Feed-Forward Network — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Machine Unlearning — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transactions on Graphics — 1× in this paper

Sources

• SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
• Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
• An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
• Aligning Text-to-Image Models using Human Feedback
• Decoupled Weight Decay Regularization
• Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
• A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models
• SmoothGrad: removing noise by adding noise
• Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA
• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
• Image and Video Tokenization with Binary Spherical Quantization
• Closing the Safety Gap: Surgical Concept Erasure in Visual Autoregressive Models

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