The paper introduces SEAL, a plug‑and‑play module that enhances single‑image sticker personalization in diffusion models by adding a semantic‑guided spatial attention loss, a split‑merge token strategy, and structure‑aware layer restriction. SEAL integrates without altering the U‑Net backbone and addresses overfitting issues such as visual entanglement and structural rigidity. Alongside SEAL, the authors release StickerBench, a large sticker dataset with six attribute tags to enable systematic evaluation of identity preservation and contextual controllability.
arXiv:2609.37198v1 Announce Type: new Abstract: Pretrained text-to-image models contain broad visual knowledge, yet they cannot reliably acquire or refine a specific visual identity from only a few r...
arXiv:2506.02015v4 Announce Type: replace Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled unified multimodal understanding and generation. However, they still strug...
arXiv:2606. 08841v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than individual taste.
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:2512.23245v3 Announce Type: replace Abstract: Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while...
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:2606. 03792v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual concepts and styles.
arXiv:2607. 22919v1 Announce Type: cross Abstract: Multimodal embedding spaces in models like CLIP enable powerful capabilities such as semantic similarity retrieval and cross-modal zero-shot classification.
RefDiT is a new framework for reference-guided image generation that addresses the shortcomings of previous methods when handling complex scenes with multiple objects. It introduces local region guidance by decomposing a single identifier token into attribute-level signals, allowing the model to learn correspondences between tokens and specific regions of a reference image. The approach incorporates a low-rank adapter (LoRA) within a diffusion transformer (DiT) to adjust the inference prompt based on user-provided guidance context, thereby enabling more precise local attribute control.
Generating and editing a person's face demands high precision, as even minor modifications can significantly alter a subject's perceived identity. Current personalization and editing methods built on general-purpose text-to-image models, however, often lack the precision required for fine-grained facial edits.
Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstrate emerging capabilities in multi-source image editing (MIE), including tasks such as object synthesis, person-background composition, and cross-image style fusion.
arXiv:2608. 07565v1 Announce Type: cross Abstract: Conversational assistants increasingly recommend follow-up edits to help users continue a task.