Search Results for author:
Found 7 papers, 4 papers with code
Phantom: Subject-consistent video generation via cross-modal alignment
The continuous development of foundational models for video generation is evolving into various applications, with subject-consistent video generation still in the exploratory stage.
Injecting Universal Jailbreak Backdoors into LLMs in Minutes
Our findings underscore the effectiveness, stealthiness, and explainability of JailbreakEdit, emphasizing the need for more advanced defense mechanisms in LLMs.
CustomContrast: A Multilevel Contrastive Perspective For Subject-Driven Text-to-Image Customization
This misconstruction leads to both overfitting or underfitting of irrelevant and intrinsic attributes of the subject, i. e., these attributes are over-represented or under-represented simultaneously, causing a trade-off between similarity and controllability.
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification
The potential of the diffusion language model (LM) for textual data augmentation (DA) remains unexplored, moreover, textual DA methods struggle to balance the diversity and consistency of new samples.
PuLID: Pure and Lightning ID Customization via Contrastive Alignment
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation.
DreamIdentity: Improved Editability for Efficient Face-identity Preserved Image Generation
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centric images, an intractable problem is how to preserve the face identity for conditioned face images.
Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantization
Existing vector quantization (VQ) based autoregressive models follow a two-stage generation paradigm that first learns a codebook to encode images as discrete codes, and then completes generation based on the learned codebook.
