Search Results for author:
Found 8 papers, 8 papers with code
UniFork: Exploring Modality Alignment for Unified Multimodal Understanding and Generation
Our analysis reveals a crucial observation: understanding tasks benefit from a progressively increasing modality alignment across network depth, which helps build up semantic information for better comprehension; In contrast, generation tasks follow a different trend: modality alignment increases in the early layers but decreases in the deep layers to recover spatial details.
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
While the progress in unified models offers new solutions, existing benchmarks are insufficient for evaluating these methods due to limitations in data size and diversity.
Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model
To tackle this challenge, we introduce Diffree, a Text-to-Image (T2I) model that facilitates text-guided object addition with only text control.
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions.
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model
Text-to-image (T2I) generative models have attracted significant attention and found extensive applications within and beyond academic research.
Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study
The poor cross-architecture generalization of dataset distillation greatly weakens its practical significance.
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
Inspired by the Dynamic Sparse Training, DSnoT minimizes the reconstruction error between the dense and sparse LLMs, in the fashion of performing iterative weight pruning-and-growing on top of sparse LLMs.
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
LWC modulates the extreme values of weights by optimizing the clipping threshold.
