6 citations · 10 across the 10 of their papers we have counts for
12 papers
NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
Enshu Liu, Xuefei Ning, Yu Wang +1
Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexibility to generate tokens in…
Distilled Decoding 2: One-step Sampling of Image Auto-regressive Models with Conditional Score Distillation
Enshu Liu, Qian Chen, Xuefei Ning +4
Image Auto-regressive (AR) models have emerged as a powerful paradigm of visual generative models. Despite their promising performance, they suffer from slow generation speed due t…
Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
Zinan Lin, Enshu Liu, Xuefei Ning +3
Generative modeling, representation learning, and classification are three core problems in machine learning (ML), yet their state-of-the-art (SoTA) solutions remain largely disjoi…
R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing
Tianyu Fu, Yi Ge, Yichen You +6
Large Language Models (LLMs) achieve impressive reasoning capabilities at the cost of substantial inference overhead, posing substantial deployment challenges. Although distilled S…
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching
Enshu Liu, Xuefei Ning, Yu Wang +1
Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation but suffer from slow generation due to the token-by-token process. We ask an ambi…
Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs
Enshu Liu, Junyi Zhu, Zinan Lin +6
The rapid advancement of large language models (LLMs) has led to architectures with billions to trillions of parameters, posing significant deployment challenges due to their subst…
ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation
Tianchen Zhao, Tongcheng Fang, Haofeng Huang +10
Diffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, la…
MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
Tianchen Zhao, Xuefei Ning, Tongcheng Fang +6
Diffusion models have achieved significant visual generation quality. However, their significant computational and memory costs pose challenge for their application on resource-con…
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
Enshu Liu, Junyi Zhu, Zinan Lin +8
Diffusion Models (DM) and Consistency Models (CM) are two types of popular generative models with good generation quality on various tasks. When training DM and CM, intermediate we…
A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models
Enshu Liu, Xuefei Ning, Huazhong Yang +1
Recent years have witnessed the rapid progress and broad application of diffusion probabilistic models (DPMs). Sampling from DPMs can be viewed as solving an ordinary differential…
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
Enshu Liu, Xuefei Ning, Zinan Lin +2
Diffusion probabilistic models (DPMs) are a new class of generative models that have achieved state-of-the-art generation quality in various domains. Despite the promise, one major…
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"
Junbo Zhao, Xuefei Ning, Enshu Liu +7
Predictor-based Neural Architecture Search (NAS) employs an architecture performance predictor to improve the sample efficiency. However, predictor-based NAS suffers from the sever…