Search Results for author:
Found 18 papers, 10 papers with code
Neighboring Autoregressive Modeling for Efficient Visual Generation
In this paper, we propose Neighboring Autoregressive Modeling (NAR), a novel paradigm that formulates autoregressive visual generation as a progressive outpainting procedure, following a near-to-far ``next-neighbor prediction" mechanism.
ZipAR: Accelerating Auto-regressive Image Generation through Spatial Locality
By decoding multiple tokens simultaneously in a single forward pass, the number of forward passes required to generate an image is significantly reduced, resulting in a substantial improvement in generation efficiency.
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.
ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification
The efficiency of large vision-language models (LVLMs) is constrained by the computational bottleneck of the attention mechanism during the prefill phase and the memory bottleneck of fetching the key-value (KV) cache in the decoding phase, particularly in scenarios involving high-resolution images or videos.
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models
LLM development involves pre-training a foundation model on massive data, followed by fine-tuning on task-specific data to create specialized experts.
MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
In this paper, we present a simple yet effective approach, called MiniCache, to compress the KV cache across layers from a novel depth perspective, significantly reducing the memory footprint for LLM inference.
ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification
In terms of efficiency, ZipCache also showcases a $37. 3\%$ reduction in prefill-phase latency, a $56. 9\%$ reduction in decoding-phase latency, and a $19. 8\%$ reduction in GPU memory usage when evaluating LLaMA3-8B model with a input length of $4096$.
DragAnything: Motion Control for Anything using Entity Representation
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation.
Towards Accurate Post-training Quantization for Reparameterized Models
Our framework outperforms previous methods by approximately 1\% for 8-bit PTQ and 2\% for 6-bit PTQ, showcasing its superior performance.
Paragraph-to-Image Generation with Information-Enriched Diffusion Model
In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation.
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM
Fine-grained quantization has smaller quantization loss, consequently achieving superior performance.
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
In this paper, we introduce a data-free and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT-level performance with PTQ-like efficiency.
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation.
PTQD: Accurate Post-Training Quantization for Diffusion Models
To address these challenges, we propose a unified formulation for the quantization noise and diffusion perturbed noise in the quantized denoising process.
BiViT: Extremely Compressed Binary Vision Transformers
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
BiViT: Extremely Compressed Binary Vision Transformer
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
Binarizing by Classification: Is soft function really necessary?
Extensive experiments demonstrate that the proposed method yields surprising performance both in image classification and human pose estimation tasks.
Ranked #1 on
Binarization
on ImageNet
(Top 1 Accuracy metric)
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization
In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization.
