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Found 10 papers, 5 papers with code
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.
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.
