Search Results for author: Shanchuan Lin

Found 8 papers, 4 papers with code

AnimateDiff-Lightning: Cross-Model Diffusion Distillation

no code implementations19 Mar 2024 Shanchuan Lin, Xiao Yang

We present AnimateDiff-Lightning for lightning-fast video generation.

Video Generation

SDXL-Lightning: Progressive Adversarial Diffusion Distillation

no code implementations21 Feb 2024 Shanchuan Lin, Anran Wang, Xiao Yang

We propose a diffusion distillation method that achieves new state-of-the-art in one-step/few-step 1024px text-to-image generation based on SDXL.

Text-to-Image Generation

Diffusion Model with Perceptual Loss

no code implementations30 Dec 2023 Shanchuan Lin, Xiao Yang

In this paper, we show that the effectiveness of classifier-free guidance partly originates from it being a form of implicit perceptual guidance.

Denoising

Common Diffusion Noise Schedules and Sample Steps are Flawed

1 code implementation15 May 2023 Shanchuan Lin, Bingchen Liu, Jiashi Li, Xiao Yang

We discover that common diffusion noise schedules do not enforce the last timestep to have zero signal-to-noise ratio (SNR), and some implementations of diffusion samplers do not start from the last timestep.

Robust High-Resolution Video Matting with Temporal Guidance

1 code implementation25 Aug 2021 Shanchuan Lin, Linjie Yang, Imran Saleemi, Soumyadip Sengupta

We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance.

4k Image Matting +2

Real-Time High-Resolution Background Matting

2 code implementations CVPR 2021 Shanchuan Lin, Andrey Ryabtsev, Soumyadip Sengupta, Brian Curless, Steve Seitz, Ira Kemelmacher-Shlizerman

We introduce a real-time, high-resolution background replacement technique which operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU.

4k Image Matting +1

Fact or Fiction: Verifying Scientific Claims

2 code implementations EMNLP 2020 David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi

We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision.

Claim Verification Domain Adaptation +1

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