From 56a65a0818dbfc7753d2b7ea8d0177b52ee7aa57 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Fri, 8 Nov 2024 15:11:47 -0500 Subject: [PATCH] vault backup: 2024-11-08 15:11:47 --- 201 Metadata/My Library.bib | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index 142841a55..7b53602cc 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -12597,6 +12597,17 @@ Subject\_term: Careers, Politics, Policy}, file = {/home/danesabo/Zotero/storage/GJ8Q4YD8/Wang et al. - 2019 - A Formal Model-Based Design Method for Robotic Sys.pdf} } +@article{wangInterpolatingImagesDiffusion2023, + title = {Interpolating between {{Images}} with {{Diffusion Models}}}, + author = {Wang, Clinton and Golland, Polina}, + date = {2023-06-23}, + url = {https://openreview.net/forum?id=L2D9Gybx0P#all}, + urldate = {2024-11-08}, + abstract = {One little-explored frontier of image generation and editing is the task of interpolating between two input images, a feature missing from all currently deployed image generation pipelines. We argue that such a feature can expand the creative applications of such models, and propose a method for zero-shot interpolation using latent diffusion models. We apply interpolation in the latent space at a sequence of decreasing noise levels, then perform denoising conditioned on interpolated text embeddings derived from textual inversion and (optionally) subject poses derived from OpenPose. For greater consistency, or to specify additional criteria, we can generate several candidates and use CLIP to select the highest quality image. We obtain convincing interpolations across diverse subject poses, image styles, and image content, and show that standard quantitative metrics such as FID are insufficient to measure the quality of an interpolation. Code and data are available at \textbackslash url\{https://clintonjwang.github.io/interpolation\}.}, + langid = {english}, + file = {/home/danesabo/Zotero/storage/FSUBB6F2/Wang and Golland - 2023 - Interpolating between Images with Diffusion Models.pdf} +} + @online{wangPINNsBasedUncertaintyQuantification2023, title = {{{PINNs-Based Uncertainty Quantification}} for {{Transient Stability Analysis}}}, author = {Wang, Ren and Zhong, Ming and Xu, Kaidi and Sánchez-Cortés, Lola Giráldez and Guerra, Ignacio de Cominges},