ragesamplingtimetogenerateonevideowhenusingthebatchsizeof10ononeNVIDIAA100GPU.Weﬁrstcomparetheresultssampledby10-stepDDIMand100-stepDDIMagainst1000-stepDDPM(de-faultsetting).Itisinterestingtoobservethat10-stepDDIMshowsnoticeablybetterperformancethan100-stepDDIMwithfastersamplingspeed.WealsotrytwotypicalvaluesofguidancescaleginEq.4,smallguidance1.1andlargeguidance5.0.Thoughusingg=1.1guidanceslightlyim-provestheFVDscore,italsodoublestheinferencetime.Soweuseg=1asourdefaultsetting.WealsotrytochangethenetworkdepthofthedecoderΩinstage-oneLFAEandthedenoisingmodel(cid:15)θinstage-twoDM;resultsofthisab-lationstudy,generatedvideoexamplesandfurtherdiscus-sionareincludedinourSupp.materials.5.ConclusionandDiscussionInthispaper,weproposeanovelconditionalimage-to-videoframework,latentﬂowdiffusionmodels(LFDM),togeneratevideosbywarpinggivenimageswithﬂowse-quencesgeneratedinthelatentspacebasedonclasscon-ditions.ComprehensiveexperimentsshowthatLFDMcanachievestate-of-the-artperformanceonmultipledatasets.T
Conditional Image-to-Video Generation with Latent Flow Diffusion Models