ngtheirlatentspaces,whichcouplespatialandtemporalfeatures.ModelL1error↓MUGMHADNATOPSLDM[57]0.4190.2940.222LFDM(Ours)0.4180.3020.260Table3.Comparisonofself-reconstructionL1errorbetweentheauto-encoderofLDMandthatofLFDMontestingsetsofmultipledatasetsundertheresolutionof128×128.ModelFVD↓sFVD↓ImaGINator[77]196.18352.52±104.54VDM[27]106.66214.21±95.96LDM64[57]125.36242.19±79.40LFDM64(Ours)42.50154.50±50.54LDM128[57]127.12247.28±74.26LFDM128(Ours)49.31167.58±56.85Table4.ComparisonamongdifferentmethodsonMUGdatasetforstochasticimage-to-videogeneration.ThoughVDMachievessimilarFVDscorestooursonNATOPSdataset,notethatVDMhashighercomputationalcostthanLFDMsincethelatentﬂowspaceinourDMhaslowerdimensions.Whensamplingwiththebatchsizeof10using1000-stepDDPMononeNVIDIAA100GPU,LFDMcostsabout0.9GBand36stogenerateonevideoof128×128resolutionwhileVDMrequiresabout2.5GBand112.5stosampleonevideoofonly64×64resolution.Tofurtheranalyzetheperformancedifferenceamongdifferentmodels,wealsocomputetheirFVDscoresofgen-erate
Conditional Image-to-Video Generation with Latent Flow Diffusion Models