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""&" U\\ ell  r;rc^\rSrSrSS\S\S\S\S\S\S\S \S \S \S \4U4S jjjrS\ RS\ R4Sjr Sr U=r $)EasyAnimateDownBlock3Dirr num_layersact_fnrhrirjrkrladd_downsampleadd_temporal_downsamplec >[T U]5 [R"/5Ul[ U5H6n U S:XaUOUnURR [UUUUUUUU S95 M8 U (a&U (a[X"SSS9Ul SUl SUl gU (a&U (d[X"SSS9Ul SUl SUl gSUl SUl SUl g) Nrrrrgrhrirjrkrlrr)rrr rr) r*r+rs ModuleListconvsrangerQrer downsamplerspatial_downsample_factortemporal_downsample_factor)r,rrrrrhrirjrkrlrrrVr8s r9r+EasyAnimateDownBlock3D.__init__s ]]2& z"A)*a+\K JJ  * +!-"($3%'9#(;  # 57 `ajstD -.D *./D + $;7 `ajstD -.D *./D +#D -.D *./D +r;rCrDcxURH nU"U5nM URbURU5nU$r=)rrr,rCrs r9rPEasyAnimateDownBlock3D.forward>s<JJD /M    ' ,,];Mr;)rrrr) rrrrTrrTTrrcs@r9rrs !#'%(#(,+0+0+0 +0  +0  +0+0!+0+0#+0+0"&+0+0ZU\\ellr;rc^\rSrSrSS\S\S\S\S\S\S\S \S \S \S \4U4S jjjrS\ RS\ R4Sjr Sr U=r $)EasyAnimateUpBlock3diFrrrrrhrirjrkrl add_upsampleadd_temporal_upsamplec ">[T U]5 [R"/5Ul[ U5H6n U S:XaUOUnURR [UUUUUUUU S95 M8 U (a[UUU US9Ul gSUl g)Nrr)rrj) r*r+rsrrrrQrer upsampler)r,rrrrrhrirjrkrlrrrVr8s r9r+EasyAnimateUpBlock3d.__init__Gs ]]2& z"A)*a+\K JJ  * +!-"($3%'9#(;  # 3"7#5 DN"DNr;rCrDcxURH nU"U5nM URbURU5nU$r=rrrs r9rPEasyAnimateUpBlock3d.forwardqs8JJD /M >> % NN=9Mr;r) rrrrFrrTTrrcs@r9rrFs !#(%(!&*("("(" ("  ("  ("("!("("#("(" $("("TU\\ellr;rc^\rSrSrSS\S\S\S\S\S\S\S \4U4S jjjrS \ RS \ R4S jr Sr U=r $)EasyAnimateMidBlock3diyrrrrhrirjrkrlc >[T U]5 UbUO[US-S5n[R"[ UUUUUUUUS9/5Ul[US- 5H,n UR R[ UUUUUUUUS95 M. g)Nrrrr) r*r+minrsrrerrrQ) r,rrrrhrirjrkrl_r8s r9r+EasyAnimateMidBlock3d.__init__zs -<-H/cR]abRbdfNg]]* +!,"($3%'9#(;     zA~&A JJ  * +!,"($3%'9#(;  'r;rCrDclURS"U5nURSSH nU"U5nM U$)Nrrr)r,rCresnets r9rPEasyAnimateMidBlock3d.forwards8 1 m4 jjnF"=1M%r;r)rrrrTrrrrcs@r9rrys!#'%(*** *  *  *!**#**XU\\ellr;rc^\rSrSrSrSrSSS/SQSS S SS 4 S \S \S\\S4S\\S4S\S\S\S\ S\ 4U4Sjjjr S\ RS\ R4Sjr SrU=r$)EasyAnimateEncoderizh Causal encoder for 3D video-like data used in [EasyAnimate](https://huggingface.co/papers/2405.18991). TrSpatialDownBlock3DSpatialTemporalDownBlock3Drrrr rrFrrdown_block_types.block_out_channelslayers_per_blockrhrdouble_zrjc >[TU]5 [XSSS9Ul[R "/5UlUSn [U5HupU n XKn U [U5S- :HnU S:Xa[U U UUUSU U(+SS9 nO+U S :Xa[U U UUUSU U(+S S9 nO[S U 35eUR RU5 M [US UUU USSSS 9Ul Xl[R"US USS9Ul[#U5UlU(aSU-OUn[US USS9UlSUlg)NrrrrrrrF) rrrrrhrirjrrrTUnknown up block type: r)rrrrjrhrirkrlrornrpr )r*r+rconv_inrsr down_blocks enumerater!r ValueErrorrQr mid_blockrjrt conv_norm_outr conv_actconv_outgradient_checkpointing)r,rrrrrrhrrrjoutput_channelsrVdown_block_typeinput_channelsis_final_block down_blockconv_out_channelsr8s r9r+EasyAnimateEncoder.__init__s" /{qB/*2.'1+ !  #5\\+B/&  'v. 19A ,l/0B20FHYghi &+#r;rCrDcbURU5nURHIn[R"5(a$UR(aUR X!5nMAU"U5nMK UR U5nUR(aoURS5nURSSSSS5RSS5nURU5nURSUS45RSSSSS5nOURU5nURU5nURU5nU$r)rrrRis_grad_enabledr_gradient_checkpointing_funcrrjrOrrrrrr)r,rCrrs r9rPEasyAnimateEncoder.forwards ]3 **J$$&&4+F+F $ A A* \ *= 9 + }5  " "&++A.J)11!Q1a@HHANM ..}=M)33A B7GHPPQRTUWXZ[]^_M ..}=M m4  m4 r;)rrrrrrrrjrZr[r\r]__doc__ _supports_gradient_checkpointingr%rr_r^r+rRr`rPrarbrcs@r9rrs(,$- /C !!#(R,R,R, S/ R,"#s(OR,R,R,R,R,!R,R,hU\\ellr;rc^\rSrSrSrSrSSS/SQSS S S 4S \S \S\\S4S\\S4S\S\S\S\ 4U4Sjjjr S\ RS\ R4Sjr SrU=r$)EasyAnimateDecoderi!zh Causal decoder for 3D video-like data used in [EasyAnimate](https://huggingface.co/papers/2405.18991). TrrSpatialUpBlock3DSpatialTemporalUpBlock3Drrrr rrFrrup_block_types.rrrhrrjc >[TU]5 [XSSS9Ul[ USUUUSSSS9Ul[ R"/5Ul[[U55n U Sn [U5HupU n Xn U [U5S- :HnU S:Xa[U U US-UUSUU(+S S 9 nO.U S :Xa[U U US-UUSUU(+S S 9 nO[S U 35eURRU5 M Xl[ R""USUSS9Ul['U5Ul[USUSS9UlS Ulg)Nrrrrrrr)rrrrhrirkrlrF) rrrrrhrirjrrrTrr)r*r+rrrrrsr up_blockslistreversedrr!rrrQrjrtrr rrr)r,rrrrrrhrrjreversed_block_out_channelsrrV up_block_typerrup_blockr8s r9r+EasyAnimateDecoder.__init__(s /{rAutoencoderKLMagvitia] A VAE model with KL loss for encoding images into latents and decoding latent representations into images. This model is used in [EasyAnimate](https://huggingface.co/papers/2405.18991). This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). Trrrrr rrg?rlatent_channelsrr.rrrrrhscaling_factorrjc >[T U]5 [UUUUUU USU S9 Ul[ UUUUUU UU S9Ul[ R"SU-SU-SS9Ul[ R"X"SS9Ul S[U5S- -Ul S[U5S- -Ul SUl SUlSUlSUlSUlSUlS UlS UlSUlS UlS UlS Ulg) NT) rrrrrrhrrrj)rrrrrrhrrjr rrrFrrir)r*r+rencoderrdecoderrsr{ quant_convpost_quant_convr!spatial_compression_ratiotemporal_compression_ratio use_slicing use_tilinguse_framewise_encodinguse_framewise_decodingnum_sample_frames_batch_sizenum_latent_frames_batch_sizetile_sample_min_heighttile_sample_min_widthtile_sample_min_num_framestile_sample_stride_heighttile_sample_stride_widthtile_sample_stride_num_frames) r,rr rrrrrrrhr rjr8s r9r+AutoencoderKLMagvit.__init__s/2 *#(-1-+1   *'%)1-+1   ))A$7_9LZ[\!yyWXY)*s3E/F/J)K&*+4F0G!0K*L'!  ',#&+#-.),-)'*#%("*+'*-&(+%-.*r;cUR5HQup[U[5(aUR5 [U[5(dMAUR5 MS gr=) named_modulesrrr@r)r,namemodules r9r@%AutoencoderKLMagvit._clear_conv_cachesK ..0LD&"9::((*&"899((* 1r;NrrrrrrrDcfSUlSUlSUlU=(d URUlU=(d URUlU=(d UR UlU=(d UR UlU=(d URUlU=(d URUlg)a Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. Args: tile_sample_min_height (`int`, *optional*): The minimum height required for a sample to be separated into tiles across the height dimension. tile_sample_min_width (`int`, *optional*): The minimum width required for a sample to be separated into tiles across the width dimension. tile_sample_stride_height (`int`, *optional*): The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are no tiling artifacts produced across the height dimension. tile_sample_stride_width (`int`, *optional*): The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling artifacts produced across the width dimension. TN) rrrrrrrrr)r,rrrrrrs r9 enable_tiling!AutoencoderKLMagvit.enable_tilings4&*#&*#&<&[@[@[#%:%Xd>X>X"*D*gHgHg')B)ddFdFd&(@(aDDaDa%-J-pdNpNp*r;cSUlg)z Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing decoding in one step. FN)rr?s r9disable_tiling"AutoencoderKLMagvit.disable_tiling(s  r;cSUlg)z Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. TNrr?s r9enable_slicing"AutoencoderKLMagvit.enable_slicing/s  r;cSUlg)z Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing decoding in one step. FNr+r?s r9disable_slicing#AutoencoderKLMagvit.disable_slicing6s !r;x return_dictc HUR(aIURSUR:dURSUR:aUR XS9$UR USS2SS2SS2SS2SS245nU/n[ SURSUR5HCnUR USS2SS2XUUR-2SS2SS245nURU5 ME [R"USS9nURU5nUR5 U$)a Encode a batch of images into latents. Args: x (`torch.Tensor`): Input batch of images. return_dict (`bool`, *optional*, defaults to `True`): Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. Returns: The latent representations of the encoded images. If `return_dict` is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned. rr2Nrr rJ) rshaperr tiled_encoder rrrQrRcatrr@)r,r1r2 first_frameshrV next_framesmomentss r9_encodeAutoencoderKLMagvit._encode=s ?? d.I.I IQWWUW[[_[u[uMu$$Q$@ @||AaBQB1n$56 Nq!''!*d&G&GHA,,qAqt7X7X3X/XZ[]^)^'_`K HH[ !I IIaQ //!$  r;c>UR(aYURSS:aFURS5Vs/sHo0RU5PM nn[R "U5nOURU5n[ U5nU(dU4$[US9$s snf)a Encode a batch of images into latents. Args: x (`torch.Tensor`): Input batch of images. return_dict (`bool`, *optional*, defaults to `True`): Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. Returns: The latent representations of the encoded videos. If `return_dict` is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned. rr) latent_dist)rr6splitr=rRr8rr )r,r1r2x_sliceencoded_slicesr: posteriors r9encodeAutoencoderKLMagvit.encode[s    QCD771:N:ll73:NN .)A QA03 < "y99OsBzc URup4pVnURUR-nURUR-n UR(a5URSU:dURSU :aUR XS9$UR U5nURUSS2SS2SS2SS2SS245n U /n [SURSUR5HCn URUSS2SS2XUR-2SS2SS245n U RU 5 ME [R"U SS9n U(dU 4$[U S9$)Nrr4r5rr rJsample)r6rrrr tiled_decoderr rrrQrRr8r)r,rGr2rrorTheightwidthtile_latent_min_heighttile_latent_min_widthr9decrVr;s r9_decodeAutoencoderKLMagvit._decodews9>?gg; *e!%!\>\ \ ?? .D DPR VkHk$$Q$@ @   #||AaBQB1n$56 nq!''!*d&G&GHA,,qAqt7X7X3X/XZ[]^)^'_`K JJ{ #Iii#6MC((r;cpUR(acURSS:aPURS5Vs/sHo0RU5RPM nn[ R "U5nOURU5RnUR5 U(dU4$[US9$s snf)a Decode a batch of images. Args: z (`torch.Tensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to `True`): Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple. Returns: [`~models.vae.DecoderOutput`] or `tuple`: If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is returned. rrrI) rr6rArQrJrRr8r@r)r,rGr2z_slicedecoded_slicesdecodeds r9decodeAutoencoderKLMagvit.decodes    QJK''RS*U*wll73::*NUii/Gll1o,,G  : G,,Vs$B3ab blend_extentc [URSURSU5n[U5HInUSS2SS2SS2U*U-SS24SXC- - -USS2SS2SS2USS24XC- --USS2SS2SS2USS24'MK U$)Nrrrr6r)r,rYrZr[ys r9blend_vAutoencoderKLMagvit.blend_vs1771:qwwqz<@ |$A Aq<-!*;Q!>?1qGWCWX[\]^`acdfgij]j[k \ AaAq!m %r;c[URSURSU5n[U5HInUSS2SS2SS2SS2U*U-4SXC- - -USS2SS2SS2SS2U4XC- --USS2SS2SS2SS2U4'MK U$)Nrrr])r,rYrZr[r1s r9blend_hAutoencoderKLMagvit.blend_hs1771:qwwqz<@ |$A Aq!l]Q->!>?1qGWCWX[\]^`acdfgij]j[k \ AaAq!m %r;c URup4pVnX`R-nXpR-n URUR-n URUR-n URUR-n UR UR-n X- nX- n/n[ SX`R5GH-n/n[ SXpR 5HnUSS2SS2SS2UUUR-2UUUR-24nURUSS2SS2SS2SS2SS245nU/n[ SXPR5HDnURUSS2SS2UUUR-2SS2SS245nURU5 MF [R"USS9nURU5nUR5 URU5 M URU5 GM0 /n[U5Hunn/n[U5HjunnUS:aURUUS- UUU5nUS:aUR!UUS- UU5nURUSS2SS2SS2SU2SU 245 Ml UR[R"USS95 M [R"USS9SS2SS2SS2SU2SU 24nU$)Nrrr rJrr)r6rrrrrrr rrQrRr8rr@rr_rb)r,r1r2rrorTrLrM latent_height latent_widthrNrOtile_latent_stride_heighttile_latent_stride_width blend_height blend_widthrowsrVrowjtiler9tile_hkr; result_rows result_rowr<s r9r7 AutoencoderKLMagvit.tiled_encodes>?gg; *e"@"@@  > >> !%!\>\ \$($B$BdFdFd$d!#'#@#@DDbDb#b -I +F q&"@"@AAC1e%B%BCD7777D6666 8 $||DAqsAq,AB &q*.O.OPA"&,,tAq!a$BcBc>c:cefhi4i/j"kKMM+.QyyQ/t,&&( 4 #D$ KK )B* oFAsJ$S>4q5<<QU AlKDq5<<AE D+FD!!$q!Q  'M"NO*   uyy; <&))KQ/1a-,0VWr;c URup4pVnX`R-nXpR-n URUR-n URUR-n URUR-n UR UR-n URUR- nURUR - n/n[ SXl5GHn/n[ SX}5HnUSS2SS2SS2UUU -2UUU -24nURU5nURUSS2SS2SS2SS2SS245nU/n[ SXPR5HDnURUSS2SS2UUUR-2SS2SS245nURU5 MF [R"USS9nUR5 URU5 M URU5 GM /n[U5Hunn/n[U5H~unnUS:aURUUS- UUU5nUS:aUR!UUS- UU5nURUSS2SS2SS2SUR2SUR 245 M UR[R"USS95 M [R"USS9SS2SS2SS2SU2SU 24nU(dU4$[#US9$)Nrrr rJrrrI)r6rrrrrrrr rrQrRr8r@rr_rbr)r,rGr2rrorTrLrM sample_height sample_widthrNrOrgrhrirjrkrVrlrmrnr9tile_decrpr;rVrqrrrPs r9rK AutoencoderKLMagvit.tiled_decodes>?gg; *e!?!?? === !%!\>\ \$($B$BdFdFd$d!#'#@#@DDbDb#b 22T5S5SS 0043P3PP q&222111 3++D1 $||DArr1a,@A (>q*.O.OPA"&,,tAq!a$BcBc>c:cefhi4i/j"kKOOK0Q ))H!4&&( 7#+?, KK 1=2 oFAsJ$S>4q5<<QU AlKDq5<<AE D+FD!!$q!Q0P$2P2P0PRqTXTqTqRq'q"rs*   uyy; <&ii +Aq!^m^]l],RS6MC((r;rJsample_posterior generatorcUnURU5RnU(aURUS9nOUR5nUR U5RnU(dU4$[ US9$)a) Args: sample (`torch.Tensor`): Input sample. sample_posterior (`bool`, *optional*, defaults to `False`): Whether to sample from the posterior. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`DecoderOutput`] instead of a plain tuple. )rzrI)rEr@rJrHrWr) r,rJryr2rzr1rDrGrPs r9rPAutoencoderKLMagvit.forward,sf KKN..   9 5A Akk!n##6MC((r;)r r rrrrrrrrrrrrrrrr)NNNNNN)rDN)T)FTN)&rZr[r\r]rrrr%rr_rr^r+r@rr%r(r,r/r rRr`rr rr=rErrQrWr_rbr7rK GeneratorrPrarbrcs@r9rrsn(,$!.B- + !"! &#'-T/T/T/ T/ "#s(O T/  S/ T/c3hT/$%T/&'T/()T/*+T/,!-T/T/l+15/34859489="q ( "q (}"q%-SM "q $,E? 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