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-r/)rrr)rrrrrTrp)rQrRrSrTr@r$rVrUrr>rrWrNrXrYrZs@r-rr^s  $!6?,,,,,, ,,  ,,  ,,,,,,,, %S#s]3,, ,,,,\ U\\ ell  r/rc^\rSrSrSrSS\S\S\\S4S\\S4S\S \S \S \S \S \SS4U4Sjjjr S\ RS\ R4Sjr Sr U=r$)HunyuanVideoEncoder3Dizp Causal encoder for 3D video-like data introduced in [Hunyuan Video](https://huggingface.co/papers/2412.03603). r3r4down_block_types.block_out_channelslayers_per_blockract_fndouble_ztemporal_compression_ratiospatial_compression_ratiorNc ,>[TU]5 [XSSSS9UlSUl[ R "/5UlUSn [U5GHBupUS:wa[SU35eU nXMn U [U5S- :Hn[[R"U 55n[[R"U 55nU S:Xa:[U U:5n[U [U5S- U- :=(a U(+5nO1U S:Xa[U U:5n[U U:5nO[S U 35eU(aS OS nU(aS OS n[UU-5n[!UUU [U=(d U5SUUUSS9 nURR#U5 GME [%USSUUSUU S9Ul[ R&"USUSS9Ul[ R*"5UlU(aSU-OUn[USUSS9UlSUlg)Nrrrr5r6rzUnsupported down_block_type: r$Unsupported time_compression_ratio: r r rrr rr) rr3r4rrrrrrrr3rrrrrr num_groupsrr r5F)r=r>r1conv_in mid_blockrBr down_blocks enumerate ValueErrorlenr@nplog2rUtuplerrrr 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!,0014/+!"6"K:KL&;$- H NN ! !( +"0 E!:J \\7I!7LYhnrs  01CA1F bcd &+#r/rHrcURU5n[R"5(aRUR(aAUR UR U5nUR HnUR X!5nM O,UR U5nUR H nU"U5nM URU5nURU5nURU5nU$rx) rrrrrrrrrr)rErHr s r-rNHunyuanVideoDecoder3D.forward^s ]3  " "t'B'B ==dnnm\M NN $ A A( Z +!NN=9M NN ( 7 +**=9  m4  m4 r/)rrrrrrrr) rrrrrrr r rr Trr)rQrRrSrTrr@rrVr>rrWrNrXrYrZs@r-rr s + /C !! $&')*!N,N,N,c3h N,"#s(ON,N,N,N,!$N, $'!N,N,`U\\ellr/rc^\rSrSrSrSr\S5S\S\S\S\\ S4S \\ S4S \\S \S \ S \S\ S\S\S\ SS4U4Sjjj5r S6S\ \S\ \S\ \S\ \ S\ \ S\ \ SS4SjjrS7SjrS7SjrS7SjrS\R&S\R&4S jr\S8S\R&S!\ S\\\\44S"jj5rS8S#\R&S!\ S\\\R&44S$jjr\S8S#\R&S!\ S\\\R&44S%jj5rS&\R&S'\R&S(\S\R&4S)jrS&\R&S'\R&S(\S\R&4S*jrS&\R&S'\R&S(\S\R&4S+jrS\R&S\4S,jr S8S#\R&S!\ S\\\R&44S-jjr!S\R&S\4S.jr"S8S#\R&S!\ S\\\R&44S/jjr#S9S0\R&S1\ S!\ S2\ \RHS\\\R&44 S3jjr%S4r&U=r'$):AutoencoderKLHunyuanVideoitaV A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Introduced in [HunyuanVideo](https://huggingface.co/papers/2412.03603). This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). Tr3r4latent_channelsr.rrrrrscaling_factorrrrrNc>[TU]5 Xl[UUUUUU USU U U S9 Ul[ UUUUUU UU U U S9 Ul[R"SU-SU-SS9Ul [R"X3SS9Ul Xl Xl SUl SUlSUlSUlSUlSUlS UlS UlS UlS Ulg) NT) r3r4rrrrrrrrr) r3r4rrrrrrrrr rrFr  )r=r>rrencoderrdecoderrBrC quant_convpost_quant_convrr use_slicing use_tilinguse_framewise_encodinguse_framewise_decodingtile_sample_min_heighttile_sample_min_widthtile_sample_min_num_framestile_sample_stride_heighttile_sample_stride_widthtile_sample_stride_num_frames)rEr3r4r'rrrrrrr(rrrrFs r-r>"AutoencoderKLHunyuanVideo.__init__s 6 &@#,#(-1-+$;'A&?   -'%)1-+#=&?$;   ))A$7_9LZ[\!yyWXY)B&*D'!  '+#&*#'*#%("*,'*-&(+%-/*r/r5r6r7r8r9r:cJSUlU=(d URUlU=(d URUlU=(d URUlU=(d URUlU=(d UR UlU=(d UR Ulg)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_min_num_frames (`int`, *optional*): The minimum number of frames required for a sample to be separated into tiles across the frame 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. tile_sample_stride_num_frames (`int`, *optional*): The stride between two consecutive frame tiles. This is to ensure that there are no tiling artifacts produced across the frame dimension. TN)r2r5r6r7r8r9r:)rEr5r6r7r8r9r:s r- enable_tiling'AutoencoderKLHunyuanVideo.enable_tilings~@&<&[@[@[#%:%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)r2rEs r-disable_tiling(AutoencoderKLHunyuanVideo.disable_tilings  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. TNr1r@s r-enable_slicing(AutoencoderKLHunyuanVideo.enable_slicings  r/cSUlg)z Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing decoding in one step. FNrDr@s r-disable_slicing)AutoencoderKLHunyuanVideo.disable_slicing s !r/r*cLURup#pEnUR(a X@R:aURU5$UR(a/X`R :dXPR :aURU5$URU5nURU5nU$rx) rr3r7_temporal_tiled_encoder2r6r5 tiled_encoder-r/)rEr*rrrrrencs r-_encode!AutoencoderKLHunyuanVideo._encodes>?gg; *e  & &:8W8W+W..q1 1 ??(B(B BfOjOjFj$$Q' ' LLOooa  r/ return_dictc>UR(aYURSS:aFURS5Vs/sHo0RU5PM nn[R "U5nOURU5n[ U5nU(dU4$[US9$s snf)a Encode a batch of images into latents. Args: x (`torch.Tensor`): Input batch of images. return_dict (`bool`, *optional*, defaults to `True`): Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. Returns: The latent representations of the encoded videos. If `return_dict` is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned. rr) latent_dist)r1rrhrNrrlrr)rEr*rPx_sliceencoded_slicesh posteriors r-encode AutoencoderKLHunyuanVideo.encode!s    QCD771:N:ll73:NN .)A QA03 < "y99OsBzcURup4pVnURUR-nURUR-n URUR -n UR (aXZ:aURXS9$UR(aXy:dXh:aURXS9$URU5nURU5n U(dU 4$[U S9$)NrPsample) rr5rr6r7rr4_temporal_tiled_decoder2 tiled_decoder0r.r) rErYrPrrrrrtile_latent_min_heighttile_latent_min_widthtile_latent_min_num_framesdecs r-_decode!AutoencoderKLHunyuanVideo._decode=s>?gg; *e!%!\>\ \%)%D%DHgHg%g"  & &:+R..q.J J ?? =A`$$Q$@ @   #ll1o6MC((r/cPUR(acURSS:aPURS5Vs/sHo0RU5RPM nn[ R "U5nOURU5RnU(dU4$[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. rrr\)r1rrhrdr]rrlr)rErYrPz_slicedecoded_slicesdecodeds r-decode AutoencoderKLHunyuanVideo.decodeQs    QJK''RS*U*wll73::*NUii/Gll1o,,G: G,,Vs$B#ab blend_extentc [URSURSU5n[U5HInUSS2SS2SS2U*U-SS24SXC- - -USS2SS2SS2USS24XC- --USS2SS2SS2USS24'MK U$)Nrrrr)rErlrmrnr+s r-blend_v!AutoencoderKLHunyuanVideo.blend_vks1772; \B |$A Aq<-!*;Q!>?1qGWCWX[\]^`acdfgij]j[k \ AaAq!m %r/c[URSURSU5n[U5HInUSS2SS2SS2SS2U*U-4SXC- - -USS2SS2SS2SS2U4XC- --USS2SS2SS2SS2U4'MK U$)NrrrqrErlrmrnr*s r-blend_h!AutoencoderKLHunyuanVideo.blend_hss1772; \B |$A Aq!l]Q->!>?1qGWCWX[\]^`acdfgij]j[k \ AaAq!m %r/c [URSURSU5n[U5HInUSS2SS2U*U-SS2SS24SXC- - -USS2SS2USS2SS24XC- --USS2SS2USS2SS24'MK U$)Nrrqrus r-blend_t!AutoencoderKLHunyuanVideo.blend_t{s1772; \B |$A A }q'8!Q!>?1qGWCWX[\]^`acdfgij]j[k \ AaAq!m %r/c PURup#pEnXPR-nX`R-nURUR-n URUR-n URUR-n UR UR-n X- n X- n/n[ SXPR5Hn/n[ SX`R 5HdnUSS2SS2SS2UUUR-2UUUR-24nURU5nURU5nURU5 Mf URU5 M /n[U5Hunn/n[U5HjunnUS:aURUUS- UUU 5nUS:aURUUS- UU5nURUSS2SS2SS2SU 2SU 245 Ml UR[R"USS95 M [R"USS9SS2SS2SS2SU2SU24nU$)zEncode a batch of images using a tiled encoder. 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