+h@SSKJr SSKJrJr SSKrSSKrSSKJ r SSK J r SSK J r SSKJr SS KJr SS KJrJrJrJr \"S S \ 55r\"S S\ 55r"SS\ R25r"SS\ R25r"SS\ R25r"SS\ R25r"SS\ R25r"SS\ R25r"SS\ 5r!"SS\ 5r""SS \ R25r#"S!S"\ R25r$g)#) dataclass)OptionalTupleN) BaseOutput) randn_tensor)get_activation) SpatialNorm)AutoencoderTinyBlockUNetMidBlock2Dget_down_block get_up_blockc8\rSrSr%Sr\R \S'Srg) EncoderOutput!z Output of encoding method. Args: latent (`torch.Tensor` of shape `(batch_size, num_channels, latent_height, latent_width)`): The encoded latent. latentN) __name__ __module__ __qualname____firstlineno____doc__torchTensor__annotations____static_attributes__r[/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/models/autoencoders/vae.pyrr!s LLrrc`\rSrSr%Sr\R \S'Sr\ \R\S'Sr g) DecoderOutput.z Output of decoding method. Args: sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`): The decoded output sample from the last layer of the model. sampleN commit_lossr) rrrrrrrrr$r FloatTensorrrrrr!r!.s( LL/3K%++,3rr!c^\rSrSrSrSS\S\S\\S4S\\S4S\S \S \S \4U4S jjjr S \ RS\ R4Sjr Sr U=r$)Encoder<aw The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation. Args: in_channels (`int`, *optional*, defaults to 3): The number of input channels. out_channels (`int`, *optional*, defaults to 3): The number of output channels. down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`): The types of down blocks to use. See `~diffusers.models.unet_2d_blocks.get_down_block` for available options. block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`): The number of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups for normalization. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. See `~diffusers.models.activations.get_activation` for available options. double_z (`bool`, *optional*, defaults to `True`): Whether to double the number of output channels for the last block. in_channels out_channelsdown_block_types.block_out_channelslayers_per_blocknorm_num_groupsact_fndouble_zc >[TU]5 XPl[R"UUSSSSS9Ul[R "/5UlUSn [U5HYupU n XKn U [U5S- :Hn[U URU U U(+SSUUU SS9 nURRU5 M[ [USSUSSUSUSU S 9 Ul [R"USUSS 9Ul[R "5UlU(aS U-OUn[R"USUSSS 9UlS Ulg)Nrr kernel_sizestridepaddingư>) num_layersr)r*add_downsample resnet_epsdownsample_padding resnet_act_fn resnet_groupsattention_head_dim temb_channelsdefault r)r:r<output_scale_factorresnet_time_scale_shiftr>r=r? add_attention num_channels num_groupsepsr r6F)super__init__r-nnConv2dconv_in ModuleList down_blocks enumeratelenrappendr mid_block GroupNorm conv_norm_outSiLUconv_actconv_outgradient_checkpointing)selfr)r*r+r,r-r.r/r0mid_block_add_attentionoutput_channelidown_block_type input_channelis_final_block down_blockconv_out_channels __class__s rrLEncoder.__init__Tsl  0yy  q !   ==,,A."+,<"= A*M/2N#&8"9A"==N'00)+#11#$$-#1" J    # #J /%#>*(*2. !$-1"5)1   \\7I"7MZiost  08A ,l "4R"8:KQXYZ &+#rr#returncURU5n[R"5(aRUR(aAURHnUR X!5nM UR UR U5nO,URH nU"U5nM UR U5nURU5nURU5nURU5nU$)z*The forward method of the `Encoder` class.) rOris_grad_enabledr[rQ_gradient_checkpointing_funcrUrWrYrZ)r\r#rcs rforwardEncoder.forwardsf%  " "t'B'B".. :::N/66t~~vNF#.. #F+/^^F+F##F+v&v& r)rYrOrWrZrQr[r-rU) rr)DownEncoderBlock2D@r siluTT)rrrrrintrstrboolrLrrrkr __classcell__res@rr'r'<s2,C.3 !! $C,C,C, S/ C, "#s(O C,  C,C,C,C,C,Jellu||rr'c^\rSrSrSrSS\S\S\\S4S\\S4S\S \S \S \4U4S jjjrSS \ RS\ \ RS\ R4Sjjr Sr U=r$)Decoderav The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample. Args: in_channels (`int`, *optional*, defaults to 3): The number of input channels. out_channels (`int`, *optional*, defaults to 3): The number of output channels. up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`): The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options. block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`): The number of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups for normalization. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. See `~diffusers.models.activations.get_activation` for available options. norm_type (`str`, *optional*, defaults to `"group"`): The normalization type to use. Can be either `"group"` or `"spatial"`. r)r*up_block_types.r,r-r.r/ norm_typec >[TU]5 XPl[R"UUSSSSS9Ul[R "/5UlUS:XaUOSn [USSUSUS:XaSOUUSUU U S 9 Ul [[U55n U S n [U5H_upU nXn U [U5S- :Hn[UURS-UU UU(+SUUU U US 9 nURRU5 U nMa US:Xa[!US U 5UlO[R$"US USS 9Ul[R&"5Ul[R"US USSS 9UlSUlg)Nr@rr2r3spatialr7grouprArBr r8r)r*prev_output_channel add_upsampler:r<r=r>r?rDrFrJF)rKrLr-rMrNrOrP up_blocksr rUlistreversedrRrSrrTr rWrVrXrYrZr[)r\r)r*rzr,r-r.r/r{r]r?reversed_block_out_channelsr^r_ up_block_typerrbup_blockres rrLDecoder.__init__s  0yy  r "   r*'0I'= 4 (*2. !1:g1EI91"5)'1  '+84F+G&H#4Q7 ). 9 A"0 8;N#&8"9A"==N#0014/+$7!//$-#1+(1 H NN ! !( +"0 +!:0  !!,-?-BM!RD !#;Ma;P]lrv!wD    "4Q"7qRST &+#rr# latent_embedsrgcURU5n[[URR 555R n[ R"5(adUR(aSURURX5nURU5nURHnURXAU5nM O=URX5nURU5nURH nU"X5nM UcURU5nOURX5nURU5nURU5nU$)z*The forward method of the `Decoder` class.)rOnextiterr parametersdtyperrir[rjrUtorWrYrZ)r\r#r upscale_dtypers rrkDecoder.forwardsf%T$..";";"=>?EE  " "t'B'B66t~~v]FYY}-F!NN::8][+^^F:FYY}-F!NN!&8+  ''/F''>Fv&v& r)rYrOrWrZr[r-rUr) rrUpDecoderBlock2Drnr rprqr~TNrrrrrrrrrsrLrrrrkrrurvs@rrxrxs0*?.3 !! $J,J,J,c3h J, "#s(O J,  J,J,J,J,J,^15# #  -#  ##rrxcv^\rSrSrSrS\S\SS4U4SjjrS\RS\R4S jr S r U=r $) UpSamplei>a  The `UpSample` layer of a variational autoencoder that upsamples its input. Args: in_channels (`int`, *optional*, defaults to 3): The number of input channels. out_channels (`int`, *optional*, defaults to 3): The number of output channels. r)r*rgNcr>[TU]5 XlX l[R "XSSSS9Ulg)Nr r2r3)rKrLr)r*rMConvTranspose2ddeconv)r\r)r*res rrLUpSample.__init__Is7 &(((PQZ[efg rxcT[R"U5nURU5nU$)z+The forward method of the `UpSample` class.)rrelurr\rs rrkUpSample.forwardSs! JJqM KKNr)rr)r* rrrrrrrrLrrrkrrurvs@rrr>sMhhh  h%,,rrc ^\rSrSrSrS S\S\S\S\SS4 U4S jjjrSS \RS\R4S jjr S r U=r $)MaskConditionEncoderiZz! used in AsymmetricAutoencoderKL in_chout_chres_chr5rgNc 8>[T U]5 /nUS:a8US-nUS-nX#:aUnUS:XaUnURXb45 US-nUS:aM8/nUHupURU 5 M URUSS5 /n Un[[ U55Hbn X{n U S:XdU S:Xa'U R[ R "XlSSSS95 O&U R[ R "XlSSSS95 U nMd [ R"U 6Ulg)Nr2r r@rrr3r) rKrLrTrangerSrMrN Sequentiallayers)r\rrrr5channelsin_ch_r*_in_ch_out_chrlout_ch_res rrLMaskConditionEncoder.__init___s qjq[FaZF{ OOV, - aKFqj 'OF    ( (HRLO,s<()A"oGAva biiQqZ[\] biiQqZ[\]F *mmV, rrc0n[[UR55HPnURUnU"U5nX[[ UR 55'[ R"U5nMR U$)z7The forward method of the `MaskConditionEncoder` class.)rrSrrstupleshaperr)r\rmaskoutrlayers rrkMaskConditionEncoder.forwards^s4;;'(AKKNEaA'(E!''N# $ 1 A )  r)r)irrrvs@rrrZsn #-#-#- #-  #-  #-#-JU\\rrc^\rSrSrSrSS\S\S\\S4S\\S4S\S \S \S \4U4S jjjrSS \ RS\ \ RS\ \ RS\ \ RS\ R4 Sjjr Sr U=r$)MaskConditionDecoderiaThe `MaskConditionDecoder` should be used in combination with [`AsymmetricAutoencoderKL`] to enhance the model's decoder with a conditioner on the mask and masked image. Args: in_channels (`int`, *optional*, defaults to 3): The number of input channels. out_channels (`int`, *optional*, defaults to 3): The number of output channels. up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`): The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options. block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`): The number of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups for normalization. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. See `~diffusers.models.activations.get_activation` for available options. norm_type (`str`, *optional*, defaults to `"group"`): The normalization type to use. Can be either `"group"` or `"spatial"`. r)r*rz.r,r-r.r/r{c *>[TU]5 XPl[R"UUSSSSS9Ul[R "/5UlUS:XaUOSn [USSUSUS:XaSOUUSUU S 9Ul [[U55n U S n [U5H_upU nXn U [U5S- :Hn[U URS-UU SU(+SUUU U US 9 nURRU5 U nMa [!UUS USS 9UlUS:Xa[%US U 5UlO[R("US USS 9Ul[R*"5Ul[R"US USSS9UlSUlg)Nr@rr2r3r}r7r~rA)r)r:r<rCrDr>r=r?rr)rrrrFrJF)rKrLr-rMrNrOrPrr rUrrrRrSrrTrcondition_encoderr rWrVrXrYrZr[)r\r)r*rzr,r-r.r/r{r?rr^r_rrrbrres rrLMaskConditionDecoder.__init__s  0yy  r "   r*'0I'= 4 (*2. !1:g1EI91"5)'  '+84F+G&H#4Q7 ). 9 A"0 8;N#&8"9A"==N#0014/+$(!//$-#1+(1 H NN ! !( +"0 +!:0"6%a(%b)"   !!,-?-BM!RD !#;Ma;P]lrv!wD    "4Q"7qRST &+#rzimagerrrgczUnURU5n[[URR 555R n[ R"5(Ga!UR(GaURURXT5nURU5nUb(Ub%SU- U-nURURUU5nURHtn Ub\UbYW[[UR55n [ R"R%X5RSSSS9n X[-U SU - --nURXU5nMv Ub/Ub,XS-W[[UR55SU- --nOURXT5nURU5nUbUbSU- U-nURXs5nURHjn Ub\UbYW[[UR55n [ R"R%X5RSSSS9n X[-U SU - --nU "XT5nMl Ub/Ub,XS-W[[UR55SU- --nUcUR'U5nOUR'XT5nUR)U5nUR+U5nU$)z7The forward method of the `MaskConditionDecoder` class.Nr2nearest)sizemode)rOrrrrrrrir[rjrUrrrsrrrM functional interpolaterWrYrZ) r\rrrrr#r masked_imageim_xrsample_mask_s rrkMaskConditionDecoder.forwardsf%T$..";";"=>?EE  " "t'B'B'B66t~~v]FYY}-F T%5 !DE1 88** !NN$)9"3uV\\':#;Fv&v& r) rrYrOrWrZr[r-rUr)rrrrnr rprqr~)NNNrrvs@rrrs0*?.3 !! O,O,O,c3h O, "#s(O O,  O,O,O,O,O,h)-'+04 ? <<? %?u||$ ?   - ?  ??rrc j^\rSrSrSrSS\S\S\S\S\S\4 U4S jjjr S \ RS \ R4S jr S \ RS \ R4S jr S\ RS \\ R\ R\44SjrS\ RS\\S4S \ R4SjrSrU=r$)VectorQuantizeri9z Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix multiplications and allows for post-hoc remapping of indices. n_e vq_embed_dimbeta unknown_indexsane_index_shapelegacyc *>[TU]5 XlX lX0lXpl[ R"URUR5UlURRRRSUR- SUR- 5 X@l URbURS[R"[ R""UR555 U UR$R&SUlXPlUR*S:Xa%UR(UlUR(S-Ul[-SURSUR(S UR*S 35 OXlX`lg) Ng?usedrextrar2z Remapping z indices to z indices. 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"  9% 2[TU]5 [R"XSSSS9[ U5/n[ U5HupU [ U5S- :Hn XIn [U 5Hn UR[XU55 M U (d#UR[R"XWS95 U (dU OUnUR[R"U USSU S95 M [R"U6Ul SUl g)Nrrr2rC) scale_factorr)r4r6rDF)rKrLrMrNr rRrSrrTr Upsamplerrr[)r\r)r*rAr,rPr/rQrr_rErbrGrFconv_out_channelres rrLDecoderTiny.__init__Ls  IIka#8aQR S 6 "  &j1LA3z?Q#67N-0L9% 2rgs "" -(-  J     4J 4  4vbiivrFbiiFRryy822992jg299gTwbiiwt565p6?"))?DH"))Hr