oMj= SSKrSSKJr SSKJs Jr SSKJr SSKJ r SSK J r "SS\R5r "S S \R5r"S S \R5r"S S\R5r"SS\R5rSS\R$S\R$S-S\S\S\R$4 Sjjrg)N) deprecate)RMSNorm)upfirdn2d_nativec ^\rSrSrSrSS\S\S\S-S\S\4 U4S jjjrS \ RS \ R4S jr S r U=r $) Downsample1DaA 1D downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. out_channels (`int`, optional): number of output channels. Defaults to `channels`. padding (`int`, default `1`): padding for the convolution. name (`str`, default `conv`): name of the downsampling 1D layer. Nchannelsuse_conv out_channelspaddingnamecP>[TU]5 XlU=(d UUlX lX@lSnXPlU(a1[R"URURSXdS9Ul gURUR:Xde[R"XfS9Ul g)Nrstrider kernel_sizer) super__init__r r r rrnnConv1dconv AvgPool1d)selfr r r rrr __class__s ګC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\diffusers/models/downsampling.pyrDownsample1D.__init__(s  (4H    $--1B1BAffDI==D$5$55 55 GDIinputsreturncbURSUR:XdeURU5$)Nr)shaper r)rr!s rforwardDownsample1D.forward>s+||A$--///yy  r )r rrr rr )FNrr__name__ __module__ __qualname____firstlineno____doc__intboolstrrtorchTensorr%__static_attributes__ __classcell__rs@rr r s{ $#' HHHDj H  H  HH,!ell!u||!!r r c ^\rSrSrSrSS\S\S\S-S\S\4 U4S jjjrS \ RS \ R4S jr S r U=r $) Downsample2DCaA 2D downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. out_channels (`int`, optional): number of output channels. Defaults to `channels`. padding (`int`, default `1`): padding for the convolution. name (`str`, default `conv`): name of the downsampling 2D layer. Nr r r rrc 0>[T U]5 XlU=(d UUlX lX@lSn XPlUS:Xa[R"XU 5Ul O1US:Xa[XU 5Ul OUcSUl O[SU35eU(a,[R"URURXkXJS9n O0URUR:Xde[R"XS9n US:Xa XlXlgUS:XaXlgXlg) Nrln_normrms_normzunknown norm_type: )rrrbiasrrConv2d_0)rrr r r rrr LayerNormnormr ValueErrorConv2d AvgPool2dr<r)rr r r rrr norm_typeepselementwise_affiner;rrrs rrDownsample2D.__init__Ss  (4H    ! X4FGDI * $/ABDI  DI29+>? ? 99 t00kbiD==D$5$55 55< MI Z IIr hidden_statesr"c[U5S:dURSS5bSn[SSU5 URSUR:XdeUR b5UR UR SSSS55R SSSS5nUR(a(URS:XaSn[R"XS SS 9nURSUR:XdeURU5nU$) NrscalezThe `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.z1.0.0rrrrrrrconstantmodevalue) lengetrr$r r>permuter rFpadr)rrFargskwargsdeprecation_messagerRs rr%Downsample2D.forwards t9q=FJJw5A#U  gw(; <""1%666 99  IIm&;&;Aq!Q&GHPPQRTUWXZ[\M ==T\\Q.CEE-:QOM""1%666 -0 r )r<r rrr>r rr ) FNrrrNNNTr'r4s@rr6r6Cs $#'  ---Dj -  -  --^U\\u||r r6c *^\rSrSrSrSS\S-S\S-S\S\\\\\44U4SjjjrSS \ RS \ RS-S \ RS-S \S \ S\ R4 Sjjr S \ RS\ R4Sjr SrU=r$)FirDownsample2DaA 2D FIR downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. out_channels (`int`, optional): number of output channels. Defaults to `channels`. fir_kernel (`tuple`, default `(1, 3, 3, 1)`): kernel for the FIR filter. Nr r r fir_kernelc>[TU]5 U(aUOUnU(a[R"XSSSS9UlX@lX0lX lg)Nrrrrr)rrrr@r<rZr r )rr r r rZrs rrFirDownsample2D.__init__sD '3| IIh!TU_`aDM$ (r rFweightkernelfactorgainr"c[U[5(aUS:deUcS/U-n[R"U[RS9nUR S:Xa[R "X35nU[R"U5-nX5-nUR(a{URu pgnURSU- US- -n XD/n [UURURURS9U S-S-U S-4S9n [R"XU SS9n U $URSU- n [UURURURS9UU S-S-U S-4S9n U $) azFused `Conv2d()` followed by `downsample_2d()`. Padding is performed only once at the beginning, not between the operations. The fused op is considerably more efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary order. Args: hidden_states (`torch.Tensor`): Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. weight (`torch.Tensor`, *optional*): Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`. kernel (`torch.Tensor`, *optional*): FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which corresponds to average pooling. factor (`int`, *optional*, default to `2`): Integer downsampling factor. gain (`float`, *optional*, default to `1.0`): Scaling factor for signal magnitude. Returns: output (`torch.Tensor`): Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same datatype as `x`. rdtyperdevicerdr)rRrdownrR) isinstancer-r0tensorfloat32ndimoutersumr r$rtorfrdrQconv2d) rrFr^r_r`ra_convHconvW pad_value stride_value upfirdn_inputoutputs r_downsample_2dFirDownsample2D._downsample_2dshB&#&&6Q;66 >S6\FfEMM: ;;! [[0F%))F## ==!' Aqa61eai@I"+L, !5!5]=P=P Q!m)9>:M XXmLRSTF  Q&0I% !5!5]=P=P Q!m)9>: F r cUR(aZURXRRURS9nX RR R SSSS5-nU$URXRSS9nU$)N)r^r_rr)r_r`)r rxr<r^rZr;reshape)rrFdownsample_inputs rr%FirDownsample2D.forwards ==#22=I]I]fjfufu2v ,}}/A/A/I/I!RQRTU/VVM!// oo^_/`Mr )r<rZr r )NNF)rrrr)NNrr)r(r)r*r+r,r-r.tuplerr0r1floatrxr%r2r3r4s@rrXrXs  $#'0< )* )Dj ) ) #sC,- ) )$'+&* @||@ t#@ t# @  @  @ @DU\\ellr rXcr^\rSrSrSrS S\4U4SjjjrS\RS\R4Sjr Sr U=r $) KDownsample2Dz{A 2D K-downsampling layer. Parameters: pad_mode (`str`, *optional*, default to `"reflect"`): the padding mode to use. pad_modec>[TU]5 Xl[R"/SQ/5nUR SS-S- UlURSURU-SS9 g)N)??rrrrr_F) persistent) rrrr0rjr$rRregister_bufferT)rr kernel_1drs rrKDownsample2D.__init__s]  LL">!?@ ??1%*Q. Xy{{Y'>5Qr r!r"c&[R"XR4S-UR5nURURSURSUR RSUR RS/5n[ R"URSURS9nUR RU5SSS24RURSSS5nXBX34'[R"XSS9$)Nrr)rfr{r)r) rQrRr new_zerosr$r_r0arangerfroexpandrp)rr!r^indicesr_s rr%KDownsample2D.forwardsv{Q >!! Q Q !!!$ !!!$   ,,v||Av}}E'a077 QRP#)w xxq11r )rRr)reflect) r(r)r*r+r,r/rr0r1r%r2r3r4s@rrrs< RRR 2ell 2u|| 2 2r rc ^\rSrSrSrSS\S\S\S\S\S\4 U4S jjjrS \RS \R4S jr S r U=r $)CogVideoXDownsample3DiaO A 3D Downsampling layer using in [CogVideoX]() by Tsinghua University & ZhipuAI Args: in_channels (`int`): Number of channels in the input image. out_channels (`int`): Number of channels produced by the convolution. kernel_size (`int`, defaults to `3`): Size of the convolving kernel. stride (`int`, defaults to `2`): Stride of the convolution. padding (`int`, defaults to `0`): Padding added to all four sides of the input. compress_time (`bool`, defaults to `False`): Whether or not to compress the time dimension. in_channelsr rrr compress_timecd>[TU]5 [R"XX4US9UlX`lg)Nr\)rrrr@rr)rrr rrrrrs rrCogVideoXDownsample3D.__init__2s+ IIk[ipq *r xr"cUR(GaURup#pEnURSSSSS5RX%-U-X45nURSS-S:XaUSUSSS24pURSS:a[R "USSS 9n[ R"US U/SS 9nURX%XcURS5RSSSSS5nOI[R "USSS 9nURX%XcURS5RSSSSS5nS n [R"XS SS9nURup#pEnURSSSSS5RX$-X5U5nURU5nURX$URSURSURS5RSSSSS5nU$)Nrrrrrr{).r.r).N)dimrIrJrK) rr$rPr|rQ avg_pool1dr0catrRr) rr batch_sizer framesheightwidthx_firstx_restrRs rr%CogVideoXDownsample3D.forward@s    :;'' 7J&% !Q1a(001Du1Lh_Awwr{Q!#"#F)QsABwZ<<#a'\\&aJFIIwy16:CIIj%1772;OWWXY[\^_abdefLL!<IIj%1772;OWWXY[\^_abdef EE!z 367gg3 fe IIaAq! $ , ,Z-@(TY Z IIaL IIj!''!*aggaj!''!* M U UVWYZ\]_`bc dr )rr)rrrF) r(r)r*r+r,r-r.rr0r1r%r2r3r4s@rrrsz,# + + + +  +  + + +%,,r rrFr_r`rar"c[U[5(aUS:deUcS/U-n[R"U[RS9nUR S:Xa[R "X5nU[R"U5-nX-nURSU- n[UURURURS9UUS-S-US-4S9nU$)aDownsample2D a batch of 2D images with the given filter. Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a multiple of the downsampling factor. Args: hidden_states (`torch.Tensor`) Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. kernel (`torch.Tensor`, *optional*): FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which corresponds to average pooling. factor (`int`, *optional*, default to `2`): Integer downsampling factor. gain (`float`, *optional*, default to `1.0`): Scaling factor for signal magnitude. Returns: output (`torch.Tensor`): Tensor of the shape `[N, C, H // factor, W // factor]` rrcrrerrg) rir-r0rjrkrlrmrnr$rrorfrd)rFr_r`rartrws r downsample_2drbs8 fc " "v{2 2 ~v \\& 6F {{aV, eiiF ]F Q&(I  --]5H5H I !m !9> 2 F Mr )Nrr)r0torch.nnrtorch.nn.functional functionalrQutilsr normalizationr upsamplingrModuler r6rXrrr1r-rrr rrs "((!299(!VP299PffbiifT2BII2