+hKCSSKJrJr SSKrSSKJs Jr SSKJr SSKJ r SSK J r J r SSK Jr SS KJrJr SS KJrJrJrJrJr SS KJrJr SS KJr SS KJrJr SSK J!r!J"r" "SS\\ \5r#"SS\RH5r%"SS\RH5r&"SS\RH5r'"SS\RH5r(g))DictUnionN)nn checkpoint) ConfigMixinregister_to_config)PeftAdapterMixin)BasicTransformerBlockSkipFFTransformerBlock)ADDED_KV_ATTENTION_PROCESSORSCROSS_ATTENTION_PROCESSORSAttentionProcessorAttnAddedKVProcessor AttnProcessor)TimestepEmbeddingget_timestep_embedding) ModelMixin)GlobalResponseNormRMSNorm) Downsample2D Upsample2Dc.^\rSrSrSr\S!S\S\S\S\S\S\S \S \S \S \S \S\S\S\S\S\S\S\S\S\S\S\4,U4Sjjj5r S"Sjr \ S\ \ \44Sj5rS\\\ \ \444SjrSrS rU=r$)# UVit2DModel'T hidden_sizeuse_biashidden_dropoutcond_embed_dimmicro_cond_encode_dimmicro_cond_embed_dimencoder_hidden_size vocab_size codebook_size in_channelsblock_out_channelsnum_res_blocks downsampleupsampleblock_num_headsnum_hidden_layersnum_attention_headsattention_dropoutintermediate_sizelayer_norm_epsln_elementwise_affine sample_sizec>[TU]5 [R"XqUS9Ul[ UUU5Ul[XUUUU5Ul[Xd-XS9Ul [U U UUUUUUUU S5 Ul [ U UU5Ul [R"XUS9Ul[R"[!U5Vs/sHn[#UUUU-UUUSUUUUUUUS9PM sn5Ul[ UUU5Ul[R"XUS9Ul[U U UUUUUUUSUS9 Ul[-XUUUU 5UlSUlgs snf)Nbias)sample_proj_biasFada_norm_continuous)dimr.attention_head_dimdropoutcross_attention_dimattention_bias norm_type-ada_norm_continous_conditioning_embedding_dimnorm_elementwise_affinenorm_eps ada_norm_bias ff_inner_dimff_biasattention_out_bias)r*r+)super__init__rLinear encoder_projrencoder_proj_layer_normUVit2DConvEmbedembedr cond_embed UVitBlock down_blockproject_to_hidden_normproject_to_hidden ModuleListranger transformer_layersproject_from_hidden_normproject_from_hiddenup_block ConvMlmLayer mlm_layergradient_checkpointing)selfrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3_ __class__s X/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/models/unets/uvit_2d.pyrGUVit2DModel.__init__*sD II&9XV'.{NLa'b$$ Z9NP^`h  , 1; $     !        '..@.Rg&h#!#+=QY!Z"$--$01# "2A!&#(;'26I'I*(3#+3BM,A+"*!2$'/ 2# # ,)0 ^Mb(c%#%99[S[#\ !     !       & X7Ln^k ',#U s#Ec^URU5nURU5n[UR5URR SSS9nUR URSS45n[R"X6/SS9nURURS9nURU5RUR5nURU5nURUUUUS9nURuppURSS S S5R XU -U 5nUR!U5nUR#U5nUR$HBm[R&"5(aUR((aU4S jn OTn U "UUUS U0S 9nMD UR+U5nUR-U5nUR XX5RSS SS 5nUR/UUUUS9nUR1U5n U $)NTr)flip_sin_to_cosdownscale_freq_shiftr9)dtype)pooled_text_embencoder_hidden_statescross_attention_kwargsr rc>[T/UQ76$Nr)argslayers r^layer_#UVit2DModel.forward..layer_s%e3d33rg)rhriadded_cond_kwargs)rIrJrflattenconfigr"reshapeshapetorchcattorfrMrLrOpermuterPrQrTis_grad_enabledrZrUrVrWrY)r[ input_idsrhrg micro_condsrimicro_cond_embeds hidden_states batch_sizechannelsheightwidthrnlogitsrms @r^forwardUVit2DModel.forwards $ 1 12G H $ < <=R S2    !4;;#D#DVZqr .55yq7I26NO))_$HaP),,4::,>///:==>S>Y>YZ 9-  +"7#9 ( /<.A.A+ f%--aAq9AA*W\n^fg 33MB ..}= ,,E$$&&4+F+F4"&;'=#4o"F M- 55mD 00? %--j%RZZ[\^_abdef  +"7#9 &  . rpreturnc^0nS[S[RRS[[[ 44U4SjjmUR 5Hup#T"X#U5 M U$)z Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. namemodule processorsc>[US5(aUR5X S3'UR5Hup4T"USU3XB5 M U$)N get_processor .processor.)hasattrrnamed_children)rrrsub_namechildfn_recursive_add_processorss r^r@UVit2DModel.attn_processors..fn_recursive_add_processorssZv//282F2F2H V:./#)#8#8#:+tfAhZ,@%T$; rp)strrvrModulerrr)r[rrrrs @r^attn_processorsUVit2DModel.attn_processorssb  c 588?? X\]`bt]tXu !//1LD 'j A2rp processorc d^[URR55n[U[5(a-[U5U:wa[ S[U5SUSUS35eS[ S[RR4U4SjjmUR5Hup4T"X4U5 M g) a Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors. z>A dict of processors was passed, but the number of processors z0 does not match the number of attention layers: z. Please make sure to pass z processor classes.rrc >[US5(aJ[U[5(dURU5 O#URUR US355 UR 5Hup4T"USU3XB5 M g)N set_processorrr)r isinstancedictrpopr)rrrrrfn_recursive_attn_processors r^rCUVit2DModel.set_attn_processor..fn_recursive_attn_processorssv//!)T22((3(($z7J)KL#)#8#8#:+tfAhZ,@%S$;rpN) lenrkeysrr ValueErrorrrvrrr)r[rcountrrrs @r^set_attn_processorUVit2DModel.set_attn_processorsD((--/0 i & &3y>U+BPQTU^Q_P`a005w6QRWQXXkm   Tc T588?? T!//1LD 'i @2rpc ~[SURR555(a [5nOr[SURR555(a [ 5nO8[ S[ [URR55535eURU5 g)zU Disables custom attention processors and sets the default attention implementation. c3F# UHoR[;v M g7frk)r]r.0procs r^ 9UVit2DModel.set_default_attn_processor..siKh4~~!>>Kh!c3F# UHoR[;v M g7frk)r]rrs r^rrshJg$#==JgrzOCannot call `set_default_attn_processor` when attention processors are of type N) allrvaluesrrrnextiterr)r[rs r^set_default_attn_processor&UVit2DModel.set_default_attn_processors i4K_K_KfKfKhi i i,.I h$J^J^JeJeJgh h h%IabfgklpmAmAmHmHmJhKcLbMN   *rp) rMrOrLrIrJrZrYrVrUrQrPrTrW)iFiri@ i rrrFF ri gư>T@rk)__name__ __module__ __qualname____firstlineno__ _supports_gradient_checkpointingr intboolfloatrGrpropertyrrrrrrr__static_attributes__ __classcell__r]s@r^rr's'+$  #!%($(#&!"% !!##%#&!% $&*?j,j, j,  j,j, #j,"j,!j,j,j,j,  !j,"#j,$%j,&'j,()j,,-j,.!/j,2!3j,67j,:;j,< $=j,>?j,j,X;zc+=&=!>0 AE2Dd3PbKbFc2c,d AF + +rprc.^\rSrSrU4SjrSrSrU=r$)rKi!c>[TU]5 [R"X15Ul[ XU5Ul[R"XSUS9UlgNrd) kernel_sizer6) rFrGr Embedding embeddingsr layer_normConv2dconv)r[r'r(r%elementwise_affineepsr6r]s r^rGUVit2DConvEmbed.__init__"sB ,,z?!+4FGIIk1SWX rpcURU5nURU5nURSSSS5nURU5nU$)Nrrrdr )rrryr)r[r{rs r^rUVit2DConvEmbed.forward(sH__Y/ __Z0 ''1a3 YYz* rp)rrrrrrrrGrrrrs@r^rKrK!sY rprKc>^\rSrSrS\S\S\4U4SjjrSrSrU=r $)rNi0r)r*r+c >[TU]5 U (a[USSSSSUUUS9 UlOSUl[R "[ U5V s/sHn [UUUUUU5PM sn 5Ul[R "[ U5V s/sHn [UUX-UUU UUUS9 PM sn 5Ul U (a[USSSSSUUUS S 9 Ul gSUl gs sn fs sn f) NTrConv2d_0r rms_norm)use_convpaddingrrr>rrr6)r=rErF) use_conv_transposerrrr>rrr6 interpolate) rFrGrr*rrRrS ConvNextBlock res_blocksrattention_blocksrr+)r[rr)rr r2r1rr,r/r*r+ir\r]s r^rGUVitBlock.__init__1s/  *$"#8 DO#DO--~. /A")" /   !# ~. /A'#/%#+'/ / ! " &#'$"#8! DM!DMY  s C%C*cURbURU5n[URUR5HkupVU"X5nURupxpUR XxX-5R SSS5nU"XUS9nUR SSS5R XxX5nMm URbURU5nU$)Nrr rd)rhri)r*ziprrruviewryr+) r[xrgrhri res_blockattention_blockrrrrs r^rUVitBlock.forwards ?? &"A*-doot?T?T*U &I!-A23'' /J&zV^<DDQ1MAWmA !Q"'' fLA+V == $ a Arp)rr*rr+) rrrrrrrGrrrrs@r^rNrN0s5L!L!L!L!\rprNc4^\rSrSrSU4SjjrSrSrU=r$)ric >[TU]5 [R"UUSSUUS9Ul[ XU5Ul[R"U[X-5US9Ul [R"5Ul [[X-55Ul [R"[X-5XS9Ul[R"U5Ul[R"XaS-U5Ulg)Nrrd)rrgroupsr6r5r )rFrGrr depthwisernormrHrchannelwise_linear_1GELUchannelwise_actrchannelwise_normchannelwise_linear_2Dropoutchannelwise_dropoutcond_embeds_mapper) r[rr1r2rr rres_ffn_factorr]s r^rGConvNextBlock.__init__s     H6KL $&IIhH[TU]5 [R"XSUS9Ul[ X%U5Ul[R"X&SUS9Ulgr)rFrGrrconv1rrconv2)r[r(r'rr2r1r&r]s r^rGConvMlmLayer.__init__sI YY1AT\] !+?TUYY{qxX rpcURU5nURURSSSS55RSSSS5nURU5nU$)Nrr rrd)rrryr )r[r~rs r^rConvMlmLayer.forwardsW =1  (=(=aAq(IJRRSTVWYZ\]^ M* rp)rr r) rrrrrrrrGrrrrs@r^rXrXsS Y Y Y Y $ Y  Y YrprX))typingrrrvtorch.nn.functionalr functionalrtorch.utils.checkpointrconfiguration_utilsr r loadersr attentionr rattention_processorrrrrrrrrmodeling_utilsr normalizationrrresnetrrrrrKrNrrXrpr^rs  -B'EC'7-w+*k+;w+t bii ` `F*BII*Z299rp