+h_ SrSSKrSSKJrJrJrJr SSKJr SSK J r J r J r \ "5(aSSK r SSKJrJrJrJr \R&"\5rSSKJr S&S \\\4S \\\S S 4S \\\S4S\SS\S4 SjjrS\4SjrSrSSS\ S\ SS4Sjr!S\ SSSSS\S4S jr"S!r#\RHS"5r%S'S#\\4S$jjr&S'S#\\4S%jjr'g!\\4a S rNf=f)(z1 PyTorch utilities: Utilities related to PyTorch N)ListOptionalTupleUnion)logging)is_torch_availableis_torch_npu_availableis_torch_version)fftnfftshiftifftn ifftshift)allow_in_graphcU$N)clss U/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/utils/torch_utils.pymaybe_allow_in_graphr#s shape generatorztorch.Generatordevicez torch.devicedtypez torch.dtypelayoutz torch.layoutc [U[5(a[R"U5nUnUSnU=(d [RnU=(d [R"S5nUb[U[ 5(dURR OUSRR nXrR :wa.US:Xa(SnUS:wa[RSUSUSUS35 O'XrR :waUS :Xa[S US US 35e[U[ 5(a[U5S :XaUSn[U[ 5(a`SUS S-n[U5Vs/sHn[R"XUXSUS9PM n n[R"U SS9RU5n U $[R"XXSUS9RU5n U $s snf)zA helper function to create random tensors on the desired `device` with the desired `dtype`. When passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor is always created on the CPU. rcpuNmpszBThe passed generator was created on 'cpu' even though a tensor on zB was expected. Tensors will be created on 'cpu' and then moved to zl. Note that one can probably slightly speed up this function by passing a generator that was created on the z device.cudazCannot generate a z! tensor from a generator of type .r)r)rrrrdim) isinstancestrtorchrstridedlisttypeloggerinfo ValueErrorlenrangerandncatto) rrrrr rand_device batch_sizegen_device_typeilatentss r randn_tensorr7's&#f%KqJ  $u}}F  *u||E*F7A)T7R7R)**//XabcXdXkXkXpXp kk )o.FK XY_X`aKKQ(Sggmfnnvx  +60I1&9Z[jZkklmn n)T""s9~':aL )T""uQRy :& & KK1k_e f&  ))G+..v6 N++ebhillmst N s)#Greturnc[SS5(d[[S5(dg[U[RR R 5$)z:Check whether the module was compiled with torch.compile()[U5(a UR$U$)z8Unwraps a module if it was compiled with torch.compile())rA _orig_modr?s r unwrap_modulerD`s1&996  EvErx_in torch.Tensor thresholdscalec8UnURupEpgXwS- -S:wd XfS- -S:waUR[RS9nO;UR[R :XaUR[RS9n[ USS9n[USS9nURupEpg[R"XEXg4URS9n US-US-pX)SX- X-2X- X-24'X-n[USS9n[USS9Rn U RURS9$) zFourier filter as introduced in FreeU (https://huggingface.co/papers/2309.11497). This version of the method comes from here: https://github.com/huggingface/diffusers/pull/5164#issuecomment-1732638706 rr)r)r")r.) rr1r&float32rbfloat16r r onesrrrreal) rErGrHxBCHWx_freqmaskcrowccol x_filtereds rfourier_filterr[es AJA! U aq5ka/ DDu}}D % ENN " DDu}}D %! "F f( +FJA! ::qQl188 4Daa$Z_d!1143CdFV3V VW ]Fv8 ,Fv8,11J ==tzz= **rresolution_idx hidden_statesres_hidden_states)rFrFc US:Xa:URSS-nUSS2SU24US-USS2SU24'[USUSS9nUS:Xa:URSS-nUSS2SU24US-USS2SU24'[USUS S9nX4$) a5Applies the FreeU mechanism as introduced in https: //arxiv.org/abs/2309.11497. Adapted from the official code repository: https://github.com/ChenyangSi/FreeU. Args: resolution_idx (`int`): Integer denoting the UNet block where FreeU is being applied. hidden_states (`torch.Tensor`): Inputs to the underlying block. res_hidden_states (`torch.Tensor`): Features from the skip block corresponding to the underlying block. s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features. s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features. b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. rrrLNb1s1)rGrHb2s2)rr[)r\r]r^ freeu_kwargsnum_half_channelss r apply_freeurfs)//2a7/rs//VV::   H %DNR37%)'+ /  /d#457HHIJ/ U3./ 0/ M " / ^ $ /dH$HF ++C+++D,,(6,KY, )*,6   HSMHSME ()sC C.-C.