+h|SSKrSSKJr SSKJr SSKrSSKJr SSK r SSK J r SSK Jr SSKJrJr SSKJr SS KJr \R,R."S S \55r\R,R."S S \55r"SS\R45r"SS\R45r"SS\R45r"SS\R45r"SS\R45r"SS\R45r "SS\R45r!"SS\R45r""SS\R45r#"S S!\$5r%\"S"S#\R4\\55r&g)$N)partial)Tuple) FrozenDict) ConfigMixinflax_register_to_config) BaseOutput)FlaxModelMixinc8\rSrSr%Sr\R \S'Srg)FlaxDecoderOutput a Output of decoding method. Args: sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): The decoded output sample from the last layer of the model. dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): The `dtype` of the parameters. sampleN) __name__ __module__ __qualname____firstlineno____doc__jnpndarray__annotations____static_attributes__rS/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/models/vae_flax.pyr r s KKrr c$\rSrSr%SrS\S'Srg)FlaxAutoencoderKLOutput/a4 Output of AutoencoderKL encoding method. Args: latent_dist (`FlaxDiagonalGaussianDistribution`): Encoded outputs of `Encoder` represented as the mean and logvar of `FlaxDiagonalGaussianDistribution`. `FlaxDiagonalGaussianDistribution` allows for sampling latents from the distribution. FlaxDiagonalGaussianDistribution latent_distrN)rrrrrrrrrrrr/s43rrcf\rSrSr%Sr\\S'\Rr \R\S'Sr Sr Sr g) FlaxUpsample2D=z Flax implementation of 2D Upsample layer Args: in_channels (`int`): Input channels dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` in_channelsdtypecf[R"URSSSURS9Ulg)Nr(r r r)r) kernel_sizestridespaddingr%nnConvr$r%convselfs rsetupFlaxUpsample2D.setupKs+GG   $**   rcURup#pE[RRUX#S-US-U4SS9nUR U5nU$)Nrnearest)shapemethod)r9jaximageresizer2)r4 hidden_statesbatchheightwidthchannelss r__call__FlaxUpsample2D.__call__TsY)6)<)<&u (( 1*eai:)  -0 rr2N rrrrrintrrfloat32r%r5rCrrrrr"r"=s+{{E399" rr"cf\rSrSr%Sr\\S'\Rr \R\S'Sr Sr Sr g) FlaxDownsample2D_z Flax implementation of 2D Downsample layer Args: in_channels (`int`): Input channels dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` r$r%cf[R"URSSSURS9Ulg)Nr')rrVALIDr+r/r3s rr5FlaxDownsample2D.setupms+GG   **   rcTSn[R"XS9nURU5nU$)N)rrrr rQrP) pad_width)rpadr2)r4r>rSs rrCFlaxDownsample2D.__call__vs). =  -0 rrENrFrrrrJrJ_s+{{E399" rrJc\rSrSr%Sr\\S'Sr\\S'Sr\ \S'Sr \\S 'Sr \ \S '\ Rr\ R\S 'S rSS jrSrg)FlaxResnetBlock2D}aC Flax implementation of 2D Resnet Block. Args: in_channels (`int`): Input channels out_channels (`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for group norm. use_nin_shortcut (:obj:`bool`, *optional*, defaults to `None`): Whether to use `nin_shortcut`. This activates a new layer inside ResNet block dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` r$N out_channelsdropoutrgroupsuse_nin_shortcutr%cURc URO URn[R"URSS9Ul[R "USSSURS9Ul[R"URSS9Ul [R"UR5Ul [R "USSSURS9Ul URcURU:gO URnSUlU(a([R "USSSURS9Ulgg)Nư> num_groupsepsilonr'r)r*r+rM)rXr$r0 GroupNormr[norm1r1r%conv1norm2DropoutrZ dropout_layerconv2r\ conv_shortcut)r4rXr\s rr5FlaxResnetBlock2D.setups+/+<+<+Dt''$J[J[ \\T[[$G WW $**   \\T[[$G ZZ 5WW $**   @D?T?T?\4++|;bfbwbw! !#"jj "D  rcLUnURU5n[R"U5nURU5nUR U5n[R"U5nUR X5nUR U5nURbURU5nX-$N)rcr0swishrdrergrhri)r4r> deterministicresiduals rrCFlaxResnetBlock2D.__call__s  =1 /  =1  =1 / **=H  =1    )))(3H''r)rdrhrirgrcreT)rrrrrrGrrXrZfloatr[r\boolrrHr%r5rCrrrrrVrV}sU$L#GUFC!d!{{E399" D(rrVc\rSrSr%Sr\\S'Sr\\S'Sr\\S'\ Rr \ R\S'S r S r S rS rg) FlaxAttentionBlocka Flax Convolutional based multi-head attention block for diffusion-based VAE. Parameters: channels (:obj:`int`): Input channels num_head_channels (:obj:`int`, *optional*, defaults to `None`): Number of attention heads num_groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for group norm dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` rBNnum_head_channelsrr`r%cbURbURUR-OSUl[[R URUR S9n[R"URSS9Ul U"5U"5U"5sUl Ul Ul U"5Ul g)Nr r%r^r_)rwrB num_headsrr0Denser%rbr` group_normquerykeyvalue proj_attn)r4denses rr5FlaxAttentionBlock.setups{DHDZDZDf$*@*@@lm$--tzzB,,$//4P+07EGUW( DHdjrcURSSURS4-nURU5n[R"US5nU$)Nrrr r()r9rzreshaper transpose)r4 projectionnew_projection_shapenew_projections rtranspose_for_scores'FlaxAttentionBlock.transpose_for_scoressJ)//47KK#++,@A~|Drc4UnURup4pVURU5nURX4U-U45nURU5nUR U5nUR U5n UR U5nUR U5nUR U 5n S[R"[R"URUR- 55- n [R"SXz-X-5n [R"U SS9n [R"SX5n[R"US5nURSSUR4-n URU 5nUR!U5nURX4XV45nX-nU$)Nr z...qc,...kc->...qkraxisz...kc,...qk->...qcr)r9r|rr}r~rrmathsqrtrBrzreinsumr0softmaxrr) r4r>ror?r@rArBr}r~rscale attn_weightsnew_hidden_states_shapes rrCFlaxAttentionBlock.__call__sp )6)<)<&u 6 %--uunh.OP  =)hh}% =)))%0'',))%0DIIdii (FGHHzz"6 s{S zz,R8  #7M  m\B "/"5"5cr":dmm=M"M%--.EF }5 %--ue.NO %0 r)r|r~rzrr}r)rrrrrrGrrwr`rrHr%r5rrCrrrrrurusD M!s!J{{E399"! rruc\rSrSr%Sr\\S'\\S'Sr\\S'Sr \\S'S r \\S 'S r \ \S '\ Rr\ R\S 'SrSSjrSrg)FlaxDownEncoderBlock2Dia Flax Resnet blocks-based Encoder block for diffusion-based VAE. Parameters: in_channels (:obj:`int`): Input channels out_channels (:obj:`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of Resnet layer block resnet_groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for the Resnet block group norm add_downsample (:obj:`bool`, *optional*, defaults to `True`): Whether to add downsample layer dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` r$rXrYrZr num_layersr resnet_groupsTadd_downsampler%c ~/n[UR5HgnUS:Xa URO URn[ UURUR UR URS9nURU5 Mi Xl UR(a$[URURS9Ul ggNrr$rXrZr[r%ry) rangerr$rXrVrZrr%appendresnetsrrJdownsamplers_0r4rir$ res_blocks rr5FlaxDownEncoderBlock2D.setup0st'A./1f$**$:K:KK)'!.. ))jj I NN9 %(   "243D3DDJJ"WD  rc|URH nU"XS9nM UR(aURU5nU$Nrn)rrrr4r>rnresnets rrCFlaxDownEncoderBlock2D.__call__Bs;llF"=NM#    // >Mr)rrNrq)rrrrrrGrrZrrrrrrsrrHr%r5rCrrrrrrsZ(GUJM3ND{{E399"X$rrc\rSrSr%Sr\\S'\\S'Sr\\S'Sr \\S'S r \\S 'S r \ \S '\ Rr\ R\S 'SrSSjrSrg)FlaxUpDecoderBlock2DiLa Flax Resnet blocks-based Decoder block for diffusion-based VAE. Parameters: in_channels (:obj:`int`): Input channels out_channels (:obj:`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of Resnet layer block resnet_groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for the Resnet block group norm add_upsample (:obj:`bool`, *optional*, defaults to `True`): Whether to add upsample layer dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` r$rXrYrZr rrrT add_upsampler%c ~/n[UR5HgnUS:Xa URO URn[ UURUR UR URS9nURU5 Mi Xl UR(a$[URURS9Ul ggr) rrr$rXrVrZrr%rrrr" upsamplers_0rs rr5FlaxUpDecoderBlock2D.setupist'A./1f$**$:K:KK)'!.. ))jj I NN9 %(    .t/@/@ SD  rc|URH nU"XS9nM UR(aURU5nU$r)rrrrs rrCFlaxUpDecoderBlock2D.__call__{s;llF"=NM#    --mrnattnrs rrCFlaxUNetMidBlock2D.__call__sS Q S ab1ABLD /M"=NMCr)rrNrq)rrrrrrGrrZrrrrrrrHr%r5rCrrrrrrsU$GUJM3  {{E399"#%Jrrc\rSrSr%SrSr\\S'Sr\\S'Sr \ \ \S'Sr \ \\S 'S r \\S 'S r\\S 'Sr\ \S'Sr\\S'\R&r\R(\S'SrSS\4SjjrSrg) FlaxEncoderia Flax Implementation of VAE Encoder. This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: in_channels (:obj:`int`, *optional*, defaults to 3): Input channels out_channels (:obj:`int`, *optional*, defaults to 3): Output channels down_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`): DownEncoder block type block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`): Tuple containing the number of output channels for each block layers_per_block (:obj:`int`, *optional*, defaults to `2`): Number of Resnet layer for each block norm_num_groups (:obj:`int`, *optional*, defaults to `32`): norm num group act_fn (:obj:`str`, *optional*, defaults to `silu`): Activation function double_z (:obj:`bool`, *optional*, defaults to `False`): Whether to double the last output channels dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` r(r$rXDownEncoderBlock2Ddown_block_types@block_out_channelsrlayers_per_blockrnorm_num_groupssiluact_fnFdouble_zr%c URn[R"USSSSURS9Ul/nUSn[ UR 5H^upEUnXnU[U5S- :Hn[UUURURU(+URS9nURU5 M` X l [USURSURS 9UlUR(aS UR -O UR n [R""URS S 9Ul[R"U SSSURS9Ulg) Nrr'r)r*r+r )r$rXrrrr%rr$rrr%rr^r_)rr0r1r%conv_in enumeraterlenrrrr down_blocksr mid_blockrrXrb conv_norm_outconv_out) r4rroutput_channelrr input_channelis_final_block down_blockconv_out_channelss rr5FlaxEncoder.setupsN!44ww q !$**    +A.d334DA*M/2N#&8"9A"==N/)+00"22#11jj J   z *5',*2... $**  6:]]A 1 11HYHY\\T5I5ISWX $**   rrncURU5nURH nU"XS9nM URXS9nURU5n[R "U5nUR U5nU$r)rrrrr0rmrr4rrnblocks rrCFlaxEncoder.__call__+sqf%%%E6?F&D##F+&!v& r)rrrrrNrq)rrrrrr$rGrrXrrstrrrrrrrsrrHr%r5rCrrrrrrs DKL##:eCj:%*c *cOSFCHd{{E399"/ bdrrc\rSrSr%SrSr\\S'Sr\\S'Sr \ \ \S'Sr \\S 'S r \\S 'S r\\S 'Sr\ \S'\R"r\R$\S'SrSS\4SjjrSrg) FlaxDecoderi>ay Flax Implementation of VAE Decoder. This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: in_channels (:obj:`int`, *optional*, defaults to 3): Input channels out_channels (:obj:`int`, *optional*, defaults to 3): Output channels up_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`): UpDecoder block type block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`): Tuple containing the number of output channels for each block layers_per_block (:obj:`int`, *optional*, defaults to `2`): Number of Resnet layer for each block norm_num_groups (:obj:`int`, *optional*, defaults to `32`): norm num group act_fn (:obj:`str`, *optional*, defaults to `silu`): Activation function double_z (:obj:`bool`, *optional*, defaults to `False`): Whether to double the last output channels dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): parameters `dtype` r(r$rXUpDecoderBlock2Dup_block_typesrrrrrrrrr%c URn[R"USSSSURS9Ul[ USUR SURS9Ul[[U55nUSn/n[UR5HcupVUnX%nU[U5S- :Hn[UUURS-UR U(+URS 9n URU 5 UnMe X@l[R""UR S S 9Ul[R"UR&SSSURS9Ulg) Nrr'r)r*r+rrr )r$rXrrrr%r^r_)rr0r1r%rrrrlistreversedrrrrrr up_blocksrbrrXr) r4rreversed_block_out_channelsrrrrprev_output_channelrup_blocks rr5FlaxDecoder.setupjsS!44ww r "$**   ,*2... $**  '+84F+G&H#4Q7 d112DA"0 8;N#&8"9A"==N+/+0014"22!//jj H   X &"0 3"# \\T5I5ISWX   $**   rrncURU5nURXS9nURH nU"XS9nM URU5n[R "U5nUR U5nU$r)rrrrr0rmrrs rrCFlaxDecoder.__call__sof%D^^E6?F$##F+&!v& r)rrrrrNrq)rrrrrr$rGrrXrrrrrrrrrHr%r5rsrCrrrrrr>s DKL#!6NE#J6##cOSFC{{E399"3 jdrrcD\rSrSrS SjrSrS Sjr/SQ4SjrSrS r g) ric[R"USSS9uUlUl[R"URSS5UlX l[R "SUR-5Ul[R "UR5UlUR (a-[R"UR5=UlUlgg)Nrrrg>g4@?) rsplitmeanlogvarcliprnexpstdvar zeros_like)r4 parametersrns r__init__)FlaxDiagonalGaussianDistribution.__init__s!$:qr!B 4;hht{{E48 *773,-774;;'   "%.."; ;DHtx rcURUR[RR XRR 5--$rl)rrr;randomnormalr9)r4r~s rr'FlaxDiagonalGaussianDistribution.samples1yy488cjj&7&7YY__&MMMMrNcUR(a[R"S/5$UcDS[R"URS-UR -S- UR - /SQS9-$S[R"[R"URUR- 5UR - UR UR - -S- UR - UR -/SQS9-$)NrYrrg?r rr(r)rnrarraysumrrrsquare)r4others rkl#FlaxDiagonalGaussianDistribution.kls   99cU# # =A!83!>!LS\]] ]SWW JJtyy5::- . :TXX =Q QTW WZ^ZeZe ehmhtht t   rrcHUR(a[R"S/5$[R"S[R-5nS[R "X0R -[R"XR- 5UR- -US9-$)NrYg@rr) rnrr logpir rr rr)r4rrlogtwopis rnll$FlaxDiagonalGaussianDistribution.nllsq   99cU# #773<(SWWX 3cjj))AS6TW[W_W_6__fjkkkrcUR$rl)rr3s rmode%FlaxDiagonalGaussianDistribution.modes yyr)rnrrrr)Frl) rrrrrrr rrrrrrrrs"<N   )lrrcj\rSrSr%SrSr\\S'Sr\\S'Sr \ \ \S'Sr \ \ \S 'S r \ \\S 'S r\\S 'Sr\ \S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'\R,r\R.\S'SrS\R4S\4SjrS#S\S\4SjjrS#S\S\4SjjrS$S\S\4S jjr S!r!g")%FlaxAutoencoderKLia Flax implementation of a VAE model with KL loss for decoding latent representations. This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matter related to its general usage and behavior. Inherent JAX features such as the following are supported: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image. out_channels (`int`, *optional*, defaults to 3): Number of channels in the output. down_block_types (`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`): Tuple of downsample block types. up_block_types (`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`): Tuple of upsample block types. block_out_channels (`Tuple[str]`, *optional*, defaults to `(64,)`): Tuple of block output channels. layers_per_block (`int`, *optional*, defaults to `2`): Number of ResNet layer for each block. act_fn (`str`, *optional*, defaults to `silu`): The activation function to use. latent_channels (`int`, *optional*, defaults to `4`): Number of channels in the latent space. norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization. sample_size (`int`, *optional*, defaults to 32): Sample input size. scaling_factor (`float`, *optional*, defaults to 0.18215): The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) paper. dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): The `dtype` of the parameters. r(r$rXrrrrrrr rrrrlatent_channelsrr sample_sizeg{P?scaling_factorr%c [URRURRURRURR URR URRURRSURS9 Ul [URRURRURRURR URR URRURRURS9Ul[R "SURR-SSSURS9Ul[R "URRSSSURS9Ulg)NT) r$rXrrrrrrr%)r$rXrrrrrr%rr)rMr+)rconfigr$rrrrrrr%encoderrrXrdecoderr0r1 quant_convpost_quant_convr3s rr5FlaxAutoencoderKL.setupsG" //44![[99#{{==![[99;;%% KK77**   # 3311;;55#{{==![[99 KK77;;%%**   ''  ++ +**   "ww KK ' '**  rrngreturncSURURUR4n[R"U[RS9n[ R RUS5upEnXEUS.nURXs5S$)Nr ryr()paramsrZgaussianr') r$rrzerosrHr;rrinit)r4r$ sample_shaper params_rng dropout_rng gaussian_rngrngss r init_weightsFlaxAutoencoderKL.init_weights;so4++T-=-=t?O?OP moments posteriors rencodeFlaxAutoencoderKL.encodeEsPv|4 V I //-04W= < &9==rcURSURR:wa[R"US5nUR U5nUR XBS9n[R"US5nU(dU4$[US9$)Nrr4r)rr(r rr)r9rrrrr"r r )r4latentsrnr2r>s rdecodeFlaxAutoencoderKL.decodeQst ==  ; ; ;mmG\:G,,W5  ] P  m\B !# # 66rcURXUS9nU(a-URS5nURRU5nOURR 5nUR XtS9RnU(dU4$[ US9$)N)rnr2r()r2r:)r7make_rngr rrr<r )r4rsample_posteriorrnr2r6r$r>s rrCFlaxAutoencoderKL.__call___s|KKQ\K] -- +C%1188=M%11668M]DKK9  //r)r rr"r!N)TT)FTT)"rrrrrr$rGrrXrrrrrrrrrrrrrrrHr%r5r;Arrayrr0rsr7r<rCrrrrrrs/bKL##:eCj:!6NE#J6%*c *cFCOSOSK#NE#{{E399"# J1 1j1 >D >d > 7T 7t 7 0d 0`d 0 0rr)'r functoolsrtypingrflax flax.linenlinenr0r; jax.numpynumpyrflax.core.frozen_dictrconfiguration_utilsrrutilsr modeling_flax_utilsr struct dataclassr rModuler"rJrVrurrrrrobjectrrrrrrRsG"   ,F/    4j 4 4RYYDryy;U0U0r