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SS K"J#r# SSK$J%r%J&r& \RN"\(5r)\"SS\55r*"SS\#\ \5r+"SS\#5r,g)) dataclass)AnyDictListOptionalTupleUnionN) ConfigMixinregister_to_config)PeftAdapterMixin)USE_PEFT_BACKEND BaseOutputloggingscale_lora_layersunscale_lora_layers)AttentionProcessor)ControlNetConditioningEmbedding zero_module)*CombinedTimestepGuidanceTextProjEmbeddings"CombinedTimestepTextProjEmbeddings FluxPosEmbed)Transformer2DModelOutput) ModelMixin)FluxSingleTransformerBlockFluxTransformerBlockc^\rSrSr%\\R \S'\\R \S'Srg)FluxControlNetOutput#controlnet_block_samplescontrolnet_single_block_samplesN) __name__ __module__ __qualname____firstlineno__rtorchTensor__annotations____static_attributes__r#f/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/models/controlnets/controlnet_flux.pyrr#s #ELL11%*5<<%88r,rc^\rSrSrSr\SSSSSSS S S /S QS S 4 S\S\S\S\S\S\S\S\S\S\\S\S\4U4Sjjj5r \ S5r Sr \ S-S\S\S\S\4Sjj5rS.S\R S\R S \R S!\S"\R S#\R S$\R$S%\R S&\R S'\R S(\\\\4S)\S*\\R0\44S+jjrS,rU=r$)/FluxControlNetModel)T@&iiF)8r8N patch_size in_channels num_layersnum_single_layersattention_head_dimnum_attention_headsjoint_attention_dimpooled_projection_dimguidance_embedsaxes_dims_ropenum_modeconditioning_embedding_channelsc H>[TU]5 X lXe-Ul[ SU S9UlU (a[ O[n U "URUS9Ul[R"XpR5Ul [RRX R5Ul [R"[U5Vs/sHn[!URUUS9PM sn5Ul[R"[U5Vs/sHn[%URUUS9PM sn5Ul[R"/5Ul[[+UR"55HPnUR(R-[/[R"URUR555 MR [R"/5Ul[[+UR&55HPnUR0R-[/[R"URUR555 MR U SLUlUR2(a%[R4"XR5UlU b>[9U SS9Ul[RRX R5UlO>SUl[/[RRX R55UlSUlgs snfs snf)Ni')thetaaxes_dim) embedding_dimr@)dimr>r=)r7r7r7r7)rDblock_out_channelsF) super__init__ out_channels inner_dimr pos_embedrrtime_text_embednnLinearcontext_embedderr( x_embedder ModuleListrangertransformer_blocksrsingle_transformer_blockscontrolnet_blockslenappendrcontrolnet_single_blocksunion Embeddingcontrolnet_mode_embedderrinput_hint_blockcontrolnet_x_embeddergradient_checkpointing)selfr9r:r;r<r=r>r?r@rArBrCrDtext_time_guidance_clsi_ __class__s r-rLFluxControlNetModel.__init__,sk ',A%ENK:I 6Oq  6..@U !# *=~~ N((//+~~F"$--z*   +A %(;'9 +   # *,01   2A +(;'9 2   * &"$r!2s42234A  " " ) )+biiPTP^P^6_*` a5)+ b(9%s499:;A  ) ) 0 0RYYt~~W[WeWe=f1g h<T) ::,.LL>>,RD ) * 6$C0Odt%D !*/nn)UD &$(D !)4UXX__[R`R`5a)bD &&+#W  s LLc^0nS[S[RRS[[[ 44U4SjjmUR 5Hup#T"X#U5 M U$)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$)N get_processor .processor.)hasattrrnnamed_children)rjrkrlsub_namechildfn_recursive_add_processorss r-ruHFluxControlNetModel.attn_processors..fn_recursive_add_processorssZv//282F2F2H V:./#)#8#8#:+tfAhZ,@%T$; r,)strr(rQModulerrrr)rcrlrjrkrus @r-attn_processors#FluxControlNetModel.attn_processorsysb  c 588?? X\]`bt]tXu !//1LD 'j A2r,c 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.rjrkc >[US5(aJ[U[5(dURU5 O#URUR US355 UR 5Hup4T"USU3XB5 M g)N set_processorrorp)rq isinstancedictr}poprr)rjrk processorrsrtfn_recursive_attn_processors r-rKFluxControlNetModel.set_attn_processor..fn_recursive_attn_processorssv//!)T22((3(($z7J)KL#)#8#8#:+tfAhZ,@%S$;r,N) rZrykeysr~r ValueErrorrwr(rQrxrr)rcrcountrjrkrs @r-set_attn_processor&FluxControlNetModel.set_attn_processorsD((--/0 i & &3y>U+BPQTU^Q_P`a005w6QRWQXXkm   Tc T588?? T!//1LD 'i @2r,c[UR5nX'S'X7S'XGS'XWS'URU5nU(GaJURR URR 55 UR R UR R 55 URR URR 55 URR URR 55 URR URR 5SS9 URR URR 5SS9 [UR5Ul U$)Nr;r<r=r>F)strict) rconfig from_configrOload_state_dict state_dictrPrSrTrWrXrra) cls transformerr;r<r=r>load_weights_from_transformerr controlnets r-from_transformer$FluxControlNetModel.from_transformersFk(())|&7"#'9#$(;$%__V, (  0 01F1F1Q1Q1S T  & & 6 6{7R7R7]7]7_ `  ' ' 7 7 8T8T8_8_8a b  ! ! 1 1+2H2H2S2S2U V  ) ) 9 9+:X:X:c:c:ens 9 t  0 0 @ @55@@B5 A 0;:;[;[/\J ,r, hidden_statescontrolnet_condcontrolnet_modeconditioning_scaleencoder_hidden_statespooled_projectionstimestepimg_idstxt_idsguidancejoint_attention_kwargs return_dictreturnc U b#U R5n U RSS5n OSn [(a [X 5 O+U b(U R SS5b[ R S5 URU5nURbURU5nURupnnUURR-nUURR-nURXUURRUURR5nURSSSSS S 5nURUUU-S 5nXRU5-nURUR 5S -nU bU RUR 5S -n OSn U cUR#Xv5OUR#XzU5nUR%U5nU R&S :Xa[ R S 5 U Sn UR&S :Xa[ R S5 USnUR((aPUc [+S5eUR-U5n[.R0"UU/SS9n[.R0"U SSU /SS9n [.R0"X4SS9nUR3U5nSn[5UR65HZunn[.R8"5(a)UR:(aUR=UUUUU5upQO U"UUUUS9upQUU4-nM\ SnR>5HZunn[.R8"5(a)UR:(aUR=UUUUU5upQO U"UUUUS9upQUU4-nM\ Sn[AUURB5HunnU"U5nUU4-nM Sn[AUURD5Hun nU"U 5n UU 4-nM UV!s/sHn!U!U-PM nn!UV!s/sHn!U!U-PM nn![GU5S:XaSOUn[GU5S:XaSOUn[(a [IX 5 U (dUU4$[KUUS9$s sn!fs sn!f)a The [`FluxTransformer2DModel`] forward method. Args: hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input `hidden_states`. controlnet_cond (`torch.Tensor`): The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`. controlnet_mode (`torch.Tensor`): The mode tensor of shape `(batch_size, 1)`. conditioning_scale (`float`, defaults to `1.0`): The scale factor for ControlNet outputs. encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`): Conditional embeddings (embeddings computed from the input conditions such as prompts) to use. pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected from the embeddings of input conditions. timestep ( `torch.LongTensor`): Used to indicate denoising step. block_controlnet_hidden_states: (`list` of `torch.Tensor`): A list of tensors that if specified are added to the residuals of transformer blocks. joint_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain tuple. Returns: If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. Nscale?z\Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective.rrr1r izrPassing `txt_ids` 3d torch.Tensor is deprecated.Please remove the batch dimension and pass it as a 2d torch TensorzrPassing `img_ids` 3d torch.Tensor is deprecated.Please remove the batch dimension and pass it as a 2d torch TensorzA`controlnet_mode` cannot be `None` when applying ControlNet-Union)rIr#)rrtembimage_rotary_emb)r!r")&copyrrrgetloggerwarningrTr`shaperr9reshapepermuteratodtyperPrSndimr]rr_r(catrO enumeraterWis_grad_enabledrb_gradient_checkpointing_funcrXziprYr\rZrr)"rcrrrrrrrrrrrr lora_scale batch_sizechannels height_pwwidth_pwheightwidthrcontrolnet_mode_embidsr block_samples index_blockblocksingle_block_samplesr! block_samplecontrolnet_blockr"single_block_samplesamples" r-forwardFluxControlNetModel.forwards^ " -%;%@%@%B "/33GSAJJ   d /%16L6P6PQXZ^6_6kr 6  ,"33ODO8G8M8M 5J)X$++"8"88F 6 66E-55fdkk.D.DeT[[McMcO.55aAq!QGO-55j&5.RTUO%(B(B?(SS ;;}223d:  {{=#6#67$>HH   >%%h:LM !% 5 56K L <<1  NNU ajG <<1  NNU ajG ::& !dee"&"?"?"P $)II/BDY.Z`a$b !ii!g 6A>Gii*2>>#. 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