biՕddlmZddlmZmZmZmZmZmZddl Z ddl m Z ddl m Z ddlmZmZddlmZdd lmZmZd d lmZmZmZmZmZd d lmZmZd d lm Z d dl!m"Z"d dl#m$Z$d dl%m&Z&m'Z'ejPe)Z*eGddeZ+Gdde jXZ-Gdde eeZ.de jXde jXfdZ/y)) dataclass)AnyDictListOptionalTupleUnionN)nn) functional) ConfigMixinregister_to_config)FromOriginalModelMixin) BaseOutputlogging)ADDED_KV_ATTENTION_PROCESSORSCROSS_ATTENTION_PROCESSORSAttentionProcessorAttnAddedKVProcessor AttnProcessor)TimestepEmbedding Timesteps) ModelMixin)UNetMidBlock2DCrossAttn)UNet2DConditionModel)CrossAttnDownBlockMotionDownBlockMotioncTeZdZUdZeej ed<ej ed<y)SparseControlNetOutputa The output of [`SparseControlNetModel`]. Args: down_block_res_samples (`tuple[torch.Tensor]`): A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be used to condition the original UNet's downsampling activations. mid_down_block_re_sample (`torch.Tensor`): The activation of the middle block (the lowest sample resolution). Each tensor should be of shape `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`. Output can be used to condition the original UNet's middle block activation. down_block_res_samplesmid_block_res_sampleN)__name__ __module__ __qualname____doc__rtorchTensor__annotations__m/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/models/controlnets/controlnet_sparsectrl.pyr r *s" "%,,//,,&r+r c teZdZ d dededeedfffd ZdejdejfdZxZ S) %SparseControlNetConditioningEmbeddingconditioning_embedding_channelsconditioning_channelsblock_out_channels.c t|tj||ddd|_tj g|_tt|dz D]t}||}||dz}|j jtj||dd|j jtj||dddvttj|d|dd|_ y)Nrr  kernel_sizepaddingr)r5r6stride) super__init__r Conv2dconv_in ModuleListblocksrangelenappend zero_moduleconv_out)selfr/r0r1i channel_in channel_out __class__s r,r:z.SparseControlNetConditioningEmbedding.__init__?s yy!68J18M[\fgh mmB' s-.23 gA+A.J,QU3K KK  ryyZQXYZ [ KK  ryy[aYZcde f  g $ II(,.M[\fg h  r+ conditioningreturnc|j|}tj|}|jD]}||}tj|}!|j |}|SN)r<Fsilur>rC)rDrI embeddingblocks r,forwardz-SparseControlNetConditioningEmbedding.forwardTs_LL. FF9% [[ *Ei(Iy)I *MM), r+)r  `) r#r$r%intrr:r'r(rQ __classcell__rHs@r,r.r.>sO&'.?  ),  # "#s(O  * ELL U\\ r+r.c<|eZdZdZdZe d9dededededee d fd e eeefd eed fd ed ede de de ede dede eeed ffde e eeefde eeed ffde eeed ffde e eeed ffdedede de eed fdede deded ed!ef:fd" Z e d:d#ede de eed fd$eded%df d&Zed%ee effd'Zd(e eee efffd)Zd*Zd+e e eeefd%dfd,Z d;d-ej4d.e ej4e efd/ej4d0ej4d1e d2e ej4d3e ej4d4e ee efd5e ej4d6ed7ed%e eeeej4d fej4fffd8ZxZS)<SparseControlNetModela3 A SparseControlNet model as described in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://huggingface.co/papers/2311.16933). Args: in_channels (`int`, defaults to 4): The number of channels in the input sample. conditioning_channels (`int`, defaults to 4): The number of input channels in the controlnet conditional embedding module. If `concat_condition_embedding` is True, the value provided here is incremented by 1. flip_sin_to_cos (`bool`, defaults to `True`): Whether to flip the sin to cos in the time embedding. freq_shift (`int`, defaults to 0): The frequency shift to apply to the time embedding. down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): The tuple of downsample blocks to use. only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`): block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. layers_per_block (`int`, defaults to 2): The number of layers per block. downsample_padding (`int`, defaults to 1): The padding to use for the downsampling convolution. mid_block_scale_factor (`float`, defaults to 1): The scale factor to use for the mid block. act_fn (`str`, defaults to "silu"): The activation function to use. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. If None, normalization and activation layers is skipped in post-processing. norm_eps (`float`, defaults to 1e-5): The epsilon to use for the normalization. cross_attention_dim (`int`, defaults to 1280): The dimension of the cross attention features. transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1): The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. transformer_layers_per_mid_block (`int` or `Tuple[int]`, *optional*, defaults to 1): The number of transformer layers to use in each layer in the middle block. attention_head_dim (`int` or `Tuple[int]`, defaults to 8): The dimension of the attention heads. num_attention_heads (`int` or `Tuple[int]`, *optional*): The number of heads to use for multi-head attention. use_linear_projection (`bool`, defaults to `False`): upcast_attention (`bool`, defaults to `False`): resnet_time_scale_shift (`str`, defaults to `"default"`): Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`. conditioning_embedding_out_channels (`Tuple[int]`, defaults to `(16, 32, 96, 256)`): The tuple of output channel for each block in the `conditioning_embedding` layer. global_pool_conditions (`bool`, defaults to `False`): TODO(Patrick) - unused parameter controlnet_conditioning_channel_order (`str`, defaults to `rgb`): motion_max_seq_length (`int`, defaults to `32`): The maximum sequence length to use in the motion module. motion_num_attention_heads (`int` or `Tuple[int]`, defaults to `8`): The number of heads to use in each attention layer of the motion module. concat_conditioning_mask (`bool`, defaults to `True`): use_simplified_condition_embedding (`bool`, defaults to `True`): TN in_channelsr0flip_sin_to_cos freq_shiftdown_block_types.only_cross_attentionr1layers_per_blockdownsample_paddingmid_block_scale_factoract_fnnorm_num_groupsnorm_epscross_attention_dimtransformer_layers_per_block transformer_layers_per_mid_block%temporal_transformer_layers_per_blockattention_head_dimnum_attention_headsuse_linear_projectionupcast_attentionresnet_time_scale_shift#conditioning_embedding_out_channelsglobal_pool_conditions%controlnet_conditioning_channel_ordermotion_max_seq_lengthmotion_num_attention_headsconcat_conditioning_mask"use_simplified_condition_embeddingct t+|||_|xs|}t|t|k7rt d|d|dt |t s)t|t|k7rt d|d|dt |ts)t|t|k7rt d|d|dt |tr|gt|z}t |tr|gt|z}d}|dz dz}tj||d || |_ |r|dz}||_ |r+ttj||d dd |_ nt|d || |_ |d d z} t|d |||_|d }!t#|!| | |_tj&g|_tj&g|_t |tr|ft|z}t |t r|gt|z}t |tr|ft|z}t |tr|ft|z}t |tr|ft|z}|d }"tj|"|"d}#t|#}#|j*j-|#t/|D]_\}$}%|"}&||$}"|$t|dz k(}'|%dk(r_t1d/id|&d|"d| dd d|d||$d| d|d| d| ddd||$d||$d|' dd d!|d"||$d#|d$||$d%|d&||$d'd }(n2|%d(k(r"t3|&|"| d || || | d|' ||$|||$d )}(n t d*|j(j-|(t5|D]@})tj|"|"d}#t|#}#|j*j-|#B|'r"tj|"|"d}#t|#}#|j*j-|#b|d+}*tj|*|*d}#t|#}#|#|_|t |d+tr|d+nd}t9d/id|*d| dd ddd|d| d|d| d| ddd|d+d,| d|d+dd d!|d#|d-d.|_y)0NzbMust provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: z. `down_block_types`: .zfMust provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: zdMust provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: r r3rrr4)r/r1r0)rd)r5rr\ out_channels temb_channelsdropout num_layersrh resnet_epsro resnet_act_fn resnet_groupsresnet_pre_normTrlrgadd_downsampledual_cross_attentionFrmr`rntemporal_num_attention_headstemporal_max_seq_lengthrjtemporal_double_self_attentionr)r\rzr{r|r}r~rorrrrrrrjrz`Invalid `block_type` encountered. Must be one of `CrossAttnDownBlockMotion` or `DownBlockMotion`r8output_scale_factorattention_typedefaultr*)r9r:rvr@ ValueError isinstanceboolrWr r;r<rurBcontrolnet_cond_embeddingr.r time_projrtime_embeddingr= down_blockscontrolnet_down_blocksrA enumeraterrr?controlnet_mid_blockr mid_block),rDr\r0r]r^r_r`r1rarbrcrdrerfrgrhrirjrkrlrmrnrorprqrrrsrtrurvconv_in_kernelconv_in_paddingtime_embed_dimtimestep_input_dimoutput_channelcontrolnet_blockrEdown_block_type input_channelis_final_block down_block_mid_block_channelsrHs, r,r:zSparseControlNetModel.__init__sL 2T/2G5G ! "c*:&; ;tvHuII_`p_qqrs .5#>R:SWZ[kWl:lxzNyOOefvewwxy -s3)(6#5*/ +@!"*>a)@#$&6%&2LA1N'(-B)*;``a:b+,49- 0!$55, -!/"0/',C"("1$('5#51KA1N,A:_`a:b38 $!v    # #J /+, E#%99^^YZ#[ #./?#@ ++223CD E "#%99^^YZ#[ #./?#@ ++223CD? ED0399%79KYZ[&'78$4! + 34>?[\^?_ad4e,R0kl -1 * (    *J    %< ! * ! !4B 7 !7 !4B 7 "' #8 .! "%# r+unetload_weights_from_unetrJcpd|jvr|jjnd}|jj}tt |D]&}d||vrd||<d||vrd||<t d|did|jj d |d |jjd |jjd |jjd |jjd|jjd|jjd|jjd|jjd|jjd|jjd|jj d|jj"d|d|jj$d|jj&d|jj(d|jj*d|jj,d|d|} |r | j.j1|j.j3d| j4j1|j4j3d| j6j1|j6j3d| j8j1|j8j3d| j:j1|j:j3d| S) a5 Instantiate a [`SparseControlNetModel`] from [`UNet2DConditionModel`]. Parameters: unet (`UNet2DConditionModel`): The UNet model weights to copy to the [`SparseControlNetModel`]. All configuration options are also copied where applicable. rhr3 CrossAttnrDownrzIInvalid `block_type` encountered. Must be a cross-attention or down blockr\r0r]r^r_r`r1rarbrcrdrerfrgrkrlrmrnrorprrF)strictr*)configrhr_r?r@rr\r]r^r`r1rarbrcrdrerfrgrkrlrmrnror<load_state_dict state_dictrrrr) clsrrrrprr0rhr_rE controlnets r, from_unetzSparseControlNetModel.from_unets$9WZ^ZeZe8eDKK 4 4kl % ;;77s+,- nA.q11&@ #+A..&7 # !lmm  n  // "7 !KK77 {{--  "[[99  "&!A!A   ${{== "[[99  ${{== $(;;#E#E ;;%% !KK77 [[)) !% ? ? *F ${{==! "!% ? ?# $#'++"C"C% &"[[99' (%)KK$G$G) *1T+ ,3X- 2 "    . .t||/F/F/HQV . W  0 01J1J1LUZ 0 [  % % 5 5d6I6I6T6T6V_d 5 e  " " 2 243C3C3N3N3PY^ 2 _  0 01J1J1LUZ 0 [r+ci}dtdtjjdttt fffd |j D]\}}||||S)z Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. namemodule processorsct|dr|j||d<|jD]\}}|d||||S)N get_processor .processorrx)hasattrrnamed_children)rrrsub_namechildfn_recursive_add_processorss r,rzJSparseControlNetModel.attn_processors..fn_recursive_add_processorssdv/282F2F2H dV:./#)#8#8#: U%+tfAhZ,@%T U r+)strr'r Modulerrr)rDrrrrs @r,attn_processorsz%SparseControlNetModel.attn_processorssm  c 588?? X\]`bt]tXu !//1 BLD& 'fj A Br+ processorc Tt|jj}t|tr,t||k7rt dt|d|d|ddt dtjjffd |jD]\}}|||y) a4 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.rrct|drEt|ts|j|n#|j|j |d|j D]\}}|d|||y)N set_processorrrx)rrdictrpopr)rrrrrfn_recursive_attn_processors r,rzMSparseControlNetModel.set_attn_processor..fn_recursive_attn_processorsxv/!)T2((3(($z7J)KL#)#8#8#: T%+tfAhZ,@%S Tr+N) r@rkeysrrrrr'r rr)rDrcountrrrs @r,set_attn_processorz(SparseControlNetModel.set_attn_processorsD((--/0 i &3y>U+BPQTU^Q_P`a005w6QRWQXXkm   Tc T588?? T!//1 ALD& 'fi @ Ar+c jtd|jjDr t}nmtd|jjDr t }n8t dt t|jj|j|y)ze Disables custom attention processors and sets the default attention implementation. c3@K|]}|jtvywrL)rHr.0procs r, zCSparseControlNetModel.set_default_attn_processor..si4t~~!>>ic3@K|]}|jtvywrL)rHrrs r,rzCSparseControlNetModel.set_default_attn_processor..sh$#==hrzOCannot call `set_default_attn_processor` when attention processors are of type N) allrvaluesrrrnextiterr)rDrs r,set_default_attn_processorz0SparseControlNetModel.set_default_attn_processors i4K_K_KfKfKhi i,.I h$J^J^JeJeJgh h%IabfgklpmAmAmHmHmJhKcLbMN   *r+ slice_sizec  g dtjjf fd |jD] }| t }|dk(r Dcgc]}|dz }}n |dk(r|dgz}t |t s||gzn|}t |t k7r=tdt |d|jd t d t d tt |D]&}||} |}|||kDstd |d|d dtjjdttf fd t t|}|jD] } || y cc}w)a Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. This is useful for saving some memory in exchange for a small decrease in speed. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. rct|drj|j|jD] }| yNset_attention_slice)rrAsliceable_head_dimchildren)rr$fn_recursive_retrieve_sliceable_dimssliceable_head_dimss r,rzWSparseControlNetModel.set_attention_slice..fn_recursive_retrieve_sliceable_dims s@v45#**6+D+DE* <4U; .fn_recursive_set_attention_sliceFsEv45**:>>+;<* D0 C Dr+) r'r rrr@rlistrrr?rrWreversed) rDrrnum_sliceable_layersdimrEsizereversed_slice_sizerrrs @@@r,rz)SparseControlNetModel.set_attention_slices! < <mmo 9F 0 8 9 ##67  /BBs#(BJB 5 -3J@J:W[@\)ZL8bl z?c"56 6$S_$5VDKK=cReNfMghQQTUhQiPjjkm  s:' VAa=D%a(CD3J 5.McURS!TUU  V DUXX__ DRVWZR[ D#8J#78mmo JF ,V5H I J=Cs E;sampletimestepencoder_hidden_statescontrolnet_condconditioning_scale timestep_condattention_maskcross_attention_kwargsconditioning_mask guess_mode return_dictc  |j\} } }}}tj|}|jj}|dk(rn,|dk(rtj |dg}nt d||2d|j|jz dz}|jd}|}tj|s|jjdk(}|jjd k(}t|tr%|s|rtjntj }n$|s|rtj"ntj$}tj&|g||j }n6t)|jd k(r|dj|j}|j+|jd }|j-|}|j|j }|j/||}|j1|d |jd |z }|j\}}}}}|j3d ddddj5||z|||}|j7|}|j\}}}}|dddfj5| ||||}|j8rtj:|| gd}|j\}}}}}|j3d ddddj5||z|||}|j=|}|j\}}}}|dddfj5|||||}||z}|j\}}}}}|j5| |z|||}|f}|j>D]?}tA|dr|jBr|||||||\}} n||||\}} || z }A|jDUtA|jDdr-|jDjBr|jE|||||}n|jE||}d}!tG||jHD]\}"}#|#|"}"|!|"fz}!|!}|jK|}$| rt|jjLs^tjNdd t)|dz|j}%|%|z}%tG||%D&cgc] \}}&||&z }}}&|$|%dz}$n|Dcgc]}||z }}|$|z}$|jjLr=|Dcgc]}tjP|dd}}tjP|$dd}$| s||$fStS||$Scc}&}wcc}wcc}w)a The [`SparseControlNetModel`] forward method. Args: sample (`torch.Tensor`): The noisy input tensor. timestep (`Union[torch.Tensor, float, int]`): The number of timesteps to denoise an input. encoder_hidden_states (`torch.Tensor`): The encoder hidden states. controlnet_cond (`torch.Tensor`): The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`. conditioning_scale (`float`, defaults to `1.0`): The scale factor for ControlNet outputs. class_labels (`torch.Tensor`, *optional*, defaults to `None`): Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. timestep_cond (`torch.Tensor`, *optional*, defaults to `None`): Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep embeddings. attention_mask (`torch.Tensor`, *optional*, defaults to `None`): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. added_cond_kwargs (`dict`): Additional conditions for the Stable Diffusion XL UNet. cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`): A kwargs dictionary that if specified is passed along to the `AttnProcessor`. guess_mode (`bool`, defaults to `False`): In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended. return_dict (`bool`, defaults to `True`): Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple. Returns: [`~models.controlnet.ControlNetOutput`] **or** `tuple`: If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. rgbbgrr3)dimsz1unknown `controlnet_conditioning_channel_order`: Ngmpsnpu)dtypedevicer)r)r output_sizerr ry)rhas_cross_attention) hidden_statestembrr num_framesr)rrr)rrrr*r8)r)rr T)rkeepdim)r!r")*shaper' zeros_likerrrfliprtor unsqueeze is_tensorrtyperfloatfloat32float64int32int64tensorr@expandrrrepeat_interleavepermutereshaper<rucatrrrrrziprrrqlogspacemeanr )'rDrrrrrrrrrrrsample_batch_sizesample_channelssample_num_frames sample_height sample_width channel_order timestepsis_mpsis_npurt_embemb batch_sizechannelsrheightwidth batch_framesr!downsample_block res_samples!controlnet_down_block_res_samplesdown_block_res_samplerr"scalesscales' r,rQzSparseControlNetModel.forwardQsh^d]i]iZ?,=}l!!&) II E !  e ##jjsCOPQ^P_`a a  %."3"3FLL"AAXMN+55a8N y)]]''50F]]''50F(E**0F (.& u{{ i[fmmTI  !Q &!$**6==9I$$V\\!_5 y) v||,!!%7##$51#))TU,YjJj#k;A,,7 Hj&%1aA.66zJ7NPXZ`bghf%06 - h4(():?DTDhDh&6"(*?#1)+A '# '7VRUbl&m# "k 1 " 2 >> %t~~'<=$..BdBd*?#1+A (4-/)7:;QSWSnSn7o m 3 !#3$45J$K !0QUjTl0l - m"C#88@ dkk@@^^B3/E+F+JSYS`S`aF00FJMNdflJm%nfun%n "%n#7&*#D Pf%gff/A&A%g "%g#7:L#L ;; - -Ka&AG 6vt<& "&$)::.BX\#] *,@A A%#9Pd  &o&h&s6U UU)ryryTr)rrrrF)i@ir*rr3r3rNrTgh㈵>ir3Nr3NFFrrRFrrTr+TT)rrRTr )g?NNNNFT)r#r$r%r& _supports_gradient_checkpointingrrWrrrr rrr: classmethodrrpropertyrrrrrrrr'r(rr rQrXrYs@r,r[r[`sB;z(,$%& $- :?.D !"#())+#&DEMQMN:;EI&+!&'0IZ',5:%'*+)-37G` `  #`  `  `  S/ ` $D%+$56` "#s(O` `  ` !&!` "#` $"#%` &'` (!)` *',CsCx,@&A+` ,+35eCj3I*J-` .05S%S/5I/J/` 0"#uS#X"671` 2&eCsCx,@&AB3` 4 $5` 67` 8"%9` :.6eCHo-F;` <!%=` >03?` @ #A` B%(C` D#'E` F-1G` ` D6;IZ'+%& ="=03=.6eCHo-F = !% = # = !==~c+=&=!>0 AE2Dd3PbKbFc2c,d AF + ?JeCd3i4G.H?JT?JN%(0415;?48  y  y  eS01y  %|| y  y " y   -y !.y !)c3h 8y $ELL1y y y  %uU5<<3D-Eu||-S'TT Uy r+r[rrJcn|jD]!}tjj|#|SrL) parametersr initzeros_)rps r,rBrBs/     q Mr+)0 dataclassesrtypingrrrrrr r'r torch.nnr rMconfiguration_utilsr rloadersrutilsrrattention_processorrrrrr embeddingsrrmodeling_utilsrunets.unet_2d_blocksrunets.unet_2d_conditionrunets.unet_motion_modelrr get_loggerr#loggerr rr.r[rBr*r+r,rBs":: $B-(6'::O   H % 'Z' '&BIIDj  J 5Kj  \ biir+