3jAHHSSKrSSKrSSKrSSKrSSKrSSKrSSKrSSKrSSKrSSK r SSKJ r SSK J r Jr SSKJr SSKJr SSKJrJr \R,"S5rSrS rS \S \4S jr"S S\R85r"SS\R85r"SS\R85r"SS\R85r "SS5r!"SS\R85r""SS\RFRH5r%"SS\R85r&"SS\ 5r'S \'4Sjr(g) N) model_trange)ComfyExtensionio) raft_large)override)RaftOpticalFlowget_noise_from_video OPTICAL_FLOWlengthreturncUS- [-S-nU[-S:XaU$[[U[-[-5nUS- [-S-$)aRound ``length`` down to a value that produces an even latent_t. VOID / CogVideoX-Fun-V1.5 uses patch_size_t=2, so the VAE-encoded latent must have an even temporal dimension. If latent_t is odd, the transformer pad_to_patch_size circular-wraps an extra latent frame onto the end; after the post-transformer crop the last real latent frame has been influenced by the wrapped phantom frame, producing visible jitter and "disappearing" subjects near the end of the decoded video. Rounding down fixes this. r)TEMPORAL_COMPRESSION PATCH_SIZE_Tmax)r latent_ttarget_latent_ts 7/home/wildlama/comfy/ComfyUI/comfy_extras/nodes_void.py_valid_void_lengthrsX! 449H,!# ,\)A\(QRO a #7 7! ;;cT\rSrSrSr\S5r\S\R4Sj5r Sr g)OpticalFlowLoader/uLoad an optical flow model from ``models/optical_flow/``. Only torchvision's RAFT-large format is recognized today (the model used by VOIDWarpedNoise). The checkpoint must be placed under ``models/optical_flow/`` — ComfyUI never downloads optical-flow weights at runtime. c [R"SSS[RRS[R "S5SS9/[ R5/S9$) NrzLoad Optical Flow Modelz model/loaders model_name optical_flowzOptical flow model to load. Files must be placed in the 'optical_flow' folder. Today only torchvision's raft_large.pth is supported.)optionstooltipnode_id display_namecategoryinputsoutputs)rSchemaComboInput folder_pathsget_filename_list OpticalFlowOutputclss r define_schemaOpticalFlowLoader.define_schema8s_yy'2$ (::>J7  ""$  rrc[R"SU5n[RR USS9n[ SU55=(a+ [ SU55=(a [ SU55nU(d [ S5e[SSS 9nURU5 UR5R[R5 [RRU[RR!5[RR#5S 9n[$R&"U5$) NrT) safe_loadc3B# UHoRS5v M g7f)zfeature_encoder.N startswith.0ks r ,OpticalFlowLoader.execute..Us="Q /00"c3B# UHoRS5v M g7f)zcontext_encoder.Nr5r7s rr:r;VsAbLL!344br<c3B# UHoRS5v M g7f)z update_block.Nr5r7s rr:r;Ws>2aLL112r<zUnrecognized optical flow model format: expected a torchvision RAFT-large state dict with 'feature_encoder.', 'context_encoder.' and 'update_block.' prefixes.F)weightsprogress) load_deviceoffload_device)r*get_full_path_or_raisecomfyutilsload_torch_fileany ValueErrorrload_state_dictevaltotorchfloat32 model_patcher ModelPatchermodel_managementget_torch_deviceunet_offload_devicer NodeOutput)r/r model_pathsd has_raft_keysmodelpatchers rexecuteOpticalFlowLoader.executeNs"88T [[ ( (t ( D ="= = ?AbAA ?>2>>  0  4%8 b!  &%%22 ..??A 11EEG3  }}W%%rN __name__ __module__ __qualname____firstlineno____doc__ classmethodr0rrSrY__static_attributes__r[rrrr/s:  *&BMM&&rrcX\rSrSrSr\S5r\SS\R4Sjj5r Sr g) VOIDQuadmaskPreprocesslaPreprocess a quadmask video for VOID inpainting. Quantizes mask values to four semantic levels, inverts, and normalizes: 0 -> primary object to remove 63 -> overlap of primary + affected 127 -> affected region (interactions) 255 -> background (keep) After inversion and normalization, the output mask has values in [0, 1] with four discrete levels: 1.0 (remove), ~0.75, ~0.50, 0.0 (keep). c[R"SSS[RRS5[RRSSSSSS S 9/[RR S S 9/S 9$)NrezVOID Quadmask Preprocessorz image/maskmask dilate_widthr2rz=Dilation radius for the primary mask region (0 = no dilation)defaultminrstepr quadmaskr#r!)rr'Maskr)Intr-r.s rr0$VOIDQuadmaskPreprocess.define_schemaysmyy,5! f% ^QA2A%df J7  rrcUR5nUR5S::aUS-nUS:aURS:aUS:R5nUS-S-nURS:XaUR S5n[ R RRXESUS9nURS :XaURS5n[ R"US :[ R"U5U5n[ R"US :*[ R"U5U5n[ R"US :US :*-[ R"US 5U5n[ R"US :US:*-[ R"US5U5n[ R"US:[ R"US5U5nSU- S- n[R"U5$)N?go@rr r) kernel_sizestridepaddingr ?_?)clonerndimfloat unsqueezerLnn functional max_pool2dsqueezewhere zeros_like full_likerrS)r/rhrimbinaryrxdilateds rrYVOIDQuadmaskPreprocess.executes JJL 557c>E A ! ! #g__&F&*Q.K{{a))!,hh))44<5G||q !//!, GcM5+;+;A+>BA KKR!1!1!!4a 8 KKRAG,eooa.Da H KKRAH-uq#/F J KKCC!8! < QY% }}Qrr[N)rr\r[rrrerels:      bmm  rrecT\rSrSrSr\S5r\S\R4Sj5r Sr g)VOIDInpaintConditioningaBuild VOID inpainting conditioning for CogVideoX. Encodes the processed quadmask and masked source video through the VAE, producing a 32-channel concat conditioning (16ch mask + 16ch masked video) that gets concatenated with the 16ch noise latent by the model. cT[R"SS[RRS5[RRS5[RRS5[R RSSS9[R RS S S9[RRS S S [RSS9[RRSSS [RSS9[RRSSS[RSSS9[RRSSSSS9/ [RRSS9[RRSS9[RRSS9/S9$)Nrzmodel/conditioning/voidpositivenegativevaevideoz Source video frames [T, H, W, 3]r roz;Preprocessed quadmask from VOIDQuadmaskPreprocess [T, H, W]widthrlrmrrnheightr -ruNumber of pixel frames to process. For CogVideoX-Fun-V1.5 (patch_size_t=2), latent_t must be even — lengths that produce odd latent_t are rounded down (e.g. 49 → 45).rk batch_size@rlrmrrplatentr"r$r%r&) rr' Conditioningr)VaeImagerqrrnodesMAX_RESOLUTIONr-Latentr.s rr0%VOIDInpaintConditioning.define_schemasKyy-.%%j1%%j1 U#w0RS j2o p Wcru?S?SZ[ \ Xs@T@T[\ ] Xrqe>R>RYZ&_` \1! D &&J&?&&J&?   h 7#  rrc [U5n X:wa[R"SX5 U nUS- [-S-n US-n US-n USUn[R R URSS5XgSS5RSS5nUSUnURS:XaURS5n[R R URSS5XgSS5RSS5nURS:Xa$URSS:XaURS5nUnURS:Xa$URS5RSSSS5nOUnS U- nUSS2SS2SS2SS24S U- -nURU5nURU5nS nU"UU 5nU"UU 5n[R"UU/SS 9n[ R""US U05n[ R""US U05n[R$"U S XU /[R&R)5S9n[*R,"XSU05$)NzVOIDInpaintConditioning: rounding length %d down to %d so that latent_t is even (required by CogVideoX-Fun-V1.5 patch_size_t=2). Using odd latent_t causes the last frame to be corrupted by circular padding.rrbilinearcenterrvr ruc URSU:aUSS2SS2SU24$URSU:aFXRS- n[R"XSS2SS2SS24RSSUSS5/SS9$U$)Nr rrdim)shaperLcatrepeat)lattarget_tpads r_match_temporal8VOIDInpaintConditioning.execute.._match_temporalsyy|h&1a(?++1(1-yy#1a9~'<' ./CXN))\3G$HaP 77 ,o>  77 ,o> {{ X :))==? }}X)\1JKKrr[Nr\r[rrrrsA  2KL68mmKLKLrrcT\rSrSrSr\S5r\S\R4Sj5r Sr g)VOIDWarpedNoiseiaGenerate optical-flow warped noise for VOID Pass 2 refinement. Takes the Pass 1 output video and produces temporally-correlated noise by warping Gaussian noise along optical flow vectors. This noise is used as the initial latent for Pass 2, resulting in better temporal consistency. c[R"SS[RSSS9[RRSSS9[R RSS S [ RS S 9[R RS SS [ RS S 9[R RSSS[ RSSS9[R RSSSSS9/[RRSS9/S9$)Nrmodel/latent/voidrz7Optical flow model from OpticalFlowLoader (RAFT-large).rrz'Pass 1 output video frames [T, H, W, 3]rrrrrrrr rruhNumber of pixel frames. Rounded down to make latent_t even (patch_size_t=2 requirement), e.g. 49 → 45.rkrrr warped_noiserpr) rr'r,r)rrrrrrr-r.s rr0VOIDWarpedNoise.define_schemasyy%(!!"U"w0YZ Wcru?S?SZ[ \ Xs@T@T[\ ] Xrqe>R>RYZ&Z[ \1! D    n =!  rrc [U5nXu:wa[R"SXW5 UnUS- [-S-nUS-n US-n [R R 5n [R RU5 [URU S9n USURU 5n [RRU RSS5X4SS5RSS5n U RSS5S -R[R 5nS nSnSn[#UU S UU[%UU-5U-U S 9nUR&SU:wa:[R("SUR&SS- UU S9R+5nUUnUR&SU :wdUR&S U :waSUR-SSSS 5n[R.R0R3UX4SSS9nUR-SS SS5nUR-SSSS 5R5S5nUS:aUR7USSSS5nUR[R R955n[:R<"SU05$)NzxVOIDWarpedNoise: rounding length %d down to %d so that latent_t is even (required by CogVideoX-Fun-V1.5 patch_size_t=2).rrrrrrrrr{r)noise_channels resize_frames resize_flowdownscale_factorrr rvF)sizemode align_cornersr)rrrrrDrPrQload_model_gpurrWrKrErrclamprLuint8r roundrlinspacelongpermuterr interpolaterrrrrS)r/rrrrr rrrrrrraftr vid_uint8FRAMEFLOW LATENT_SCALEwarpedindices warped_tensors rrYVOIDWarpedNoise.execute6sp-V4  $ OOT  %FaZ$88A=Q;A: ''88: --l;|11&AGVn'kk(( KKA z8 '!R. YYq!_s*..u{{;  %  "54<0<?  <<?h &nnQ Q!(;X,2448DF G_F <<?h &&,,q/X*E^^Aq!Q/FXX((44h1u5F^^Aq!Q/Fq!Q2<)00Q1aHM%(()?)?)S)S)UV }}i788rr[Nr\r[rrrrs@  ,@9PRP]P]@9@9rrc$\rSrSrSrSrSrSrg)Noise_FromLatentizz5Wraps a pre-computed LATENT tensor as a NOISE source.c&SUlUSUlg)Nrr)seed_samples)self latent_dicts r__init__Noise_FromLatent.__init__|s #I. rcRURR5R5$N)rrcpu)r input_latents rgenerate_noiseNoise_FromLatent.generate_noises}}""$((**r)rrN)r]r^r_r`rarrrcr[rrrrzs?/+rrcT\rSrSrSr\S5r\S\R4Sj5r Sr g)VOIDWarpedNoiseSourceizdConvert a LATENT (e.g. from VOIDWarpedNoise) into a NOISE source for use with SamplerCustomAdvanced.c [R"SS[RRSSS9/[RR 5/S9$)Nrrrz(Warped noise latent from VOIDWarpedNoiserr)rr'rr)Noiser-r.s rr0#VOIDWarpedNoiseSource.define_schemasLyy+( F HXX__&'  rrc@[R"[U55$r)rrSr)r/rs rrYVOIDWarpedNoiseSource.executes}}-l;<AIEAJ!!T\ZH#HaVq9Quz!12 C*/$9: C8 ;;g RU?UU3&(2cJ6F35NQY5YY!?$rr[)NNF)r]r^r_r`rar(rcr[rrrrs rrcX\rSrSrSr\S5r\S\R4Sj5r \ r Sr g) VOIDSampleria6VOID DDIM sampler for use with SamplerCustom / SamplerCustomAdvanced. Required for VOID inpainting models. Implements the same DDIM loop that VOID was trained with (diffusers CogVideoXDDIMScheduler), without the noise_scaling that the standard KSampler applies. Use with RandomNoise or VOIDWarpedNoiseSource. cj[R"SS/[RR5/S9$)Nr+zmodel/sampling/samplersr)rr'Samplerr-r.s rr0VOIDSampler.define_schemas/yy!.ZZ&&()   rrc>[R"[55$r)rrSrr.s rrYVOIDSampler.executes}}Y[))rr[N) r]r^r_r`rarbr0rrSrY get_samplerrcr[rrr+r+sA  * **Krr+cL\rSrSr\S\\\R4Sj5r Sr g) VOIDExtensionircJ# [[[[[[ /$7fr)rrerrrr+)rs r get_node_listVOIDExtension.get_node_lists#  " #  !    s!#r[N) r]r^r_r`rlisttyper ComfyNoder5rcr[rrr3r3s)  T$r||*<%=  rr3c# [5$7fr)r3r[rrcomfy_entrypointr;s ?s ))rrLrDcomfy.model_managementcomfy.model_patchercomfy.samplers comfy.utilsr*rrrrcomfy_api.latestrrtorchvision.models.optical_flowrtyping_extensionsrcomfy_extras.void_noise_warprr Customr,rrintrr9rrerrrrsamplersr-rr+r3r;r[rrrGs  ./6&Oii'