3j|bSSKJr SSKrSSKJr SSKJr SSKJr \"SS9"S S55r g) )AnyN) is_device_cpu)GrowMask) comfy_nodeLTXVPreprocessMasks)namec\rSrSrSr\SSS\\\44Sj5r Sr Sr Sr S r S \RS \S \S \S\S\S\S\S\\R4SjrSrg)r a Preprocesses masks for use with the LTXVideo model's latent masking. This node handles temporal mask processing by: 1. Validating mask dimensions against VAE downscaling factors 2. Optionally inverting masks 3. Handling the first frame mask separately (can be ignored) 4. Combining temporal masks using max pooling 5. Growing/shrinking masks with morphological operations 6. Clamping mask values to ensure proper opacity The output is a set of masks ready for latent-space masking operations. clsreturnczSSSSSSS.4SSS S.4/S QS S S.4S SSSSSS.4SSSS.4SSSSSSS.4SSSSSSS.4S. 0$)NrequiredMASK)VAEBOOLEANFzTInvert the input masks before processing.Useful for masking vs unmasking operations.)defaulttooltipTzUZero out the first mask, typically used whenit corresponds to the conditioning frame.maxmeanminrzoMethod to combine temporal masks. Max preserves strongest values, mean averages them, min takes weakest values.INTrii@rzVPixels to grow (positive) or shrink (negative) the mask.Uses morphological operations.)rrrsteprzHUse tapered corners in morphological operations for smoother mask edges.FLOATg?g?g{Gz?z:Minimum value to clamp masks to, ensuring minimum opacity.z Maximum value to clamp masks to.) masksvaeinvert_input_masksignore_first_maskpooling_method grow_masktapered_corners clamp_min clamp_max)r s C/home/wildlama/comfy/ComfyUI/custom_nodes/ComfyUI-LTXVideo/masks.py INPUT_TYPESLTXVPreprocessMasks.INPUT_TYPESs "#($F'#'$D&+#($H##$%$ !$9  #'#m$#&"" $#_  #&"" $#E  oAC C rpreprocess_maskszLightricks/mask_operationszFPreprocess masks to be used for masking latents in the LTXVideo model.rr"r#r$r%r&r!r c URS:wa [S5eURupn URupnU S- U -S:wa[SU S35eX-S:wa[SUS35eX-S:wa[S US35eU (aS U- nU(aUSS2S S 2S S 24S-nOUSS2S S 2S S 24nUSS R S XU 5nS SSS.nUU"U5nUS:wai[ 5nUR n[U5(dUR5nURUXES9un[U5(dURU5n[R"U[R"UXg5/SS9nU4$)a Preprocess masks for temporal latent masking. Args: masks: Input masks tensor of shape (batch_size, H, W) where batch_size = 1 + (num_frames - 1) The first mask is for the conditioning frame, remaining masks are for generated frames vae: VAE model object containing downscale_index_formula attribute pooling_method: Method to combine temporal masks ('max', 'mean', 'min') grow_mask: Pixels to grow (positive) or shrink (negative) the mask tapered_corners: Whether to use tapered corners in morphological operations clamp_min: Minimum value to clamp masks to (ensures minimum opacity) clamp_max: Maximum value to clamp masks to ignore_first_mask: Whether to zero out the first mask (often used for conditioning) invert_input_masks: Whether to invert all input masks (1 - mask) Returns: Tuple containing the processed masks tensor Raises: ValueError: If mask dimensions are not compatible with VAE downscaling factors z*Masks must be of shape (batch_size, H, W).rrz)Masks batch size must have a multiple of z masks + 1.z%Masks height must have a multiple of .z$Masks width must have a multiple of rNc&URSS9S$Nrdimr)rxs r(6LTXVPreprocessMasks.preprocess_masks..QUUqU\!_r+c URSS9$)Nrr3)rr5s r(r7r8safffmr+c&URSS9S$r2)rr5s r(r7r8r9r+r)maskexpandr$r3)ndim ValueErrorshapedownscale_index_formulaviewrdevicercpu expand_masktotorchcatclamp)selfrrr"r#r$r%r&r!r batch_sizeHWtime_scale_factorwidth_scale_factorheight_scale_factor first_mask remainingpooling_methodscombinedgrow_mask_node input_deviceprocessed_maskss r(r,$LTXVPreprocessMasks.preprocess_maskshsD ::?IJ J ;; q  ' ' C/B N/ /1 4;29= >%ZN$??L ..#<<>(44i5KX !..#;;|4 )) XyD E1  !!r+r'N)__name__ __module__ __qualname____firstlineno____doc__ classmethoddictstrrr) RETURN_TYPESFUNCTIONCATEGORY DESCRIPTIONrGTensorintboolfloattupler,__static_attributes__r'r+r(rr s D .D 4S>D D LL!H+HPl"||l" l"  l"  l"l"l" l"!l" u|| l"r+) typingrrGcomfy.model_managementrcomfy_extras.nodes_maskrnodes_registryrrr'r+r(ros6 0,& &'I"I"(I"r+