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WIP: Added LTX Automated Sequencer and Diagnostic Node before structural fix

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Marco
2026-04-15 23:29:57 -03:00
parent 793402f93f
commit 63b3d2fc86
5 changed files with 433 additions and 49 deletions
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__init__.py
+79 -42
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@@ -7,62 +7,76 @@ __version__ = "1.0.0"
__author__ = "marcoags"
__package_name__ = "AnotherUtils"
import logging
# Basic Image Processing
from .image_processing.custom_crop import CustomCropNode
from .image_processing.smart_resize import SmartResizeNode
from .image_processing.nearest_upscale import NearestUpscaleNode
from .image_processing.image_grid_slicer import ImageGridSlicer
from .image_processing.remove_alpha import RemoveAlphaNode
from .image_processing.interactive_crop import InteractiveCropNode
from .image_processing.image_composite_masked import AnotherImageCompositeMasked
from .image_processing.segs_adapter import SEGStoBBox, SEGStoSAM2Points, GetFirstFrame, ManualPointToSAM2, RefineMask
from .image_processing.point_collector import PointCollectorSAM2
# Loaders
from .loaders.load_images import LoadImagesOriginalSize
from .pixel_art.pixel_normalizer import PixelArtNormalizerNode
from .characters.fighting_game_character import FightingGameCharacter
from .characters.walking_pose import WalkingPoseGenerator
from .loaders.load_remove_alpha import LoadImageRemoveAlpha
from .loaders.last_image import LastImage
from .loaders.csv_prompt_loader import CSVPromptLoader
from .loaders.trello_prompt_loader import TrelloPromptLoader
from .loaders.trello_browser import TrelloBrowser
from .loaders.caption_image_loader import CaptionImageLoader
from .loaders.load_image_metadata import LoadImageAndExtractPrompt
from .loaders.folder_image_metadata import FolderImageAndExtractPrompt
from .loaders.folder_image_metadata_by_name import FolderImageMetadataByName
from .loaders.load_gif_frames import LoadGifFrames, RemapGifFrames
from .loaders.batch_image_list import BatchToImageList
from .loaders.folder_image_loader import FolderImageLoader
# Pixel Art
from .pixel_art.pixel_normalizer import PixelArtNormalizerNode
from .pixel_art.pixel_art_converter import PixelArtConverterNode
from .pixel_art.pixel_art_converter_parallel import PixelArtConverterNodeParallel
from .loaders.last_image import LastImage
# Characters
from .characters.fighting_game_character import FightingGameCharacter
from .characters.walking_pose import WalkingPoseGenerator
from .characters.character_constructor import CharacterConstructor
from .characters.character_generator import CharacterRandomizer
from .image_processing.remove_alpha import RemoveAlphaNode
# GIMP / GEGL Like
from .gimp_nodes.adaptive_noise import AdaptiveNoise
from .gimp_nodes.cie_lch_noise_gegl_like import CIELChNoiseGEGLLike
from .gimp_nodes.image_type_detector import ImageTypeDetector
from .gimp_nodes.mean_curvature_blur_gegl_like import MeanCurvatureBlurGEGLLike
from .gimp_nodes.rgb_noise_gegl_like import RGBNoiseGEGLLike
from .loaders.csv_prompt_loader import CSVPromptLoader
from .loaders.trello_prompt_loader import TrelloPromptLoader
from .loaders.trello_browser import TrelloBrowser
# Video General
from .video.comparison_swipe import ComparisonSwipeNode
from .video.folder_video_concatenator import FolderVideoConcatenator
from .image_processing.interactive_crop import InteractiveCropNode
from .image_processing.image_composite_masked import AnotherImageCompositeMasked
from .loaders.caption_image_loader import CaptionImageLoader
from .video.animated_composite import AnotherTransformKeyframes, AnotherAnimatedCompositeMasked, AnotherTransformOrchestrator
from .video.camera_switcher import AnotherCameraSwitcher
from .video.video_auto_sync_hstack import VideoAutoSyncHStack
from .video.video_audio_combiner import (
VideoAudioCombiner,
VideoAudioCombinerSimple,
HAS_NEW_VIDEO_API,
)
from .video.animated_composite import AnotherTransformKeyframes, AnotherAnimatedCompositeMasked, AnotherTransformOrchestrator
from .video.camera_switcher import AnotherCameraSwitcher
from .loaders.load_image_metadata import LoadImageAndExtractPrompt
from .loaders.folder_image_metadata import FolderImageAndExtractPrompt
from .loaders.folder_image_metadata_by_name import FolderImageMetadataByName
if HAS_NEW_VIDEO_API:
from .video.video_audio_combiner import VideoAudioCombinerV3
# Audio
from .audio.audio_waveform_slicer import AudioWaveformSlicer
from .audio.audio_slice_selector import AudioSliceSelector
from .audio.audio_concatenate import AudioConcatenate
from .loaders.load_gif_frames import LoadGifFrames, RemapGifFrames
from .loaders.batch_image_list import BatchToImageList
from .video.video_auto_sync_hstack import VideoAutoSyncHStack
from .video.ltxv_multi_guide import LTXVMultiGuide
from .video.ltxv_multi_concat import LTXVMultiConcat
from .video.ltxv_multi_concat_beta import LTXVMultiConcatBeta
from .video.ltxv_vid2vid import LTXVVid2Vid
from .loaders.folder_image_loader import FolderImageLoader
# Logic & Management
from .logic_management.image_list_to_batch import ImageListToBatch
from .logic_management.indices_list_to_50 import IndicesListTo50
from .logic_management.dataset_loader import DatasetLoader
from .logic_management.image_list_sampler import ImageListSampler
from .image_processing.segs_adapter import SEGStoBBox, SEGStoSAM2Points, GetFirstFrame, ManualPointToSAM2, RefineMask
from .image_processing.point_collector import PointCollectorSAM2
# Inference
from .inference_nodes import (
AnotherLoadYOLO,
AnotherLoadSAM2,
@@ -79,8 +93,10 @@ from .inference_nodes import (
AnotherMaskMath,
AnotherMaskBlur
)
from .core import server_routes # Register Custom API Routes
# Initial Mappings
NODE_CLASS_MAPPINGS = {
"CustomCrop": CustomCropNode,
"SmartResize": SmartResizeNode,
@@ -119,10 +135,6 @@ NODE_CLASS_MAPPINGS = {
"FolderImageLoader": FolderImageLoader,
"DatasetLoader": DatasetLoader,
"ImageListSampler": ImageListSampler,
"LTXVMultiGuide": LTXVMultiGuide,
"LTXVMultiConcat": LTXVMultiConcat,
"LTXVMultiConcatBeta": LTXVMultiConcatBeta,
"LTXVVid2Vid": LTXVVid2Vid,
"SEGStoBBox": SEGStoBBox,
"SEGStoSAM2Points": SEGStoSAM2Points,
"GetFirstFrame": GetFirstFrame,
@@ -153,12 +165,10 @@ NODE_CLASS_MAPPINGS = {
"LoadImageAndExtractPrompt": LoadImageAndExtractPrompt,
"FolderImageAndExtractPrompt": FolderImageAndExtractPrompt,
"FolderImageMetadataByName": FolderImageMetadataByName,
"ImageListToBatch": ImageListToBatch,
"IndicesListTo50": IndicesListTo50,
}
# Add V3 nodes if the new API is available
if HAS_NEW_VIDEO_API:
NODE_CLASS_MAPPINGS["VideoAudioCombinerV3"] = VideoAudioCombinerV3
NODE_DISPLAY_NAME_MAPPINGS = {
"CustomCrop": "Custom Crop",
"SmartResize": "Smart Resize with Border Fill",
@@ -196,10 +206,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"FolderImageLoader": "Folder Image Loader",
"DatasetLoader": "Dataset Loader (Images + Captions)",
"ImageListSampler": "Image List Sampler",
"LTXVMultiGuide": "LTXV Multi Guide (N Frames)",
"LTXVMultiConcat": "LTXV Multi Concat (N Frames)",
"LTXVMultiConcatBeta": "LTXV Multi Concat (N Frames) (beta)",
"LTXVVid2Vid": "LTXV Vid2Vid Encode",
"SEGStoBBox": "SEGS to BBox",
"SEGStoSAM2Points": "SEGS to SAM2 Points (JSON)",
"GetFirstFrame": "Get First Frame (Batch to Single)",
@@ -230,11 +236,42 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"LoadImageAndExtractPrompt": "Load Image and Extract Prompt",
"FolderImageAndExtractPrompt": "Folder Image and Extract Prompt",
"FolderImageMetadataByName": "Folder Metadata by Node Name",
"ImageListToBatch": "Image List To Multi Batch",
"IndicesListTo 50": "Indices List To 50 Inputs",
}
# Add V3 display names if available
# LTX Video Specific - Conditional Loading
if HAS_NEW_VIDEO_API:
NODE_DISPLAY_NAME_MAPPINGS["VideoAudioCombinerV3"] = "Video + Audio Combiner (V3)"
try:
from .video.video_audio_combiner import VideoAudioCombinerV3
from .video.ltxv_multi_guide import LTXVMultiGuide
from .video.another_ltx_sequencer import AnotherLTXSequencer
from .video.ltxv_multi_concat import LTXVMultiConcat
from .video.ltxv_multi_concat_beta import LTXVMultiConcatBeta
from .video.ltxv_vid2vid import LTXVVid2Vid
from .video.ltxv_diagnostic import LTXVDiagnosticNode
NODE_CLASS_MAPPINGS.update({
"VideoAudioCombinerV3": VideoAudioCombinerV3,
"LTXVMultiGuide": LTXVMultiGuide,
"AnotherLTXSequencer": AnotherLTXSequencer,
"LTXVMultiConcat": LTXVMultiConcat,
"LTXVMultiConcatBeta": LTXVMultiConcatBeta,
"LTXVVid2Vid": LTXVVid2Vid,
"LTXVDiagnosticNode": LTXVDiagnosticNode,
})
NODE_DISPLAY_NAME_MAPPINGS.update({
"VideoAudioCombinerV3": "Video + Audio Combiner (V3)",
"LTXVMultiGuide": "LTXV Multi Guide (N Frames)",
"AnotherLTXSequencer": "LTX Sequencer (Automated)",
"LTXVMultiConcat": "LTXV Multi Concat (N Frames)",
"LTXVMultiConcatBeta": "LTXV Multi Concat (N Frames) (beta)",
"LTXVVid2Vid": "LTXV Vid2Vid Encode",
"LTXVDiagnosticNode": "LTXV Diagnostic (Shape Checker)",
})
except Exception as e:
logging.error(f"Failed to load LTX Video extension nodes: {e}")
WEB_DIRECTORY = "./js"
video/another_ltx_sequencer.py
+171
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@@ -0,0 +1,171 @@
import torch
import logging
logger = logging.getLogger(__name__)
class AnotherLTXSequencer:
"""
Automated version of LTXSequencer (Guide mode).
Takes 'multi_input' (batched images) and 'indices' (list of frame positions).
IMPORTANT: This node uses LTXVAddGuide.append_keyframe which EXTENDS the video
latent along the time dimension. For LTX 2.3 AV workflows, this node should be
placed BEFORE LTXVConcatAVLatent (i.e., it should receive a plain video latent,
NOT a NestedTensor). The audio latent is handled separately by ConcatAVLatent.
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"vae": ("VAE",),
"latent": ("LATENT",),
"multi_input": ("IMAGE",),
"indices": ("INT",),
"num_images": ("INT", {"default": 1, "min": 0, "max": 50, "step": 1, "tooltip": "Number of images to process from the batch."}),
"insert_mode": (["frames", "seconds"], {"default": "frames", "tooltip": "Select the method for determining insertion points."}),
"frame_rate": ("INT", {"default": 24, "min": 1, "max": 120, "step": 1, "tooltip": "Video FPS (used for calculating second insertions)."}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Global strength for all guide images."}),
}
}
INPUT_IS_LIST = True
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "execute"
CATEGORY = "AnotherUtils/video"
def execute(self, positive, negative, vae, latent, multi_input, indices, num_images, insert_mode, frame_rate, strength):
from comfy_extras.nodes_lt import LTXVAddGuide, get_noise_mask, _append_guide_attention_entry
# Unwrap list inputs because INPUT_IS_LIST = True
positive = positive[0] if isinstance(positive, list) else positive
negative = negative[0] if isinstance(negative, list) else negative
vae = vae[0] if isinstance(vae, list) else vae
latent = latent[0] if isinstance(latent, list) else latent
multi_input = multi_input[0] if isinstance(multi_input, list) else multi_input
num_images = num_images[0] if isinstance(num_images, list) else num_images
insert_mode = insert_mode[0] if isinstance(insert_mode, list) else insert_mode
frame_rate = frame_rate[0] if isinstance(frame_rate, list) else frame_rate
strength = strength[0] if isinstance(strength, list) else strength
# Keep indices as a list
idx_list = indices if isinstance(indices, list) else [indices]
if len(idx_list) > 0 and isinstance(idx_list[0], list):
idx_list = idx_list[0]
scale_factors = vae.downscale_index_formula
# --- Extract video latent, separating from audio if nested ---
try:
from comfy.nested_tensor import NestedTensor
is_nested = isinstance(latent["samples"], NestedTensor)
except ImportError:
is_nested = False
if is_nested:
# Extract video-only portion for guide processing
latent_samples = latent["samples"].tensors[0].clone()
audio_samples = latent["samples"].tensors[1]
# Extract noise masks
noise_mask_raw = latent.get("noise_mask", None)
is_mask_nested = isinstance(noise_mask_raw, NestedTensor) if noise_mask_raw is not None else False
if is_mask_nested:
noise_mask = noise_mask_raw.tensors[0].clone()
audio_mask = noise_mask_raw.tensors[1]
else:
# Create default video noise mask
batch, _, lat_t, _, _ = latent_samples.shape
noise_mask = torch.ones((batch, 1, lat_t, 1, 1), dtype=torch.float32, device=latent_samples.device)
audio_mask = None
logger.info(f"[AnotherLTXSequencer] Nested input - Video: {latent_samples.shape}, Audio: {audio_samples.shape}")
else:
latent_samples = latent["samples"].clone()
noise_mask = get_noise_mask(latent).clone()
audio_samples = None
audio_mask = None
is_mask_nested = False
_, _, latent_length, latent_height, latent_width = latent_samples.shape
batch_size = multi_input.shape[0] if multi_input is not None else 0
cur_pos = positive
cur_neg = negative
# Process guide images
for i in range(1, num_images + 1):
if i > batch_size:
continue
img = multi_input[i-1:i]
if img is None:
continue
if (i-1) >= len(idx_list):
continue
# Calculate frame index
f_idx = idx_list[i-1]
if insert_mode == "seconds":
f_idx = int(f_idx * frame_rate)
# Encode and get latent index
image_1, t = LTXVAddGuide.encode(vae, latent_width, latent_height, img, scale_factors)
frame_idx, latent_idx = LTXVAddGuide.get_latent_index(cur_pos, latent_length, len(image_1), f_idx, scale_factors)
delta_t = t.shape[2]
if latent_idx + delta_t > latent_length:
logger.warning(f"[AnotherLTXSequencer] Guide at frame {f_idx} exceeds latent length. Skipping.")
continue
# append_keyframe concatenates guide frames onto the video latent (dim=2)
cur_pos, cur_neg, latent_samples, noise_mask = LTXVAddGuide.append_keyframe(
cur_pos, cur_neg,
frame_idx,
latent_samples,
noise_mask,
t,
strength,
scale_factors,
)
# Add attention entry for the guide
try:
pre_filter_count = t.shape[2] * t.shape[3] * t.shape[4]
guide_latent_shape = list(t.shape[2:])
cur_pos, cur_neg = _append_guide_attention_entry(
cur_pos, cur_neg, pre_filter_count, guide_latent_shape, strength=strength
)
except Exception as e:
logger.error(f"[AnotherLTXSequencer] Failed to append attention entry: {e}")
# --- Build output latent ---
new_latent = latent.copy()
if is_nested:
# Re-wrap as NestedTensor. The audio is NOT padded because
# the sampler's pack/unpack handles mismatched time dimensions.
# CropGuides will trim the video back after sampling.
from comfy.nested_tensor import NestedTensor
new_latent["samples"] = NestedTensor((latent_samples, audio_samples))
if is_mask_nested and audio_mask is not None:
new_latent["noise_mask"] = NestedTensor((noise_mask, audio_mask))
else:
new_latent["noise_mask"] = noise_mask
logger.info(f"[AnotherLTXSequencer] Nested output - Video: {latent_samples.shape}, Audio: {audio_samples.shape}")
else:
new_latent["samples"] = latent_samples
new_latent["noise_mask"] = noise_mask
return (cur_pos, cur_neg, new_latent)
video/ltxv_diagnostic.py
+85
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@@ -0,0 +1,85 @@
import torch
import logging
logger = logging.getLogger(__name__)
class LTXVDiagnosticNode:
"""
Diagnostic node to inspect LTX Video latent structures (NestedTensors, shapes, etc.)
Place this before Audio Decode or Sampler to see what's happening inside.
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"latent": ("LATENT",),
"node_name": ("STRING", {"default": "LTXV Diagnostic"}),
"print_to_console": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("LATENT", "STRING")
RETURN_NAMES = ("latent", "info")
FUNCTION = "inspect"
CATEGORY = "AnotherUtils/video"
def inspect(self, latent, node_name, print_to_console):
info_lines = [f"--- {node_name} Diagnostic ---"]
samples = latent.get("samples", None)
if samples is None:
info_lines.append("ERROR: No 'samples' found in latent.")
else:
info_lines.append(f"Samples Type: {type(samples)}")
# Check for ComfyUI NestedTensor wrapper
is_wrapper = False
try:
from comfy.nested_tensor import NestedTensor as ComfyNested
if isinstance(samples, ComfyNested):
is_wrapper = True
info_lines.append("Format: ComfyUI NestedTensor Wrapper")
for i, t in enumerate(samples.tensors):
info_lines.append(f" Inner Tensor {i} Shape: {list(t.shape)} | dtype: {t.dtype}")
except ImportError:
pass
# Check for Native PyTorch Nested Tensor
if not is_wrapper and hasattr(samples, "is_nested"):
if samples.is_nested:
info_lines.append("Format: Native PyTorch Nested Tensor")
try:
# Try to unbind to see components
unbound = samples.unbind()
for i, t in enumerate(unbound):
info_lines.append(f" Unbound Component {i} Shape: {list(t.shape)} | dtype: {t.dtype}")
except Exception as e:
info_lines.append(f" Error unbinding native nested tensor: {e}")
else:
info_lines.append(f"Format: Standard Tensor | Shape: {list(samples.shape)} | dtype: {samples.dtype}")
elif not is_wrapper:
info_lines.append(f"Format: Standard Tensor | Shape: {list(samples.shape)} | dtype: {samples.dtype}")
# Check for noise_mask
mask = latent.get("noise_mask", None)
if mask is not None:
info_lines.append(f"Noise Mask Type: {type(mask)}")
if hasattr(mask, "shape"):
info_lines.append(f" Mask Shape: {list(mask.shape)}")
elif is_wrapper:
info_lines.append(" Mask is likely also a Nested Wrapper (checking components...)")
# Many LTX implementations wrap mask too
else:
info_lines.append("Noise Mask: Not present")
# Other keys
other_keys = [k for k in latent.keys() if k not in ["samples", "noise_mask"]]
if other_keys:
info_lines.append(f"Other Keys: {other_keys}")
info_text = "\n".join(info_lines)
if print_to_console:
print(info_text)
return (latent, info_text)
video/ltxv_multi_concat.py
+45 -3
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@@ -1,5 +1,7 @@
import torch
from comfy_extras.nodes_lt import LTXVAddGuide, get_noise_mask
from typing import Dict, Tuple, Any
import logging
from .ltxv_utils import resolve_frame_indices, parse_strengths, flatten_images
class LTXVMultiConcat:
@@ -83,7 +85,20 @@ class LTXVMultiConcat:
scale_factors = vae.downscale_index_formula
time_scale_factor = scale_factors[0]
latent_samples = latent["samples"].clone()
# Handle NestedTensor from audio-enabled latents
try:
from comfy.nested_tensor import NestedTensor
is_nested = isinstance(latent["samples"], NestedTensor)
except ImportError:
is_nested = False
if is_nested:
latent_samples = latent["samples"].tensors[0].clone()
audio_samples = latent["samples"].tensors[1]
else:
latent_samples = latent["samples"].clone()
latent_length = latent_samples.shape[2]
total_pixel_frames = (latent_length - 1) * time_scale_factor + 1
@@ -94,7 +109,17 @@ class LTXVMultiConcat:
n = min(num_images, len(frame_indices))
noise_mask = get_noise_mask(latent).clone()
has_nested_mask = False
noise_mask_obj = get_noise_mask(latent)
if is_nested:
from comfy.nested_tensor import NestedTensor
if isinstance(noise_mask_obj, NestedTensor):
noise_mask = noise_mask_obj.tensors[0].clone()
else:
noise_mask = noise_mask_obj.clone()
else:
noise_mask = noise_mask_obj.clone()
_, _, lat_len, lat_h, lat_w = latent_samples.shape
for i in range(n):
@@ -118,6 +143,23 @@ class LTXVMultiConcat:
# noise_mask is [B, 1, T, 1, 1] typically
noise_mask[:, :, latent_idx:latent_idx+t.shape[2], :, :] = 1.0 - strength
new_latent = latent.copy()
if is_nested:
from comfy.nested_tensor import NestedTensor
new_latent["samples"] = NestedTensor((latent_samples, audio_samples))
# Re-wrap noise_mask as NestedTensor to match samples structure.
# The audio mask stays unchanged (all ones = fully denoised).
noise_mask_raw = latent.get("noise_mask", None)
if noise_mask_raw is not None and isinstance(noise_mask_raw, NestedTensor):
audio_noise_mask = noise_mask_raw.tensors[1]
else:
audio_noise_mask = torch.ones_like(audio_samples)
new_latent["noise_mask"] = NestedTensor((noise_mask, audio_noise_mask))
else:
new_latent["samples"] = latent_samples
new_latent["noise_mask"] = noise_mask
# In Concat mode, we DON'T modify conditioning with attention entries.
# We just return the modified latent with frames and mask.
return (positive, negative, {"samples": latent_samples, "noise_mask": noise_mask})
return (positive, negative, new_latent)
video/ltxv_multi_concat_beta.py
+53 -4
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@@ -1,5 +1,7 @@
import torch
from comfy_extras.nodes_lt import LTXVAddGuide, get_noise_mask
from typing import Dict, Tuple, Any
import logging
from .ltxv_utils import resolve_frame_indices, parse_strengths, flatten_images
class LTXVMultiConcatBeta:
@@ -83,7 +85,20 @@ class LTXVMultiConcatBeta:
scale_factors = vae.downscale_index_formula
time_scale_factor = scale_factors[0]
latent_samples = latent["samples"].clone()
# Handle NestedTensor from audio-enabled latents
try:
from comfy.nested_tensor import NestedTensor
is_nested = isinstance(latent["samples"], NestedTensor)
except ImportError:
is_nested = False
if is_nested:
latent_samples = latent["samples"].tensors[0].clone()
audio_samples = latent["samples"].tensors[1]
else:
latent_samples = latent["samples"].clone()
latent_length = latent_samples.shape[2]
total_pixel_frames = (latent_length - 1) * time_scale_factor + 1
@@ -93,9 +108,22 @@ class LTXVMultiConcatBeta:
strength_list = parse_strengths(strengths_str, num_images, strength)
n = min(num_images, len(frame_indices))
noise_mask = get_noise_mask(latent).clone()
has_nested_mask = False
noise_mask_obj = get_noise_mask(latent)
if is_nested:
from comfy.nested_tensor import NestedTensor
if isinstance(noise_mask_obj, NestedTensor):
noise_mask = noise_mask_obj.tensors[0].clone()
else:
noise_mask = noise_mask_obj.clone()
else:
noise_mask = noise_mask_obj.clone()
_, _, lat_len, lat_h, lat_w = latent_samples.shape
new_latent = latent.copy()
if not smooth_strength:
# Original behavior
for i in range(n):
@@ -122,7 +150,14 @@ class LTXVMultiConcatBeta:
anchors[latent_idx] = (t, strength_list[i])
if not anchors:
return (positive, negative, {"samples": latent_samples, "noise_mask": noise_mask})
if is_nested:
from comfy.nested_tensor import NestedTensor
new_latent["samples"] = NestedTensor((latent_samples, audio_samples))
new_latent["noise_mask"] = noise_mask
else:
new_latent["samples"] = latent_samples
new_latent["noise_mask"] = noise_mask
return (positive, negative, new_latent)
sorted_idx = sorted(anchors.keys())
@@ -182,4 +217,18 @@ class LTXVMultiConcatBeta:
latent_samples[:, :, i:i+1, :, :] = t_interp
noise_mask[:, :, i:i+1, :, :] = 1.0 - s_interp
return (positive, negative, {"samples": latent_samples, "noise_mask": noise_mask})
if is_nested:
from comfy.nested_tensor import NestedTensor
new_latent["samples"] = NestedTensor((latent_samples, audio_samples))
# Re-wrap noise_mask as NestedTensor to match samples structure.
noise_mask_raw = latent.get("noise_mask", None)
if noise_mask_raw is not None and isinstance(noise_mask_raw, NestedTensor):
audio_noise_mask = noise_mask_raw.tensors[1]
else:
audio_noise_mask = torch.ones_like(audio_samples)
new_latent["noise_mask"] = NestedTensor((noise_mask, audio_noise_mask))
else:
new_latent["samples"] = latent_samples
new_latent["noise_mask"] = noise_mask
return (positive, negative, new_latent)