From 75faa4100ed16dffe270ffebbd278d29b7538873 Mon Sep 17 00:00:00 2001
From: drozbay <17261091+drozbay@users.noreply.github.com>
Date: Tue, 2 Jun 2026 18:36:57 -0600
Subject: [PATCH] Initial commit
---
.gitignore | 6 +
LICENSE | 674 +++++++++++++++++++++++++++++++++++++++++
README.md | 30 ++
__init__.py | 1 +
context_windows.py | 736 +++++++++++++++++++++++++++++++++++++++++++++
guides.py | 136 +++++++++
nodes.py | 117 +++++++
pyproject.toml | 14 +
8 files changed, 1714 insertions(+)
create mode 100644 .gitignore
create mode 100644 LICENSE
create mode 100644 README.md
create mode 100644 __init__.py
create mode 100644 context_windows.py
create mode 100644 guides.py
create mode 100644 nodes.py
create mode 100644 pyproject.toml
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..eb0e0a3
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+.DS_Store
+*.pyc
+.vscode
+__pycache__
+*.egg-info
+*.bak
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f288702
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
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+ 16. Limitation of Liability.
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+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
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+SUCH DAMAGES.
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+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+
+ Copyright (C)
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ Copyright (C)
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
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+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e15fc87
--- /dev/null
+++ b/README.md
@@ -0,0 +1,30 @@
+# LTExperiments
+
+Context-window sampling for **LTXV / LTX-2 (LTXAV)** video models in native ComfyUI, including the
+multimodal (video + audio) path and **IC-LoRA guide** splitting — guides placed at arbitrary positions
+are spliced into each window they overlap.
+
+This is a self-contained extraction of the work in
+[Comfy-Org/ComfyUI#13325](https://github.com/Comfy-Org/ComfyUI/pull/13325). It plugs into ComfyUI's
+existing extension points (`model_options["context_handler"]` + sampling wrappers) and **does not edit
+or monkey-patch core**. It works whether or not that PR is merged into your ComfyUI: when the base
+model provides the guide hooks it defers to them, otherwise it uses its own bundled implementation.
+
+## Node
+
+**LTEx LTXV Context Windows** — drop it between your model and the sampler. Inputs are in real (pixel)
+frames; `context_length` snaps to `8n+1`, `context_overlap` to multiples of 8. `retain_first_frame`
+keeps latent sub-pos 0 in both the conditioning and the noise latent (the right combo for
+inplace-style LTX-2 first-frame / AnimateDiff-style I2V). Advanced settings (stride, closed_loop,
+freenoise) are collapsed by default.
+
+## IC-LoRA guides
+
+Guide support consumes the conds produced by the native **LTXVAddGuide** node — no extra setup beyond
+adding your guide(s) as usual. Guides are stripped from the working latent, then re-injected only into
+the windows they overlap, with `keyframe_idxs` / `guide_attention_entries` regenerated per window
+(downscaled IC-LoRA guides included).
+
+## Requirements
+
+Native ComfyUI with LTXV / LTX-2 support. No extra Python dependencies.
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..ebd98b7
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1 @@
+from .nodes import comfy_entrypoint # noqa: F401
diff --git a/context_windows.py b/context_windows.py
new file mode 100644
index 0000000..ff06fa5
--- /dev/null
+++ b/context_windows.py
@@ -0,0 +1,736 @@
+"""Forked context-windowing engine for the LTExperiments pack.
+
+This is a self-contained copy of PR #13325's ``IndexListContextHandler`` and friends so the feature
+works on any ComfyUI core (PR-merged or not). It is the object set into
+``model.model_options["context_handler"]`` by this pack's nodes, so when one of those nodes patches
+the model, this code — not core — drives windowing.
+
+Stable, unchanged primitives (ABCs, schedules, weight helpers) are imported from
+``comfy.context_windows`` rather than copied. Only what PR #13325 changed is forked here, plus two
+deltas that let the pack defer to base model hooks when present and supply its own LTXAV guide logic
+otherwise (see ``guides``).
+"""
+from __future__ import annotations
+from typing import TYPE_CHECKING, Callable
+import torch
+import logging
+from dataclasses import dataclass
+import comfy.model_management
+import comfy.patcher_extension
+import comfy.utils
+import comfy.conds
+
+from comfy.context_windows import (
+ ContextWindowABC,
+ ContextHandlerABC,
+ ContextResults,
+ IndexListCallbacks,
+ ContextSchedule,
+ ContextFuseMethod,
+ ContextSchedules,
+ ContextFuseMethods,
+ get_matching_context_schedule,
+ apply_freenoise,
+ match_weights_to_dim,
+ get_shape_for_dim,
+ create_weights_flat,
+ create_weights_pyramid,
+)
+from . import guides
+
+if TYPE_CHECKING:
+ from comfy.model_base import BaseModel
+ from comfy.model_patcher import ModelPatcher
+
+
+class IndexListContextWindow(ContextWindowABC):
+ def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0, modality_windows: dict=None, context_overlap: int=0):
+ self.index_list = index_list
+ self.context_length = len(index_list)
+ self.context_overlap = context_overlap
+ self.dim = dim
+ self.total_frames = total_frames
+ self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames)
+ self.modality_windows = modality_windows # dict of {mod_idx: IndexListContextWindow}
+ self.guide_frames_indices: list[int] = []
+ self.guide_overlap_info: list[tuple[int, int]] = []
+ self.guide_kf_local_positions: list[int] = []
+ self.guide_downscale_factors: list[int] = []
+
+ def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor:
+ if dim is None:
+ dim = self.dim
+ if dim == 0 and full.shape[dim] == 1:
+ return full
+ indices = self.index_list
+ anchor_idx = getattr(self, 'causal_anchor_index', None)
+ if anchor_idx is not None and anchor_idx >= 0:
+ indices = [anchor_idx] + list(indices)
+ idx = tuple([slice(None)] * dim + [indices])
+ window = full[idx]
+ if retain_index_list:
+ idx = tuple([slice(None)] * dim + [retain_index_list])
+ window[idx] = full[idx]
+ return window.to(device)
+
+ def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor:
+ if dim is None:
+ dim = self.dim
+ idx = tuple([slice(None)] * dim + [self.index_list])
+ full[idx] += to_add
+ return full
+
+ def get_region_index(self, num_regions: int) -> int:
+ region_idx = int(self.center_ratio * num_regions)
+ return min(max(region_idx, 0), num_regions - 1)
+
+ def get_window_for_modality(self, modality_idx: int) -> 'IndexListContextWindow':
+ if modality_idx == 0:
+ return self
+ return self.modality_windows[modality_idx]
+
+
+def compute_guide_overlap(guide_entries: list[dict], keyframe_idxs: torch.Tensor, temporal_downscale_ratio: int, window_index_list: list[int]):
+ """Compute which concatenated guide frames overlap with a context window.
+
+ Each guide's latent-space start is derived from its first token's pixel-t-start
+ in keyframe_idxs (shape (B, [t,h,w], num_tokens, [start, end])), divided by the
+ model's temporal_downscale_ratio.
+
+ Returns suffix_indices into the guide_frames tensor, overlap_info per entry,
+ window-local kf positions for keyframe_idxs regeneration, and total overlap count.
+ """
+ window_set = set(window_index_list)
+ window_list = list(window_index_list)
+ suffix_indices = []
+ overlap_info = []
+ kf_local_positions = []
+ suffix_base = 0
+ token_offset = 0
+
+ for entry_idx, entry in enumerate(guide_entries):
+ first_t_pixel = int(keyframe_idxs[0, 0, token_offset, 0].item())
+ latent_start = (first_t_pixel + temporal_downscale_ratio - 1) // temporal_downscale_ratio
+ guide_len = entry["latent_shape"][0]
+ entry_overlap = 0
+
+ for local_offset in range(guide_len):
+ video_pos = latent_start + local_offset
+ if video_pos in window_set:
+ suffix_indices.append(suffix_base + local_offset)
+ kf_local_positions.append(window_list.index(video_pos))
+ entry_overlap += 1
+
+ if entry_overlap > 0:
+ overlap_info.append((entry_idx, entry_overlap))
+ suffix_base += guide_len
+ token_offset += entry["pre_filter_count"]
+
+ return suffix_indices, overlap_info, kf_local_positions, len(suffix_indices)
+
+
+@dataclass
+class WindowingState:
+ """Per-modality context windowing state for each step,
+ built using IndexListContextHandler._build_window_state().
+ For non-multimodal models the lists are length 1
+ """
+ latents: list[torch.Tensor] # per-modality working latents (guide frames stripped)
+ guide_latents: list[torch.Tensor | None] # per-modality guide frames stripped from latents
+ guide_entries: list[list[dict] | None] # per-modality guide_attention_entry metadata
+ keyframe_idxs: list[torch.Tensor | None] # per-modality keyframe_idxs tensor for guide latent_start derivation
+ latent_shapes: list | None # original packed shapes for unpack/pack (None if not multimodal)
+ dim: int = 0 # primary modality temporal dim for context windowing
+ is_multimodal: bool = False
+ temporal_downscale_ratio: int = 1 # model's pixel-to-latent temporal compression ratio
+
+ def prepare_window(self, window: IndexListContextWindow, model) -> IndexListContextWindow:
+ """Reformat window for multimodal contexts by deriving per-modality index lists.
+ Non-multimodal contexts return the input window unchanged.
+ """
+ if not self.is_multimodal:
+ return window
+
+ x = self.latents[0]
+ primary_total = self.latent_shapes[0][self.dim]
+ primary_overlap = window.context_overlap
+ map_shapes = self.latent_shapes
+ if x.size(self.dim) != primary_total:
+ map_shapes = list(self.latent_shapes)
+ video_shape = list(self.latent_shapes[0])
+ video_shape[self.dim] = x.size(self.dim)
+ map_shapes[0] = torch.Size(video_shape)
+ # Defer to the model's own mapping when present; else use the pack's generic mapping.
+ fn = getattr(model, "map_context_window_to_modalities", None)
+ per_modality_indices = fn(window.index_list, map_shapes, self.dim) if fn is not None \
+ else guides.map_context_window_to_modalities(window.index_list, map_shapes, self.dim)
+ modality_windows = {}
+ for mod_idx in range(1, len(self.latents)):
+ modality_total_frames = self.latents[mod_idx].shape[self.dim]
+ ratio = modality_total_frames / primary_total if primary_total > 0 else 1
+ modality_overlap = max(round(primary_overlap * ratio), 0)
+ modality_windows[mod_idx] = IndexListContextWindow(
+ per_modality_indices[mod_idx], dim=self.dim,
+ total_frames=modality_total_frames,
+ context_overlap=modality_overlap)
+ return IndexListContextWindow(
+ window.index_list, dim=self.dim, total_frames=x.shape[self.dim],
+ modality_windows=modality_windows, context_overlap=primary_overlap)
+
+ def slice_for_window(self, window: IndexListContextWindow, retain_index_list: list[int], device=None) -> tuple[list[torch.Tensor], list[int]]:
+ """Slice latents for a context window, injecting guide frames where applicable.
+ For multimodal contexts, uses the modality-specific windows derived in prepare_window().
+ """
+ sliced = []
+ guide_frame_counts = []
+ for idx in range(len(self.latents)):
+ modality_window = window.get_window_for_modality(idx)
+ retain = retain_index_list if idx == 0 else []
+ s = modality_window.get_tensor(self.latents[idx], device, retain_index_list=retain)
+ if self.guide_entries[idx] is not None:
+ s, ng = self._inject_guide_frames(s, modality_window, modality_idx=idx)
+ else:
+ ng = 0
+ sliced.append(s)
+ guide_frame_counts.append(ng)
+ return sliced, guide_frame_counts
+
+ def strip_guide_frames(self, out_per_modality: list[list[torch.Tensor]], guide_frame_counts: list[int], window: IndexListContextWindow):
+ """Strip injected guide frames from per-cond, per-modality outputs in place."""
+ for idx in range(len(self.latents)):
+ if guide_frame_counts[idx] > 0:
+ window_len = len(window.get_window_for_modality(idx).index_list)
+ for ci in range(len(out_per_modality)):
+ out_per_modality[ci][idx] = out_per_modality[ci][idx].narrow(self.dim, 0, window_len)
+
+ def _inject_guide_frames(self, latent_slice: torch.Tensor, window: IndexListContextWindow, modality_idx: int = 0) -> tuple[torch.Tensor, int]:
+ guide_entries = self.guide_entries[modality_idx]
+ guide_frames = self.guide_latents[modality_idx]
+ keyframe_idxs = self.keyframe_idxs[modality_idx]
+ suffix_idx, overlap_info, kf_local_pos, guide_frame_count = compute_guide_overlap(
+ guide_entries, keyframe_idxs, self.temporal_downscale_ratio, window.index_list)
+ # Shift keyframe positions to account for causal_window_fix anchor occupying sub-pos 0.
+ anchor_idx = getattr(window, 'causal_anchor_index', None)
+ if anchor_idx is not None and anchor_idx >= 0:
+ kf_local_pos = [p + 1 for p in kf_local_pos]
+ window.guide_frames_indices = suffix_idx
+ window.guide_overlap_info = overlap_info
+ window.guide_kf_local_positions = kf_local_pos
+
+ # Derive per-overlap-entry latent_downscale_factor from guide entry latent_shape vs guide frame spatial dims.
+ # guide_frames has full (post-dilation) spatial dims; entry["latent_shape"] has pre-dilation dims.
+ guide_downscale_factors = []
+ if guide_frame_count > 0:
+ full_H = guide_frames.shape[3]
+ for entry_idx, _ in overlap_info:
+ entry_H = guide_entries[entry_idx]["latent_shape"][1]
+ guide_downscale_factors.append(full_H // entry_H)
+ window.guide_downscale_factors = guide_downscale_factors
+
+ if guide_frame_count > 0:
+ idx = tuple([slice(None)] * self.dim + [suffix_idx])
+ return torch.cat([latent_slice, guide_frames[idx]], dim=self.dim), guide_frame_count
+ return latent_slice, 0
+
+ def patch_latent_shapes(self, sub_conds, new_shapes):
+ if not self.is_multimodal:
+ return
+
+ for cond_list in sub_conds:
+ if cond_list is None:
+ continue
+ for cond_dict in cond_list:
+ model_conds = cond_dict.get('model_conds', {})
+ if 'latent_shapes' in model_conds:
+ model_conds['latent_shapes'] = comfy.conds.CONDConstant(new_shapes)
+
+
+class IndexListContextHandler(ContextHandlerABC):
+ def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,
+ closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False,
+ latent_retain_index_list: list[int]=[], causal_window_fix: bool=True):
+ self.context_schedule = context_schedule
+ self.fuse_method = fuse_method
+ self.context_length = context_length
+ self.context_overlap = context_overlap
+ self.context_stride = context_stride
+ self.closed_loop = closed_loop
+ self.dim = dim
+ self._step = 0
+ self.freenoise = freenoise
+ self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else []
+ self.split_conds_to_windows = split_conds_to_windows
+ self.latent_retain_index_list = [int(x.strip()) for x in latent_retain_index_list.split(",")] if latent_retain_index_list else []
+ self.causal_window_fix = causal_window_fix
+
+ self.callbacks = {}
+
+ @staticmethod
+ def _get_latent_shapes(conds):
+ for cond_list in conds:
+ if cond_list is None:
+ continue
+ for cond_dict in cond_list:
+ model_conds = cond_dict.get('model_conds', {})
+ if 'latent_shapes' in model_conds:
+ return model_conds['latent_shapes'].cond
+ return None
+
+ @staticmethod
+ def _get_guide_entries(conds):
+ for cond_list in conds:
+ if cond_list is None:
+ continue
+ for cond_dict in cond_list:
+ model_conds = cond_dict.get('model_conds', {})
+ entries = model_conds.get('guide_attention_entries')
+ if entries is not None and hasattr(entries, 'cond') and entries.cond:
+ return entries.cond
+ return None
+
+ @staticmethod
+ def _get_keyframe_idxs(conds):
+ for cond_list in conds:
+ if cond_list is None:
+ continue
+ for cond_dict in cond_list:
+ model_conds = cond_dict.get('model_conds', {})
+ kf = model_conds.get('keyframe_idxs')
+ if kf is not None and hasattr(kf, 'cond') and kf.cond is not None:
+ return kf.cond
+ return None
+
+ def _apply_freenoise(self, noise: torch.Tensor, conds: list[list[dict]], seed: int) -> torch.Tensor:
+ """Apply FreeNoise shuffling, scaling context length/overlap per-modality by frame ratio.
+ If guide frames are present on the primary modality, only the video portion is shuffled.
+ """
+ guide_entries = self._get_guide_entries(conds)
+ guide_count = sum(e["latent_shape"][0] for e in guide_entries) if guide_entries else 0
+
+ latent_shapes = self._get_latent_shapes(conds)
+ if latent_shapes is not None and len(latent_shapes) > 1:
+ modalities = comfy.utils.unpack_latents(noise, latent_shapes)
+ primary_total = latent_shapes[0][self.dim]
+ primary_video_count = modalities[0].size(self.dim) - guide_count
+ apply_freenoise(modalities[0].narrow(self.dim, 0, primary_video_count), self.dim, self.context_length, self.context_overlap, seed)
+ for i in range(1, len(modalities)):
+ mod_total = latent_shapes[i][self.dim]
+ ratio = mod_total / primary_total if primary_total > 0 else 1
+ mod_ctx_len = max(round(self.context_length * ratio), 1)
+ mod_ctx_overlap = max(round(self.context_overlap * ratio), 0)
+ modalities[i] = apply_freenoise(modalities[i], self.dim, mod_ctx_len, mod_ctx_overlap, seed)
+ noise, _ = comfy.utils.pack_latents(modalities)
+ return noise
+ video_count = noise.size(self.dim) - guide_count
+ apply_freenoise(noise.narrow(self.dim, 0, video_count), self.dim, self.context_length, self.context_overlap, seed)
+ return noise
+
+ def _build_window_state(self, x_in: torch.Tensor, conds: list[list[dict]], model: BaseModel) -> WindowingState:
+ """Build windowing state for the current step, including unpacking latents and extracting guide frame info from conds."""
+ latent_shapes = self._get_latent_shapes(conds)
+ is_multimodal = latent_shapes is not None and len(latent_shapes) > 1
+ unpacked_latents = comfy.utils.unpack_latents(x_in, latent_shapes) if is_multimodal else [x_in]
+
+ unpacked_latents_list = list(unpacked_latents)
+ guide_latents_list = [None] * len(unpacked_latents)
+ guide_entries_list = [None] * len(unpacked_latents)
+ keyframe_idxs_list = [None] * len(unpacked_latents)
+
+ extracted_guide_entries = self._get_guide_entries(conds)
+ extracted_keyframe_idxs = self._get_keyframe_idxs(conds)
+
+ # Strip guide frames (only from first modality for now)
+ if extracted_guide_entries is not None:
+ guide_count = sum(e["latent_shape"][0] for e in extracted_guide_entries)
+ if guide_count > 0:
+ x = unpacked_latents[0]
+ latent_count = x.size(self.dim) - guide_count
+ unpacked_latents_list[0] = x.narrow(self.dim, 0, latent_count)
+ guide_latents_list[0] = x.narrow(self.dim, latent_count, guide_count)
+ guide_entries_list[0] = extracted_guide_entries
+ keyframe_idxs_list[0] = extracted_keyframe_idxs
+
+ return WindowingState(
+ latents=unpacked_latents_list,
+ guide_latents=guide_latents_list,
+ guide_entries=guide_entries_list,
+ keyframe_idxs=keyframe_idxs_list,
+ latent_shapes=latent_shapes,
+ dim=self.dim,
+ is_multimodal=is_multimodal,
+ temporal_downscale_ratio=model.latent_format.temporal_downscale_ratio)
+
+ def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
+ window_state = self._build_window_state(x_in, conds, model) # build window_state to check frame counts, will be built again in execute
+ total_frame_count = window_state.latents[0].size(self.dim)
+ if total_frame_count > self.context_length:
+ logging.info(f"\nUsing context windows: Context length {self.context_length} with overlap {self.context_overlap} for {total_frame_count} frames.")
+ if self.cond_retain_index_list:
+ logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}")
+ if self.latent_retain_index_list:
+ logging.info(f"Retaining original latent for indexes: {self.latent_retain_index_list}")
+ return True
+ logging.info(f"\nNot using context windows since context length ({self.context_length}) exceeds input frames ({total_frame_count}).")
+ return False
+
+ def prepare_control_objects(self, control, device=None):
+ if control.previous_controlnet is not None:
+ self.prepare_control_objects(control.previous_controlnet, device)
+ return control
+
+ def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: IndexListContextWindow, device=None) -> list:
+ if cond_in is None:
+ return None
+ # reuse or resize cond items to match context requirements
+ resized_cond = []
+ # if multiple conds, split based on primary region
+ if self.split_conds_to_windows and len(cond_in) > 1:
+ region = window.get_region_index(len(cond_in))
+ logging.info(f"Splitting conds to windows; using region {region} for window {window.index_list[0]}-{window.index_list[-1]} with center ratio {window.center_ratio:.3f}")
+ cond_in = [cond_in[region]]
+ # cond object is a list containing a dict - outer list is irrelevant, so just loop through it
+ for actual_cond in cond_in:
+ resized_actual_cond = actual_cond.copy()
+ # now we are in the inner dict - "pooled_output" is a tensor, "control" is a ControlBase object, "model_conds" is dictionary
+ for key in actual_cond:
+ try:
+ cond_item = actual_cond[key]
+ if isinstance(cond_item, torch.Tensor):
+ # check that tensor is the expected length - x.size(0)
+ if self.dim < cond_item.ndim and cond_item.size(self.dim) == x_in.size(self.dim):
+ # if so, it's subsetting time - tell controls the expected indeces so they can handle them
+ actual_cond_item = window.get_tensor(cond_item)
+ resized_actual_cond[key] = actual_cond_item.to(device)
+ else:
+ resized_actual_cond[key] = cond_item.to(device)
+ # look for control
+ elif key == "control":
+ resized_actual_cond[key] = self.prepare_control_objects(cond_item, device)
+ elif isinstance(cond_item, dict):
+ new_cond_item = cond_item.copy()
+ # snapshot items so guide-less windows can drop keys (e.g. keyframe_idxs) during the loop
+ for cond_key, cond_value in list(new_cond_item.items()):
+ # Allow callbacks to handle custom conditioning items
+ handled = False
+ for callback in comfy.patcher_extension.get_all_callbacks(
+ IndexListCallbacks.RESIZE_COND_ITEM, self.callbacks
+ ):
+ result = callback(cond_key, cond_value, window, x_in, device, new_cond_item)
+ if result is not None:
+ new_cond_item[cond_key] = result
+ handled = True
+ break
+ if not handled and self._model is not None:
+ # Defer to the base model hook first (PR-merged cores handle LTXAV guides here);
+ # otherwise use the pack's bundled LTXAV logic so stock cores work too.
+ result = self._model.resize_cond_for_context_window(
+ cond_key, cond_value, window, x_in, device,
+ retain_index_list=self.cond_retain_index_list)
+ if result is None:
+ result = guides.resize_ltxav_cond_for_context_window(
+ self._model, cond_key, cond_value, window, x_in, device,
+ self.cond_retain_index_list)
+ if result is guides.DROP:
+ del new_cond_item[cond_key]
+ handled = True
+ elif result is not None:
+ new_cond_item[cond_key] = result
+ handled = True
+ if handled:
+ continue
+ if isinstance(cond_value, torch.Tensor):
+ if (self.dim < cond_value.ndim and cond_value.size(self.dim) == x_in.size(self.dim)) or \
+ (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):
+ new_cond_item[cond_key] = window.get_tensor(cond_value, device)
+ # Handle audio_embed (temporal dim is 1)
+ elif cond_key == "audio_embed" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+ audio_cond = cond_value.cond
+ if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):
+ new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))
+ # Handle vace_context (temporal dim is 3)
+ elif cond_key == "vace_context" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+ vace_cond = cond_value.cond
+ if vace_cond.ndim >= 4 and vace_cond.size(3) == x_in.size(self.dim):
+ sliced_vace = window.get_tensor(vace_cond, device, dim=3, retain_index_list=self.cond_retain_index_list)
+ new_cond_item[cond_key] = cond_value._copy_with(sliced_vace)
+ # if has cond that is a Tensor, check if needs to be subset
+ elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+ if (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \
+ (cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim)):
+ new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device, retain_index_list=self.cond_retain_index_list))
+ elif cond_key == "num_video_frames": # for SVD
+ new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond)
+ new_cond_item[cond_key].cond = window.context_length
+ resized_actual_cond[key] = new_cond_item
+ else:
+ resized_actual_cond[key] = cond_item
+ finally:
+ del cond_item # just in case to prevent VRAM issues
+ resized_cond.append(resized_actual_cond)
+ return resized_cond
+
+ def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
+ sample_sigmas = model_options["transformer_options"]["sample_sigmas"]
+ current_timestep = timestep[0].to(sample_sigmas.dtype)
+ mask = torch.isclose(sample_sigmas, current_timestep, rtol=0.0001)
+ matches = torch.nonzero(mask)
+ if torch.numel(matches) == 0:
+ return # substep from multi-step sampler: keep self._step from the last full step
+ self._step = int(matches[0].item())
+
+ def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:
+ full_length = x_in.size(self.dim) # TODO: choose dim based on model
+ context_windows = self.context_schedule.func(full_length, self, model_options)
+ context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length, context_overlap=self.context_overlap) for window in context_windows]
+ return context_windows
+
+ def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
+ self._model = model
+ self.set_step(timestep, model_options)
+
+ window_state = self._build_window_state(x_in, conds, model)
+ num_modalities = len(window_state.latents)
+
+ context_windows = self.get_context_windows(model, window_state.latents[0], model_options)
+ enumerated_context_windows = list(enumerate(context_windows))
+ total_windows = len(enumerated_context_windows)
+
+ # Initialize per-modality accumulators (length 1 for single-modality)
+ accum = [[torch.zeros_like(m) for _ in conds] for m in window_state.latents]
+ if self.fuse_method.name == ContextFuseMethods.RELATIVE:
+ counts = [[torch.ones(get_shape_for_dim(m, self.dim), device=m.device) for _ in conds] for m in window_state.latents]
+ else:
+ counts = [[torch.zeros(get_shape_for_dim(m, self.dim), device=m.device) for _ in conds] for m in window_state.latents]
+ biases = [[([0.0] * m.shape[self.dim]) for _ in conds] for m in window_state.latents]
+
+ for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks):
+ callback(self, model, x_in, conds, timestep, model_options)
+
+ # accumulate results from each context window
+ for enum_window in enumerated_context_windows:
+ results = self.evaluate_context_windows(
+ calc_cond_batch, model, x_in, conds, timestep, [enum_window],
+ model_options, window_state=window_state, total_windows=total_windows)
+ for result in results:
+ # result.sub_conds_out is per-cond, per-modality: list[list[Tensor]]
+ for mod_idx in range(num_modalities):
+ mod_out = [result.sub_conds_out[ci][mod_idx] for ci in range(len(conds))]
+ modality_window = result.window.get_window_for_modality(mod_idx)
+ self.combine_context_window_results(
+ window_state.latents[mod_idx], mod_out, result.sub_conds, modality_window,
+ result.window_idx, total_windows, timestep,
+ accum[mod_idx], counts[mod_idx], biases[mod_idx])
+
+ # fuse accumulated results into final conds
+ try:
+ result_out = []
+ for ci in range(len(conds)):
+ finalized = []
+ for mod_idx in range(num_modalities):
+ if self.fuse_method.name != ContextFuseMethods.RELATIVE:
+ accum[mod_idx][ci] /= counts[mod_idx][ci]
+ f = accum[mod_idx][ci]
+
+ # if guide frames were injected, append them to the end of the fused latents for the next step
+ if window_state.guide_latents[mod_idx] is not None:
+ f = torch.cat([f, window_state.guide_latents[mod_idx]], dim=self.dim)
+ finalized.append(f)
+
+ # pack modalities together if needed
+ if window_state.is_multimodal and len(finalized) > 1:
+ packed, _ = comfy.utils.pack_latents(finalized)
+ else:
+ packed = finalized[0]
+
+ result_out.append(packed)
+ return result_out
+ finally:
+ for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks):
+ callback(self, model, x_in, conds, timestep, model_options)
+
+ def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds,
+ timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]],
+ model_options, window_state: WindowingState, total_windows: int = None,
+ device=None, first_device=None):
+ """Evaluate context windows and return per-cond, per-modality outputs in ContextResults.sub_conds_out
+
+ For each window: build per-modality windows, slice each, inject concatenated guide frames,
+ pack if multimodal and call the model, then unpack and strip any guides from outputs.
+ """
+ x = window_state.latents[0]
+
+ results: list[ContextResults] = []
+ for window_idx, window in enumerated_context_windows:
+ # allow processing to end between context window executions for faster Cancel
+ comfy.model_management.throw_exception_if_processing_interrupted()
+
+ # prepare the window accounting for multimodal windows
+ window = window_state.prepare_window(window, model)
+
+ # causal_window_fix: prepend a pre-window frame that will be stripped post-forward.
+ # Set anchor before slice_for_window so the latent slice and downstream cond slices both pick it up.
+ anchor_applied = False
+ if self.causal_window_fix:
+ anchor_idx = window.index_list[0] - 1
+ if 0 <= anchor_idx < x_in.size(self.dim):
+ window.causal_anchor_index = anchor_idx
+ anchor_applied = True
+
+ # slice the window for each modality, injecting guide frames where applicable
+ sliced, guide_frame_counts_per_modality = window_state.slice_for_window(window, self.latent_retain_index_list, device)
+
+ for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks):
+ callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device)
+
+ logging.info(f"Context window {window_idx + 1}/{total_windows or len(enumerated_context_windows)}: frames {window.index_list[0]}-{window.index_list[-1]} of {x.shape[self.dim]}"
+ + (f" (+{guide_frame_counts_per_modality[0]} guide frames)" if guide_frame_counts_per_modality[0] > 0 else "")
+ )
+
+ # if multimodal, pack modalities together
+ if window_state.is_multimodal and len(sliced) > 1:
+ sub_x, sub_shapes = comfy.utils.pack_latents(sliced)
+ else:
+ sub_x, sub_shapes = sliced[0], [sliced[0].shape]
+
+ # get resized conds for window
+ model_options["transformer_options"]["context_window"] = window
+ sub_timestep = window.get_tensor(timestep, dim=0)
+ sub_conds = [self.get_resized_cond(cond, x, window) for cond in conds]
+
+ # if multimodal, patch latent_shapes in conds for correct unpacking in model
+ window_state.patch_latent_shapes(sub_conds, sub_shapes)
+
+ # call model on window
+ sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options)
+
+ # unpack outputs
+ out_per_modality = [comfy.utils.unpack_latents(sub_conds_out[i], sub_shapes) for i in range(len(sub_conds_out))]
+
+ # strip causal_window_fix anchor from primary modality before guide strip so window_len math stays correct
+ if anchor_applied:
+ for ci in range(len(out_per_modality)):
+ t = out_per_modality[ci][0]
+ out_per_modality[ci][0] = t.narrow(self.dim, 1, t.shape[self.dim] - 1)
+
+ # strip injected guide frames
+ window_state.strip_guide_frames(out_per_modality, guide_frame_counts_per_modality, window)
+
+ results.append(ContextResults(window_idx, out_per_modality, sub_conds, window))
+ return results
+
+ def combine_context_window_results(self, x_in: torch.Tensor, sub_conds_out, sub_conds, window: IndexListContextWindow, window_idx: int, total_windows: int, timestep: torch.Tensor,
+ conds_final: list[torch.Tensor], counts_final: list[torch.Tensor], biases_final: list[torch.Tensor]):
+ if self.fuse_method.name == ContextFuseMethods.RELATIVE:
+ for pos, idx in enumerate(window.index_list):
+ # bias is the influence of a specific index in relation to the whole context window
+ bias = 1 - abs(idx - (window.index_list[0] + window.index_list[-1]) / 2) / ((window.index_list[-1] - window.index_list[0] + 1e-2) / 2)
+ bias = max(1e-2, bias)
+ # take weighted average relative to total bias of current idx
+ for i in range(len(sub_conds_out)):
+ bias_total = biases_final[i][idx]
+ prev_weight = (bias_total / (bias_total + bias))
+ new_weight = (bias / (bias_total + bias))
+ # account for dims of tensors
+ idx_window = tuple([slice(None)] * self.dim + [idx])
+ pos_window = tuple([slice(None)] * self.dim + [pos])
+ # apply new values
+ conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight
+ biases_final[i][idx] = bias_total + bias
+ else:
+ # add conds and counts based on weights of fuse method
+ weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep, context_overlap=window.context_overlap)
+ weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device)
+ for i in range(len(sub_conds_out)):
+ window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor)
+ window.add_window(counts_final[i], weights_tensor)
+
+ for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.COMBINE_CONTEXT_WINDOW_RESULTS, self.callbacks):
+ callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final)
+
+
+def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, conds, *args, **kwargs):
+ # Scale noise_shape to a single context window so VRAM estimation budgets per-window.
+ model_options = kwargs.get("model_options", None)
+ if model_options is None:
+ raise Exception("model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.")
+ handler: IndexListContextHandler = model_options.get("context_handler", None)
+ if handler is not None:
+ noise_shape = list(noise_shape)
+ is_packed = len(noise_shape) == 3 and noise_shape[1] == 1
+ if is_packed:
+ # latent_shapes cond isn't attached yet at this point, so we can't compute a
+ # per-window flat latent here. Skipping the clamp over-estimates but prevents immediate OOM.
+ pass
+ elif handler.dim < len(noise_shape) and noise_shape[handler.dim] > handler.context_length:
+ noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length)
+ return executor(model, noise_shape, conds, *args, **kwargs)
+
+
+def create_prepare_sampling_wrapper(model: ModelPatcher):
+ model.add_wrapper_with_key(
+ comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING,
+ "LTExperiments_prepare_sampling",
+ _prepare_sampling_wrapper
+ )
+
+
+def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, noise, *args, **kwargs):
+ model_options = extra_args.get("model_options", None)
+ if model_options is None:
+ raise Exception("model_options not found in sampler_sample_wrapper; this should never happen, something went wrong.")
+ handler: IndexListContextHandler = model_options.get("context_handler", None)
+ if handler is None:
+ raise Exception("context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.")
+ if not handler.freenoise:
+ return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
+
+ conds = [guider.conds.get('positive', guider.conds.get('negative', []))]
+ noise = handler._apply_freenoise(noise, conds, extra_args["seed"])
+
+ return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
+
+
+def create_sampler_sample_wrapper(model: ModelPatcher):
+ model.add_wrapper_with_key(
+ comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,
+ "LTExperiments_sampler_sample",
+ _sampler_sample_wrapper
+ )
+
+
+def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None, context_overlap: int=None):
+ context_overlap = handler.context_overlap if context_overlap is None else context_overlap
+ return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs, context_overlap=context_overlap)
+
+
+def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], context_overlap: int, **kwargs):
+ # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302
+ # only expected overlap is given different weights
+ weights_torch = torch.ones((length))
+ # blend left-side on all except first window
+ if min(idxs) > 0:
+ ramp_up = torch.linspace(1e-37, 1, context_overlap)
+ weights_torch[:context_overlap] = ramp_up
+ # blend right-side on all except last window
+ if max(idxs) < full_length-1:
+ ramp_down = torch.linspace(1, 1e-37, context_overlap)
+ weights_torch[-context_overlap:] = ramp_down
+ return weights_torch
+
+
+# Resolve fuse methods to the pack's own weight functions. The overlap-linear weight now takes a
+# per-window context_overlap, so binding it here (instead of core's get_matching_fuse_method) keeps
+# per-window blending correct even on a stock core whose overlap-linear ignores that argument.
+_FUSE_MAPPING = {
+ ContextFuseMethods.FLAT: create_weights_flat,
+ ContextFuseMethods.PYRAMID: create_weights_pyramid,
+ ContextFuseMethods.RELATIVE: create_weights_pyramid,
+ ContextFuseMethods.OVERLAP_LINEAR: create_weights_overlap_linear,
+}
+
+
+def get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod:
+ func = _FUSE_MAPPING.get(fuse_method, None)
+ if func is None:
+ raise ValueError(f"Unknown fuse_method '{fuse_method}'.")
+ return ContextFuseMethod(fuse_method, func)
diff --git a/guides.py b/guides.py
new file mode 100644
index 0000000..94564b3
--- /dev/null
+++ b/guides.py
@@ -0,0 +1,136 @@
+"""LTXAV guide-cond fallback used when the running ComfyUI core lacks PR #13325.
+
+The handler in context_windows.py defers to ``model.resize_cond_for_context_window`` /
+``model.map_context_window_to_modalities`` first; these functions are only used when the base
+model does not provide them. They are ported from PR #13325's LTXAV model methods so the pack
+behaves identically on stock and PR-merged cores, without editing or monkey-patching core.
+"""
+from __future__ import annotations
+import torch
+import comfy.model_base
+import comfy.ldm.lightricks.symmetric_patchifier
+
+
+# Sentinel returned by resize_ltxav_cond_for_context_window to mean "omit this cond key for the
+# window". Used for keyframe_idxs on guide-less windows so the unmodified model forward takes its
+# keyframe_idxs-is-None path, avoiding the empty-tensor branch that PR #13325 guards in core.
+DROP = object()
+
+
+def _is_ltxav(model) -> bool:
+ cls = getattr(comfy.model_base, "LTXAV", None)
+ return cls is not None and isinstance(model, cls)
+
+
+def map_context_window_to_modalities(primary_indices, latent_shapes, dim):
+ result = [primary_indices]
+ if len(latent_shapes) < 2:
+ return result
+
+ video_total = latent_shapes[0][dim]
+
+ for i in range(1, len(latent_shapes)):
+ mod_total = latent_shapes[i][dim]
+ # Map each primary index to its proportional range of modality indices and
+ # concatenate in order. Preserves wrapped/strided geometry so the modality
+ # attends to the same temporal regions as the primary window.
+ mod_indices = []
+ seen = set()
+ for v_idx in primary_indices:
+ a_start = min(int(round(v_idx * mod_total / video_total)), mod_total - 1)
+ a_end = min(int(round((v_idx + 1) * mod_total / video_total)), mod_total)
+ if a_end <= a_start:
+ a_end = a_start + 1
+ for a in range(a_start, a_end):
+ if a not in seen:
+ seen.add(a)
+ mod_indices.append(a)
+ result.append(mod_indices)
+
+ return result
+
+
+def resize_ltxav_cond_for_context_window(model, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+ if not _is_ltxav(model):
+ return None
+
+ # Audio denoise mask — slice using audio modality window
+ if cond_key == "audio_denoise_mask" and hasattr(window, 'modality_windows') and window.modality_windows:
+ audio_window = window.modality_windows.get(1)
+ if audio_window is not None and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+ sliced = audio_window.get_tensor(cond_value.cond, device, dim=2)
+ return cond_value._copy_with(sliced)
+
+ # Video denoise mask — split into video + guide portions, slice each
+ if cond_key == "denoise_mask" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+ cond_tensor = cond_value.cond
+ guide_count = cond_tensor.size(window.dim) - x_in.size(window.dim)
+ if guide_count > 0:
+ T_video = x_in.size(window.dim)
+ video_mask = cond_tensor.narrow(window.dim, 0, T_video)
+ guide_mask = cond_tensor.narrow(window.dim, T_video, guide_count)
+ sliced_video = window.get_tensor(video_mask, device, retain_index_list=retain_index_list)
+ suffix_indices = window.guide_frames_indices
+ if suffix_indices:
+ idx = tuple([slice(None)] * window.dim + [suffix_indices])
+ sliced_guide = guide_mask[idx].to(device)
+ return cond_value._copy_with(torch.cat([sliced_video, sliced_guide], dim=window.dim))
+ else:
+ return cond_value._copy_with(sliced_video)
+
+ # Keyframe indices — regenerate pixel coords for window, select guide positions
+ if cond_key == "keyframe_idxs":
+ kf_local_pos = window.guide_kf_local_positions
+ if not kf_local_pos:
+ return DROP # no guide overlap: drop the key so the model takes its keyframe_idxs-is-None path
+ H, W = x_in.shape[3], x_in.shape[4]
+ window_len = len(window.index_list)
+ # account for causal_window_fix anchor in coord space size
+ anchor_idx = getattr(window, 'causal_anchor_index', None)
+ if anchor_idx is not None and anchor_idx >= 0:
+ window_len += 1
+ patchifier = model.diffusion_model.patchifier
+ latent_coords = patchifier.get_latent_coords(window_len, H, W, 1, cond_value.cond.device)
+ scale_factors = model.diffusion_model.vae_scale_factors
+ pixel_coords = comfy.ldm.lightricks.symmetric_patchifier.latent_to_pixel_coords(
+ latent_coords,
+ scale_factors,
+ causal_fix=model.diffusion_model.causal_temporal_positioning)
+ tokens = []
+ for pos in kf_local_pos:
+ tokens.extend(range(pos * H * W, (pos + 1) * H * W))
+ pixel_coords = pixel_coords[:, :, tokens, :]
+
+ # Adjust spatial end positions for dilated (downscaled) guides.
+ # Each guide entry may have a different downscale factor; expand the
+ # per-entry factor to cover all tokens belonging to that entry.
+ downscale_factors = window.guide_downscale_factors
+ overlap_info = window.guide_overlap_info
+ if downscale_factors:
+ per_token_factor = []
+ for (entry_idx, overlap_count), dsf in zip(overlap_info, downscale_factors):
+ per_token_factor.extend([dsf] * (overlap_count * H * W))
+ factor_tensor = torch.tensor(per_token_factor, device=pixel_coords.device, dtype=pixel_coords.dtype)
+ spatial_end_offset = (factor_tensor.unsqueeze(0).unsqueeze(0).unsqueeze(-1) - 1) * torch.tensor(
+ scale_factors[1:], device=pixel_coords.device, dtype=pixel_coords.dtype,
+ ).view(1, -1, 1, 1)
+ pixel_coords[:, 1:, :, 1:] += spatial_end_offset
+
+ B = cond_value.cond.shape[0]
+ if B > 1:
+ pixel_coords = pixel_coords.expand(B, -1, -1, -1)
+ return cond_value._copy_with(pixel_coords)
+
+ # Guide attention entries — adjust per-guide counts based on window overlap
+ if cond_key == "guide_attention_entries":
+ overlap_info = window.guide_overlap_info
+ H, W = x_in.shape[3], x_in.shape[4]
+ new_entries = []
+ for entry_idx, overlap_count in overlap_info:
+ e = cond_value.cond[entry_idx]
+ new_entries.append({**e,
+ "pre_filter_count": overlap_count * H * W,
+ "latent_shape": [overlap_count, H, W]})
+ return cond_value._copy_with(new_entries)
+
+ return None
diff --git a/nodes.py b/nodes.py
new file mode 100644
index 0000000..0ac8d52
--- /dev/null
+++ b/nodes.py
@@ -0,0 +1,117 @@
+"""LTExperiments context-window nodes (V3 io).
+
+Patches the model with this pack's forked context-window handler (see context_windows.py) for
+LTXV / LTX-2 sampling.
+"""
+from comfy_api.latest import ComfyExtension, io
+import nodes
+
+from . import context_windows as cw
+
+CATEGORY = "LTExperiments"
+
+
+class LTEx_ContextWindowsManualNode(io.ComfyNode):
+ @classmethod
+ def define_schema(cls) -> io.Schema:
+ return io.Schema(
+ node_id="LTEx_ContextWindowsManual",
+ display_name="LTEx Context Windows (Manual)",
+ category=CATEGORY,
+ description="Manually set context windows.",
+ inputs=[
+ io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
+ io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."),
+ io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."),
+ io.Combo.Input("context_schedule", options=[
+ cw.ContextSchedules.STATIC_STANDARD,
+ cw.ContextSchedules.UNIFORM_STANDARD,
+ cw.ContextSchedules.UNIFORM_LOOPED,
+ cw.ContextSchedules.BATCHED,
+ ], default=cw.ContextSchedules.STATIC_STANDARD, tooltip="Step-dependent scheduling algorithm for context windows."),
+ io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
+ io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
+ io.Combo.Input("fuse_method", options=cw.ContextFuseMethods.LIST_STATIC, default=cw.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
+ io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
+ io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
+ io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window. For concat-style I2V models (e.g. Wan I2V, HunyuanVideo I2V, Cosmos I2V, SVD) the encoded start image lives in the c_concat conditioning channels; setting this to '0' will retain that start image content at sub-pos 0 of every window."),
+ io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
+ io.String.Input("latent_retain_index_list", default="", tooltip="List of latent indices to retain in the noise latent itself for each window. Use for workflows where reference content (e.g. a start image) lives directly in the noise latent rather than in separate conditioning channels (e.g. inplace-style I2V like LTXV, AnimateDiff). Independent of cond_retain_index_list."),
+ io.Boolean.Input("causal_window_fix", default=True, tooltip="Whether to add a causal fix frame to non-0-indexed context windows."),
+ ],
+ outputs=[
+ io.Model.Output(tooltip="The model with context windows applied during sampling."),
+ ],
+ is_experimental=True,
+ )
+
+ @classmethod
+ def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int, freenoise: bool,
+ cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False, latent_retain_index_list: list[int]=[], causal_window_fix: bool=True) -> io.Model:
+ model = model.clone()
+ model.model_options["context_handler"] = cw.IndexListContextHandler(
+ context_schedule=cw.get_matching_context_schedule(context_schedule),
+ fuse_method=cw.get_matching_fuse_method(fuse_method),
+ context_length=context_length,
+ context_overlap=context_overlap,
+ context_stride=context_stride,
+ closed_loop=closed_loop,
+ dim=dim,
+ freenoise=freenoise,
+ cond_retain_index_list=cond_retain_index_list,
+ split_conds_to_windows=split_conds_to_windows,
+ latent_retain_index_list=latent_retain_index_list,
+ causal_window_fix=causal_window_fix,
+ )
+ # make memory usage calculation only take into account the context window latents
+ cw.create_prepare_sampling_wrapper(model)
+ if freenoise: # no other use for this wrapper at this time
+ cw.create_sampler_sample_wrapper(model)
+ return io.NodeOutput(model)
+
+
+class LTEx_LTXVContextWindowsNode(LTEx_ContextWindowsManualNode):
+ @classmethod
+ def define_schema(cls) -> io.Schema:
+ schema = super().define_schema()
+ schema.node_id = "LTEx_LTXVContextWindows"
+ schema.display_name = "LTEx LTXV Context Windows"
+ schema.description = "Set context windows for LTXV / LTX-2 models."
+ schema.inputs = [
+ io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
+ io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=8, default=145, tooltip="The length of the context window in real frames. Must be 8*n + 1."),
+ io.Int.Input("context_overlap", min=0, step=8, default=40, tooltip="The overlap of the context window in real frames."),
+ io.Combo.Input("context_schedule", options=[
+ cw.ContextSchedules.STATIC_STANDARD,
+ cw.ContextSchedules.UNIFORM_STANDARD,
+ cw.ContextSchedules.UNIFORM_LOOPED,
+ cw.ContextSchedules.BATCHED,
+ ], default=cw.ContextSchedules.UNIFORM_STANDARD, tooltip="Step-dependent scheduling algorithm for context windows."),
+ io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
+ io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules.", advanced=True),
+ io.Combo.Input("fuse_method", options=cw.ContextFuseMethods.LIST_STATIC, default=cw.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
+ io.Boolean.Input("freenoise", default=True, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending.", advanced=True),
+ io.Boolean.Input("retain_first_frame", default=False, tooltip="Retain the first latent frame in every context window (may help retain initial reference)."),
+ io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index.", advanced=True),
+ ]
+ return schema
+
+ @classmethod
+ def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, fuse_method: str, freenoise: bool,
+ retain_first_frame: bool=False, split_conds_to_windows: bool=False, context_stride: int=1, closed_loop: bool=False) -> io.Model:
+ context_length = max(((context_length - 1) // 8) + 1, 1) # at least length 1
+ context_overlap = max(context_overlap // 8, 0) # at least overlap 0
+ retain_index_list = "0" if retain_first_frame else ""
+ return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2, freenoise=freenoise,
+ cond_retain_index_list=retain_index_list, latent_retain_index_list=retain_index_list, split_conds_to_windows=split_conds_to_windows)
+
+
+class LTExperimentsExtension(ComfyExtension):
+ async def get_node_list(self) -> list[type[io.ComfyNode]]:
+ return [
+ LTEx_LTXVContextWindowsNode,
+ ]
+
+
+def comfy_entrypoint():
+ return LTExperimentsExtension()
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..6cd3c6f
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,14 @@
+[project]
+name = "ltexperiments"
+description = "Context window sampling for LTXV / LTX-2 (LTXAV) video+audio models, including IC-LoRA guide splitting across windows. Self-contained; uses ComfyUI's context_handler and wrapper hooks (no core edits)."
+version = "0.1.0"
+license = {file = "LICENSE"}
+
+[project.urls]
+Repository = "https://github.com/drozbay/LTExperiments"
+# Used by Comfy Registry https://registry.comfy.org
+
+[tool.comfy]
+PublisherId = "drozbay"
+DisplayName = "LTExperiments"
+Icon = ""