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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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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
<https://www.gnu.org/licenses/why-not-lgpl.html>.
README.md
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# 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.
__init__.py
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from .nodes import comfy_entrypoint # noqa: F401
context_windows.py
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"""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)
guides.py
+136
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"""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
nodes.py
+117
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"""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()
pyproject.toml
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[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 = ""