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1051 lines (961 loc) · 43.2 KB
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import math
import re
import node_helpers
import torch
from comfy.ldm.lightricks.symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
from comfy_api.latest import ComfyExtension, io
from comfy_extras.nodes_lt import (
LTXVAddGuide,
_append_guide_attention_entry,
conditioning_get_any_value,
get_keyframe_idxs,
get_noise_mask,
)
from typing_extensions import override
DEFAULT_TEMPORAL_SCALE = 8
_OCCUPIED_MASK_MAX = 1.0 - 1e-4
def get_generated_keyframes(cond):
return conditioning_get_any_value(cond, "generated_keyframes", None)
def _parse_frame_index_list(value, field, expected_count, first, last, expected_desc, empty_hint):
"""Parse a manual frame index override and validate count, uniqueness and range.
Order is not significant — only the set of positions matters — so the list
is returned as written rather than sorted. ``expected_count`` may be None
when the list itself defines how many keyframes to add.
"""
parts = [part for part in re.split(r"[,\s]+", value.strip()) if part]
if not parts:
raise ValueError(
f"{field} is empty. Provide at least one index, or leave {field} empty {empty_hint}."
)
try:
indices = [int(part) for part in parts]
except ValueError:
bad = ", ".join(repr(part) for part in parts if not re.fullmatch(r"-?\d+", part))
raise ValueError(
f"{field} must be a comma-separated list of integers, but could not parse {bad}."
) from None
if expected_count is not None and len(indices) != expected_count:
raise ValueError(
f"{field} lists {len(indices)} index/indices but {expected_desc}. Provide exactly "
f"{expected_count}, or leave {field} empty {empty_hint}."
)
duplicates = sorted({index for index in indices if indices.count(index) > 1})
if duplicates:
raise ValueError(
f"{field} must not place two keyframes on the same pixel frame, but "
f"{', '.join(str(index) for index in duplicates)} appear(s) more than once."
)
out_of_range = [index for index in indices if not first <= index <= last]
if out_of_range:
raise ValueError(
f"{field} must lie between {first} and {last}, but got "
f"{', '.join(str(index) for index in out_of_range)}."
)
return indices
def _grow_guide_attention_entry(positive, negative, index, extra_pre_filter_count, extra_frames):
"""Grow an existing guide_attention_entry when extending generated keyframes."""
results = []
for cond in (positive, negative):
existing = []
for t in cond:
found = t[1].get("guide_attention_entries", None)
if found is not None:
existing = found
break
if index >= len(existing):
raise ValueError(
f"The generated keyframes recorded guide entry {index} but the conditioning only has "
f"{len(existing)}. The conditioning was rebuilt after they were added."
)
entries = list(existing)
grown = dict(entries[index])
grown["pre_filter_count"] = grown["pre_filter_count"] + extra_pre_filter_count
shape = list(grown["latent_shape"])
shape[0] = shape[0] + extra_frames
grown["latent_shape"] = shape
entries[index] = grown
results.append(node_helpers.conditioning_set_values(cond, {"guide_attention_entries": entries}))
return results[0], results[1]
def _spaced_positions_keep_last(num_keyframes: int, num_frames: int) -> list[int]:
return (
torch.linspace(0, num_frames - 1, num_keyframes + 1)
.round()
.to(torch.int64)
.tolist()[1:]
)
def detailing_positions(num_frames: int, interval_frames: float) -> list[int]:
"""About one detailing keyframe every ``interval_frames`` pixel frames.
Skips frame 0 (already a standalone token) and keeps the last frame.
``interval_frames`` 24 is about 1/s at 24 fps.
"""
if interval_frames <= 0:
raise ValueError(f"interval_frames must be > 0, got {interval_frames}")
if num_frames <= 1:
raise ValueError(
f"A {num_frames}-frame target has no pixel frames to place keyframes on."
)
count = max(1, round((num_frames - 1) / interval_frames))
positions = [index for index in _spaced_positions_keep_last(count, num_frames) if index != 0]
if not positions:
raise ValueError(
f"A {num_frames}-frame target has no pixel frames to place keyframes on."
)
return positions
def free_detailing_slots(num_frames: int, interval_frames: float, occupied: set[int]) -> list[int]:
"""Density candidates that are not already I2V / guides / detailing KFs."""
taken = set(occupied)
positions = [
index
for index in detailing_positions(num_frames, interval_frames)
if index not in taken
]
if not positions:
raise ValueError(
"Every candidate detailing-keyframe pixel already has an image keyframe "
"or a guide. Leave at least one unoccupied frame, or pass frame_indices."
)
return positions
def scale_frame_indices(indices: list[int], old_num_frames: int, new_num_frames: int) -> list[int]:
"""Map pixel indices from one canvas length onto another (e.g. temporal x2)."""
if old_num_frames <= 1:
raise ValueError(f"Cannot scale keyframe indices from a {old_num_frames}-frame canvas.")
if new_num_frames <= 1:
raise ValueError(f"Cannot scale keyframe indices onto a {new_num_frames}-frame canvas.")
scale = (new_num_frames - 1) / (old_num_frames - 1)
remapped = [int(round(index * scale)) for index in indices]
duplicates = sorted({index for index in remapped if remapped.count(index) > 1})
if duplicates:
raise ValueError(
"Scaling keyframe indices onto the new canvas collapsed "
f"{', '.join(str(index) for index in duplicates)} onto the same pixel frame."
)
return remapped
def _as_int_set(value) -> set[int]:
if value is None:
return set()
if isinstance(value, (int, float)):
return {int(round(value))}
if isinstance(value, (set, frozenset)):
return {int(round(item)) for item in value}
if isinstance(value, (list, tuple)):
out = set()
for item in value:
out |= _as_int_set(item)
return out
if hasattr(value, "detach"):
value = value.detach()
if hasattr(value, "cpu"):
value = value.cpu()
if hasattr(value, "tolist"):
return _as_int_set(value.tolist())
return {int(round(float(value)))}
def pixel_frames_from_keyframe_idxs(keyframe_idxs) -> set[int]:
"""Unique RoPE *start* pixel times of extra guide / keyframe tokens.
``keyframe_idxs`` is ``(B, 3, tokens, 2)`` (t/h/w × start/end). A keyframe
at pixel 24 spans the half-open interval ``[24, 25)``, so only 24 is
occupied — the exclusive end is not a slot.
"""
if keyframe_idxs is None:
return set()
if keyframe_idxs.ndim >= 4:
starts = keyframe_idxs[:, 0, :, 0]
else:
starts = keyframe_idxs[:, 0]
return _as_int_set(starts)
def occupied_pixel_frames(latent, temporal_scale: int, num_frames: int, video_latent_frames=None) -> set[int]:
"""Pixel frames that already hold an in-place image keyframe.
Prefers ``noise_mask`` (0 means frozen / guided). Falls back to
non-zero latent frames when no mask is present. Each occupied latent
index maps to its representative pixel: 0, ``t * scale``, or last frame.
``video_latent_frames`` limits the scan to the video portion of T so
appended guide / detailing-keyframe tokens are not treated as video frames.
Extra-token pixel times come from ``pixel_frames_from_keyframe_idxs``.
"""
samples = latent["samples"]
latent_frames = samples.shape[2]
if video_latent_frames is None:
scan_frames = latent_frames
else:
scan_frames = min(max(int(video_latent_frames), 0), latent_frames)
taken = set()
def add_latent_index(index: int) -> None:
if index <= 0:
pixel = 0
elif index >= scan_frames - 1:
pixel = num_frames - 1
else:
pixel = index * temporal_scale
taken.add(min(max(pixel, 0), num_frames - 1))
mask = latent.get("noise_mask")
if mask is not None and getattr(mask, "ndim", 0) >= 3:
for index in range(min(mask.shape[2], scan_frames)):
if torch.any(mask[:, :, index : index + 1] < _OCCUPIED_MASK_MAX):
add_latent_index(index)
return taken
for index in range(scan_frames):
if torch.any(samples[:, :, index : index + 1] != 0):
add_latent_index(index)
return taken
def nearest_latent_index(pixel_frame: int, temporal_scale: int, num_latent_frames: int) -> int:
return min(max(round(pixel_frame / temporal_scale), 0), num_latent_frames - 1)
def should_copy_nearest_video_frames(keyframe_t, requested_count, has_recorded_indices, batched_singles):
"""True when ``keyframes`` is a longer video to sample, not stacked keyframes."""
return (
not has_recorded_indices
and requested_count is not None
and not batched_singles
and keyframe_t > requested_count
)
def keyframes_from_video(samples, indices, temporal_scale: int):
"""Stack the nearest video latent frame at each pixel index."""
if not torch.is_tensor(samples) or samples.ndim != 5:
raise ValueError(
"Initializing keyframes from a video needs a plain 5D video latent. "
"Split audio with Separate AV Latent first, and peel generated "
"keyframes before copying from the video."
)
temporal_scale = int(temporal_scale)
if temporal_scale < 1:
raise ValueError(f"temporal_scale must be >= 1, got {temporal_scale}")
num_latent_frames = samples.shape[2]
if num_latent_frames < 1:
raise ValueError("The video latent has no frames to copy from.")
frames = []
for pixel_frame in indices:
idx = nearest_latent_index(pixel_frame, temporal_scale, num_latent_frames)
frames.append(samples[:, :, idx : idx + 1])
return torch.cat(frames, dim=2)
def _fit_keyframe_samples(keyframe_samples, samples, num_slots):
"""Pad stacked keyframe tokens with zeros up to ``num_slots``; reject extras."""
expected = (
samples.shape[0],
samples.shape[1],
num_slots,
samples.shape[3],
samples.shape[4],
)
have = keyframe_samples.shape[2]
if (
keyframe_samples.shape[0] != expected[0]
or keyframe_samples.shape[1] != expected[1]
or keyframe_samples.shape[3:] != expected[3:]
):
raise ValueError(
"The keyframes latent must hold whole latent frames at this latent's shape, expected "
f"{list(expected)} but got {list(keyframe_samples.shape)}. Resize it to this stage's "
"resolution first."
)
if have > num_slots:
raise ValueError(
f"The keyframes latent holds {have} frame(s) but only {num_slots} free slot(s) "
"are available on this canvas. Pass fewer keyframes, or set frame_indices."
)
if have < num_slots:
pad = torch.zeros(
(expected[0], expected[1], num_slots - have, expected[3], expected[4]),
dtype=keyframe_samples.dtype,
device=keyframe_samples.device,
)
keyframe_samples = torch.cat([keyframe_samples, pad], dim=2)
return keyframe_samples
class LTXVAddGeneratedKeyframes(io.ComfyNode):
PATCHIFIER = SymmetricPatchifier(1, start_end=True)
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LTXVAddGeneratedKeyframes",
display_name="LTXV Add Generated Keyframes",
category="model/conditioning/ltxv",
search_aliases=["detailing", "dfr", "generated keyframes"],
description=(
"Append detailing keyframes to a video latent. Each keyframe is one latent "
"frame of tokens whose RoPE position spans a single pixel frame; they are "
"denoised with the video and are not part of the decoded output. Placement "
"is one slot every interval_frames pixels, skipping I2V frames, existing "
"guides, and detailing keyframes already on the cond. Connected keyframes "
"are content only and are re-placed on this canvas unless frame_indices is "
"set. Pull them back out with LTXV Separate Generated Keyframes. Requires a "
"checkpoint trained for generated keyframes (one carrying "
"keyframes_abs_pos_embedding)."
),
inputs=[
io.Conditioning.Input(
"positive",
tooltip="Positive conditioning the keyframes are attached to.",
),
io.Conditioning.Input(
"negative",
tooltip="Negative conditioning the keyframes are attached to.",
),
io.Vae.Input(
"vae", tooltip="Only used to read the latent scale factors."
),
io.Latent.Input(
"latent",
tooltip=(
"Plain 5D video latent to generate keyframes alongside. Add them "
"before Concat AV Latent."
),
),
io.Int.Input(
"interval_frames",
optional=True,
default=24,
min=1,
max=1024,
tooltip=(
"Pixel-frame stride for auto placement. Default 24 is about one "
"keyframe per second at 24 fps. Occupied pixels are skipped. "
"Ignored when frame_indices is set."
),
),
io.Latent.Input(
"keyframes",
optional=True,
tooltip=(
"Optional content to initialize the new keyframes with. Connect "
"keyframes from an earlier Separate (same spatial size), or a "
"plain video latent to copy the nearest frame at each new slot "
"(e.g. after temporal upscale). These are still denoised, not "
"pinned as guides. Recorded indices on a keyframes latent are "
"ignored unless frame_indices is set. Only has an effect when "
"sampling starts below sigma 1."
),
),
io.String.Input(
"frame_indices",
optional=True,
default="",
tooltip=(
"Optional pixel-frame indices. Leave empty to place from "
"interval_frames on the current canvas. When set, this list is "
"the placement (connected keyframes are matched in order). The "
"last frame is allowed; frame 0 is not (it is already a "
"standalone token)."
),
),
],
outputs=[
io.Conditioning.Output(
display_name="positive",
tooltip="Positive conditioning with generated-keyframe attention attached.",
),
io.Conditioning.Output(
display_name="negative",
tooltip="Negative conditioning with generated-keyframe attention attached.",
),
io.Latent.Output(
display_name="latent",
tooltip="Video latent with generated keyframes appended on T.",
),
],
)
@classmethod
def parse_frame_indices(cls, frame_indices, num_pixel_frames, expected_count=None):
"""Pixel frame positions from a manual override.
Frame 0 is excluded (already a standalone token). The terminal frame is
allowed so a DFR segment grid that includes N-1 is legal.
"""
first, last = 1, num_pixel_frames - 1
if last < first:
raise ValueError(
f"A {num_pixel_frames}-frame target has no pixel frames to place keyframes on."
)
return _parse_frame_index_list(
frame_indices,
"frame_indices",
expected_count,
first,
last,
expected_desc=(
f"{expected_count} keyframe(s)"
if expected_count is not None
else "the list sets the count"
),
empty_hint="to place them from interval_frames",
)
@classmethod
def keyframe_coords(cls, latent, frame_index, scale_factors):
"""Pixel coordinates of one keyframe: the full spatial grid over [t, t + 1)."""
_, latent_coords = cls.PATCHIFIER.patchify(latent[:, :, :1])
pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, causal_fix=True)
pixel_coords[:, 0] += frame_index
return pixel_coords
@classmethod
def execute(
cls,
positive,
negative,
vae,
latent,
interval_frames=24,
keyframes=None,
frame_indices="",
) -> io.NodeOutput:
samples = latent["samples"]
if not torch.is_tensor(samples) or samples.ndim != 5:
raise ValueError(
"Generated keyframes must be added to a plain video latent. Add them before "
"merging the video and audio latents with Concat AV Latent."
)
existing_record = get_generated_keyframes(positive)
if existing_record is not None:
prev_tokens_per_frame = existing_record["tokens_per_frame"]
if prev_tokens_per_frame != samples.shape[3] * samples.shape[4]:
raise ValueError(
f"The existing generated keyframes were added at {prev_tokens_per_frame} tokens per latent "
f"frame but this latent has {samples.shape[3] * samples.shape[4]}. The latent was rescaled "
"after they were added, so more keyframes cannot be appended to them."
)
prev_first = existing_record["first_latent_frame"]
prev_count = existing_record["num_keyframes"]
if prev_first + prev_count != samples.shape[2]:
raise ValueError(
f"The existing generated keyframes end at latent frame {prev_first + prev_count} but the "
f"latent has {samples.shape[2]}. Something was appended after them, so more keyframes would "
"not be contiguous with the existing block. Add all the keyframes before any guides."
)
scale_factors = vae.downscale_index_formula
time_scale_factor = scale_factors[0]
keyframe_idxs, num_guide_frames = get_keyframe_idxs(positive, samples.shape)
num_target_frames = samples.shape[2] - num_guide_frames
num_pixel_frames = (num_target_frames - 1) * time_scale_factor + 1
occupied = occupied_pixel_frames(
latent,
time_scale_factor,
num_pixel_frames,
video_latent_frames=num_target_frames,
)
occupied |= pixel_frames_from_keyframe_idxs(keyframe_idxs)
prev_indices = list(existing_record["frame_indices"]) if existing_record is not None else []
occupied |= set(prev_indices)
if frame_indices and str(frame_indices).strip():
indices = cls.parse_frame_indices(frame_indices, num_pixel_frames)
else:
indices = free_detailing_slots(num_pixel_frames, float(interval_frames), occupied)
clashes = sorted(set(indices) & occupied)
if clashes:
raise ValueError(
f"frame_indices reuses pixel frame(s) {', '.join(str(i) for i in clashes)}, which already hold "
"an image keyframe, a guide, or a generated keyframe. Each keyframe needs its own pixel frame."
)
num_keyframes = len(indices)
if keyframes is not None:
keyframe_samples = keyframes["samples"]
if keyframe_samples.ndim != 5:
raise ValueError(
f"The keyframes latent must be 5 dimensional, got {list(keyframe_samples.shape)}."
)
recorded_positions = keyframes.get("generated_keyframe_indices")
batched_singles = (
keyframe_samples.shape[2] == 1
and keyframe_samples.shape[0] != samples.shape[0]
)
if batched_singles:
if keyframe_samples.shape[0] % samples.shape[0] != 0:
raise ValueError(
f"The keyframes latent batch ({keyframe_samples.shape[0]}) is not a "
f"multiple of the video latent batch ({samples.shape[0]}), so it "
"cannot be reshaped into per-video keyframes."
)
stacked = keyframe_samples.shape[0] // samples.shape[0]
keyframe_samples = keyframe_samples.reshape(
samples.shape[0],
stacked,
keyframe_samples.shape[1],
*keyframe_samples.shape[3:],
).movedim(1, 2)
batched_singles = False
if should_copy_nearest_video_frames(
keyframe_samples.shape[2],
num_keyframes,
recorded_positions is not None,
batched_singles,
):
keyframe_samples = keyframes_from_video(
keyframe_samples,
indices,
time_scale_factor,
)
else:
keyframe_samples = _fit_keyframe_samples(
keyframe_samples.to(samples), samples, num_keyframes
)
keyframe_samples = keyframe_samples.to(samples)
else:
keyframe_samples = torch.zeros(
(
samples.shape[0],
samples.shape[1],
num_keyframes,
samples.shape[3],
samples.shape[4],
),
dtype=samples.dtype,
device=samples.device,
)
generated_coords = torch.cat(
[cls.keyframe_coords(samples, index, scale_factors) for index in indices],
dim=2,
)
if keyframe_idxs is not None:
generated_coords = torch.cat([keyframe_idxs, generated_coords.to(keyframe_idxs)], dim=2)
existing_entries = conditioning_get_any_value(positive, "guide_attention_entries", None) or []
generated_keyframes = {
"first_latent_frame": (
existing_record["first_latent_frame"] if existing_record else samples.shape[2]
),
"num_keyframes": (
existing_record["num_keyframes"] + num_keyframes if existing_record else num_keyframes
),
"frame_indices": prev_indices + list(indices),
"num_pixel_frames": num_pixel_frames,
"guide_entry_index": (
existing_record["guide_entry_index"] if existing_record else len(existing_entries)
),
"tokens_per_frame": samples.shape[3] * samples.shape[4],
}
values = {
"keyframe_idxs": generated_coords,
"generated_keyframes": generated_keyframes,
}
positive = node_helpers.conditioning_set_values(positive, values)
negative = node_helpers.conditioning_set_values(negative, values)
if existing_record is not None:
positive, negative = _grow_guide_attention_entry(
positive,
negative,
existing_record["guide_entry_index"],
extra_pre_filter_count=num_keyframes * samples.shape[3] * samples.shape[4],
extra_frames=num_keyframes,
)
else:
positive, negative = _append_guide_attention_entry(
positive,
negative,
pre_filter_count=num_keyframes * samples.shape[3] * samples.shape[4],
latent_shape=[num_keyframes, samples.shape[3], samples.shape[4]],
strength=1.0,
)
noise_mask = get_noise_mask(latent)
keyframe_noise_mask = torch.ones(
(
noise_mask.shape[0],
1,
num_keyframes,
noise_mask.shape[3],
noise_mask.shape[4],
),
dtype=noise_mask.dtype,
device=noise_mask.device,
)
out = latent.copy()
out["samples"] = torch.cat([samples, keyframe_samples], dim=2)
out["noise_mask"] = torch.cat([noise_mask, keyframe_noise_mask], dim=2)
return io.NodeOutput(positive, negative, out)
class LTXVSeparateGeneratedKeyframes(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LTXVSeparateGeneratedKeyframes",
display_name="LTXV Separate Generated Keyframes",
category="model/conditioning/ltxv",
search_aliases=["detailing", "dfr", "peel keyframes"],
description=(
"Split the generated keyframes added by LTXV Add Generated Keyframes "
"back out of a sampled latent, and remove them from the conditioning. "
"Separate them before spatially upscaling the video latent. Do not run "
"LTXV Crop Guides first — it treats generated keyframes as disposable "
"guides and drops them."
),
inputs=[
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Latent.Input("latent"),
io.Boolean.Input(
"keyframes_to_batch",
default=False,
tooltip=(
"Return the keyframes as a batch of single-frame latents. Leave off "
"to get them as one multi-frame latent, which is what the latent "
"upsampler and a later Add Generated Keyframes expect."
),
),
],
outputs=[
io.Conditioning.Output(
display_name="positive",
tooltip="Positive conditioning with generated-keyframe metadata removed.",
),
io.Conditioning.Output(
display_name="negative",
tooltip="Negative conditioning with generated-keyframe metadata removed.",
),
io.Latent.Output(
display_name="latent",
tooltip="Video latent with the generated keyframes stripped.",
),
io.Latent.Output(
display_name="keyframes",
tooltip=(
"The peeled keyframes, labeled with generated_keyframe_indices and "
"generated_keyframe_num_frames. Feed these to a later Add Generated "
"Keyframes to initialize new slots, or to Generated Keyframes To "
"Guides to pin them as frozen image guides (indices are remapped "
"if the canvas length changed)."
),
),
],
)
@classmethod
def strip_keyframe_idxs(cls, cond, first_token, num_tokens):
keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None)
if keyframe_idxs is None:
return None
remaining = torch.cat(
[
keyframe_idxs[:, :, :first_token],
keyframe_idxs[:, :, first_token + num_tokens :],
],
dim=2,
)
return remaining if remaining.shape[2] > 0 else None
@classmethod
def strip_guide_entry(cls, cond, entry_index):
entries = conditioning_get_any_value(cond, "guide_attention_entries", None)
if not entries:
return None
if entry_index >= len(entries):
raise ValueError(
f"The generated keyframes recorded guide entry {entry_index} but the conditioning only has "
f"{len(entries)}. The conditioning was rebuilt after they were added."
)
remaining = entries[:entry_index] + entries[entry_index + 1 :]
return remaining or None
@classmethod
def execute(cls, positive, negative, latent, keyframes_to_batch=False) -> io.NodeOutput:
generated_keyframes = get_generated_keyframes(positive)
if generated_keyframes is None:
raise ValueError(
"This latent has no generated keyframes. Add them with LTXV Add Generated Keyframes first."
)
samples = latent["samples"]
if not torch.is_tensor(samples) or samples.ndim != 5:
raise ValueError(
"Generated keyframes must be separated from a plain video latent. Split the video and "
"audio latents with Separate AV Latent first."
)
tokens_per_frame = samples.shape[3] * samples.shape[4]
if generated_keyframes["tokens_per_frame"] != tokens_per_frame:
raise ValueError(
f"The generated keyframes were added at {generated_keyframes['tokens_per_frame']} tokens per "
f"latent frame but this latent has {tokens_per_frame}. The latent was rescaled after they were "
"added, so the keyframes no longer line up. Separate them before upscaling the latent."
)
first_frame = generated_keyframes["first_latent_frame"]
num_keyframes = generated_keyframes["num_keyframes"]
end_frame = first_frame + num_keyframes
if end_frame > samples.shape[2]:
raise ValueError(
f"The generated keyframes span latent frames [{first_frame}, {end_frame}) but the latent "
f"only has {samples.shape[2]}. It was recorded against a different latent."
)
keyframe_samples = samples[:, :, first_frame:end_frame].clone()
if keyframes_to_batch:
batch, channels, _, height, width = keyframe_samples.shape
keyframe_samples = keyframe_samples.movedim(2, 1).reshape(
batch * num_keyframes, channels, 1, height, width
)
video_samples = torch.cat(
[samples[:, :, :first_frame], samples[:, :, end_frame:]], dim=2
)
noise_mask = get_noise_mask(latent)
video_noise_mask = torch.cat(
[noise_mask[:, :, :first_frame], noise_mask[:, :, end_frame:]], dim=2
)
_, num_guide_frames = get_keyframe_idxs(positive, samples.shape)
first_token = (first_frame - (samples.shape[2] - num_guide_frames)) * tokens_per_frame
entry_index = generated_keyframes["guide_entry_index"]
outputs = []
for cond in (positive, negative):
outputs.append(
node_helpers.conditioning_set_values(
cond,
{
"keyframe_idxs": cls.strip_keyframe_idxs(
cond, first_token, num_keyframes * tokens_per_frame
),
"guide_attention_entries": cls.strip_guide_entry(cond, entry_index),
"generated_keyframes": None,
},
)
)
out = latent.copy()
out["samples"] = video_samples
out["noise_mask"] = video_noise_mask
num_pixel_frames = generated_keyframes.get("num_pixel_frames")
if num_pixel_frames is None:
num_pixel_frames = (first_frame - 1) * DEFAULT_TEMPORAL_SCALE + 1
keyframes_out = {
"samples": keyframe_samples,
"generated_keyframe_indices": list(generated_keyframes["frame_indices"]),
"generated_keyframe_num_frames": int(num_pixel_frames),
}
return io.NodeOutput(outputs[0], outputs[1], out, keyframes_out)
class LTXVGeneratedKeyframesToGuides(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LTXVGeneratedKeyframesToGuides",
display_name="LTXV Generated Keyframes to Guides",
category="model/conditioning/ltxv",
search_aliases=["detailing", "dfr", "keyframe guides"],
description=(
"Pin generated keyframes from an earlier stage as frozen image guides on "
"a later canvas. They are decoded as standalone frames, resized if needed, "
"and written with noise_mask=0 so they are not denoised again. After a "
"temporal upscale, recorded indices are scaled from the canvas they were "
"generated on onto this one (same moments). Use override_frame_indices to "
"set positions explicitly. To keep generating (and denoising) keyframes at "
"new positions, use Add Generated Keyframes instead."
),
inputs=[
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Vae.Input("vae"),
io.Latent.Input(
"latent",
tooltip="The target video latent to add the guides to, e.g. the temporally upscaled one.",
),
io.Latent.Input(
"keyframes",
tooltip=(
"The keyframes output of LTXV Separate Generated Keyframes, which "
"carries the pixel frame index each keyframe was generated at."
),
),
io.Float.Input(
"strength",
default=1.0,
min=0.0,
max=10.0,
step=0.01,
tooltip="Guide strength. 1.0 is a hard pin; lower values relax it.",
),
io.String.Input(
"override_frame_indices",
optional=True,
default="",
tooltip=(
"Optional — pin at these pixel frames instead of the recorded "
"(or auto-scaled) positions. Provide one index per keyframe. "
"Leave empty to reuse recorded positions, or to scale them when "
"the target canvas is a different length (e.g. after temporal x2)."
),
),
],
outputs=[
io.Conditioning.Output(
display_name="positive",
tooltip="Positive conditioning with the keyframes pinned as image guides.",
),
io.Conditioning.Output(
display_name="negative",
tooltip="Negative conditioning with the keyframes pinned as image guides.",
),
io.Latent.Output(
display_name="latent",
tooltip="Target video latent with the keyframes added as frozen guides.",
),
],
)
@classmethod
def decode_single_frames(cls, vae, keyframe_samples):
"""Decode each keyframe latent on its own, never as one clip."""
if keyframe_samples.shape[2] != 1:
batch, channels, num_keyframes, height, width = keyframe_samples.shape
keyframe_samples = keyframe_samples.movedim(2, 1).reshape(
batch * num_keyframes, channels, 1, height, width
)
images = vae.decode(keyframe_samples)
if images.ndim == 5:
images = images.reshape(-1, *images.shape[-3:])
return images
@classmethod
def execute(
cls,
positive,
negative,
vae,
latent,
keyframes,
strength,
override_frame_indices="",
) -> io.NodeOutput:
indices = keyframes.get("generated_keyframe_indices", None)
if indices is None:
raise ValueError(
"This latent does not carry generated keyframe positions. Connect the keyframes output "
"of LTXV Separate Generated Keyframes."
)
if get_generated_keyframes(positive) is not None:
raise ValueError(
"This conditioning still carries generated keyframes. Connect the positive and "
"negative outputs of LTXV Separate Generated Keyframes."
)
samples = latent["samples"]
if not torch.is_tensor(samples) or samples.ndim != 5:
raise ValueError(
"Generated keyframe guides must be added to a plain video latent. Add them before "
"merging the video and audio latents with Concat AV Latent."
)
if samples.shape[0] != 1:
raise ValueError(
f"Only a batch size of 1 is supported, got {samples.shape[0]}. Each guide is encoded from "
"one image, so it cannot differ across batch elements."
)
kf_samples = keyframes["samples"]
if kf_samples.shape[2] != 1:
batch, channels, num_keyframes, height, width = kf_samples.shape
kf_samples = kf_samples.movedim(2, 1).reshape(
batch * num_keyframes, channels, 1, height, width
)
resize_needed = kf_samples.shape[3:] != samples.shape[3:]
guides = cls.decode_single_frames(vae, kf_samples) if resize_needed else kf_samples
if guides.shape[0] != len(indices):
raise ValueError(
f"Got {guides.shape[0]} keyframes for {len(indices)} recorded positions."
)
_, num_guide_frames = get_keyframe_idxs(positive, samples.shape)
time_scale_factor = vae.downscale_index_formula[0]
num_pixel_frames = (samples.shape[2] - num_guide_frames - 1) * time_scale_factor + 1
if override_frame_indices and str(override_frame_indices).strip():
indices = _parse_frame_index_list(
override_frame_indices,
"override_frame_indices",
len(indices),
1,
num_pixel_frames - 1,
expected_desc=f"the keyframes latent carries {len(indices)}",
empty_hint="to reuse the recorded positions",
)
else:
old_len = keyframes.get("generated_keyframe_num_frames")
if old_len is not None and int(old_len) != num_pixel_frames:
indices = scale_frame_indices(list(indices), int(old_len), num_pixel_frames)
if indices and max(indices) >= num_pixel_frames:
raise ValueError(
f"Keyframe position {max(indices)} is outside this latent's {num_pixel_frames} frames. The "
"target was resized temporally after the keyframes were generated."
)
for index, frame_idx in enumerate(indices):
if resize_needed:
added = LTXVAddGuide.execute(
positive,
negative,
vae,
latent,
guides[index].unsqueeze(0),
int(frame_idx),
strength,
)
positive, negative, latent = added[0], added[1], added[2]
else:
positive, negative, latent = cls.append_latent_keyframe(
positive,
negative,
vae,
latent,
guides[index : index + 1],
int(frame_idx),
strength,
)
return io.NodeOutput(positive, negative, latent)
@classmethod
def append_latent_keyframe(cls, positive, negative, vae, latent, guiding_latent, frame_idx, strength):
"""Append an already encoded keyframe latent the same way LTXVAddGuide would after encoding."""
noise_mask = get_noise_mask(latent)
positive, negative, latent_image, noise_mask = LTXVAddGuide.append_keyframe(
positive,
negative,
frame_idx,
latent["samples"],
noise_mask,
guiding_latent,
strength,
vae.downscale_index_formula,
)
positive, negative = _append_guide_attention_entry(
positive,
negative,
pre_filter_count=math.prod(guiding_latent.shape[2:]),
latent_shape=list(guiding_latent.shape[2:]),
strength=strength,
)
out = latent.copy()
out["samples"] = latent_image
out["noise_mask"] = noise_mask
return positive, negative, out
class LTXVFreezeLatent(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LTXVFreezeLatent",
display_name="LTXV Freeze Latent",
category="model/latent/ltxv",
search_aliases=["noise mask", "freeze audio", "freeze video"],
description=(
"Set noise_mask to 0 so this latent is kept clean during sampling. "
"Works on video or audio. Typical uses: freeze audio before Concat AV "
"so it only provides cross-attention, or freeze any latent that should "
"not be denoised."
),
inputs=[
io.Latent.Input(
"latent",
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