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fix: Stop LTXVCropGuides leaving stray latent frames when guides share a start position

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This commit is contained in:
ozbayb 2026-05-13 18:14:52 -06:00
parent 26515acd23
commit 09f15d82da
1 changed files with 16 additions and 6 deletions
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22
comfy_extras/nodes_lt.py
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@ -183,10 +183,20 @@ def get_noise_mask(latent):
noise_mask = noise_mask.clone() noise_mask = noise_mask.clone()
return noise_mask return noise_mask
def get_keyframe_idxs(cond): def get_keyframe_idxs(cond, latent_shape=None):
keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None) keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None)
if keyframe_idxs is None: if keyframe_idxs is None:
return None, 0 return None, 0
# Get number of keyframes from latent_shape or guide_attention_entries if available
if latent_shape is not None and len(latent_shape) == 5:
tokens_per_frame = latent_shape[-2] * latent_shape[-1]
num_keyframes = keyframe_idxs.shape[2] // tokens_per_frame
return keyframe_idxs, num_keyframes
entries = conditioning_get_any_value(cond, "guide_attention_entries", None)
if entries:
num_keyframes = sum(e["latent_shape"][0] for e in entries)
return keyframe_idxs, num_keyframes
# fallback, may under-count if keyframes share t-start
# keyframe_idxs contains start/end positions (last dimension), checking for unqiue values only for start # keyframe_idxs contains start/end positions (last dimension), checking for unqiue values only for start
num_keyframes = torch.unique(keyframe_idxs[:, 0, :, 0]).shape[0] num_keyframes = torch.unique(keyframe_idxs[:, 0, :, 0]).shape[0]
return keyframe_idxs, num_keyframes return keyframe_idxs, num_keyframes
@ -238,9 +248,9 @@ class LTXVAddGuide(io.ComfyNode):
return encode_pixels, t return encode_pixels, t
@classmethod @classmethod
def get_latent_index(cls, cond, latent_length, guide_length, frame_idx, scale_factors): def get_latent_index(cls, cond, latent_length, guide_length, frame_idx, scale_factors, latent_shape=None):
time_scale_factor, _, _ = scale_factors time_scale_factor, _, _ = scale_factors
_, num_keyframes = get_keyframe_idxs(cond) _, num_keyframes = get_keyframe_idxs(cond, latent_shape)
latent_count = latent_length - num_keyframes latent_count = latent_length - num_keyframes
frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * time_scale_factor + 1 + frame_idx, 0) frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * time_scale_factor + 1 + frame_idx, 0)
if guide_length > 1 and frame_idx != 0: if guide_length > 1 and frame_idx != 0:
@ -344,7 +354,7 @@ class LTXVAddGuide(io.ComfyNode):
num_frames_to_keep = ((image.shape[0] - 1) // time_scale_factor) * time_scale_factor + 1 num_frames_to_keep = ((image.shape[0] - 1) // time_scale_factor) * time_scale_factor + 1
resolved_frame_idx = frame_idx resolved_frame_idx = frame_idx
if frame_idx < 0: if frame_idx < 0:
_, num_keyframes = get_keyframe_idxs(positive) _, num_keyframes = get_keyframe_idxs(positive, latent_image.shape)
resolved_frame_idx = max((latent_length - num_keyframes - 1) * time_scale_factor + 1 + frame_idx, 0) resolved_frame_idx = max((latent_length - num_keyframes - 1) * time_scale_factor + 1 + frame_idx, 0)
causal_fix = resolved_frame_idx == 0 or num_frames_to_keep == 1 causal_fix = resolved_frame_idx == 0 or num_frames_to_keep == 1
@ -357,7 +367,7 @@ class LTXVAddGuide(io.ComfyNode):
t = t[:, :, 1:, :, :] t = t[:, :, 1:, :, :]
image = image[1:] image = image[1:]
frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors) frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors, latent_shape=latent_image.shape)
assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence." assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence."
positive, negative, latent_image, noise_mask = cls.append_keyframe( positive, negative, latent_image, noise_mask = cls.append_keyframe(
@ -407,7 +417,7 @@ class LTXVCropGuides(io.ComfyNode):
latent_image = latent["samples"].clone() latent_image = latent["samples"].clone()
noise_mask = get_noise_mask(latent) noise_mask = get_noise_mask(latent)
_, num_keyframes = get_keyframe_idxs(positive) _, num_keyframes = get_keyframe_idxs(positive, latent_image.shape)
if num_keyframes == 0: if num_keyframes == 0:
return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)