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Rename NormalizeVideoLatentFrames -node

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For bit better clarity what it does
This commit is contained in:
kijai 2025-12-05 23:34:22 +02:00
parent ce079b0cf5
commit 4848793aac
1 changed files with 10 additions and 10 deletions
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20
comfy_extras/nodes_kandinsky5.py
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@ -66,17 +66,17 @@ def adaptive_mean_std_normalization(source, reference, clump_mean_low=0.3, clump
return normalized
class NormalizeVideoLatentFrames(io.ComfyNode):
class NormalizeVideoLatentStart(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="NormalizeVideoLatentFrames",
node_id="NormalizeVideoLatentStart",
category="conditioning/video_models",
description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames.",
description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.",
inputs=[
io.Latent.Input("latent"),
io.Int.Input("frames_to_normalize", default=4, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of initial frames to normalize, counted from the start"),
io.Int.Input("reference_frames", default=5, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of frames after the normalized frames to use as reference"),
io.Int.Input("start_frame_count", default=4, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of latent frames to normalize, counted from the start"),
io.Int.Input("reference_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of latent frames after the start frames to use as reference"),
],
outputs=[
io.Latent.Output(display_name="latent"),
@ -84,17 +84,17 @@ class NormalizeVideoLatentFrames(io.ComfyNode):
)
@classmethod
def execute(cls, latent, frames_to_normalize, reference_frames) -> io.NodeOutput:
def execute(cls, latent, start_frame_count, reference_frame_count) -> io.NodeOutput:
if latent["samples"].shape[2] <= 1:
return latent
s = latent.copy()
samples = latent["samples"].clone()
first_frames = samples[:, :, :frames_to_normalize]
reference_frames_data = samples[:, :, frames_to_normalize:frames_to_normalize+min(reference_frames, samples.shape[2]-1)]
first_frames = samples[:, :, :start_frame_count]
reference_frames_data = samples[:, :, start_frame_count:start_frame_count+min(reference_frame_count, samples.shape[2]-1)]
normalized_first_frames = adaptive_mean_std_normalization(first_frames, reference_frames_data)
samples[:, :, :frames_to_normalize] = normalized_first_frames
samples[:, :, :start_frame_count] = normalized_first_frames
s["samples"] = samples
return io.NodeOutput(s)
@ -128,7 +128,7 @@ class Kandinsky5Extension(ComfyExtension):
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
Kandinsky5ImageToVideo,
NormalizeVideoLatentFrames,
NormalizeVideoLatentStart,
CLIPTextEncodeKandinsky5,
]