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Gate SeedVR2 frames_per_chunk behind a manual/auto DynamicCombo

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Make chunking_mode a DynamicCombo on the Chunk SeedVR2 Latent node so frames_per_chunk is shown only when chunking_mode is manual. In auto mode the chunk size is predicted from free VRAM, so frames_per_chunk is irrelevant and is now hidden; temporal_overlap stays visible in both modes. Options are alphabetized (auto, manual).
This commit is contained in:
John Pollock 2026-07-07 04:11:55 -05:00
parent 76719afe7c
commit e5f018d7a4
2 changed files with 25 additions and 14 deletions
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34
comfy_extras/nodes_seedvr.py
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@ -431,12 +431,17 @@ class SeedVR2TemporalChunk(io.ComfyNode):
search_aliases=["seedvr2", "chunk", "temporal", "video upscale", "rebatch"],
inputs=[
io.Latent.Input("latent", tooltip="The VAE-encoded SeedVR2 latent to split."),
io.Int.Input("frames_per_chunk", default=21, min=1, max=16384, step=4,
tooltip="Pixel frames per temporal chunk (4n+1: 1, 5, 9, 13, ...)."),
io.Int.Input("temporal_overlap", default=0, min=0, max=16384,
tooltip="Latent frames shared between adjacent chunks and crossfaded at merge; 0 = no overlap."),
io.Combo.Input("chunking_mode", options=["auto", "manual"], default="manual",
tooltip="manual = use frames_per_chunk exactly; auto = predict the largest chunk that fits free VRAM."),
io.DynamicCombo.Input("chunking_mode",
tooltip="manual = use frames_per_chunk exactly; auto = predict the largest chunk that fits free VRAM.",
options=[
io.DynamicCombo.Option("auto", []),
io.DynamicCombo.Option("manual", [
io.Int.Input("frames_per_chunk", default=21, min=1, max=16384, step=4,
tooltip="Pixel frames per temporal chunk (4n+1: 1, 5, 9, 13, ...)."),
]),
]),
],
outputs=[
io.Latent.Output(display_name="latent_chunks", is_output_list=True,
@ -447,7 +452,7 @@ class SeedVR2TemporalChunk(io.ComfyNode):
)
@classmethod
def execute(cls, latent, frames_per_chunk, temporal_overlap, chunking_mode) -> io.NodeOutput:
def execute(cls, latent, temporal_overlap, chunking_mode) -> io.NodeOutput:
samples = latent["samples"]
if samples.ndim != 5:
raise ValueError(
@ -463,15 +468,16 @@ class SeedVR2TemporalChunk(io.ComfyNode):
raise ValueError(
f"SeedVR2TemporalChunk: temporal_overlap must be >= 0; got {temporal_overlap}."
)
if chunking_mode not in ("auto", "manual"):
mode = chunking_mode["chunking_mode"]
if mode not in ("auto", "manual"):
raise ValueError(
f"SeedVR2TemporalChunk: chunking_mode must be 'auto' or 'manual'; "
f"got {chunking_mode!r}."
f"got {mode!r}."
)
t_latent = samples.shape[2]
t_pixel = 4 * (t_latent - 1) + 1
if chunking_mode == "auto":
if mode == "auto":
free_gb = comfy.model_management.get_free_memory(
comfy.model_management.get_torch_device()) / (1024 ** 3)
mpx_per_frame = (samples.shape[0] * samples.shape[3] * samples.shape[4]) * (BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE ** 2) / 1e6
@ -482,11 +488,13 @@ class SeedVR2TemporalChunk(io.ComfyNode):
"SeedVR2TemporalChunk auto: free=%.2fGiB, %.2fMpx -> frames_per_chunk=%d (t_pixel=%d).",
free_gb, mpx_per_frame, frames_per_chunk, t_pixel,
)
elif frames_per_chunk < 1 or (frames_per_chunk - 1) % 4 != 0:
raise ValueError(
f"SeedVR2TemporalChunk: frames_per_chunk must be a 4n+1 pixel-frame count "
f"(1, 5, 9, 13, 17, 21, ...); got {frames_per_chunk}."
)
else:
frames_per_chunk = chunking_mode["frames_per_chunk"]
if frames_per_chunk < 1 or (frames_per_chunk - 1) % 4 != 0:
raise ValueError(
f"SeedVR2TemporalChunk: frames_per_chunk must be a 4n+1 pixel-frame count "
f"(1, 5, 9, 13, 17, 21, ...); got {frames_per_chunk}."
)
if t_pixel <= frames_per_chunk:
return io.NodeOutput([latent], 0)

5
tests-unit/comfy_extras_test/test_seedvr2_temporal_chunk.py
View File

@ -17,7 +17,10 @@ def _latent(t_latent, h=8, w=8, b=1):
return {"samples": torch.randn(b, SEEDVR2_LATENT_CHANNELS, t_latent, h, w, generator=g)}
def _split(latent, frames_per_chunk, temporal_overlap, chunking_mode="manual"):
return SeedVR2TemporalChunk.execute(latent, frames_per_chunk, temporal_overlap, chunking_mode).args
combo = {"chunking_mode": chunking_mode}
if chunking_mode != "auto":
combo["frames_per_chunk"] = frames_per_chunk
return SeedVR2TemporalChunk.execute(latent, temporal_overlap, combo).args
def _merge(chunks, temporal_overlap):
return SeedVR2TemporalMerge.execute(chunks, [temporal_overlap]).args[0]