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Use temporal downscale to make empty audio latent nodes more reusable. (#13975)

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comfyanonymous 2026-05-18 21:14:30 -07:00 committed by GitHub
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commit a4382e056e
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3 changed files with 4 additions and 2 deletions
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2
comfy/latent_formats.py
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@ -150,6 +150,7 @@ class SD3(LatentFormat):
class StableAudio1(LatentFormat): class StableAudio1(LatentFormat):
latent_channels = 64 latent_channels = 64
latent_dimensions = 1 latent_dimensions = 1
temporal_downscale_ratio = 2048
class Flux(SD3): class Flux(SD3):
latent_channels = 16 latent_channels = 16
@ -766,6 +767,7 @@ class ACEAudio(LatentFormat):
class ACEAudio15(LatentFormat): class ACEAudio15(LatentFormat):
latent_channels = 64 latent_channels = 64
latent_dimensions = 1 latent_dimensions = 1
temporal_downscale_ratio = 1764
class ChromaRadiance(LatentFormat): class ChromaRadiance(LatentFormat):
latent_channels = 3 latent_channels = 3

2
comfy_extras/nodes_ace.py
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@ -104,7 +104,7 @@ class EmptyAceStep15LatentAudio(IO.ComfyNode):
def execute(cls, seconds, batch_size) -> IO.NodeOutput: def execute(cls, seconds, batch_size) -> IO.NodeOutput:
length = round((seconds * 48000 / 1920)) length = round((seconds * 48000 / 1920))
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
return IO.NodeOutput({"samples": latent, "type": "audio"}) return IO.NodeOutput({"samples": latent, "type": "audio", "downscale_ratio_temporal": 1764})
class ReferenceAudio(IO.ComfyNode): class ReferenceAudio(IO.ComfyNode):
@classmethod @classmethod

2
comfy_extras/nodes_audio.py
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@ -33,7 +33,7 @@ class EmptyLatentAudio(IO.ComfyNode):
def execute(cls, seconds, batch_size) -> IO.NodeOutput: def execute(cls, seconds, batch_size) -> IO.NodeOutput:
length = round((seconds * 44100 / 2048) / 2) * 2 length = round((seconds * 44100 / 2048) / 2) * 2
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device()) latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
return IO.NodeOutput({"samples":latent, "type": "audio"}) return IO.NodeOutput({"samples": latent, "type": "audio", "downscale_ratio_temporal": 2048})
generate = execute # TODO: remove generate = execute # TODO: remove