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https://github.com/comfyanonymous/ComfyUI.git
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V3 Nodes: Load,Save,Vae audio nodes; sort imports; ruff
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bigcat88
@@ -1,38 +1,79 @@
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from __future__ import annotations
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import torchaudio
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import folder_paths
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import os
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import io
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import hashlib
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import json
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import os
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from io import BytesIO
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import av
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import torch
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import torchaudio
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import comfy.model_management
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import folder_paths
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import node_helpers
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from comfy.cli_args import args
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from comfy_api.v3 import io, ui
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class PreviewAudio_V3(io.ComfyNodeV3):
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class ConditioningStableAudio_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="PreviewAudio_V3",
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display_name="Preview Audio _V3",
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category="audio",
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node_id="ConditioningStableAudio_V3",
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category="conditioning",
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inputs=[
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io.Audio.Input("audio"),
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io.Conditioning.Input(id="positive"),
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io.Conditioning.Input(id="negative"),
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io.Float.Input(id="seconds_start", default=0.0, min=0.0, max=1000.0, step=0.1),
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io.Float.Input(id="seconds_total", default=47.0, min=0.0, max=1000.0, step=0.1),
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],
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outputs=[
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io.Conditioning.Output(id="positive_out", display_name="positive"),
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io.Conditioning.Output(id="negative_out", display_name="negative"),
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],
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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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)
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@classmethod
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def execute(cls, audio):
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return io.NodeOutput(ui=ui.PreviewAudio(audio, cls=cls))
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def execute(cls, positive, negative, seconds_start, seconds_total) -> io.NodeOutput:
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return io.NodeOutput(
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node_helpers.conditioning_set_values(
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positive, {"seconds_start": seconds_start, "seconds_total": seconds_total}
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),
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node_helpers.conditioning_set_values(
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negative, {"seconds_start": seconds_start, "seconds_total": seconds_total}
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),
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)
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class EmptyLatentAudio_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="EmptyLatentAudio_V3",
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category="latent/audio",
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inputs=[
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io.Float.Input(id="seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
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io.Int.Input(
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id="batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
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),
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],
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outputs=[io.Latent.Output()],
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)
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@classmethod
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def execute(cls, seconds, batch_size) -> io.NodeOutput:
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length = round((seconds * 44100 / 2048) / 2) * 2
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latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
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return io.NodeOutput({"samples":latent, "type": "audio"})
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class LoadAudio_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="LoadAudio_V3",
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display_name="Load Audio _V3",
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node_id="LoadAudio_V3", # frontend expects "LoadAudio" to work
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display_name="Load Audio _V3", # frontend ignores "display_name" for this node
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category="audio",
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inputs=[
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io.Combo.Input("audio", upload=io.UploadType.audio, options=cls.get_files_options()),
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@@ -65,14 +106,242 @@ class LoadAudio_V3(io.ComfyNodeV3):
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return True
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class PreviewAudio_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="PreviewAudio_V3", # frontend expects "PreviewAudio" to work
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display_name="Preview Audio _V3", # frontend ignores "display_name" for this node
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category="audio",
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inputs=[
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io.Audio.Input("audio"),
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],
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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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)
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@classmethod
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def execute(cls, audio) -> io.NodeOutput:
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return io.NodeOutput(ui=ui.PreviewAudio(audio, cls=cls))
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class SaveAudioMP3_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="SaveAudioMP3_V3", # frontend expects "SaveAudioMP3" to work
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display_name="Save Audio(MP3) _V3", # frontend ignores "display_name" for this node
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category="audio",
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inputs=[
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io.Audio.Input("audio"),
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io.String.Input("filename_prefix", default="audio/ComfyUI"),
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io.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
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],
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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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)
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@classmethod
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def execute(self, audio, filename_prefix="ComfyUI", format="mp3", quality="V0") -> io.NodeOutput:
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return _save_audio(self, audio, filename_prefix, format, quality)
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class SaveAudioOpus_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="SaveAudioOpus_V3", # frontend expects "SaveAudioOpus" to work
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display_name="Save Audio(Opus) _V3", # frontend ignores "display_name" for this node
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category="audio",
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inputs=[
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io.Audio.Input("audio"),
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io.String.Input("filename_prefix", default="audio/ComfyUI"),
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io.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
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],
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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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)
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@classmethod
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def execute(self, audio, filename_prefix="ComfyUI", format="opus", quality="128k") -> io.NodeOutput:
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return _save_audio(self, audio, filename_prefix, format, quality)
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class SaveAudio_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="SaveAudio_V3", # frontend expects "SaveAudio" to work
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display_name="Save Audio _V3", # frontend ignores "display_name" for this node
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category="audio",
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inputs=[
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io.Audio.Input("audio"),
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io.String.Input("filename_prefix", default="audio/ComfyUI"),
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],
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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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)
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@classmethod
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def execute(cls, audio, filename_prefix="ComfyUI", format="flac") -> io.NodeOutput:
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return _save_audio(cls, audio, filename_prefix, format)
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class VAEDecodeAudio_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="VAEDecodeAudio_V3",
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category="latent/audio",
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inputs=[
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io.Latent.Input(id="samples"),
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io.Vae.Input(id="vae"),
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],
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outputs=[io.Audio.Output()],
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)
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@classmethod
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def execute(cls, vae, samples) -> io.NodeOutput:
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audio = vae.decode(samples["samples"]).movedim(-1, 1)
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std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
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std[std < 1.0] = 1.0
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audio /= std
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return io.NodeOutput({"waveform": audio, "sample_rate": 44100})
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class VAEEncodeAudio_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="VAEEncodeAudio_V3",
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category="latent/audio",
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inputs=[
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io.Audio.Input(id="audio"),
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io.Vae.Input(id="vae"),
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],
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outputs=[io.Latent.Output()],
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)
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@classmethod
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def execute(cls, vae, audio) -> io.NodeOutput:
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sample_rate = audio["sample_rate"]
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if 44100 != sample_rate:
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waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
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else:
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waveform = audio["waveform"]
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return io.NodeOutput({"samples": vae.encode(waveform.movedim(1, -1))})
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def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="128k") -> io.NodeOutput:
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
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filename_prefix, folder_paths.get_output_directory()
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)
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# Prepare metadata dictionary
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metadata = {}
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if not args.disable_metadata:
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if cls.hidden.prompt is not None:
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metadata["prompt"] = json.dumps(cls.hidden.prompt)
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if cls.hidden.extra_pnginfo is not None:
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for x in cls.hidden.extra_pnginfo:
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metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
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# Opus supported sample rates
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OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
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results = []
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for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
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filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
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file = f"{filename_with_batch_num}_{counter:05}_.{format}"
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output_path = os.path.join(full_output_folder, file)
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# Use original sample rate initially
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sample_rate = audio["sample_rate"]
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# Handle Opus sample rate requirements
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if format == "opus":
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if sample_rate > 48000:
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sample_rate = 48000
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elif sample_rate not in OPUS_RATES:
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# Find the next highest supported rate
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for rate in sorted(OPUS_RATES):
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if rate > sample_rate:
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sample_rate = rate
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break
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if sample_rate not in OPUS_RATES: # Fallback if still not supported
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sample_rate = 48000
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# Resample if necessary
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if sample_rate != audio["sample_rate"]:
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waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate)
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# Create output with specified format
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output_buffer = BytesIO()
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output_container = av.open(output_buffer, mode='w', format=format)
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# Set metadata on the container
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for key, value in metadata.items():
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output_container.metadata[key] = value
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# Set up the output stream with appropriate properties
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if format == "opus":
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out_stream = output_container.add_stream("libopus", rate=sample_rate)
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if quality == "64k":
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out_stream.bit_rate = 64000
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elif quality == "96k":
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out_stream.bit_rate = 96000
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elif quality == "128k":
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out_stream.bit_rate = 128000
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elif quality == "192k":
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out_stream.bit_rate = 192000
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elif quality == "320k":
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out_stream.bit_rate = 320000
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elif format == "mp3":
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out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
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if quality == "V0":
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#TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
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out_stream.codec_context.qscale = 1
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elif quality == "128k":
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out_stream.bit_rate = 128000
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elif quality == "320k":
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out_stream.bit_rate = 320000
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else: # format == "flac":
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out_stream = output_container.add_stream("flac", rate=sample_rate)
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frame = av.AudioFrame.from_ndarray(
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waveform.movedim(0, 1).reshape(1, -1).float().numpy(),
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format='flt',
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layout='mono' if waveform.shape[0] == 1 else 'stereo',
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)
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frame.sample_rate = sample_rate
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frame.pts = 0
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output_container.mux(out_stream.encode(frame))
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# Flush encoder
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output_container.mux(out_stream.encode(None))
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# Close containers
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output_container.close()
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# Write the output to file
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output_buffer.seek(0)
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with open(output_path, 'wb') as f:
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f.write(output_buffer.getbuffer())
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results.append(ui.SavedResult(file, subfolder, io.FolderType.output))
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counter += 1
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return io.NodeOutput(ui={"audio": results})
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NODES_LIST: list[type[io.ComfyNodeV3]] = [
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# EmptyLatentAudio_V3,
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# VAEEncodeAudio_V3,
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# VAEDecodeAudio_V3,
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# SaveAudio_V3,
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# SaveAudioMP3_V3,
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# SaveAudioOpus_V3,
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ConditioningStableAudio_V3,
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EmptyLatentAudio_V3,
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LoadAudio_V3,
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PreviewAudio_V3,
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# ConditioningStableAudio_V3,
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SaveAudioMP3_V3,
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SaveAudioOpus_V3,
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SaveAudio_V3,
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VAEDecodeAudio_V3,
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VAEEncodeAudio_V3,
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]
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