mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-03-30 21:43:43 +08:00
fix ACE-Step 1.5 language list to match upstream VALID_LANGUAGES
The node combo input only had 23 languages (arbitrary subset), causing
validation errors for valid languages like Thai ('th'). Updated to match
the full 51-entry VALID_LANGUAGES from ace-step/ACE-Step-1.5.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Mark Ledford
parent
6d11cc7354
commit
6a2ceb470b
@ -16,7 +16,9 @@ class TextEncodeAceStepAudio(io.ComfyNode):
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io.Clip.Input("clip"),
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io.String.Input("tags", multiline=True, dynamic_prompts=True),
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io.String.Input("lyrics", multiline=True, dynamic_prompts=True),
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io.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01),
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io.Float.Input(
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"lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01
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),
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],
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outputs=[io.Conditioning.Output()],
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)
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@ -25,9 +27,12 @@ class TextEncodeAceStepAudio(io.ComfyNode):
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def execute(cls, clip, tags, lyrics, lyrics_strength) -> io.NodeOutput:
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tokens = clip.tokenize(tags, lyrics=lyrics)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength})
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conditioning = node_helpers.conditioning_set_values(
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conditioning, {"lyrics_strength": lyrics_strength}
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)
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return io.NodeOutput(conditioning)
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class TextEncodeAceStepAudio15(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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@ -38,25 +43,168 @@ class TextEncodeAceStepAudio15(io.ComfyNode):
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io.Clip.Input("clip"),
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io.String.Input("tags", multiline=True, dynamic_prompts=True),
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io.String.Input("lyrics", multiline=True, dynamic_prompts=True),
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io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
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io.Int.Input(
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"seed",
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default=0,
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min=0,
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max=0xFFFFFFFFFFFFFFFF,
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control_after_generate=True,
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),
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io.Int.Input("bpm", default=120, min=10, max=300),
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io.Float.Input("duration", default=120.0, min=0.0, max=2000.0, step=0.1),
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io.Combo.Input("timesignature", options=['2', '3', '4', '6']),
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io.Combo.Input("language", options=["en", "ja", "zh", "es", "de", "fr", "pt", "ru", "it", "nl", "pl", "tr", "vi", "cs", "fa", "id", "ko", "uk", "hu", "ar", "sv", "ro", "el"]),
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io.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]),
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io.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True),
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io.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True),
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io.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True),
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io.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True),
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io.Float.Input(
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"duration", default=120.0, min=0.0, max=2000.0, step=0.1
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),
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io.Combo.Input("timesignature", options=["2", "3", "4", "6"]),
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io.Combo.Input(
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"language",
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options=[
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"ar",
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"az",
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"bg",
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"bn",
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"ca",
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"cs",
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"da",
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"de",
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"el",
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"en",
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"es",
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"fa",
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"fi",
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"fr",
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"he",
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"hi",
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"hr",
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"ht",
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"hu",
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"id",
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"is",
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"it",
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"ja",
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"ko",
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"la",
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"lt",
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"ms",
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"ne",
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"nl",
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"no",
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"pa",
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"pl",
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"pt",
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"ro",
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"ru",
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"sa",
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"sk",
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"sr",
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"sv",
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"sw",
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"ta",
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"te",
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"th",
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"tl",
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"tr",
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"uk",
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"ur",
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"vi",
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"yue",
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"zh",
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"unknown",
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],
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),
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io.Combo.Input(
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"keyscale",
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options=[
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f"{root} {quality}"
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for quality in ["major", "minor"]
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for root in [
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"C",
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"C#",
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"Db",
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"D",
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"D#",
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"Eb",
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"E",
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"F",
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"F#",
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"Gb",
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"G",
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"G#",
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"Ab",
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"A",
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"A#",
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"Bb",
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"B",
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]
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],
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),
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io.Boolean.Input(
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"generate_audio_codes",
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default=True,
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tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.",
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advanced=True,
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),
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io.Float.Input(
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"cfg_scale",
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default=2.0,
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min=0.0,
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max=100.0,
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step=0.1,
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advanced=True,
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),
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io.Float.Input(
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"temperature",
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default=0.85,
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min=0.0,
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max=2.0,
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step=0.01,
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advanced=True,
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),
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io.Float.Input(
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"top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True
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),
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io.Int.Input("top_k", default=0, min=0, max=100, advanced=True),
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io.Float.Input("min_p", default=0.000, min=0.0, max=1.0, step=0.001, advanced=True),
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io.Float.Input(
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"min_p", default=0.000, min=0.0, max=1.0, step=0.001, advanced=True
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),
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],
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outputs=[io.Conditioning.Output()],
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)
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@classmethod
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def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k, min_p) -> io.NodeOutput:
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tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed, generate_audio_codes=generate_audio_codes, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p)
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def execute(
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cls,
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clip,
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tags,
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lyrics,
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seed,
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bpm,
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duration,
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timesignature,
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language,
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keyscale,
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generate_audio_codes,
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cfg_scale,
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temperature,
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top_p,
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top_k,
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min_p,
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) -> io.NodeOutput:
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tokens = clip.tokenize(
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tags,
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lyrics=lyrics,
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bpm=bpm,
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duration=duration,
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timesignature=int(timesignature),
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language=language,
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keyscale=keyscale,
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seed=seed,
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generate_audio_codes=generate_audio_codes,
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cfg_scale=cfg_scale,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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min_p=min_p,
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)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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return io.NodeOutput(conditioning)
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@ -71,7 +219,11 @@ class EmptyAceStepLatentAudio(io.ComfyNode):
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inputs=[
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io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
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io.Int.Input(
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"batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
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"batch_size",
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default=1,
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min=1,
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max=4096,
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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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@ -80,7 +232,10 @@ class EmptyAceStepLatentAudio(io.ComfyNode):
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@classmethod
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def execute(cls, seconds, batch_size) -> io.NodeOutput:
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length = int(seconds * 44100 / 512 / 8)
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latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device())
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latent = torch.zeros(
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[batch_size, 8, 16, length],
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device=comfy.model_management.intermediate_device(),
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)
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return io.NodeOutput({"samples": latent, "type": "audio"})
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@ -92,9 +247,15 @@ class EmptyAceStep15LatentAudio(io.ComfyNode):
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display_name="Empty Ace Step 1.5 Latent Audio",
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category="latent/audio",
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inputs=[
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io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01),
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io.Float.Input(
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"seconds", default=120.0, min=1.0, max=1000.0, step=0.01
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),
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io.Int.Input(
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"batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
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"batch_size",
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default=1,
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min=1,
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max=4096,
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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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@ -103,9 +264,13 @@ class EmptyAceStep15LatentAudio(io.ComfyNode):
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@classmethod
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def execute(cls, seconds, batch_size) -> io.NodeOutput:
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length = round((seconds * 48000 / 1920))
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latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
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latent = torch.zeros(
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[batch_size, 64, length],
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device=comfy.model_management.intermediate_device(),
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)
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return io.NodeOutput({"samples": latent, "type": "audio"})
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class ReferenceAudio(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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@ -121,15 +286,20 @@ class ReferenceAudio(io.ComfyNode):
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],
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outputs=[
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io.Conditioning.Output(),
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]
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],
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)
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@classmethod
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def execute(cls, conditioning, latent=None) -> io.NodeOutput:
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if latent is not None:
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conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_audio_timbre_latents": [latent["samples"]]}, append=True)
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conditioning = node_helpers.conditioning_set_values(
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conditioning,
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{"reference_audio_timbre_latents": [latent["samples"]]},
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append=True,
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)
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return io.NodeOutput(conditioning)
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class AceExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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@ -141,5 +311,6 @@ class AceExtension(ComfyExtension):
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ReferenceAudio,
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]
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async def comfy_entrypoint() -> AceExtension:
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return AceExtension()
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