feat: init
This commit is contained in:
Sayo
@@ -0,0 +1,19 @@
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name: Publish to Comfy Registry
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on:
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push:
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branches:
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- main
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- master
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paths:
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- "pyproject.toml"
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workflow_dispatch:
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jobs:
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publish:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: Comfy-Org/publish-node-action@main
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with:
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personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
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@@ -0,0 +1,3 @@
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.config/
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__pycache__/
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*.pyc
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@@ -0,0 +1,21 @@
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MIT License
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Copyright (c) 2026 ACE-Step
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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+13
@@ -0,0 +1,13 @@
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from .nodes import AceStepMusicGen
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NODE_CLASS_MAPPINGS = {
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"AceStepMusicGen": AceStepMusicGen,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AceStepMusicGen": "ACE-Step Music Generate",
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}
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WEB_DIRECTORY = "./js"
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
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@@ -0,0 +1,86 @@
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import { app } from "../../scripts/app.js";
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function hideWidget(node, widget) {
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if (widget._hidden) return;
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widget._hidden = true;
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widget._origType = widget.type;
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widget._origComputeSize = widget.computeSize;
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widget.type = "hidden";
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widget.computeSize = () => [0, -4];
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}
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function showWidget(node, widget) {
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if (!widget._hidden) return;
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widget._hidden = false;
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widget.type = widget._origType;
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widget.computeSize = widget._origComputeSize;
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delete widget._origType;
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delete widget._origComputeSize;
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}
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app.registerExtension({
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name: "AceStep.NodeExtensions",
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nodeCreated(node) {
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if (node.comfyClass !== "AceStepMusicGen") return;
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const REPAINT_WIDGETS = ["repainting_start", "repainting_end"];
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const COVER_WIDGETS = ["audio_cover_strength"];
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function updateVisibility() {
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const taskWidget = node.widgets.find(w => w.name === "task_type");
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if (!taskWidget) return;
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const task = taskWidget.value;
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for (const w of node.widgets) {
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if (REPAINT_WIDGETS.includes(w.name)) {
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task === "repaint" ? showWidget(node, w) : hideWidget(node, w);
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} else if (COVER_WIDGETS.includes(w.name)) {
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task === "cover" ? showWidget(node, w) : hideWidget(node, w);
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}
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}
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const sz = node.computeSize();
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node.setSize([Math.max(sz[0], node.size[0]), sz[1]]);
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}
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requestAnimationFrame(() => {
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const taskWidget = node.widgets.find(w => w.name === "task_type");
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if (taskWidget) {
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const origCallback = taskWidget.callback;
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taskWidget.callback = function (...args) {
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if (origCallback) origCallback.apply(this, args);
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updateVisibility();
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};
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}
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updateVisibility();
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});
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// --- API Key masking ---
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const apiKeyWidget = node.widgets.find(w => w.name === "api_key");
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if (apiKeyWidget) {
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let realKey = "";
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// Return real key for execution, not the masked display value
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apiKeyWidget.serializeValue = async () => realKey;
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apiKeyWidget.callback = function (value) {
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if (!value) {
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realKey = "";
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return;
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}
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// Ignore if user submitted the mask unchanged
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if (/^●+$/.test(value)) return;
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realKey = value;
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apiKeyWidget.value = "●".repeat(Math.min(value.length, 16));
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};
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}
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// --- Get API Key button ---
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const btn = node.addWidget("button", "🔑 Get API Key", null, () => {
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window.open("https://acemusic.ai/api-key", "_blank");
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});
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btn.serialize = false;
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},
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});
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@@ -0,0 +1,387 @@
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import json
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import base64
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import io
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import struct
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import wave
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import os
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import numpy as np
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import torch
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import requests
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_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".config")
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_API_KEY_FILE = os.path.join(_CONFIG_DIR, "api_key")
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def _save_api_key(key):
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os.makedirs(_CONFIG_DIR, exist_ok=True)
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with open(_API_KEY_FILE, "w", encoding="utf-8") as f:
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f.write(key)
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def _load_api_key():
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try:
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with open(_API_KEY_FILE, "r", encoding="utf-8") as f:
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return f.read().strip()
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except FileNotFoundError:
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return ""
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class AceStepMusicGen:
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"""Generate music via ACE-Step OpenRouter-compatible API server."""
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CATEGORY = "api node/audio/AceStep"
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FUNCTION = "generate"
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RETURN_TYPES = ("AUDIO", "STRING")
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RETURN_NAMES = ("audio", "info")
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"server_url": ("STRING", {
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"default": "https://api.acemusic.ai",
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"tooltip": "ACE-Step API server base URL",
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}),
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"prompt": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "Music description / caption",
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}),
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},
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"optional": {
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"lyrics": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "Song lyrics (leave empty for instrumental)",
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}),
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"task_type": (["text2music", "cover", "repaint"], {
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"default": "text2music",
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}),
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"instrumental": ("BOOLEAN", {
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"default": False,
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"tooltip": "Generate instrumental only (no vocals)",
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}),
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"duration": ("FLOAT", {
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"default": 30.0,
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"min": 1.0,
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"max": 300.0,
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"step": 1.0,
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"tooltip": "Audio duration in seconds",
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}),
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"bpm": ("INT", {
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"default": 0,
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"min": 0,
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"max": 300,
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"step": 1,
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"tooltip": "Beats per minute (0 = auto)",
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}),
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"key_scale": ("STRING", {
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"default": "",
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"tooltip": "Musical key and scale (e.g. 'C major', 'A minor')",
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}),
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"time_signature": ("STRING", {
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"default": "",
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"tooltip": "Time signature (e.g. '4/4', '3/4', '6/8')",
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}),
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"vocal_language": (["en", "zh", "ja", "ko", "es", "fr", "de", "unknown"], {
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"default": "en",
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}),
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"sample_mode": ("BOOLEAN", {
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"default": False,
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"tooltip": "Use LLM to generate prompt/lyrics from query",
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}),
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"use_format": ("BOOLEAN", {
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"default": False,
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"tooltip": "Use LLM to enhance caption and lyrics",
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}),
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"batch_size": ("INT", {
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"default": 1,
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"min": 1,
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"max": 2,
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"step": 1,
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}),
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"seed": ("STRING", {
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"default": "-1",
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"tooltip": "Random seed (-1 = random). Comma-separated for batch (e.g. '42,123')",
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}),
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"temperature": ("FLOAT", {
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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": 0.05,
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}),
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"guidance_scale": ("FLOAT", {
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"default": 7.0,
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"min": 0.0,
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"max": 20.0,
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"step": 0.5,
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}),
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"src_audio": ("AUDIO", {
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"tooltip": "Source audio for cover/repaint tasks",
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}),
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"ref_audio": ("AUDIO", {
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"tooltip": "Reference audio for style/timbre guidance",
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}),
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"repainting_start": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 300.0,
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"step": 0.1,
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"tooltip": "Repaint region start time in seconds",
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}),
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"repainting_end": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 300.0,
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"step": 0.1,
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"tooltip": "Repaint region end time in seconds (0 = full length)",
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}),
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"audio_cover_strength": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.05,
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"tooltip": "Cover strength (1.0 = full cover)",
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}),
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"api_key": ("STRING", {
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"default": "",
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"tooltip": "API key (auto-saved locally after first input, also reads ACESTEP_API_KEY env var)",
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}),
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},
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}
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return float("NaN")
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def generate(
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self,
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server_url,
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prompt,
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lyrics="",
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task_type="text2music",
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instrumental=False,
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duration=30.0,
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bpm=0,
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key_scale="",
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time_signature="",
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vocal_language="en",
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sample_mode=False,
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use_format=False,
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batch_size=1,
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seed="-1",
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temperature=0.85,
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guidance_scale=7.0,
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src_audio=None,
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ref_audio=None,
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repainting_start=0.0,
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repainting_end=0.0,
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audio_cover_strength=1.0,
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api_key="",
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):
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url = server_url.rstrip("/") + "/v1/chat/completions"
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# Build message content with audio inputs
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# API routing: cover/repaint → audio[0]=src, audio[1]=ref; text2music → audio[0]=ref
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audio_list = []
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if task_type in ("cover", "repaint"):
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if src_audio is not None:
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audio_list.append(src_audio)
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if ref_audio is not None:
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audio_list.append(ref_audio)
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else:
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if ref_audio is not None:
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audio_list.append(ref_audio)
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content = self._build_content(prompt, audio_list)
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# Build request body
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body = {
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"model": "acemusic/acestep-v15-turbo",
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"messages": [{"role": "user", "content": content}],
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"modalities": ["audio"],
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"stream": False,
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"temperature": temperature,
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"guidance_scale": guidance_scale,
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"batch_size": batch_size,
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"task_type": task_type,
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"sample_mode": sample_mode,
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"use_format": use_format,
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"audio_config": {
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"duration": duration,
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"format": "wav",
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"vocal_language": vocal_language,
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"instrumental": instrumental,
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},
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}
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# Seed: "-1" means random, otherwise pass as-is (supports comma-separated)
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seed_str = seed.strip()
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if seed_str and seed_str != "-1":
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body["seed"] = seed_str
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if bpm > 0:
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body["audio_config"]["bpm"] = bpm
|
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body["audio_config"]["key_scale"] = key_scale.strip() or None
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body["audio_config"]["time_signature"] = time_signature.strip() or None
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if lyrics:
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body["lyrics"] = lyrics
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# Repaint/cover specific parameters (only when relevant)
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if task_type == "repaint":
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body["repainting_start"] = repainting_start
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if repainting_end > 0.0:
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body["repainting_end"] = repainting_end
|
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if task_type == "cover":
|
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body["audio_cover_strength"] = audio_cover_strength
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|
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# HTTP request – resolve API key: widget input > env var > saved config
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resolved_key = api_key.strip() if api_key else ""
|
||||
if resolved_key:
|
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_save_api_key(resolved_key)
|
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os.environ["ACESTEP_API_KEY"] = resolved_key
|
||||
else:
|
||||
resolved_key = os.environ.get("ACESTEP_API_KEY", "") or _load_api_key()
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
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"User-Agent": "AceMusic-ComfyUI/1.0",
|
||||
}
|
||||
if resolved_key:
|
||||
headers["Authorization"] = f"Bearer {resolved_key}"
|
||||
|
||||
try:
|
||||
resp = requests.post(
|
||||
url,
|
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data=json.dumps(body, ensure_ascii=False).encode("utf-8"),
|
||||
headers=headers,
|
||||
timeout=600,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
result = resp.json()
|
||||
except requests.exceptions.HTTPError as e:
|
||||
raise RuntimeError(f"API error {resp.status_code}: {resp.text}")
|
||||
except requests.exceptions.ConnectionError as e:
|
||||
raise RuntimeError(f"Cannot connect to {server_url}: {e}")
|
||||
except requests.exceptions.Timeout:
|
||||
raise RuntimeError(f"Request to {server_url} timed out")
|
||||
|
||||
# Parse response
|
||||
choices = result.get("choices", [])
|
||||
if not choices:
|
||||
raise RuntimeError("API returned no choices")
|
||||
|
||||
message = choices[0].get("message", {})
|
||||
text_content = message.get("content", "")
|
||||
audio_items = message.get("audio", [])
|
||||
|
||||
if not audio_items:
|
||||
raise RuntimeError(f"API returned no audio. Response: {text_content}")
|
||||
|
||||
# Decode all audio items and batch them
|
||||
waveforms = []
|
||||
sample_rate = None
|
||||
for item in audio_items:
|
||||
audio_url = item.get("audio_url", {}).get("url", "")
|
||||
if not audio_url:
|
||||
continue
|
||||
wf, sr = self._decode_audio_data_url(audio_url)
|
||||
# wf shape: (1, channels, samples)
|
||||
waveforms.append(wf)
|
||||
sample_rate = sr
|
||||
|
||||
if not waveforms:
|
||||
raise RuntimeError("API returned no valid audio data")
|
||||
|
||||
# Pad to same length and concat along batch dim
|
||||
max_len = max(w.shape[-1] for w in waveforms)
|
||||
padded = []
|
||||
for wf in waveforms:
|
||||
if wf.shape[-1] < max_len:
|
||||
pad_size = max_len - wf.shape[-1]
|
||||
wf = torch.nn.functional.pad(wf, (0, pad_size))
|
||||
padded.append(wf)
|
||||
|
||||
# (N, channels, samples)
|
||||
audio_tensor = torch.cat(padded, dim=0)
|
||||
|
||||
return (
|
||||
{"waveform": audio_tensor, "sample_rate": sample_rate},
|
||||
text_content,
|
||||
)
|
||||
|
||||
def _build_content(self, prompt, audio_list):
|
||||
"""Build message content, optionally with multimodal audio inputs."""
|
||||
if not audio_list:
|
||||
return prompt
|
||||
|
||||
# Multimodal: text + input_audio(s)
|
||||
parts = []
|
||||
|
||||
if prompt:
|
||||
parts.append({"type": "text", "text": prompt})
|
||||
|
||||
for audio in audio_list:
|
||||
waveform = audio["waveform"].squeeze(0) # (channels, samples)
|
||||
sr = audio["sample_rate"]
|
||||
b64 = self._tensor_to_wav_b64(waveform, sr)
|
||||
parts.append({
|
||||
"type": "input_audio",
|
||||
"input_audio": {"data": b64, "format": "wav"},
|
||||
})
|
||||
|
||||
return parts
|
||||
|
||||
def _decode_audio_data_url(self, data_url):
|
||||
"""Decode base64 data URL to torch audio tensor using stdlib wave."""
|
||||
if "," in data_url:
|
||||
b64_data = data_url.split(",", 1)[1]
|
||||
else:
|
||||
b64_data = data_url
|
||||
|
||||
audio_bytes = base64.b64decode(b64_data)
|
||||
buf = io.BytesIO(audio_bytes)
|
||||
|
||||
with wave.open(buf, "rb") as wf:
|
||||
n_channels = wf.getnchannels()
|
||||
sampwidth = wf.getsampwidth()
|
||||
sample_rate = wf.getframerate()
|
||||
n_frames = wf.getnframes()
|
||||
raw = wf.readframes(n_frames)
|
||||
|
||||
# Use numpy for fast PCM decoding
|
||||
if sampwidth == 2:
|
||||
samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
|
||||
elif sampwidth == 4:
|
||||
samples = np.frombuffer(raw, dtype=np.int32).astype(np.float32) / 2147483648.0
|
||||
else:
|
||||
raise RuntimeError(f"Unsupported WAV sample width: {sampwidth}")
|
||||
|
||||
# Interleaved -> (n_frames, n_channels) -> (n_channels, n_frames)
|
||||
samples = samples.reshape(n_frames, n_channels).T
|
||||
# Add batch dim: (1, n_channels, n_frames)
|
||||
tensor = torch.from_numpy(samples).unsqueeze(0)
|
||||
return tensor, sample_rate
|
||||
|
||||
def _tensor_to_wav_b64(self, waveform, sample_rate):
|
||||
"""Encode torch tensor to WAV base64 string using stdlib wave."""
|
||||
# waveform shape: (channels, samples)
|
||||
n_channels = waveform.shape[0]
|
||||
n_frames = waveform.shape[1]
|
||||
|
||||
# Clamp and convert to 16-bit PCM
|
||||
clamped = waveform.clamp(-1.0, 1.0)
|
||||
pcm = (clamped * 32767).to(torch.int16)
|
||||
|
||||
# Interleave channels: (channels, samples) -> (samples * channels,)
|
||||
interleaved = pcm.T.contiguous().reshape(-1)
|
||||
raw = struct.pack(f"<{interleaved.numel()}h", *interleaved.tolist())
|
||||
|
||||
buf = io.BytesIO()
|
||||
with wave.open(buf, "wb") as wf:
|
||||
wf.setnchannels(n_channels)
|
||||
wf.setsampwidth(2)
|
||||
wf.setframerate(sample_rate)
|
||||
wf.writeframes(raw)
|
||||
|
||||
return base64.b64encode(buf.getvalue()).decode("utf-8")
|
||||
@@ -0,0 +1,28 @@
|
||||
[project]
|
||||
name = "ACE-Step-ComfyUI"
|
||||
description = "Official ComfyUI node for ACE-Step AI music generation via ACE-Step API"
|
||||
version = "0.0.1"
|
||||
license = {file = "LICENSE"}
|
||||
dependencies = [
|
||||
"torch",
|
||||
"numpy",
|
||||
"requests",
|
||||
]
|
||||
classifiers = [
|
||||
"Operating System :: OS Independent",
|
||||
]
|
||||
|
||||
|
||||
[project.urls]
|
||||
Repository = "https://github.com/ace-step/ACE-Step-ComfyUI"
|
||||
# Used by Comfy Registry https://registry.comfy.org
|
||||
Documentation = "https://github.com/ace-step/ACE-Step-ComfyUI/wiki"
|
||||
"Bug Tracker" = "https://github.com/ace-step/ACE-Step-ComfyUI/issues"
|
||||
|
||||
[tool.comfy]
|
||||
PublisherId = "ace-step"
|
||||
DisplayName = "ACE-Step-ComfyUI"
|
||||
Icon = ""
|
||||
includes = ["__init__.py", "nodes.py", "requirements.txt", "js/"]
|
||||
# "requires-comfyui" = ">=1.0.0" # ComfyUI version compatibility
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
torch
|
||||
numpy
|
||||
requests
|
||||
Reference in New Issue
Block a user