This workflow turns a plain-language text prompt into finished music using the ACE-Step 1.5 XL Turbo model. Your prompt is encoded by TextEncodeAceStepAudio1.5 (backed by DualCLIPLoader) into audio-aware conditioning. EmptyAceStep1.5LatentAudio creates a blank latent audio canvas at your chosen length, and UNETLoader loads the distilled 4B ACE-Step checkpoint (acestep_v1.5_xl_turbo_bf16.safetensors). ModelSamplingAuraFlow configures the sampler schedule tuned for ACE-Step, and KSampler performs just 8 diffusion steps to synthesize the latent audio. The audio is then reconstructed with VAELoader + VAEDecodeAudio and saved via SaveAudioMP3.
What makes this workflow practical is its “turbo” configuration: no classifier-free guidance (CFG) is used, enabled by ConditioningZeroOut feeding an intentionally empty negative conditioning. This keeps prompts straightforward (focus only on what you want), while ModelSamplingAuraFlow and KSampler deliver fast, stable results. The graph is organized into Model and Prompt groups for clarity, and uses PrimitiveInt/PrimitiveNode for quick edits to seed, steps, and duration. The result is a tight, teachable text-to-music pipeline you can run repeatedly for variations or integrate as a reusable subgraph.
API
코드에서 이 워크플로우 사용하기
모든 Comfy 워크플로우는 JSON 그래프입니다. 아래 페이로드는 이 워크플로우 그 자체로, ComfyUI에서 실행되는 것과 동일합니다 — URL에서 가져오거나, 버전 관리에 보관하거나, ComfyUI에서 불러와 노드별로 실행할 수 있습니다.
Comfy SDK로 TypeScript 또는 Python에서 실행하세요. 동일한 코드는 Comfy Cloud 또는 직접 호스팅하는 ComfyUI 모두에 적용됩니다 — 기본 URL만 다릅니다.
// Install (beta)
npm i @comfyorg/sdk
// Run "ACE-Step 1.5XL Turbo: 텍스트를 음악으로" (TypeScript)
import { Comfy } from "@comfyorg/sdk";
const client = new Comfy({ apiKey: "comfyui-..." });
// This workflow, exported in API format (see note below)
const wf = await client.workflows.fromFile("audio_ace_step1_5_xl_turbo_api.json");
const job = await client.run(wf);
await job.getOutputs("107")[0].toFile("output.png"); // SaveAudioMP3SDK는 API 형식의 워크플로우를 사용합니다: 이 워크플로우를 ComfyUI에서 열고 파일 → 워크플로우 내보내기(API)를 사용하세요.
FAQ
