docs/en/API.md

ACE-Step API Client Documentation

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This service provides an HTTP-based asynchronous music generation API.

Basic Workflow:

1. Call POST /release_task to submit a task and obtain a task_id.
2. Call POST /query_result to batch query task status until status is 1 (succeeded) or 2 (failed).
3. Download audio files via GET /v1/audio?path=... URLs returned in the result.

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Table of Contents

• Authentication
• Response Format
• Task Status Description
• Create Generation Task
• Batch Query Task Results
• Format Input
• Get Random Sample
• List Available Models
• Server Statistics
• Download Audio Files
• Health Check
• Environment Variables

---

1. Authentication

The API supports optional API key authentication. When enabled, a valid key must be provided in requests.

Authentication Methods

Two authentication methods are supported:

Method A: ai_token in request body

{
  "ai_token": "your-api-key",
  "prompt": "upbeat pop song",
  ...
}

Method B: Authorization header

curl -X POST http://localhost:8001/release_task \
  -H 'Authorization: Bearer your-api-key' \
  -H 'Content-Type: application/json' \
  -d '{"prompt": "upbeat pop song"}'

Configuring API Key

Set via environment variable or command-line argument:

# Environment variable
export ACESTEP_API_KEY=your-secret-key

# Or command-line argument
python -m acestep.api_server --api-key your-secret-key

---

2. Response Format

All API responses use a unified wrapper format:

{
  "data": { ... },
  "code": 200,
  "error": null,
  "timestamp": 1700000000000,
  "extra": null
}

Field	Type	Description
data	any	Actual response data
code	int	Status code (200=success)
error	string	Error message (null on success)
timestamp	int	Response timestamp (milliseconds)
extra	any	Extra information (usually null)

---

3. Task Status Description

Task status (status) is represented as integers:

Status Code	Status Name	Description
0	queued/running	Task is queued or in progress
1	succeeded	Generation succeeded, result is ready
2	failed	Generation failed

---

4. Create Generation Task

4.1 API Definition

• URL: /release_task
• Method: POST
• Content-Type: application/json, multipart/form-data, or application/x-www-form-urlencoded

4.2 Request Parameters

Parameter Naming Convention

The API supports both snake_case and camelCase naming for most parameters. For example:

• audio_duration / duration / audioDuration
• key_scale / keyscale / keyScale
• time_signature / timesignature / timeSignature
• sample_query / sampleQuery / description / desc
• use_format / useFormat / format

Additionally, metadata can be passed in a nested object (metas, metadata, or user_metadata).

Method A: JSON Request (application/json)

Suitable for passing only text parameters, or referencing audio file paths that already exist on the server.

Basic Parameters:

Parameter Name	Type	Default	Description
prompt	string	""	Music description prompt (alias: caption)
lyrics	string	""	Lyrics content
thinking	bool	false	Whether to use 5Hz LM to generate audio codes (lm-dit behavior)
vocal_language	string	"en"	Lyrics language (en, zh, ja, etc.)
audio_format	string	"mp3"	Output format (mp3, wav, flac)

Sample/Description Mode Parameters:

Parameter Name	Type	Default	Description
sample_mode	bool	false	Enable random sample generation mode (auto-generates caption/lyrics/metas via LM)
sample_query	string	""	Natural language description for sample generation (e.g., "a soft Bengali love song"). Aliases: description, desc
use_format	bool	false	Use LM to enhance/format the provided caption and lyrics. Alias: format

Multi-Model Support:

Parameter Name	Type	Default	Description
model	string	null	Select which DiT model to use (e.g., "acestep-v15-turbo", "acestep-v15-turbo-shift3"). Use /v1/models to list available models. If not specified, uses the default model.

thinking Semantics (Important):

• thinking=false:
  • The server will NOT use 5Hz LM to generate audio_code_string.
  • DiT runs in text2music mode and ignores any provided audio_code_string.
• thinking=true:
  • The server will use 5Hz LM to generate audio_code_string (lm-dit behavior).
  • DiT runs with LM-generated codes for enhanced music quality.

Metadata Auto-Completion (Conditional):

When use_cot_caption=true or use_cot_language=true or metadata fields are missing, the server may call 5Hz LM to fill the missing fields based on caption/lyrics:

• bpm
• key_scale
• time_signature
• audio_duration

User-provided values always win; LM only fills the fields that are empty/missing.

Music Attribute Parameters:

Parameter Name	Type	Default	Description
bpm	int	null	Specify tempo (BPM), range 30-300
key_scale	string	""	Key/scale (e.g., "C Major", "Am"). Aliases: keyscale, keyScale
time_signature	string	""	Time signature (2, 3, 4, 6 for 2/4, 3/4, 4/4, 6/8). Aliases: timesignature, timeSignature
audio_duration	float	null	Generation duration (seconds), range 10-600. Aliases: duration, target_duration

Audio Codes (Optional):

Parameter Name	Type	Default	Description
audio_code_string	string or string[]	""	Audio semantic tokens (5Hz) for llm_dit. Alias: audioCodeString

Generation Control Parameters:

Parameter Name	Type	Default	Description
inference_steps	int	8	Number of inference steps. Turbo model: 1-20 (recommended 8). Base model: 1-200 (recommended 32-64).
guidance_scale	float	7.0	Prompt guidance coefficient. Only effective for base model.
use_random_seed	bool	true	Whether to use random seed
seed	int	-1	Specify seed (when use_random_seed=false)
batch_size	int	2	Batch generation count (max 8)

Advanced DiT Parameters:

Parameter Name	Type	Default	Description
shift	float	3.0	Timestep shift factor (range 1.0-5.0). Only effective for base models, not turbo models.
infer_method	string	"ode"	Diffusion inference method: "ode" (Euler, faster) or "sde" (stochastic).
timesteps	string	null	Custom timesteps as comma-separated values (e.g., "0.97,0.76,0.615,0.5,0.395,0.28,0.18,0.085,0"). Overrides inference_steps and shift.
use_adg	bool	false	Use Adaptive Dual Guidance (base model only)
cfg_interval_start	float	0.0	CFG application start ratio (0.0-1.0)
cfg_interval_end	float	1.0	CFG application end ratio (0.0-1.0)

5Hz LM Parameters (Optional, server-side):

These parameters control 5Hz LM sampling, used for metadata auto-completion and (when thinking=true) codes generation.

Parameter Name	Type	Default	Description
lm_model_path	string	null	5Hz LM checkpoint dir name (e.g. acestep-5Hz-lm-0.6B)
lm_backend	string	"vllm"	vllm or pt
lm_temperature	float	0.85	Sampling temperature
lm_cfg_scale	float	2.5	CFG scale (>1 enables CFG)
lm_negative_prompt	string	"NO USER INPUT"	Negative prompt used by CFG
lm_top_k	int	null	Top-k (0/null disables)
lm_top_p	float	0.9	Top-p (>=1 will be treated as disabled)
lm_repetition_penalty	float	1.0	Repetition penalty

LM CoT (Chain-of-Thought) Parameters:

Parameter Name	Type	Default	Description
use_cot_caption	bool	true	Let LM rewrite/enhance the input caption via CoT reasoning. Aliases: cot_caption, cot-caption
use_cot_language	bool	true	Let LM detect vocal language via CoT. Aliases: cot_language, cot-language
constrained_decoding	bool	true	Enable FSM-based constrained decoding for structured LM output. Aliases: constrainedDecoding, constrained
constrained_decoding_debug	bool	false	Enable debug logging for constrained decoding
allow_lm_batch	bool	true	Allow LM batch processing for efficiency

Edit/Reference Audio Parameters (requires absolute path on server):

Parameter Name	Type	Default	Description
reference_audio_path	string	null	Reference audio path (Style Transfer)
src_audio_path	string	null	Source audio path (Repainting/Cover)
task_type	string	"text2music"	Task type: text2music, cover, repaint, lego, extract, complete
instruction	string	auto	Edit instruction (auto-generated based on task_type if not provided)
repainting_start	float	0.0	Repainting start time (seconds)
repainting_end	float	null	Repainting end time (seconds), -1 for end of audio
audio_cover_strength	float	1.0	Cover strength (0.0-1.0). Lower values (0.2) for style transfer.

Method B: File Upload (multipart/form-data)

Use this when you need to upload local audio files as reference or source audio.

In addition to supporting all the above fields as Form Fields, the following file fields are also supported:

• reference_audio or ref_audio: (File) Upload reference audio file
• src_audio or ctx_audio: (File) Upload source audio file

Note: After uploading files, the corresponding _path parameters will be automatically ignored, and the system will use the temporary file path after upload.

4.3 Response Example

{
  "data": {
    "task_id": "550e8400-e29b-41d4-a716-446655440000",
    "status": "queued",
    "queue_position": 1
  },
  "code": 200,
  "error": null,
  "timestamp": 1700000000000,
  "extra": null
}

4.4 Usage Examples (cURL)

Basic JSON Method:

curl -X POST http://localhost:8001/release_task \
  -H 'Content-Type: application/json' \
  -d '{
    "prompt": "upbeat pop song",
    "lyrics": "Hello world",
    "inference_steps": 8
  }'

With thinking=true (LM generates codes + fills missing metas):

curl -X POST http://localhost:8001/release_task \
  -H 'Content-Type: application/json' \
  -d '{
    "prompt": "upbeat pop song",
    "lyrics": "Hello world",
    "thinking": true,
    "lm_temperature": 0.85,
    "lm_cfg_scale": 2.5
  }'

Description-driven generation (sample_query):

curl -X POST http://localhost:8001/release_task \
  -H 'Content-Type: application/json' \
  -d '{
    "sample_query": "a soft Bengali love song for a quiet evening",
    "thinking": true
  }'

With format enhancement (use_format=true):

curl -X POST http://localhost:8001/release_task \
  -H 'Content-Type: application/json' \
  -d '{
    "prompt": "pop rock",
    "lyrics": "[Verse 1]\nWalking down the street...",
    "use_format": true,
    "thinking": true
  }'

Select specific model:

curl -X POST http://localhost:8001/release_task \
  -H 'Content-Type: application/json' \
  -d '{
    "prompt": "electronic dance music",
    "model": "acestep-v15-turbo",
    "thinking": true
  }'

With custom timesteps:

curl -X POST http://localhost:8001/release_task \
  -H 'Content-Type: application/json' \
  -d '{
    "prompt": "jazz piano trio",
    "timesteps": "0.97,0.76,0.615,0.5,0.395,0.28,0.18,0.085,0",
    "thinking": true
  }'

File Upload Method:

curl -X POST http://localhost:8001/release_task \
  -F "prompt=remix this song" \
  -F "src_audio=@/path/to/local/song.mp3" \
  -F "task_type=repaint"

---

5. Batch Query Task Results

5.1 API Definition

• URL: /query_result
• Method: POST
• Content-Type: application/json or application/x-www-form-urlencoded

5.2 Request Parameters

Parameter Name	Type	Description
task_id_list	string (JSON array) or array	List of task IDs to query

5.3 Response Example

{
  "data": [
    {
      "task_id": "550e8400-e29b-41d4-a716-446655440000",
      "status": 1,
      "result": "[{\"file\": \"/v1/audio?path=...\", \"wave\": \"\", \"status\": 1, \"create_time\": 1700000000, \"env\": \"development\", \"prompt\": \"upbeat pop song\", \"lyrics\": \"Hello world\", \"metas\": {\"bpm\": 120, \"duration\": 30, \"genres\": \"\", \"keyscale\": \"C Major\", \"timesignature\": \"4\"}, \"generation_info\": \"...\", \"seed_value\": \"12345,67890\", \"lm_model\": \"acestep-5Hz-lm-0.6B\", \"dit_model\": \"acestep-v15-turbo\"}]"
    }
  ],
  "code": 200,
  "error": null,
  "timestamp": 1700000000000,
  "extra": null
}

Result Field Description (result is a JSON string, after parsing contains):

Field	Type	Description
file	string	Audio file URL (use with /v1/audio endpoint)
wave	string	Waveform data (usually empty)
status	int	Status code (0=in progress, 1=success, 2=failed)
create_time	int	Creation time (Unix timestamp)
env	string	Environment identifier
prompt	string	Prompt used
lyrics	string	Lyrics used
metas	object	Metadata (bpm, duration, genres, keyscale, timesignature)
generation_info	string	Generation info summary
seed_value	string	Seed values used (comma-separated)
lm_model	string	LM model name used
dit_model	string	DiT model name used

5.4 Usage Example

curl -X POST http://localhost:8001/query_result \
  -H 'Content-Type: application/json' \
  -d '{
    "task_id_list": ["550e8400-e29b-41d4-a716-446655440000"]
  }'

---

6. Format Input

6.1 API Definition

• URL: /format_input
• Method: POST

This endpoint uses LLM to enhance and format user-provided caption and lyrics.

6.2 Request Parameters

Parameter Name	Type	Default	Description
prompt	string	""	Music description prompt
lyrics	string	""	Lyrics content
temperature	float	0.85	LM sampling temperature
param_obj	string (JSON)	"{}"	JSON object containing metadata (duration, bpm, key, time_signature, language)

6.3 Response Example

{
  "data": {
    "caption": "Enhanced music description",
    "lyrics": "Formatted lyrics...",
    "bpm": 120,
    "key_scale": "C Major",
    "time_signature": "4",
    "duration": 180,
    "vocal_language": "en"
  },
  "code": 200,
  "error": null,
  "timestamp": 1700000000000,
  "extra": null
}

6.4 Usage Example

curl -X POST http://localhost:8001/format_input \
  -H 'Content-Type: application/json' \
  -d '{
    "prompt": "pop rock",
    "lyrics": "Walking down the street",
    "param_obj": "{\"duration\": 180, \"language\": \"en\"}"
  }'

---

7. Get Random Sample

7.1 API Definition

• URL: /create_random_sample
• Method: POST

This endpoint returns random sample parameters from pre-loaded example data for form filling.

7.2 Request Parameters

Parameter Name	Type	Default	Description
sample_type	string	"simple_mode"	Sample type: "simple_mode" or "custom_mode"

7.3 Response Example

{
  "data": {
    "caption": "Upbeat pop song with guitar accompaniment",
    "lyrics": "[Verse 1]\nSunshine on my face...",
    "bpm": 120,
    "key_scale": "G Major",
    "time_signature": "4",
    "duration": 180,
    "vocal_language": "en"
  },
  "code": 200,
  "error": null,
  "timestamp": 1700000000000,
  "extra": null
}

7.4 Usage Example

curl -X POST http://localhost:8001/create_random_sample \
  -H 'Content-Type: application/json' \
  -d '{"sample_type": "simple_mode"}'

---

8. List Available Models

8.1 API Definition

• URL: /v1/models
• Method: GET

Returns a list of available DiT models loaded on the server.

8.2 Response Example

{
  "data": {
    "models": [
      {
        "name": "acestep-v15-turbo",
        "is_default": true
      },
      {
        "name": "acestep-v15-turbo-shift3",
        "is_default": false
      }
    ],
    "default_model": "acestep-v15-turbo"
  },
  "code": 200,
  "error": null,
  "timestamp": 1700000000000,
  "extra": null
}

8.3 Usage Example

curl http://localhost:8001/v1/models

---

9. Server Statistics

9.1 API Definition

• URL: /v1/stats
• Method: GET

Returns server runtime statistics.

9.2 Response Example

{
  "data": {
    "jobs": {
      "total": 100,
      "queued": 5,
      "running": 1,
      "succeeded": 90,
      "failed": 4
    },
    "queue_size": 5,
    "queue_maxsize": 200,
    "avg_job_seconds": 8.5
  },
  "code": 200,
  "error": null,
  "timestamp": 1700000000000,
  "extra": null
}

9.3 Usage Example

curl http://localhost:8001/v1/stats

---

10. Download Audio Files

10.1 API Definition

• URL: /v1/audio
• Method: GET

Download generated audio files by path.

10.2 Request Parameters

Parameter Name	Type	Description
path	string	URL-encoded path to the audio file

10.3 Usage Example

# Download using the URL from task result
curl "http://localhost:8001/v1/audio?path=%2Ftmp%2Fapi_audio%2Fabc123.mp3" -o output.mp3

---

11. Health Check

11.1 API Definition

• URL: /health
• Method: GET

Returns service health status.

11.2 Response Example

{
  "data": {
    "status": "ok",
    "service": "ACE-Step API",
    "version": "1.0"
  },
  "code": 200,
  "error": null,
  "timestamp": 1700000000000,
  "extra": null
}

---

12. Environment Variables

The API server can be configured using environment variables:

Server Configuration

Variable	Default	Description
ACESTEP_API_HOST	127.0.0.1	Server bind host
ACESTEP_API_PORT	8001	Server bind port
ACESTEP_API_KEY	(empty)	API authentication key (empty disables auth)
ACESTEP_API_WORKERS	1	API worker thread count

Model Configuration

Variable	Default	Description
ACESTEP_CONFIG_PATH	acestep-v15-turbo	Primary DiT model path
ACESTEP_CONFIG_PATH2	(empty)	Secondary DiT model path (optional)
ACESTEP_CONFIG_PATH3	(empty)	Third DiT model path (optional)
ACESTEP_DEVICE	auto	Device for model loading
ACESTEP_USE_FLASH_ATTENTION	true	Enable flash attention
ACESTEP_OFFLOAD_TO_CPU	false	Offload models to CPU when idle
ACESTEP_OFFLOAD_DIT_TO_CPU	false	Offload DiT specifically to CPU

LM Configuration

Variable	Default	Description
ACESTEP_INIT_LLM	auto	Whether to initialize LM at startup (auto determines based on GPU)
ACESTEP_LM_MODEL_PATH	acestep-5Hz-lm-0.6B	Default 5Hz LM model
ACESTEP_LM_BACKEND	vllm	LM backend (vllm or pt)
ACESTEP_LM_DEVICE	(same as ACESTEP_DEVICE)	Device for LM
ACESTEP_LM_OFFLOAD_TO_CPU	false	Offload LM to CPU

Queue Configuration

Variable	Default	Description
ACESTEP_QUEUE_MAXSIZE	200	Maximum queue size
ACESTEP_QUEUE_WORKERS	1	Number of queue workers
ACESTEP_AVG_JOB_SECONDS	5.0	Initial average job duration estimate
ACESTEP_AVG_WINDOW	50	Window for averaging job duration

Cache Configuration

Variable	Default	Description
ACESTEP_TMPDIR	.cache/acestep/tmp	Temporary file directory
TRITON_CACHE_DIR	.cache/acestep/triton	Triton cache directory
TORCHINDUCTOR_CACHE_DIR	.cache/acestep/torchinductor	TorchInductor cache directory

---

Error Handling

HTTP Status Codes:

• 200: Success
• 400: Invalid request (bad JSON, missing fields)
• 401: Unauthorized (missing or invalid API key)
• 404: Resource not found
• 415: Unsupported Content-Type
• 429: Server busy (queue is full)
• 500: Internal server error

Error Response Format:

{
  "detail": "Error message describing the issue"
}

---

Best Practices

1. Use thinking=true for best quality results with LM-enhanced generation.

2. Use sample_query/description for quick generation from natural language descriptions.

3. Use use_format=true when you have caption/lyrics but want LM to enhance them.

4. Batch query task status using the /query_result endpoint to query multiple tasks at once.

5. Check /v1/stats to understand server load and average job time.

6. Use multi-model support by setting ACESTEP_CONFIG_PATH2 and ACESTEP_CONFIG_PATH3 environment variables, then select with the model parameter.

7. For production, set ACESTEP_API_KEY to enable authentication and secure your API.

8. For low VRAM environments, enable ACESTEP_OFFLOAD_TO_CPU=true to support longer audio generation.