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import re
import torch
from shared.utils import files_locator as fl
from .prompt_enhancers import HEARTMULA_LYRIC_PROMPT
ACE_STEP_REPO_ID = "DeepBeepMeep/TTS"
ACE_STEP_REPO_FOLDER = "ace_step"
ACE_STEP_TRANSFORMER_CONFIG_NAME = "ace_step_v1_transformer_config.json"
ACE_STEP_DCAE_WEIGHTS_NAME = "ace_step_v1_music_dcae_f8c8_bf16.safetensors"
ACE_STEP_DCAE_CONFIG_NAME = "ace_step_v1_dcae_config.json"
ACE_STEP_VOCODER_WEIGHTS_NAME = "ace_step_v1_music_vocoder_bf16.safetensors"
ACE_STEP_VOCODER_CONFIG_NAME = "ace_step_v1_vocoder_config.json"
ACE_STEP_TEXT_ENCODER_NAME = "umt5_base_bf16.safetensors"
ACE_STEP_TEXT_ENCODER_FOLDER = "umt5_base"
ACE_STEP_TEXT_ENCODER_URL = (
f"https://huggingface.co/{ACE_STEP_REPO_ID}/resolve/main/"
f"{ACE_STEP_TEXT_ENCODER_FOLDER}/{ACE_STEP_TEXT_ENCODER_NAME}"
)
ACE_STEP15_REPO_ID = "DeepBeepMeep/TTS"
ACE_STEP15_REPO_FOLDER = "ace_step15"
ACE_STEP15_CONFIG_DIR = os.path.join(os.path.dirname(__file__), "ace_step15", "configs")
ACE_STEP15_TRANSFORMER_CONFIG_NAME = "ace_step_v1_5_transformer_config.json"
ACE_STEP15_VAE_WEIGHTS_NAME = "ace_step_v1_5_audio_vae_bf16.safetensors"
ACE_STEP15_VAE_CONFIG_NAME = "ace_step_v1_5_audio_vae_config.json"
ACE_STEP15_TEXT_ENCODER_2_FOLDER = "Qwen3-Embedding-0.6B"
ACE_STEP15_TEXT_ENCODER_2_NAME = "model.safetensors"
ACE_STEP15_LM_FOLDER = "acestep-5Hz-lm-1.7B"
ACE_STEP15_SILENCE_LATENT_NAME = "silence_latent.pt"
ACE_STEP15_TRANSFORMER_VARIANTS = {
"base": "ace_step_v1_5_transformer_config_base.json",
"sft": "ace_step_v1_5_transformer_config_sft.json",
"turbo": "ace_step_v1_5_transformer_config_turbo.json",
"turbo_shift1": "ace_step_v1_5_transformer_config_turbo_shift1.json",
"turbo_shift3": "ace_step_v1_5_transformer_config_turbo_shift3.json",
"turbo_continuous": "ace_step_v1_5_transformer_config_turbo_continuous.json",
}
def _ace_step15_lm_weights_name(lm_folder):
folder_name = os.path.basename(os.path.normpath(str(lm_folder)))
return f"{folder_name}_bf16.safetensors"
ACE_STEP_DURATION_SLIDER = {
"label": "Duration (seconds)",
"min": 5,
"max": 240,
"increment": 1,
"default": 20,
}
ACE_STEP15_DURATION_SLIDER = {
"label": "Duration (seconds)",
"min": 5,
"max": 360,
"increment": 1,
"default": 20,
}
ACE_STEP_BPM_MIN = 30
ACE_STEP_BPM_MAX = 300
ACE_STEP_BPM_HINT = f"Use an integer from {ACE_STEP_BPM_MIN} to {ACE_STEP_BPM_MAX} (leave empty for N/A)."
ACE_STEP_TIME_SIGNATURE_VALUES = {2, 3, 4, 6}
ACE_STEP_TIME_SIGNATURE_HINT = "Use a single digit supported by ACE: 2, 3, 4, or 6 (leave empty for N/A)."
ACE_STEP_KEYSCALE_HINT = (
"Use <NOTE><ACCIDENTAL> <MODE> where NOTE is A/B/C/D/E/F/G, "
"ACCIDENTAL is optional (# or b, Unicode ♯/♭ also accepted), "
"and MODE is major or minor. "
"Short form <NOTE><ACCIDENTAL>m is also accepted. Leave empty for N/A."
)
ACE_STEP15_VALID_LANGUAGES = [
"ar", "az", "bg", "bn", "ca", "cs", "da", "de", "el", "en",
"es", "fa", "fi", "fr", "he", "hi", "hr", "ht", "hu", "id",
"is", "it", "ja", "ko", "la", "lt", "ms", "ne", "nl", "no",
"pa", "pl", "pt", "ro", "ru", "sa", "sk", "sr", "sv", "sw",
"ta", "te", "th", "tl", "tr", "uk", "ur", "vi", "yue", "zh",
"unknown",
]
ACE_STEP15_VALID_LANGUAGE_SET = set(ACE_STEP15_VALID_LANGUAGES)
ACE_STEP15_LANGUAGE_CODES_TEXT = ", ".join(ACE_STEP15_VALID_LANGUAGES)
ACE_STEP15_CUSTOM_SETTINGS = [
{
"id": "bpm",
"label": f"BPM ({ACE_STEP_BPM_MIN}-{ACE_STEP_BPM_MAX})",
"name": "BPM",
"type": "int",
},
{
"id": "keyscale",
"label": "KeyScale (C major, F# minor, ...)",
"name": "KeyScale",
"type": "text",
},
{
"id": "timesignature",
"label": "Time Signature (2,3,4,6)",
"name": "Time Signature",
"type": "int",
},
{
"id": "language",
"label": "Language (ISO code, empty = auto/unknown)",
"name": "Language",
"type": "text",
"default": "",
},
]
ACE_STEP15_MODEL_MODES = {
"choices": [
("Generate Audio Codes based on Lyrics for better Semantic Understanding", 0),
("+ Compute empty Bpm, Keyscale, Time Signature, Language using Lyrics & Music Caption", 1),
("++ Refine Caption", 2),
("++ Determine Best Song Duration based on Lyrics & Music Caption", 4),
("+++ Refine Caption & Determine Best Song Duration based on Lyrics & Music Caption", 3),
],
"default": 0,
"label": "LM Chain Of Thought Preprocessing",
}
ACE_STEP15_SETTING_ALIASES = {
"bpm": "bpm",
"keyscale": "keyscale",
"key_scale": "keyscale",
"timesignature": "timesignature",
"time_signature": "timesignature",
"language": "language",
"lang": "language",
"language_code": "language",
}
ACE_STEP_V1_SAMPLE_SOLVERS = [
("Euler", "euler"),
("Heun", "heun"),
("PingPong", "pingpong"),
]
def _normalize_ace_setting_name(name):
return re.sub(r"[^a-z0-9]+", "_", str(name or "").strip().lower()).strip("_")
def _resolve_ace_setting_id(setting_def):
raw_name = setting_def.get("id") or setting_def.get("param") or setting_def.get("name") or ""
normalized_name = _normalize_ace_setting_name(raw_name)
return ACE_STEP15_SETTING_ALIASES.get(normalized_name, normalized_name)
def _normalize_keyscale_value(value):
if value is None:
return None, None
keyscale = str(value).strip()
if len(keyscale) == 0:
return None, None
lowered = keyscale.lower()
if lowered in {"n/a", "na", "none"}:
return None, None
keyscale = keyscale.replace("\u266f", "#").replace("\u266d", "b")
short_minor = re.fullmatch(r"([A-Ga-g])\s*([#b]?)\s*[mM]", keyscale)
if short_minor:
note = short_minor.group(1).upper()
accidental = short_minor.group(2)
return f"{note}{accidental} minor", None
full = re.fullmatch(r"([A-Ga-g])\s*([#b]?)\s*(major|minor|maj|min)", keyscale, flags=re.IGNORECASE)
if not full:
return None, ACE_STEP_KEYSCALE_HINT
note = full.group(1).upper()
accidental = full.group(2)
mode = full.group(3).lower()
if mode == "maj":
mode = "major"
elif mode == "min":
mode = "minor"
return f"{note}{accidental} {mode}", None
def _get_model_path(model_def, key, default):
if not model_def:
return default
value = model_def.get(key, None)
if value is None or value == "":
model_block = model_def.get("model", {}) if isinstance(model_def, dict) else {}
value = model_block.get(key, None)
return value or default
def _ace_step_ckpt_file(filename):
rel_path = os.path.join(ACE_STEP_REPO_FOLDER, filename)
return fl.locate_file(rel_path, error_if_none=False) or rel_path
def _ace_step_ckpt_dir(dirname):
rel_path = os.path.join(ACE_STEP_REPO_FOLDER, dirname)
return fl.locate_folder(rel_path, error_if_none=False) or rel_path
def _ckpt_dir(dirname):
return fl.locate_folder(dirname, error_if_none=False) or dirname
def _ace_step15_ckpt_file(filename):
rel_path = os.path.join(ACE_STEP15_REPO_FOLDER, filename)
return fl.locate_file(rel_path, error_if_none=False) or rel_path
def _ace_step15_ckpt_dir(dirname):
rel_path = os.path.join(ACE_STEP15_REPO_FOLDER, dirname)
return fl.locate_folder(rel_path, error_if_none=False) or rel_path
def _ace_step15_lm_ckpt_file(filename):
return fl.locate_file(filename, error_if_none=False) or filename
def _ace_step15_lm_ckpt_dir(dirname):
return fl.locate_folder(dirname, error_if_none=False) or dirname
def _ace_step15_config_path(filename):
return os.path.join(ACE_STEP15_CONFIG_DIR, filename)
def _is_ace_step15(base_model_type):
return base_model_type == "ace_step_v1_5"
def _ace_step15_has_lm_definition(model_def):
text_encoder_urls = _get_model_path(model_def, "text_encoder_URLs", None)
if isinstance(text_encoder_urls, str):
return len(text_encoder_urls.strip()) > 0
if isinstance(text_encoder_urls, (list, tuple)):
return any(isinstance(one, str) and len(one.strip()) > 0 for one in text_encoder_urls)
return False
class family_handler:
@staticmethod
def query_supported_types():
return ["ace_step_v1", "ace_step_v1_5"]
@staticmethod
def query_family_maps():
return {}, {}
@staticmethod
def query_model_family():
return "tts"
@staticmethod
def query_family_infos():
return {"tts": (200, "TTS")}
@staticmethod
def register_lora_cli_args(parser, lora_root):
parser.add_argument(
"--lora-dir-ace-step",
type=str,
default=None,
help=f"Path to a directory that contains Ace Step settings (default: {os.path.join(lora_root, 'ace_step')})",
)
parser.add_argument(
"--lora-dir-ace-step15",
dest="lora_ace_step15",
type=str,
default=None,
help=f"Path to a directory that contains Ace Step 1.5 settings (default: {os.path.join(lora_root, 'ace_step_v1_5')})",
)
@staticmethod
def get_lora_dir(base_model_type, args, lora_root):
if _is_ace_step15(base_model_type):
return getattr(args, "lora_ace_step15", None) or os.path.join(lora_root, "ace_step_v1_5")
return getattr(args, "lora_ace_step", None) or os.path.join(lora_root, "ace_step")
@staticmethod
def query_model_def(base_model_type, model_def):
if _is_ace_step15(base_model_type):
extra_model_def = {
"audio_only": True,
"image_outputs": False,
"sliding_window": False,
"guidance_max_phases": 0,
"lock_inference_steps": True,
"no_negative_prompt": True,
"image_prompt_types_allowed": "",
"profiles_dir": ["ace_step_v1_5"],
"text_encoder_folder": _get_model_path(model_def, "text_encoder_folder", ACE_STEP15_LM_FOLDER),
"inference_steps": True,
"temperature": True,
"top_p_slider": True,
"top_k_slider": True,
"any_audio_prompt": True,
"audio_guide_label": "Source Audio",
"audio_guide2_label": "Reference Timbre",
"audio_scale_name": "Source Audio Strength",
"audio_prompt_choices": True,
"enabled_audio_lora": True,
"lm_engines": ["vllm"],
"prompt_class": "Lyrics",
"alt_guidance": "LM Guidance (CFG)",
"prompt_description": "Lyrics / Prompt (Write [Instrumental] for Instrumental Generation only)",
"audio_prompt_type_sources": {
"selection": ["", "A", "B", "AB"],
"labels": {
"": "Text (Lyrics) 2 Audio",
"A": "Cover Mode of Source Audio (need to provide original Lyrics and set a Source Audio Strength)",
"B": "Transfer Reference Audio Timbre",
"AB": "Cover Mode of Source Audio + Transfer Reference Audio Timbre",
},
"default": "",
"label": "Audio Task",
"letters_filter": "AB",
},
"alt_prompt": {
"label": "Music Caption (Describe the style, genre, instruments, and mood)",
"name": "Music Caption",
"placeholder": "disco",
"lines": 2,
},
"duration_slider": dict(ACE_STEP15_DURATION_SLIDER),
"custom_settings": [one.copy() for one in ACE_STEP15_CUSTOM_SETTINGS],
"text_prompt_enhancer_instructions": HEARTMULA_LYRIC_PROMPT,
"text_prompt_enhancer_max_tokens": 1024,
"prompt_enhancer_button_label": "Compose Lyrics",
}
if _ace_step15_has_lm_definition(model_def):
extra_model_def["model_modes"] = ACE_STEP15_MODEL_MODES.copy()
return extra_model_def
return {
"audio_only": True,
"image_outputs": False,
"sliding_window": False,
"guidance_max_phases": 1,
"no_negative_prompt": True,
"image_prompt_types_allowed": "",
"profiles_dir": ["ace_step_v1"],
"text_encoder_URLs": [ACE_STEP_TEXT_ENCODER_URL],
"text_encoder_folder": ACE_STEP_TEXT_ENCODER_FOLDER,
"inference_steps": True,
"sample_solvers": ACE_STEP_V1_SAMPLE_SOLVERS,
"temperature": False,
"any_audio_prompt": True,
"audio_guide_label": "Source Audio",
"audio_scale_name": "Prompt Audio Strength",
"audio_prompt_choices": True,
"enabled_audio_lora": True,
"audio_prompt_type_sources": {
"selection": ["", "A"],
"labels": {
"": "No Source Audio",
"A": "Remix Audio (need to provide original lyrics and set an Audio Prompt strength)",
},
"default": "",
"label": "Source Audio Mode",
"letters_filter": "A",
},
"alt_prompt": {
"label": "Genres / Tags",
"placeholder": "disco",
"lines": 2,
},
"duration_slider": dict(ACE_STEP_DURATION_SLIDER),
"text_prompt_enhancer_instructions": HEARTMULA_LYRIC_PROMPT,
"prompt_enhancer_button_label": "Compose Lyrics",
}
@staticmethod
def query_model_files(computeList, base_model_type, model_def=None):
if _is_ace_step15(base_model_type):
enable_lm = _ace_step15_has_lm_definition(model_def)
text_encoder_2_folder = _get_model_path(model_def, "ACE_STEP15_TEXT_ENCODER_2_FOLDER", ACE_STEP15_TEXT_ENCODER_2_FOLDER)
base_files = [
ACE_STEP15_VAE_WEIGHTS_NAME,
ACE_STEP15_SILENCE_LATENT_NAME,
]
text_encoder_2_files = [
ACE_STEP15_TEXT_ENCODER_2_NAME,
"config.json",
"tokenizer.json",
"tokenizer_config.json",
"special_tokens_map.json",
]
source_folders = [
ACE_STEP15_REPO_FOLDER,
text_encoder_2_folder,
]
file_lists = [
base_files,
text_encoder_2_files,
]
target_folders = [None, None]
if enable_lm:
lm_folder = _get_model_path(model_def, "text_encoder_folder", ACE_STEP15_LM_FOLDER)
lm_files = [
"config.json",
"tokenizer.json",
"tokenizer_config.json",
"special_tokens_map.json",
"added_tokens.json",
"merges.txt",
"vocab.json",
"chat_template.jinja",
]
source_folders.append(lm_folder)
file_lists.append(lm_files)
target_folders.append(None)
return {
"repoId": ACE_STEP15_REPO_ID,
"sourceFolderList": source_folders,
"targetFolderList": target_folders,
"fileList": file_lists,
}
text_encoder_folder = _get_model_path(model_def, "text_encoder_folder", ACE_STEP_TEXT_ENCODER_FOLDER)
base_files = [
ACE_STEP_TRANSFORMER_CONFIG_NAME,
ACE_STEP_DCAE_WEIGHTS_NAME,
ACE_STEP_DCAE_CONFIG_NAME,
ACE_STEP_VOCODER_WEIGHTS_NAME,
ACE_STEP_VOCODER_CONFIG_NAME,
]
tokenizer_files = [
"config.json",
"tokenizer.json",
"tokenizer_config.json",
"special_tokens_map.json",
]
return {
"repoId": ACE_STEP_REPO_ID,
"sourceFolderList": [
ACE_STEP_REPO_FOLDER,
text_encoder_folder,
],
"targetFolderList": [None, None],
"fileList": [base_files, tokenizer_files],
}
@staticmethod
def load_model(
model_filename,
model_type,
base_model_type,
model_def,
quantizeTransformer=False,
text_encoder_quantization=None,
dtype=None,
VAE_dtype=None,
mixed_precision_transformer=False,
save_quantized=False,
submodel_no_list=None,
text_encoder_filename=None,
profile=0,
lm_decoder_engine="legacy",
**kwargs,
):
transformer_weights = None
if isinstance(model_filename, (list, tuple)):
transformer_weights = model_filename[0] if model_filename else None
else:
transformer_weights = model_filename
if _is_ace_step15(base_model_type):
from .ace_step15.pipeline_ace_step15 import ACEStep15Pipeline
transformer_variant = _get_model_path(model_def, "ace_step15_transformer_variant", "")
transformer_config_name = ACE_STEP15_TRANSFORMER_CONFIG_NAME
if transformer_variant:
transformer_config_name = ACE_STEP15_TRANSFORMER_VARIANTS.get(
str(transformer_variant).lower(),
transformer_config_name,
)
transformer_config = _get_model_path(model_def, "ace_step15_transformer_config", _ace_step15_config_path(transformer_config_name))
vae_weights = _get_model_path(model_def, "ace_step15_vae_weights", _ace_step15_ckpt_file(ACE_STEP15_VAE_WEIGHTS_NAME))
vae_config = _get_model_path(model_def, "ace_step15_vae_config", _ace_step15_config_path(ACE_STEP15_VAE_CONFIG_NAME))
text_encoder_2_folder = _get_model_path(model_def, "ACE_STEP15_TEXT_ENCODER_2_FOLDER", ACE_STEP15_TEXT_ENCODER_2_FOLDER)
text_encoder_2_weights = _get_model_path(
model_def,
"ace_step15_text_encoder_2_weights",
fl.locate_file(os.path.join(text_encoder_2_folder, ACE_STEP15_TEXT_ENCODER_2_NAME), error_if_none=False)
or os.path.join(text_encoder_2_folder, ACE_STEP15_TEXT_ENCODER_2_NAME),
)
pre_text_tokenizer_dir = _get_model_path(model_def, "ace_step15_pre_text_tokenizer_dir", _ckpt_dir(text_encoder_2_folder))
enable_lm = bool(text_encoder_filename)
ignore_lm_cache_seed = bool(_get_model_path(model_def, "ace_step15_lm_cache_ignore_seed", False))
lm_folder = _get_model_path(model_def, "text_encoder_folder", ACE_STEP15_LM_FOLDER)
lm_weights = text_encoder_filename
lm_tokenizer_dir = _get_model_path(model_def, "ace_step15_lm_tokenizer_dir", _ace_step15_lm_ckpt_dir(lm_folder))
silence_latent = _get_model_path(model_def, "ace_step15_silence_latent", _ace_step15_ckpt_file(ACE_STEP15_SILENCE_LATENT_NAME))
if enable_lm:
lm_weight_name = os.path.basename(str(lm_weights)) if lm_weights else ""
print(f"[ace_step15] LM engine='{lm_decoder_engine}' | LM weights='{lm_weight_name}'")
pipeline = ACEStep15Pipeline(
transformer_weights_path=transformer_weights,
transformer_config_path=transformer_config,
vae_weights_path=vae_weights,
vae_config_path=vae_config,
text_encoder_2_weights_path=text_encoder_2_weights,
text_encoder_2_tokenizer_dir=pre_text_tokenizer_dir,
lm_weights_path=lm_weights,
lm_tokenizer_dir=lm_tokenizer_dir,
silence_latent_path=silence_latent,
enable_lm=enable_lm,
ignore_lm_cache_seed=ignore_lm_cache_seed,
lm_decoder_engine=lm_decoder_engine,
dtype=dtype or torch.bfloat16,
)
pipe = {
"transformer": pipeline.ace_step_transformer,
"text_encoder_2": pipeline.text_encoder_2,
"codec": pipeline.audio_vae,
}
if text_encoder_filename and pipeline.lm_model is not None:
pipe["text_encoder"] = pipeline.lm_model
pipe = { "pipe": pipe, "coTenantsMap": {}, }
if save_quantized and transformer_weights:
from wgp import save_quantized_model
save_quantized_model(
pipeline.ace_step_transformer,
model_type,
transformer_weights,
dtype or torch.bfloat16,
transformer_config,
)
return pipeline, pipe
else:
from .ace_step.pipeline_ace_step import ACEStepPipeline
transformer_config = _get_model_path(model_def, "ace_step_transformer_config", _ace_step_ckpt_file(ACE_STEP_TRANSFORMER_CONFIG_NAME))
dcae_weights = _get_model_path(model_def, "ace_step_dcae_weights", _ace_step_ckpt_file(ACE_STEP_DCAE_WEIGHTS_NAME))
dcae_config = _get_model_path(model_def, "ace_step_dcae_config", _ace_step_ckpt_file(ACE_STEP_DCAE_CONFIG_NAME))
vocoder_weights = _get_model_path(model_def, "ace_step_vocoder_weights", _ace_step_ckpt_file(ACE_STEP_VOCODER_WEIGHTS_NAME))
vocoder_config = _get_model_path(model_def, "ace_step_vocoder_config", _ace_step_ckpt_file(ACE_STEP_VOCODER_CONFIG_NAME))
text_encoder_folder = _get_model_path(model_def, "text_encoder_folder", ACE_STEP_TEXT_ENCODER_FOLDER)
text_encoder_weights = text_encoder_filename or _get_model_path(model_def, "ace_step_text_encoder_weights", os.path.join(text_encoder_folder, ACE_STEP_TEXT_ENCODER_NAME))
tokenizer_dir = _get_model_path(model_def, "ace_step_tokenizer_dir", _ckpt_dir(text_encoder_folder))
pipeline = ACEStepPipeline(
transformer_weights_path=transformer_weights,
transformer_config_path=transformer_config,
dcae_weights_path=dcae_weights,
dcae_config_path=dcae_config,
vocoder_weights_path=vocoder_weights,
vocoder_config_path=vocoder_config,
text_encoder_weights_path=text_encoder_weights,
text_encoder_tokenizer_dir=tokenizer_dir,
dtype=dtype or torch.bfloat16,
)
pipe = {
"transformer": pipeline.ace_step_transformer,
"text_encoder": pipeline.text_encoder_model,
"codec": pipeline.music_dcae,
}
if save_quantized and transformer_weights:
from wgp import get_model_def, save_quantized_model
save_quantized_model(
pipeline.ace_step_transformer,
model_type,
transformer_weights,
dtype or torch.bfloat16,
transformer_config,
)
return pipeline, pipe
@staticmethod
def update_default_settings(base_model_type, model_def, ui_defaults):
duration_def = model_def.get("duration_slider", {})
if _is_ace_step15(base_model_type):
ui_defaults.update(
{
"audio_prompt_type": "",
"prompt": "[Verse]\\nNeon rain on the city line\\n"
"You hum the tune and I fall in time\\n"
"[Chorus]\\nHold me close and keep the time",
"alt_prompt": "dreamy synth-pop, shimmering pads, soft vocals",
"duration_seconds": duration_def.get("default", 60),
"repeat_generation": 1,
"video_length": 0,
"num_inference_steps": 8,
"negative_prompt": "",
"temperature": 0.85,
"top_p": 0.9,
"top_k": 0,
"guidance_scale": 1.0,
"alt_guidance_scale": 2.5,
"multi_prompts_gen_type": 2,
"audio_scale": 0.5,
}
)
# default_custom_settings = {}
# for setting_def in model_def.get("custom_settings", []):
# setting_id = _resolve_ace_setting_id(setting_def)
# default_value = setting_def.get("default", None)
# if default_value is None:
# continue
# if isinstance(default_value, str) and len(default_value.strip()) == 0:
# continue
# default_custom_settings[setting_id] = default_value
# ui_defaults["custom_settings"] = default_custom_settings if len(default_custom_settings) > 0 else None
return
ui_defaults.update(
{
"audio_prompt_type": "",
"prompt": "[Verse]\\nNeon rain on the city line\\n"
"You hum the tune and I fall in time\\n"
"[Chorus]\\nHold me close and keep the time",
"alt_prompt": "dreamy synth-pop, shimmering pads, soft vocals",
"sample_solver": ui_defaults.get("sample_solver", ui_defaults.get("scheduler_type", "euler")),
"duration_seconds": duration_def.get("default", 60),
"repeat_generation": 1,
"video_length": 0,
"num_inference_steps": 60,
"negative_prompt": "",
"temperature": 1.0,
"guidance_scale": 7.0,
"multi_prompts_gen_type": 2,
"audio_scale": 0.5,
}
)
@staticmethod
def fix_settings(base_model_type, settings_version, model_def, ui_defaults):
if _is_ace_step15(base_model_type):
if settings_version < 2.51:
# ACE-Step 1.5 implicit LM defaults are: top-p 0.9, top-k disabled.
ui_defaults["top_p"] = 0.9
ui_defaults["top_k"] = 0
else:
ui_defaults.setdefault("top_p", 0.9)
ui_defaults.setdefault("top_k", 0)
if settings_version < 2.53:
ui_defaults["alt_guidance_scale"] = 2.5
return
if ui_defaults.get("sample_solver", "") in ("", None):
legacy_scheduler = ui_defaults.get("scheduler_type", "")
if legacy_scheduler in {"euler", "heun", "pingpong"}:
ui_defaults["sample_solver"] = legacy_scheduler
@staticmethod
def validate_generative_prompt(base_model_type, model_def, inputs, one_prompt):
if one_prompt is None or len(str(one_prompt).strip()) == 0:
return "Lyrics prompt cannot be empty for ACE-Step."
audio_prompt_type = inputs.get("audio_prompt_type", "") or ""
if "A" in audio_prompt_type and inputs.get("audio_guide") is None:
return "Reference audio is required for Only Lyrics or Remix modes."
return None
@staticmethod
def validate_generative_settings(base_model_type, model_def, inputs):
if not _is_ace_step15(base_model_type):
return None
raw_custom_settings = inputs.get("custom_settings", None)
if raw_custom_settings is None:
return None
if not isinstance(raw_custom_settings, dict):
return "Custom settings must be a dictionary."
canonical_custom_settings = {}
for raw_key, raw_value in raw_custom_settings.items():
canonical_key = ACE_STEP15_SETTING_ALIASES.get(_normalize_ace_setting_name(raw_key), _normalize_ace_setting_name(raw_key))
if len(canonical_key) == 0:
continue
canonical_custom_settings[canonical_key] = raw_value
validated_custom_settings = {}
for setting_def in model_def.get("custom_settings", []):
setting_id = _resolve_ace_setting_id(setting_def)
raw_value = canonical_custom_settings.get(setting_id, None)
if raw_value is None:
continue
if isinstance(raw_value, str):
raw_value = raw_value.strip()
if len(raw_value) == 0:
continue
if setting_id == "bpm":
try:
if isinstance(raw_value, bool):
raise ValueError()
if isinstance(raw_value, int):
bpm_value = raw_value
elif isinstance(raw_value, float):
if not raw_value.is_integer():
raise ValueError()
bpm_value = int(raw_value)
else:
bpm_as_float = float(str(raw_value).strip())
if not bpm_as_float.is_integer():
raise ValueError()
bpm_value = int(bpm_as_float)
except Exception:
return f"Invalid BPM. {ACE_STEP_BPM_HINT}"
if bpm_value < ACE_STEP_BPM_MIN or bpm_value > ACE_STEP_BPM_MAX:
return f"Invalid BPM. {ACE_STEP_BPM_HINT}"
validated_custom_settings["bpm"] = bpm_value
continue
if setting_id == "timesignature":
timesig_value = None
if isinstance(raw_value, bool):
return f"Invalid Time Signature. {ACE_STEP_TIME_SIGNATURE_HINT}"
if isinstance(raw_value, int):
timesig_value = raw_value
elif isinstance(raw_value, float):
if not raw_value.is_integer():
return f"Invalid Time Signature. {ACE_STEP_TIME_SIGNATURE_HINT}"
timesig_value = int(raw_value)
else:
time_text = str(raw_value).strip()
if len(time_text) == 0 or time_text.lower() in {"n/a", "na", "none"}:
timesig_value = None
else:
compact = time_text.replace(" ", "")
compact_lower = compact.lower()
if compact_lower in {"2/4", "3/4", "4/4", "6/8"}:
timesig_value = int(compact_lower.split("/", 1)[0])
elif compact in {"2", "3", "4", "6"}:
timesig_value = int(compact)
else:
return f"Invalid Time Signature. {ACE_STEP_TIME_SIGNATURE_HINT}"
if timesig_value is not None and timesig_value not in ACE_STEP_TIME_SIGNATURE_VALUES:
return f"Invalid Time Signature. {ACE_STEP_TIME_SIGNATURE_HINT}"
if timesig_value is not None:
validated_custom_settings["timesignature"] = timesig_value
continue
if setting_id == "keyscale":
normalized_keyscale, keyscale_error = _normalize_keyscale_value(raw_value)
if keyscale_error is not None:
return f"Invalid KeyScale. {keyscale_error}"
if normalized_keyscale is not None:
validated_custom_settings["keyscale"] = normalized_keyscale
continue
if setting_id == "language":
language_value = str(raw_value).strip().lower()
if len(language_value) == 0:
continue
if language_value not in ACE_STEP15_VALID_LANGUAGE_SET:
return f"Invalid Language code '{raw_value}'. Available codes: {ACE_STEP15_LANGUAGE_CODES_TEXT}"
validated_custom_settings["language"] = language_value
continue
for key, value in canonical_custom_settings.items():
if key in validated_custom_settings:
continue
if value is None:
continue
if isinstance(value, str):
value = value.strip()
if len(value) == 0:
continue
validated_custom_settings[key] = value
inputs["custom_settings"] = validated_custom_settings if len(validated_custom_settings) > 0 else None
return None
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