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[Feat] support AutoPipelineForText2Audio (#13511)

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* [Feat] support AutoPipelineForText2Audio

Signed-off-by: Lancer <maruixiang6688@gmail.com>

* upd

Signed-off-by: Lancer <maruixiang6688@gmail.com>

* Apply style fixes

---------

Signed-off-by: Lancer <maruixiang6688@gmail.com>
Co-authored-by: dg845 <58458699+dg845@users.noreply.github.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
This commit is contained in:
Lancer
2026-05-30 10:06:36 +08:00
committed by GitHub
parent c943837899
commit b003a47354
6 changed files with 297 additions and 2 deletions
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7
docs/source/en/api/pipelines/auto_pipeline.md
View File

@@ -37,3 +37,10 @@ The `AutoPipeline` is designed to make it easy to load a checkpoint for a task w
- all
- from_pretrained
- from_pipe
## AutoPipelineForText2Audio
[[autodoc]] AutoPipelineForText2Audio
- all
- from_pretrained
- from_pipe

9
docs/source/en/tutorials/autopipeline.md
View File

@@ -62,6 +62,13 @@ pipeline = AutoPipelineForImage2Image.from_pretrained(
"ValueError: AutoPipeline can't find a pipeline linked to ShapEImg2ImgPipeline for None"
```
There are three types of [AutoPipeline](../api/models/auto_model) classes, [`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`] and [`AutoPipelineForInpainting`]. Each of these classes have a predefined mapping, linking a pipeline to their task-specific subclass.
There are four types of [AutoPipeline](../api/models/auto_model) classes:
- [`AutoPipelineForText2Image`]
- [`AutoPipelineForImage2Image`]
- [`AutoPipelineForInpainting`]
- [`AutoPipelineForText2Audio`]
Each of these classes have a predefined mapping, linking a pipeline to their task-specific subclass.
When [`~AutoPipelineForText2Image.from_pretrained`] is called, it extracts the class name from the `model_index.json` file and selects the appropriate pipeline subclass for the task based on the mapping.

2
src/diffusers/__init__.py
View File

@@ -345,6 +345,7 @@ else:
"AudioPipelineOutput",
"AutoPipelineForImage2Image",
"AutoPipelineForInpainting",
"AutoPipelineForText2Audio",
"AutoPipelineForText2Image",
"ConsistencyModelPipeline",
"DanceDiffusionPipeline",
@@ -1179,6 +1180,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
AudioPipelineOutput,
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Audio,
AutoPipelineForText2Image,
BlipDiffusionControlNetPipeline,
BlipDiffusionPipeline,

2
src/diffusers/pipelines/__init__.py
View File

@@ -45,6 +45,7 @@ else:
"AutoPipelineForImage2Image",
"AutoPipelineForInpainting",
"AutoPipelineForText2Image",
"AutoPipelineForText2Audio",
]
_import_structure["consistency_models"] = ["ConsistencyModelPipeline"]
_import_structure["ddim"] = ["DDIMPipeline"]
@@ -557,6 +558,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .auto_pipeline import (
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Audio,
AutoPipelineForText2Image,
)
from .consistency_models import ConsistencyModelPipeline

264
src/diffusers/pipelines/auto_pipeline.py
View File

@@ -21,6 +21,7 @@ from ..configuration_utils import ConfigMixin
from ..models.controlnets import ControlNetUnionModel
from ..utils import is_sentencepiece_available
from .anyflow import AnyFlowFARPipeline, AnyFlowPipeline
from .audioldm2 import AudioLDM2Pipeline
from .aura_flow import AuraFlowPipeline
from .chroma import ChromaPipeline
from .cogview3 import CogView3PlusPipeline
@@ -76,6 +77,7 @@ from .kandinsky2_2 import (
)
from .kandinsky3 import Kandinsky3Img2ImgPipeline, Kandinsky3Pipeline
from .latent_consistency_models import LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline
from .longcat_audio_dit import LongCatAudioDiTPipeline
from .lumina import LuminaPipeline
from .lumina2 import Lumina2Pipeline
from .nucleusmoe_image import NucleusMoEImagePipeline
@@ -110,6 +112,7 @@ from .qwenimage import (
QwenImagePipeline,
)
from .sana import SanaPipeline
from .stable_audio import StableAudioPipeline
from .stable_cascade import StableCascadeCombinedPipeline, StableCascadeDecoderPipeline
from .stable_diffusion import (
StableDiffusionImg2ImgPipeline,
@@ -193,6 +196,14 @@ AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
]
)
AUTO_TEXT2AUDIO_PIPELINES_MAPPING = OrderedDict(
[
("audioldm2", AudioLDM2Pipeline),
("stable-audio", StableAudioPipeline),
("longcat-audio-dit", LongCatAudioDiTPipeline),
]
)
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING = OrderedDict(
[
("stable-diffusion", StableDiffusionImg2ImgPipeline),
@@ -305,6 +316,7 @@ SUPPORTED_TASKS_MAPPINGS = [
AUTO_TEXT2VIDEO_PIPELINES_MAPPING,
AUTO_IMAGE2VIDEO_PIPELINES_MAPPING,
AUTO_VIDEO2VIDEO_PIPELINES_MAPPING,
AUTO_TEXT2AUDIO_PIPELINES_MAPPING,
_AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING,
_AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING,
_AUTO_INPAINT_DECODER_PIPELINES_MAPPING,
@@ -851,7 +863,6 @@ class AutoPipelineForImage2Image(ConfigMixin):
original_config = dict(pipeline.config)
original_cls_name = pipeline.__class__.__name__
# derive the pipeline class to instantiate
image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, original_cls_name)
@@ -1239,3 +1250,254 @@ class AutoPipelineForInpainting(ConfigMixin):
model.register_to_config(**unused_original_config)
return model
class AutoPipelineForText2Audio(ConfigMixin):
r"""
[`AutoPipelineForText2Audio`] is a generic pipeline class that instantiates a text-to-audio pipeline class. The
specific underlying pipeline class is automatically selected from either the
[`~AutoPipelineForText2Audio.from_pretrained`] or [`~AutoPipelineForText2Audio.from_pipe`] methods.
This class cannot be instantiated using `__init__()` (throws an error).
Class attributes:
- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
diffusion pipeline's components.
"""
config_name = "model_index.json"
def __init__(self, *args, **kwargs):
raise EnvironmentError(
f"{self.__class__.__name__} is designed to be instantiated "
f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
)
@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
r"""
Instantiates a text-to-audio Pytorch diffusion pipeline from pretrained pipeline weight.
The from_pretrained() method takes care of returning the correct pipeline class instance by:
1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
config object
2. Find the text-to-audio pipeline linked to the pipeline class using pattern matching on pipeline class
name.
The pipeline is set in evaluation mode (`model.eval()`) by default.
Parameters:
pretrained_model_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* (for example `stabilityai/stable-audio-open-1.0`) of a pretrained
pipeline hosted on the Hub.
- A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
saved using
[`~DiffusionPipeline.save_pretrained`].
torch_dtype (`torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model with another dtype.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
cache_dir (`str | os.PathLike`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only (`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
custom_revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
`revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
mirror (`str`, *optional*):
Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.
device_map (`str` or `dict[str, int | str | torch.device]`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be defined for each
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device.
Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, *optional*):
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
each GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, *optional*):
The path to offload weights if device_map contains the value `"disk"`.
offload_state_dict (`bool`, *optional*):
If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
when there is some disk offload.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the safetensors weights are downloaded if they're available **and** if the
safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
weights. If set to `False`, safetensors weights are not loaded.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
class). The overwritten components are passed directly to the pipelines `__init__` method. See example
below for more information.
variant (`str`, *optional*):
Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
loading `from_flax`.
> [!TIP] > To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in
with `hf > auth login`.
Examples:
```py
>>> import torch
>>> import soundfile as sf
>>> from diffusers import AutoPipelineForText2Audio
>>> pipeline = AutoPipelineForText2Audio.from_pretrained(
... "stabilityai/stable-audio-open-1.0", torch_dtype=torch.float16
... )
>>> pipeline = pipeline.to("cuda")
>>> output = pipeline(
... "Generate a male voice reading a paragraph",
... num_inference_steps=200,
... audio_end_in_s=10.0,
... )
>>> audio = output.audios[0].T.float().cpu().numpy()
>>> sf.write("audio.wav", audio, pipeline.vae.sampling_rate)
```
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
load_config_kwargs = {
"cache_dir": cache_dir,
"force_download": force_download,
"proxies": proxies,
"token": token,
"local_files_only": local_files_only,
"revision": revision,
}
config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
orig_class_name = config["_class_name"]
text_2_audio_cls = _get_task_class(AUTO_TEXT2AUDIO_PIPELINES_MAPPING, orig_class_name)
kwargs = {**load_config_kwargs, **kwargs}
return text_2_audio_cls.from_pretrained(pretrained_model_or_path, **kwargs)
@classmethod
def from_pipe(cls, pipeline, **kwargs):
r"""
Instantiates a text-to-audio Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-audio
pipeline linked to the pipeline class using pattern matching on pipeline class name.
All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
additional memory.
The pipeline is set in evaluation mode (`model.eval()`) by default.
Parameters:
pipeline (`DiffusionPipeline`):
an instantiated `DiffusionPipeline` object
```py
>>> import torch
>>> import soundfile as sf
>>> from diffusers import AutoPipelineForText2Audio, StableAudioPipeline
>>> pipe = StableAudioPipeline.from_pretrained("stabilityai/stable-audio-open-1.0", torch_dtype=torch.float16)
>>> pipe_audio = AutoPipelineForText2Audio.from_pipe(pipe)
>>> output = pipe_audio(
... "Generate a sound",
... num_inference_steps=200,
... audio_end_in_s=10.0,
... )
>>> audio = output.audios[0].T.float().cpu().numpy()
>>> sf.write("audio.wav", audio, pipe_audio.vae.sampling_rate)
```
"""
original_config = dict(pipeline.config)
original_cls_name = pipeline.__class__.__name__
text_2_audio_cls = _get_task_class(AUTO_TEXT2AUDIO_PIPELINES_MAPPING, original_cls_name)
expected_modules, optional_kwargs = text_2_audio_cls._get_signature_keys(text_2_audio_cls)
pretrained_model_name_or_path = original_config.pop("_name_or_path", None)
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
original_class_obj = {
k: pipeline.components[k]
for k, v in pipeline.components.items()
if k in expected_modules and k not in passed_class_obj
}
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
original_pipe_kwargs = {
k: original_config[k]
for k, v in original_config.items()
if k in optional_kwargs and k not in passed_pipe_kwargs
}
additional_pipe_kwargs = [
k[1:]
for k in original_config.keys()
if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
]
for k in additional_pipe_kwargs:
original_pipe_kwargs[k] = original_config.pop(f"_{k}")
text_2_audio_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
unused_original_config = {
f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
for k, v in original_config.items()
if k not in text_2_audio_kwargs
}
missing_modules = (
set(expected_modules) - set(text_2_audio_cls._optional_components) - set(text_2_audio_kwargs.keys())
)
if len(missing_modules) > 0:
raise ValueError(
f"Pipeline {text_2_audio_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"
)
model = text_2_audio_cls(**text_2_audio_kwargs)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
model.register_to_config(**unused_original_config)
return model

15
src/diffusers/utils/dummy_pt_objects.py
View File

@@ -2492,6 +2492,21 @@ class AutoPipelineForInpainting(metaclass=DummyObject):
requires_backends(cls, ["torch"])
class AutoPipelineForText2Audio(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
class AutoPipelineForText2Image(metaclass=DummyObject):
_backends = ["torch"]