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[core] deprecate onnx (#14658)

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* deprecate onnx

* up
This commit is contained in:
Sayak Paul
2026-09-01 18:24:03 +05:30
committed by GitHub
parent 77c13f8a49
commit 7c79e70607
16 changed files with 35 additions and 1074 deletions
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1
.github/workflows/nightly_tests.yml vendored
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@@ -461,7 +461,6 @@ jobs:
run_big_gpu_torch_tests,
run_nightly_quantization_tests,
run_nightly_pipeline_level_quantization_tests,
# run_nightly_onnx_tests,
torch_minimum_version_cuda_tests,
]
if: always()

6
src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py
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@@ -10,7 +10,7 @@ from ....configuration_utils import FrozenDict
from ....schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ....utils import deprecate, logging
from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ...pipeline_utils import DiffusionPipeline
from ...pipeline_utils import DeprecatedPipelineMixin, DiffusionPipeline
from ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
@@ -38,7 +38,9 @@ def preprocess_mask(mask, scale_factor=8):
return mask
class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
class OnnxStableDiffusionInpaintPipelineLegacy(DeprecatedPipelineMixin, DiffusionPipeline):
_last_supported_version = "0.43.0"
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. This is a *legacy feature* for Onnx pipelines to
provide compatibility with StableDiffusionInpaintPipelineLegacy and may be removed in the future.

10
src/diffusers/pipelines/onnx_utils.py
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@@ -24,7 +24,7 @@ import numpy as np
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import validate_hf_hub_args
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, deprecate, is_onnx_available, logging
if is_onnx_available():
@@ -50,8 +50,14 @@ ORT_TO_NP_TYPE = {
class OnnxRuntimeModel:
_last_supported_version = "0.43.0"
def __init__(self, model=None, **kwargs):
logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")
deprecate(
"OnnxRuntimeModel",
"0.43.0",
"Please use Optimum for ONNX Runtime support.",
)
self.model = model
self.model_save_dir = kwargs.get("model_save_dir", None)
self.latest_model_name = kwargs.get("latest_model_name", ONNX_WEIGHTS_NAME)

6
src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py
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@@ -23,14 +23,16 @@ from ...configuration_utils import FrozenDict
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import deprecate, logging
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import DiffusionPipeline
from ..pipeline_utils import DeprecatedPipelineMixin, DiffusionPipeline
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__)
class OnnxStableDiffusionPipeline(DiffusionPipeline):
class OnnxStableDiffusionPipeline(DeprecatedPipelineMixin, DiffusionPipeline):
_last_supported_version = "0.43.0"
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel

6
src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py
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@@ -24,7 +24,7 @@ from ...configuration_utils import FrozenDict
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import PIL_INTERPOLATION, deprecate, logging
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import DiffusionPipeline
from ..pipeline_utils import DeprecatedPipelineMixin, DiffusionPipeline
from . import StableDiffusionPipelineOutput
@@ -55,7 +55,9 @@ def preprocess(image):
return image
class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
class OnnxStableDiffusionImg2ImgPipeline(DeprecatedPipelineMixin, DiffusionPipeline):
_last_supported_version = "0.43.0"
r"""
Pipeline for text-guided image to image generation using Stable Diffusion.

6
src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py
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@@ -24,7 +24,7 @@ from ...configuration_utils import FrozenDict
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import PIL_INTERPOLATION, deprecate, logging
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import DiffusionPipeline
from ..pipeline_utils import DeprecatedPipelineMixin, DiffusionPipeline
from . import StableDiffusionPipelineOutput
@@ -53,7 +53,9 @@ def prepare_mask_and_masked_image(image, mask, latents_shape):
return mask, masked_image
class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):
class OnnxStableDiffusionInpaintPipeline(DeprecatedPipelineMixin, DiffusionPipeline):
_last_supported_version = "0.43.0"
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.

6
src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py
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@@ -24,7 +24,7 @@ from ...configuration_utils import FrozenDict
from ...schedulers import DDPMScheduler, KarrasDiffusionSchedulers
from ...utils import deprecate, logging
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import DiffusionPipeline
from ..pipeline_utils import DeprecatedPipelineMixin, DiffusionPipeline
from . import StableDiffusionPipelineOutput
@@ -53,7 +53,9 @@ def preprocess(image):
return image
class OnnxStableDiffusionUpscalePipeline(DiffusionPipeline):
class OnnxStableDiffusionUpscalePipeline(DeprecatedPipelineMixin, DiffusionPipeline):
_last_supported_version = "0.43.0"
vae: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer

7
tests/others/test_check_dummies.py
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@@ -43,23 +43,16 @@ class TestCheckDummies:
# )
# assert double_backend_with_underscore == "sentencepiece_and_tensorflow_text"
triple_backend = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"
)
assert triple_backend == "torch_and_transformers_and_onnx"
def test_read_init(self):
objects = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
assert "torch" in objects
assert "torch_and_transformers" in objects
assert "torch_and_transformers_and_onnx" in objects
# Likewise, we can't assert on the exact content of a key
assert "UNet2DModel" in objects["torch"]
assert "StableDiffusionPipeline" in objects["torch_and_transformers"]
assert "LMSDiscreteScheduler" in objects["torch_and_scipy"]
assert "OnnxStableDiffusionPipeline" in objects["torch_and_transformers_and_onnx"]
def test_create_dummy_object(self):
dummy_constant = create_dummy_object("CONSTANT", "'torch'")

376
tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py
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@@ -1,376 +0,0 @@
# coding=utf-8
# Copyright 2026 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from ...testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def get_dummy_inputs(self, seed=0):
generator = np.random.RandomState(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_pipeline_default_ddim(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_pndm(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_lms(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_euler(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_euler_ancestral(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_dpm_multistep(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_prompt_embeds(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs()
prompt = 3 * [inputs.pop("prompt")]
text_inputs = pipe.tokenizer(
prompt,
padding="max_length",
max_length=pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_inputs = text_inputs["input_ids"]
prompt_embeds = pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0]
inputs["prompt_embeds"] = prompt_embeds
# forward
output = pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_negative_prompt_embeds(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs()
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = pipe.tokenizer(
p,
padding="max_length",
max_length=pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_inputs = text_inputs["input_ids"]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0])
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_pndm(self):
# using the PNDM scheduler by default
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
np.random.seed(0)
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=10, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_ddim(self):
ddim_scheduler = DDIMScheduler.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
)
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
revision="onnx",
scheduler=ddim_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "open neural network exchange"
generator = np.random.RandomState(0)
output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_k_lms(self):
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
)
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
revision="onnx",
scheduler=lms_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "open neural network exchange"
generator = np.random.RandomState(0)
output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_intermediate_state(self):
number_of_steps = 0
def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
test_callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
test_callback_fn.has_been_called = False
pipe = OnnxStableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "Andromeda galaxy in a bottle"
generator = np.random.RandomState(0)
pipe(
prompt=prompt,
num_inference_steps=5,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def test_stable_diffusion_no_safety_checker(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
assert isinstance(pipe, OnnxStableDiffusionPipeline)
assert pipe.safety_checker is None
image = pipe("example prompt", num_inference_steps=2).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = OnnxStableDiffusionPipeline.from_pretrained(tmpdirname)
# sanity check that the pipeline still works
assert pipe.safety_checker is None
image = pipe("example prompt", num_inference_steps=2).images[0]
assert image is not None

245
tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py
View File

@@ -1,245 +0,0 @@
# coding=utf-8
# Copyright 2026 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImg2ImgPipeline,
PNDMScheduler,
)
from ...testing_utils import (
floats_tensor,
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class OnnxStableDiffusionImg2ImgPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def get_dummy_inputs(self, seed=0):
image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed))
generator = np.random.RandomState(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_pipeline_default_ddim(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087])
assert np.abs(image_slice - expected_slice).max() < 1e-1
def test_pipeline_pndm(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_lms(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
# warmup pass to apply optimizations
_ = pipe(**self.get_dummy_inputs())
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler_ancestral(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_dpm_multistep(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_pndm(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
init_image = init_image.resize((768, 512))
# using the PNDM scheduler by default
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=10,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
expected_slice = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
def test_inference_k_lms(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
init_image = init_image.resize((768, 512))
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
)
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
revision="onnx",
scheduler=lms_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=20,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
expected_slice = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2

141
tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py
View File

@@ -1,141 +0,0 @@
# coding=utf-8
# Copyright 2026 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from ...testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_pndm(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
)
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"botp/stable-diffusion-v1-5-inpainting",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A red cat sitting on a park bench"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
guidance_scale=7.5,
num_inference_steps=10,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_k_lms(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
)
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"botp/stable-diffusion-v1-5-inpainting", subfolder="scheduler", revision="onnx"
)
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"botp/stable-diffusion-v1-5-inpainting",
revision="onnx",
scheduler=lms_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A red cat sitting on a park bench"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
guidance_scale=7.5,
num_inference_steps=20,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

231
tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py
View File

@@ -1,231 +0,0 @@
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from ...testing_utils import (
floats_tensor,
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
# TODO: (Dhruv) Update hub_checkpoint repo_id
@unittest.skip(
"There is a potential backdoor vulnerability in the hub_checkpoint. Skip running this test until resolved"
)
class OnnxStableDiffusionUpscalePipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
# TODO: is there an appropriate internal test set?
hub_checkpoint = "ssube/stable-diffusion-x4-upscaler-onnx"
def get_dummy_inputs(self, seed=0):
image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed))
generator = np.random.RandomState(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_pipeline_default_ddpm(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.6957, 0.7002, 0.7186, 0.6881, 0.6693, 0.6910, 0.7445, 0.7274, 0.7056])
assert np.abs(image_slice - expected_slice).max() < 1e-1
def test_pipeline_pndm(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.7349, 0.7347, 0.7034, 0.7696, 0.7876, 0.7597, 0.7916, 0.8085, 0.8036])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_dpm_multistep(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler_ancestral(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_ddpm(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
init_image = init_image.resize((128, 128))
# using the PNDM scheduler by default
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx",
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
guidance_scale=7.5,
num_inference_steps=10,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
def test_inference_k_lms(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
init_image = init_image.resize((128, 128))
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx", subfolder="scheduler"
)
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx",
scheduler=lms_scheduler,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
guidance_scale=7.5,
num_inference_steps=20,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]
)
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2

11
tests/pipelines/test_pipeline_utils.py
View File

@@ -625,17 +625,6 @@ class TestVariantCompatibleSiblings:
)
assert all(variant in f for f in model_filenames)
def test_download_onnx_models(self):
ignore_patterns = ["*.safetensors"]
filenames = [
"vae/model.onnx",
"unet/model.onnx",
]
model_filenames, variant_filenames = variant_compatible_siblings(
filenames, variant=None, ignore_patterns=ignore_patterns
)
assert model_filenames == set(filenames)
class TestProgressBar:
def get_dummy_components_image_generation(self):

37
tests/pipelines/test_pipelines.py
View File

@@ -60,6 +60,7 @@ from diffusers import (
UniPCMultistepScheduler,
logging,
)
from diffusers.pipelines.onnx_utils import OnnxRuntimeModel
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, is_transformers_version
from diffusers.utils.import_utils import is_peft_available
@@ -76,7 +77,6 @@ from ..testing_utils import (
load_numpy,
nightly,
require_compel,
require_onnxruntime,
require_peft_backend,
require_peft_version_greater,
require_torch_2,
@@ -328,35 +328,6 @@ class TestDownload:
assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
assert not any("openvino_" in f for f in files)
def test_download_no_onnx_by_default(self, tmp_path):
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-xl-pipe",
cache_dir=tmp_path,
use_safetensors=False,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# make sure that by default no onnx weights are downloaded for non-ONNX pipelines
assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
assert not any((f.endswith(".onnx") or f.endswith(".pb")) for f in files)
@require_onnxruntime
def test_download_onnx_by_default_for_onnx_pipelines(self, tmp_path):
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline",
cache_dir=tmp_path,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# make sure that by default onnx weights are downloaded for ONNX pipelines
assert any((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
assert any((f.endswith(".onnx")) for f in files)
assert any((f.endswith(".pb")) for f in files)
@pytest.mark.xfail(
condition=is_transformers_version(">", "4.56.2"),
reason="CLIPTextModel architecture was flattened in transformers>4.56.2 without backward-compat key mapping. "
@@ -1316,6 +1287,12 @@ class TestPipelineFast:
gc.collect()
backend_empty_cache(torch_device)
def test_onnx_runtime_model_deprecation(self):
with pytest.warns(
FutureWarning, match=r"`OnnxRuntimeModel` is deprecated and will be removed in version 0\.43\.0"
):
OnnxRuntimeModel()
def dummy_image(self):
batch_size = 1
num_channels = 3

12
tests/pipelines/test_pipelines_onnx_common.py
View File

@@ -1,12 +0,0 @@
from ..testing_utils import require_onnxruntime
@require_onnxruntime
class OnnxPipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline,
equivalence of dict and tuple outputs, etc.
"""
pass

8
tests/testing_utils.py
View File

@@ -40,7 +40,6 @@ from diffusers.utils.import_utils import (
is_kernels_available,
is_note_seq_available,
is_nvidia_modelopt_version,
is_onnx_available,
is_opencv_available,
is_peft_available,
is_sdnq_available,
@@ -702,13 +701,6 @@ def require_compel(test_case):
return pytest.mark.skipif(not is_compel_available(), reason="test requires compel")(test_case)
def require_onnxruntime(test_case):
"""
Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed.
"""
return pytest.mark.skipif(not is_onnx_available(), reason="test requires onnxruntime")(test_case)
def require_note_seq(test_case):
"""
Decorator marking a test that requires note_seq. These tests are skipped when note_seq isn't installed.