From 7c79e7060750eb35c19bd8fcc7aaca57e692ae48 Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Tue, 1 Sep 2026 18:24:03 +0530 Subject: [PATCH] [core] deprecate onnx (#14658) * deprecate onnx * up --- .github/workflows/nightly_tests.yml | 1 - ...ne_onnx_stable_diffusion_inpaint_legacy.py | 6 +- src/diffusers/pipelines/onnx_utils.py | 10 +- .../pipeline_onnx_stable_diffusion.py | 6 +- .../pipeline_onnx_stable_diffusion_img2img.py | 6 +- .../pipeline_onnx_stable_diffusion_inpaint.py | 6 +- .../pipeline_onnx_stable_diffusion_upscale.py | 6 +- tests/others/test_check_dummies.py | 7 - .../test_onnx_stable_diffusion.py | 376 ------------------ .../test_onnx_stable_diffusion_img2img.py | 245 ------------ .../test_onnx_stable_diffusion_inpaint.py | 141 ------- .../test_onnx_stable_diffusion_upscale.py | 231 ----------- tests/pipelines/test_pipeline_utils.py | 11 - tests/pipelines/test_pipelines.py | 37 +- tests/pipelines/test_pipelines_onnx_common.py | 12 - tests/testing_utils.py | 8 - 16 files changed, 35 insertions(+), 1074 deletions(-) delete mode 100644 tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py delete mode 100644 tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py delete mode 100644 tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py delete mode 100644 tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py delete mode 100644 tests/pipelines/test_pipelines_onnx_common.py diff --git a/.github/workflows/nightly_tests.yml b/.github/workflows/nightly_tests.yml index 0fedb53506..02a8ae6c18 100644 --- a/.github/workflows/nightly_tests.yml +++ b/.github/workflows/nightly_tests.yml @@ -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() diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py index f526dc419c..ac752dba5e 100644 --- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py +++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py @@ -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. diff --git a/src/diffusers/pipelines/onnx_utils.py b/src/diffusers/pipelines/onnx_utils.py index 657750f90a..52ef93aa59 100644 --- a/src/diffusers/pipelines/onnx_utils.py +++ b/src/diffusers/pipelines/onnx_utils.py @@ -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) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py index a7a81d2c13..d7776c7b51 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py @@ -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 diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py index 543881ab87..88b84cff80 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py @@ -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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py index 865cdd82cd..cf04bdf4da 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py @@ -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*. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py index c36204203d..8494a253f5 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py @@ -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 diff --git a/tests/others/test_check_dummies.py b/tests/others/test_check_dummies.py index c9a3ba3d11..ea53101566 100644 --- a/tests/others/test_check_dummies.py +++ b/tests/others/test_check_dummies.py @@ -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'") diff --git a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py b/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py deleted file mode 100644 index 77718dbf5a..0000000000 --- a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py +++ /dev/null @@ -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 diff --git a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py b/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py deleted file mode 100644 index 1a5ac94462..0000000000 --- a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py +++ /dev/null @@ -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 diff --git a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py b/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py deleted file mode 100644 index 80986ca313..0000000000 --- a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py +++ /dev/null @@ -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 diff --git a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py b/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py deleted file mode 100644 index 55d9d38d64..0000000000 --- a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py +++ /dev/null @@ -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 diff --git a/tests/pipelines/test_pipeline_utils.py b/tests/pipelines/test_pipeline_utils.py index 6bf79ae5dd..c62e582597 100644 --- a/tests/pipelines/test_pipeline_utils.py +++ b/tests/pipelines/test_pipeline_utils.py @@ -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): diff --git a/tests/pipelines/test_pipelines.py b/tests/pipelines/test_pipelines.py index 1d4edc4001..7029489da4 100644 --- a/tests/pipelines/test_pipelines.py +++ b/tests/pipelines/test_pipelines.py @@ -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 diff --git a/tests/pipelines/test_pipelines_onnx_common.py b/tests/pipelines/test_pipelines_onnx_common.py deleted file mode 100644 index fa077efb8a..0000000000 --- a/tests/pipelines/test_pipelines_onnx_common.py +++ /dev/null @@ -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 diff --git a/tests/testing_utils.py b/tests/testing_utils.py index 30f633032a..9da89e6261 100644 --- a/tests/testing_utils.py +++ b/tests/testing_utils.py @@ -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.