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| # coding=utf-8 | |
| # Copyright 2023 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 diffusers.utils.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": "numpy", | |
| } | |
| 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 | |
| class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase): | |
| def gpu_provider(self): | |
| return ( | |
| "CUDAExecutionProvider", | |
| { | |
| "gpu_mem_limit": "15000000000", # 15GB | |
| "arena_extend_strategy": "kSameAsRequested", | |
| }, | |
| ) | |
| 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( | |
| "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx" | |
| ) | |
| sd_pipe = OnnxStableDiffusionPipeline.from_pretrained( | |
| "runwayml/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( | |
| "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx" | |
| ) | |
| sd_pipe = OnnxStableDiffusionPipeline.from_pretrained( | |
| "runwayml/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( | |
| "runwayml/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( | |
| "runwayml/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 | |