296 lines
12 KiB
Python
296 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tempfile
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import unittest
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import numpy as np
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from diffusers import (
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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OnnxStableDiffusionPipeline,
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PNDMScheduler,
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)
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from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
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from ...test_pipelines_onnx_common import OnnxPipelineTesterMixin
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if is_onnx_available():
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import onnxruntime as ort
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class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
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hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
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def get_dummy_inputs(self, seed=0):
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generator = np.random.RandomState(seed)
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inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"generator": generator,
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"num_inference_steps": 2,
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"guidance_scale": 7.5,
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"output_type": "numpy",
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}
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return inputs
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def test_pipeline_default_ddim(self):
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pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_pipeline_pndm(self):
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pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_pipeline_lms(self):
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pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_pipeline_euler(self):
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pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_pipeline_euler_ancestral(self):
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pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_pipeline_dpm_multistep(self):
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pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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@nightly
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@require_onnxruntime
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@require_torch_gpu
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class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
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@property
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def gpu_provider(self):
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return (
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"CUDAExecutionProvider",
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{
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"gpu_mem_limit": "15000000000", # 15GB
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"arena_extend_strategy": "kSameAsRequested",
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},
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)
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@property
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def gpu_options(self):
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options = ort.SessionOptions()
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options.enable_mem_pattern = False
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return options
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def test_inference_default_pndm(self):
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# using the PNDM scheduler by default
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sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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revision="onnx",
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safety_checker=None,
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feature_extractor=None,
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provider=self.gpu_provider,
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sess_options=self.gpu_options,
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)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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np.random.seed(0)
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output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=10, output_type="np")
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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def test_inference_ddim(self):
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ddim_scheduler = DDIMScheduler.from_pretrained(
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"runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
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)
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sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5",
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revision="onnx",
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scheduler=ddim_scheduler,
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safety_checker=None,
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feature_extractor=None,
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provider=self.gpu_provider,
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sess_options=self.gpu_options,
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)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "open neural network exchange"
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generator = np.random.RandomState(0)
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output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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def test_inference_k_lms(self):
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lms_scheduler = LMSDiscreteScheduler.from_pretrained(
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"runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
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)
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sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5",
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revision="onnx",
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scheduler=lms_scheduler,
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safety_checker=None,
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feature_extractor=None,
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provider=self.gpu_provider,
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sess_options=self.gpu_options,
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)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "open neural network exchange"
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generator = np.random.RandomState(0)
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output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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def test_intermediate_state(self):
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number_of_steps = 0
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def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
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test_callback_fn.has_been_called = True
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nonlocal number_of_steps
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number_of_steps += 1
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if step == 0:
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assert latents.shape == (1, 4, 64, 64)
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latents_slice = latents[0, -3:, -3:, -1]
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expected_slice = np.array(
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[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]
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)
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assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
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elif step == 5:
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assert latents.shape == (1, 4, 64, 64)
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latents_slice = latents[0, -3:, -3:, -1]
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expected_slice = np.array(
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[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]
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)
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assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
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test_callback_fn.has_been_called = False
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pipe = OnnxStableDiffusionPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5",
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revision="onnx",
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safety_checker=None,
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feature_extractor=None,
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provider=self.gpu_provider,
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sess_options=self.gpu_options,
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)
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pipe.set_progress_bar_config(disable=None)
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prompt = "Andromeda galaxy in a bottle"
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generator = np.random.RandomState(0)
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pipe(
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prompt=prompt,
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num_inference_steps=5,
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guidance_scale=7.5,
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generator=generator,
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callback=test_callback_fn,
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callback_steps=1,
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)
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assert test_callback_fn.has_been_called
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assert number_of_steps == 6
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def test_stable_diffusion_no_safety_checker(self):
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pipe = OnnxStableDiffusionPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5",
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revision="onnx",
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safety_checker=None,
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feature_extractor=None,
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provider=self.gpu_provider,
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sess_options=self.gpu_options,
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)
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assert isinstance(pipe, OnnxStableDiffusionPipeline)
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assert pipe.safety_checker is None
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image = pipe("example prompt", num_inference_steps=2).images[0]
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assert image is not None
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# check that there's no error when saving a pipeline with one of the models being None
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with tempfile.TemporaryDirectory() as tmpdirname:
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pipe.save_pretrained(tmpdirname)
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pipe = OnnxStableDiffusionPipeline.from_pretrained(tmpdirname)
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# sanity check that the pipeline still works
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assert pipe.safety_checker is None
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image = pipe("example prompt", num_inference_steps=2).images[0]
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assert image is not None
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