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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 random | |
import unittest | |
import numpy as np | |
from diffusers import ( | |
DPMSolverMultistepScheduler, | |
EulerAncestralDiscreteScheduler, | |
EulerDiscreteScheduler, | |
LMSDiscreteScheduler, | |
OnnxStableDiffusionImg2ImgPipeline, | |
PNDMScheduler, | |
) | |
from diffusers.utils import floats_tensor | |
from diffusers.utils.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 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": "numpy", | |
} | |
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 | |
class OnnxStableDiffusionImg2ImgPipelineIntegrationTests(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): | |
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( | |
"runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx" | |
) | |
pipe = OnnxStableDiffusionImg2ImgPipeline.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, | |
) | |
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 | |