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# 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 | |
import torch | |
from diffusers import ( | |
DPMSolverMultistepScheduler, | |
EulerAncestralDiscreteScheduler, | |
EulerDiscreteScheduler, | |
LMSDiscreteScheduler, | |
OnnxStableDiffusionUpscalePipeline, | |
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 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 = torch.manual_seed(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": "numpy", | |
} | |
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( | |
[0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223] | |
) | |
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( | |
[0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964] | |
) | |
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 | |
class OnnxStableDiffusionUpscalePipelineIntegrationTests(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_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 = torch.manual_seed(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 = torch.manual_seed(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 | |