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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
@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(
"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
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