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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 unittest | |
import numpy as np | |
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline | |
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 OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase): | |
# FIXME: add fast tests | |
pass | |
class OnnxStableDiffusionInpaintPipelineIntegrationTests(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" | |
"/in_paint/overture-creations-5sI6fQgYIuo.png" | |
) | |
mask_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" | |
) | |
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained( | |
"runwayml/stable-diffusion-inpainting", | |
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 red cat sitting on a park bench" | |
generator = np.random.RandomState(0) | |
output = pipe( | |
prompt=prompt, | |
image=init_image, | |
mask_image=mask_image, | |
guidance_scale=7.5, | |
num_inference_steps=10, | |
generator=generator, | |
output_type="np", | |
) | |
images = output.images | |
image_slice = images[0, 255:258, 255:258, -1] | |
assert images.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
def test_inference_k_lms(self): | |
init_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/in_paint/overture-creations-5sI6fQgYIuo.png" | |
) | |
mask_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" | |
) | |
lms_scheduler = LMSDiscreteScheduler.from_pretrained( | |
"runwayml/stable-diffusion-inpainting", subfolder="scheduler", revision="onnx" | |
) | |
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained( | |
"runwayml/stable-diffusion-inpainting", | |
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 red cat sitting on a park bench" | |
generator = np.random.RandomState(0) | |
output = pipe( | |
prompt=prompt, | |
image=init_image, | |
mask_image=mask_image, | |
guidance_scale=7.5, | |
num_inference_steps=20, | |
generator=generator, | |
output_type="np", | |
) | |
images = output.images | |
image_slice = images[0, 255:258, 255:258, -1] | |
assert images.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |