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/
diffusers
/tests
/pipelines
/stable_diffusion
/test_onnx_stable_diffusion_inpaint_legacy.py
# 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 OnnxStableDiffusionInpaintPipelineLegacy | |
from diffusers.utils.testing_utils import ( | |
is_onnx_available, | |
load_image, | |
load_numpy, | |
nightly, | |
require_onnxruntime, | |
require_torch_gpu, | |
) | |
if is_onnx_available(): | |
import onnxruntime as ort | |
class StableDiffusionOnnxInpaintLegacyPipelineIntegrationTests(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(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" | |
) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy" | |
) | |
# using the PNDM scheduler by default | |
pipe = OnnxStableDiffusionInpaintPipelineLegacy.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 red cat sitting on a park bench" | |
generator = np.random.RandomState(0) | |
output = pipe( | |
prompt=prompt, | |
image=init_image, | |
mask_image=mask_image, | |
strength=0.75, | |
guidance_scale=7.5, | |
num_inference_steps=15, | |
generator=generator, | |
output_type="np", | |
) | |
image = output.images[0] | |
assert image.shape == (512, 512, 3) | |
assert np.abs(expected_image - image).max() < 1e-2 | |