mirror of
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142 lines
4.7 KiB
Python
142 lines
4.7 KiB
Python
# coding=utf-8
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# Copyright 2025 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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from diffusers.utils.testing_utils import (
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is_onnx_available,
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load_image,
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nightly,
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require_onnxruntime,
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require_torch_gpu,
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)
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from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
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if is_onnx_available():
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import onnxruntime as ort
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class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
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# FIXME: add fast tests
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pass
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@nightly
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@require_onnxruntime
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@require_torch_gpu
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class OnnxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
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@property
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def gpu_provider(self):
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return (
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"CUDAExecutionProvider",
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{
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"gpu_mem_limit": "15000000000", # 15GB
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"arena_extend_strategy": "kSameAsRequested",
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},
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)
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@property
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def gpu_options(self):
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options = ort.SessionOptions()
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options.enable_mem_pattern = False
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return options
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def test_inference_default_pndm(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
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)
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pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
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"botp/stable-diffusion-v1-5-inpainting",
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revision="onnx",
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safety_checker=None,
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feature_extractor=None,
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provider=self.gpu_provider,
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sess_options=self.gpu_options,
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)
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pipe.set_progress_bar_config(disable=None)
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prompt = "A red cat sitting on a park bench"
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generator = np.random.RandomState(0)
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output = pipe(
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prompt=prompt,
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image=init_image,
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mask_image=mask_image,
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guidance_scale=7.5,
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num_inference_steps=10,
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generator=generator,
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output_type="np",
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)
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images = output.images
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image_slice = images[0, 255:258, 255:258, -1]
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assert images.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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def test_inference_k_lms(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
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)
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lms_scheduler = LMSDiscreteScheduler.from_pretrained(
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"botp/stable-diffusion-v1-5-inpainting", subfolder="scheduler", revision="onnx"
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)
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pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
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"botp/stable-diffusion-v1-5-inpainting",
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revision="onnx",
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scheduler=lms_scheduler,
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safety_checker=None,
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feature_extractor=None,
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provider=self.gpu_provider,
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sess_options=self.gpu_options,
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)
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pipe.set_progress_bar_config(disable=None)
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prompt = "A red cat sitting on a park bench"
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generator = np.random.RandomState(0)
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output = pipe(
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prompt=prompt,
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image=init_image,
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mask_image=mask_image,
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guidance_scale=7.5,
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num_inference_steps=20,
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generator=generator,
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output_type="np",
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)
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images = output.images
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image_slice = images[0, 255:258, 255:258, -1]
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assert images.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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