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7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
232 lines
8.6 KiB
Python
232 lines
8.6 KiB
Python
# coding=utf-8
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# Copyright 2022 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 random
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import unittest
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import numpy as np
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from diffusers import (
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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OnnxStableDiffusionUpscalePipeline,
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PNDMScheduler,
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)
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from ...testing_utils import (
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floats_tensor,
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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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# TODO: (Dhruv) Update hub_checkpoint repo_id
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@unittest.skip(
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"There is a potential backdoor vulnerability in the hub_checkpoint. Skip running this test until resolved"
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)
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class OnnxStableDiffusionUpscalePipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
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# TODO: is there an appropriate internal test set?
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hub_checkpoint = "ssube/stable-diffusion-x4-upscaler-onnx"
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def get_dummy_inputs(self, seed=0):
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image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed))
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generator = np.random.RandomState(seed)
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inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"image": image,
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"generator": generator,
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"num_inference_steps": 3,
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"guidance_scale": 7.5,
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"output_type": "np",
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}
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return inputs
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def test_pipeline_default_ddpm(self):
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pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1].flatten()
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# started as 128, should now be 512
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.6957, 0.7002, 0.7186, 0.6881, 0.6693, 0.6910, 0.7445, 0.7274, 0.7056])
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assert np.abs(image_slice - expected_slice).max() < 1e-1
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def test_pipeline_pndm(self):
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pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.7349, 0.7347, 0.7034, 0.7696, 0.7876, 0.7597, 0.7916, 0.8085, 0.8036])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
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def test_pipeline_dpm_multistep(self):
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pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
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def test_pipeline_euler(self):
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pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
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def test_pipeline_euler_ancestral(self):
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pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
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@nightly
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@require_onnxruntime
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@require_torch_gpu
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class OnnxStableDiffusionUpscalePipelineIntegrationTests(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_ddpm(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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"/img2img/sketch-mountains-input.jpg"
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)
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init_image = init_image.resize((128, 128))
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# using the PNDM scheduler by default
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pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(
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"ssube/stable-diffusion-x4-upscaler-onnx",
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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 fantasy landscape, trending on artstation"
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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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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, 383:386, -1]
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assert images.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972])
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# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
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assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
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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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"/img2img/sketch-mountains-input.jpg"
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)
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init_image = init_image.resize((128, 128))
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lms_scheduler = LMSDiscreteScheduler.from_pretrained(
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"ssube/stable-diffusion-x4-upscaler-onnx", subfolder="scheduler"
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)
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pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(
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"ssube/stable-diffusion-x4-upscaler-onnx",
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scheduler=lms_scheduler,
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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 fantasy landscape, trending on artstation"
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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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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, 383:386, -1]
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assert images.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]
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)
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# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
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assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
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