# 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 random import unittest import numpy as np from diffusers import ( DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, OnnxStableDiffusionImg2ImgPipeline, PNDMScheduler, ) from diffusers.utils.testing_utils import ( floats_tensor, 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 OnnxStableDiffusionImg2ImgPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase): hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline" def get_dummy_inputs(self, seed=0): image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed)) generator = np.random.RandomState(seed) inputs = { "prompt": "A painting of a squirrel eating a burger", "image": image, "generator": generator, "num_inference_steps": 3, "strength": 0.75, "guidance_scale": 7.5, "output_type": "numpy", } return inputs def test_pipeline_default_ddim(self): pipe = OnnxStableDiffusionImg2ImgPipeline.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].flatten() assert image.shape == (1, 128, 128, 3) expected_slice = np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]) assert np.abs(image_slice - expected_slice).max() < 1e-1 def test_pipeline_pndm(self): pipe = OnnxStableDiffusionImg2ImgPipeline.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([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 def test_pipeline_lms(self): pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider") pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) pipe.set_progress_bar_config(disable=None) # warmup pass to apply optimizations _ = pipe(**self.get_dummy_inputs()) 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([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 def test_pipeline_euler(self): pipe = OnnxStableDiffusionImg2ImgPipeline.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([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 def test_pipeline_euler_ancestral(self): pipe = OnnxStableDiffusionImg2ImgPipeline.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([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 def test_pipeline_dpm_multistep(self): pipe = OnnxStableDiffusionImg2ImgPipeline.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([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 @nightly @require_onnxruntime @require_torch_gpu class OnnxStableDiffusionImg2ImgPipelineIntegrationTests(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): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/sketch-mountains-input.jpg" ) init_image = init_image.resize((768, 512)) # using the PNDM scheduler by default pipe = OnnxStableDiffusionImg2ImgPipeline.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 fantasy landscape, trending on artstation" generator = np.random.RandomState(0) output = pipe( prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np", ) images = output.images image_slice = images[0, 255:258, 383:386, -1] assert images.shape == (1, 512, 768, 3) expected_slice = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]) # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2 def test_inference_k_lms(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/sketch-mountains-input.jpg" ) init_image = init_image.resize((768, 512)) lms_scheduler = LMSDiscreteScheduler.from_pretrained( "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx" ) pipe = OnnxStableDiffusionImg2ImgPipeline.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, ) pipe.set_progress_bar_config(disable=None) prompt = "A fantasy landscape, trending on artstation" generator = np.random.RandomState(0) output = pipe( prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, num_inference_steps=20, generator=generator, output_type="np", ) images = output.images image_slice = images[0, 255:258, 383:386, -1] assert images.shape == (1, 512, 768, 3) expected_slice = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]) # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2