# coding=utf-8 # Copyright 2024 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 tempfile import unittest import numpy as np from diffusers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, OnnxStableDiffusionPipeline, PNDMScheduler, ) from diffusers.utils.testing_utils import is_onnx_available, 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): hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline" def get_dummy_inputs(self, seed=0): generator = np.random.RandomState(seed) inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 7.5, "output_type": "np", } return inputs def test_pipeline_default_ddim(self): pipe = OnnxStableDiffusionPipeline.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] assert image.shape == (1, 128, 128, 3) expected_slice = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_pipeline_pndm(self): pipe = OnnxStableDiffusionPipeline.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([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_pipeline_lms(self): pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider") pipe.scheduler = LMSDiscreteScheduler.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([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_pipeline_euler(self): pipe = OnnxStableDiffusionPipeline.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([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_pipeline_euler_ancestral(self): pipe = OnnxStableDiffusionPipeline.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([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_pipeline_dpm_multistep(self): pipe = OnnxStableDiffusionPipeline.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([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_prompt_embeds(self): pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider") pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs() inputs["prompt"] = 3 * [inputs["prompt"]] # forward output = pipe(**inputs) image_slice_1 = output.images[0, -3:, -3:, -1] inputs = self.get_dummy_inputs() prompt = 3 * [inputs.pop("prompt")] text_inputs = pipe.tokenizer( prompt, padding="max_length", max_length=pipe.tokenizer.model_max_length, truncation=True, return_tensors="np", ) text_inputs = text_inputs["input_ids"] prompt_embeds = pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0] inputs["prompt_embeds"] = prompt_embeds # forward output = pipe(**inputs) image_slice_2 = output.images[0, -3:, -3:, -1] assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4 def test_stable_diffusion_negative_prompt_embeds(self): pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider") pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs() negative_prompt = 3 * ["this is a negative prompt"] inputs["negative_prompt"] = negative_prompt inputs["prompt"] = 3 * [inputs["prompt"]] # forward output = pipe(**inputs) image_slice_1 = output.images[0, -3:, -3:, -1] inputs = self.get_dummy_inputs() prompt = 3 * [inputs.pop("prompt")] embeds = [] for p in [prompt, negative_prompt]: text_inputs = pipe.tokenizer( p, padding="max_length", max_length=pipe.tokenizer.model_max_length, truncation=True, return_tensors="np", ) text_inputs = text_inputs["input_ids"] embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0]) inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds # forward output = pipe(**inputs) image_slice_2 = output.images[0, -3:, -3:, -1] assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4 @nightly @require_onnxruntime @require_torch_gpu class OnnxStableDiffusionPipelineIntegrationTests(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): # using the PNDM scheduler by default sd_pipe = OnnxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="onnx", safety_checker=None, feature_extractor=None, provider=self.gpu_provider, sess_options=self.gpu_options, ) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" np.random.seed(0) output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=10, output_type="np") image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 def test_inference_ddim(self): ddim_scheduler = DDIMScheduler.from_pretrained( "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx" ) sd_pipe = OnnxStableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", revision="onnx", scheduler=ddim_scheduler, safety_checker=None, feature_extractor=None, provider=self.gpu_provider, sess_options=self.gpu_options, ) sd_pipe.set_progress_bar_config(disable=None) prompt = "open neural network exchange" generator = np.random.RandomState(0) output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np") image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 def test_inference_k_lms(self): lms_scheduler = LMSDiscreteScheduler.from_pretrained( "runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx" ) sd_pipe = OnnxStableDiffusionPipeline.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, ) sd_pipe.set_progress_bar_config(disable=None) prompt = "open neural network exchange" generator = np.random.RandomState(0) output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np") image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 def test_intermediate_state(self): number_of_steps = 0 def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None: test_callback_fn.has_been_called = True nonlocal number_of_steps number_of_steps += 1 if step == 0: assert latents.shape == (1, 4, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array( [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167] ) assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 elif step == 5: assert latents.shape == (1, 4, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array( [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875] ) assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 test_callback_fn.has_been_called = False pipe = OnnxStableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", 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 = "Andromeda galaxy in a bottle" generator = np.random.RandomState(0) pipe( prompt=prompt, num_inference_steps=5, guidance_scale=7.5, generator=generator, callback=test_callback_fn, callback_steps=1, ) assert test_callback_fn.has_been_called assert number_of_steps == 6 def test_stable_diffusion_no_safety_checker(self): pipe = OnnxStableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", revision="onnx", safety_checker=None, feature_extractor=None, provider=self.gpu_provider, sess_options=self.gpu_options, ) assert isinstance(pipe, OnnxStableDiffusionPipeline) assert pipe.safety_checker is None image = pipe("example prompt", num_inference_steps=2).images[0] assert image is not None # check that there's no error when saving a pipeline with one of the models being None with tempfile.TemporaryDirectory() as tmpdirname: pipe.save_pretrained(tmpdirname) pipe = OnnxStableDiffusionPipeline.from_pretrained(tmpdirname) # sanity check that the pipeline still works assert pipe.safety_checker is None image = pipe("example prompt", num_inference_steps=2).images[0] assert image is not None