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| # 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 gc | |
| import random | |
| import tempfile | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from transformers import ( | |
| CLIPTextConfig, | |
| CLIPTextModel, | |
| CLIPTokenizer, | |
| DPTConfig, | |
| DPTFeatureExtractor, | |
| DPTForDepthEstimation, | |
| ) | |
| from diffusers import ( | |
| AutoencoderKL, | |
| DDIMScheduler, | |
| DPMSolverMultistepScheduler, | |
| LMSDiscreteScheduler, | |
| PNDMScheduler, | |
| StableDiffusionDepth2ImgPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.utils import ( | |
| floats_tensor, | |
| is_accelerate_available, | |
| is_accelerate_version, | |
| load_image, | |
| load_numpy, | |
| nightly, | |
| slow, | |
| torch_device, | |
| ) | |
| from diffusers.utils.testing_utils import require_torch_gpu, skip_mps | |
| from ...pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS | |
| from ...test_pipelines_common import PipelineTesterMixin | |
| torch.backends.cuda.matmul.allow_tf32 = False | |
| class StableDiffusionDepth2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = StableDiffusionDepth2ImgPipeline | |
| test_save_load_optional_components = False | |
| params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"} | |
| required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"} | |
| batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| unet = UNet2DConditionModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| sample_size=32, | |
| in_channels=5, | |
| out_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
| cross_attention_dim=32, | |
| attention_head_dim=(2, 4), | |
| use_linear_projection=True, | |
| ) | |
| scheduler = PNDMScheduler(skip_prk_steps=True) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL( | |
| block_out_channels=[32, 64], | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| latent_channels=4, | |
| ) | |
| torch.manual_seed(0) | |
| text_encoder_config = CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=32, | |
| intermediate_size=37, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=4, | |
| num_hidden_layers=5, | |
| pad_token_id=1, | |
| vocab_size=1000, | |
| ) | |
| text_encoder = CLIPTextModel(text_encoder_config) | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| backbone_config = { | |
| "global_padding": "same", | |
| "layer_type": "bottleneck", | |
| "depths": [3, 4, 9], | |
| "out_features": ["stage1", "stage2", "stage3"], | |
| "embedding_dynamic_padding": True, | |
| "hidden_sizes": [96, 192, 384, 768], | |
| "num_groups": 2, | |
| } | |
| depth_estimator_config = DPTConfig( | |
| image_size=32, | |
| patch_size=16, | |
| num_channels=3, | |
| hidden_size=32, | |
| num_hidden_layers=4, | |
| backbone_out_indices=(0, 1, 2, 3), | |
| num_attention_heads=4, | |
| intermediate_size=37, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| is_decoder=False, | |
| initializer_range=0.02, | |
| is_hybrid=True, | |
| backbone_config=backbone_config, | |
| backbone_featmap_shape=[1, 384, 24, 24], | |
| ) | |
| depth_estimator = DPTForDepthEstimation(depth_estimator_config) | |
| feature_extractor = DPTFeatureExtractor.from_pretrained( | |
| "hf-internal-testing/tiny-random-DPTForDepthEstimation" | |
| ) | |
| components = { | |
| "unet": unet, | |
| "scheduler": scheduler, | |
| "vae": vae, | |
| "text_encoder": text_encoder, | |
| "tokenizer": tokenizer, | |
| "depth_estimator": depth_estimator, | |
| "feature_extractor": feature_extractor, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)) | |
| image = image.cpu().permute(0, 2, 3, 1)[0] | |
| image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32)) | |
| if str(device).startswith("mps"): | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| inputs = { | |
| "prompt": "A painting of a squirrel eating a burger", | |
| "image": image, | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "guidance_scale": 6.0, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_save_load_local(self): | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(torch_device) | |
| output = pipe(**inputs)[0] | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| pipe.save_pretrained(tmpdir) | |
| pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) | |
| pipe_loaded.to(torch_device) | |
| pipe_loaded.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(torch_device) | |
| output_loaded = pipe_loaded(**inputs)[0] | |
| max_diff = np.abs(output - output_loaded).max() | |
| self.assertLess(max_diff, 1e-4) | |
| def test_save_load_float16(self): | |
| components = self.get_dummy_components() | |
| for name, module in components.items(): | |
| if hasattr(module, "half"): | |
| components[name] = module.to(torch_device).half() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(torch_device) | |
| output = pipe(**inputs)[0] | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| pipe.save_pretrained(tmpdir) | |
| pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16) | |
| pipe_loaded.to(torch_device) | |
| pipe_loaded.set_progress_bar_config(disable=None) | |
| for name, component in pipe_loaded.components.items(): | |
| if hasattr(component, "dtype"): | |
| self.assertTrue( | |
| component.dtype == torch.float16, | |
| f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.", | |
| ) | |
| inputs = self.get_dummy_inputs(torch_device) | |
| output_loaded = pipe_loaded(**inputs)[0] | |
| max_diff = np.abs(output - output_loaded).max() | |
| self.assertLess(max_diff, 2e-2, "The output of the fp16 pipeline changed after saving and loading.") | |
| def test_float16_inference(self): | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| for name, module in components.items(): | |
| if hasattr(module, "half"): | |
| components[name] = module.half() | |
| pipe_fp16 = self.pipeline_class(**components) | |
| pipe_fp16.to(torch_device) | |
| pipe_fp16.set_progress_bar_config(disable=None) | |
| output = pipe(**self.get_dummy_inputs(torch_device))[0] | |
| output_fp16 = pipe_fp16(**self.get_dummy_inputs(torch_device))[0] | |
| max_diff = np.abs(output - output_fp16).max() | |
| self.assertLess(max_diff, 1.3e-2, "The outputs of the fp16 and fp32 pipelines are too different.") | |
| def test_cpu_offload_forward_pass(self): | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(torch_device) | |
| output_without_offload = pipe(**inputs)[0] | |
| pipe.enable_sequential_cpu_offload() | |
| inputs = self.get_dummy_inputs(torch_device) | |
| output_with_offload = pipe(**inputs)[0] | |
| max_diff = np.abs(output_with_offload - output_without_offload).max() | |
| self.assertLess(max_diff, 1e-4, "CPU offloading should not affect the inference results") | |
| def test_dict_tuple_outputs_equivalent(self): | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| output = pipe(**self.get_dummy_inputs(torch_device))[0] | |
| output_tuple = pipe(**self.get_dummy_inputs(torch_device), return_dict=False)[0] | |
| max_diff = np.abs(output - output_tuple).max() | |
| self.assertLess(max_diff, 1e-4) | |
| def test_progress_bar(self): | |
| super().test_progress_bar() | |
| def test_stable_diffusion_depth2img_default_case(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| pipe = StableDiffusionDepth2ImgPipeline(**components) | |
| pipe = pipe.to(device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| image = pipe(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1] | |
| assert image.shape == (1, 32, 32, 3) | |
| if torch_device == "mps": | |
| expected_slice = np.array([0.6071, 0.5035, 0.4378, 0.5776, 0.5753, 0.4316, 0.4513, 0.5263, 0.4546]) | |
| else: | |
| expected_slice = np.array([0.6312, 0.4984, 0.4154, 0.4788, 0.5535, 0.4599, 0.4017, 0.5359, 0.4716]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| def test_stable_diffusion_depth2img_negative_prompt(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| pipe = StableDiffusionDepth2ImgPipeline(**components) | |
| pipe = pipe.to(device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| negative_prompt = "french fries" | |
| output = pipe(**inputs, negative_prompt=negative_prompt) | |
| image = output.images | |
| image_slice = image[0, -3:, -3:, -1] | |
| assert image.shape == (1, 32, 32, 3) | |
| if torch_device == "mps": | |
| expected_slice = np.array([0.5825, 0.5135, 0.4095, 0.5452, 0.6059, 0.4211, 0.3994, 0.5177, 0.4335]) | |
| else: | |
| expected_slice = np.array([0.6296, 0.5125, 0.3890, 0.4456, 0.5955, 0.4621, 0.3810, 0.5310, 0.4626]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| def test_stable_diffusion_depth2img_multiple_init_images(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| pipe = StableDiffusionDepth2ImgPipeline(**components) | |
| pipe = pipe.to(device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| inputs["prompt"] = [inputs["prompt"]] * 2 | |
| inputs["image"] = 2 * [inputs["image"]] | |
| image = pipe(**inputs).images | |
| image_slice = image[-1, -3:, -3:, -1] | |
| assert image.shape == (2, 32, 32, 3) | |
| if torch_device == "mps": | |
| expected_slice = np.array([0.6501, 0.5150, 0.4939, 0.6688, 0.5437, 0.5758, 0.5115, 0.4406, 0.4551]) | |
| else: | |
| expected_slice = np.array([0.6267, 0.5232, 0.6001, 0.6738, 0.5029, 0.6429, 0.5364, 0.4159, 0.4674]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| def test_stable_diffusion_depth2img_pil(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| pipe = StableDiffusionDepth2ImgPipeline(**components) | |
| pipe = pipe.to(device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| image = pipe(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1] | |
| if torch_device == "mps": | |
| expected_slice = np.array([0.53232, 0.47015, 0.40868, 0.45651, 0.4891, 0.4668, 0.4287, 0.48822, 0.47439]) | |
| else: | |
| expected_slice = np.array([0.6312, 0.4984, 0.4154, 0.4788, 0.5535, 0.4599, 0.4017, 0.5359, 0.4716]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| def test_attention_slicing_forward_pass(self): | |
| return super().test_attention_slicing_forward_pass() | |
| class StableDiffusionDepth2ImgPipelineSlowTests(unittest.TestCase): | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def get_inputs(self, device="cpu", dtype=torch.float32, seed=0): | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| init_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png" | |
| ) | |
| inputs = { | |
| "prompt": "two tigers", | |
| "image": init_image, | |
| "generator": generator, | |
| "num_inference_steps": 3, | |
| "strength": 0.75, | |
| "guidance_scale": 7.5, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_stable_diffusion_depth2img_pipeline_default(self): | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-depth", safety_checker=None | |
| ) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs() | |
| image = pipe(**inputs).images | |
| image_slice = image[0, 253:256, 253:256, -1].flatten() | |
| assert image.shape == (1, 480, 640, 3) | |
| expected_slice = np.array([0.9057, 0.9365, 0.9258, 0.8937, 0.8555, 0.8541, 0.8260, 0.7747, 0.7421]) | |
| assert np.abs(expected_slice - image_slice).max() < 1e-4 | |
| def test_stable_diffusion_depth2img_pipeline_k_lms(self): | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-depth", safety_checker=None | |
| ) | |
| pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs() | |
| image = pipe(**inputs).images | |
| image_slice = image[0, 253:256, 253:256, -1].flatten() | |
| assert image.shape == (1, 480, 640, 3) | |
| expected_slice = np.array([0.6363, 0.6274, 0.6309, 0.6370, 0.6226, 0.6286, 0.6213, 0.6453, 0.6306]) | |
| assert np.abs(expected_slice - image_slice).max() < 1e-4 | |
| def test_stable_diffusion_depth2img_pipeline_ddim(self): | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-depth", safety_checker=None | |
| ) | |
| pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs() | |
| image = pipe(**inputs).images | |
| image_slice = image[0, 253:256, 253:256, -1].flatten() | |
| assert image.shape == (1, 480, 640, 3) | |
| expected_slice = np.array([0.6424, 0.6524, 0.6249, 0.6041, 0.6634, 0.6420, 0.6522, 0.6555, 0.6436]) | |
| assert np.abs(expected_slice - image_slice).max() < 1e-4 | |
| def test_stable_diffusion_depth2img_intermediate_state(self): | |
| number_of_steps = 0 | |
| def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: | |
| callback_fn.has_been_called = True | |
| nonlocal number_of_steps | |
| number_of_steps += 1 | |
| if step == 1: | |
| latents = latents.detach().cpu().numpy() | |
| assert latents.shape == (1, 4, 60, 80) | |
| latents_slice = latents[0, -3:, -3:, -1] | |
| expected_slice = np.array( | |
| [-0.7168, -1.5137, -0.1418, -2.9219, -2.7266, -2.4414, -2.1035, -3.0078, -1.7051] | |
| ) | |
| assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
| elif step == 2: | |
| latents = latents.detach().cpu().numpy() | |
| assert latents.shape == (1, 4, 60, 80) | |
| latents_slice = latents[0, -3:, -3:, -1] | |
| expected_slice = np.array( | |
| [-0.7109, -1.5068, -0.1403, -2.9160, -2.7207, -2.4414, -2.1035, -3.0059, -1.7090] | |
| ) | |
| assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
| callback_fn.has_been_called = False | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-depth", safety_checker=None, torch_dtype=torch.float16 | |
| ) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs(dtype=torch.float16) | |
| pipe(**inputs, callback=callback_fn, callback_steps=1) | |
| assert callback_fn.has_been_called | |
| assert number_of_steps == 2 | |
| def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-depth", safety_checker=None, torch_dtype=torch.float16 | |
| ) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing(1) | |
| pipe.enable_sequential_cpu_offload() | |
| inputs = self.get_inputs(dtype=torch.float16) | |
| _ = pipe(**inputs) | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| # make sure that less than 2.9 GB is allocated | |
| assert mem_bytes < 2.9 * 10**9 | |
| class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase): | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def get_inputs(self, device="cpu", dtype=torch.float32, seed=0): | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| init_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png" | |
| ) | |
| inputs = { | |
| "prompt": "two tigers", | |
| "image": init_image, | |
| "generator": generator, | |
| "num_inference_steps": 3, | |
| "strength": 0.75, | |
| "guidance_scale": 7.5, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_depth2img_pndm(self): | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth") | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs() | |
| image = pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_depth2img/stable_diffusion_2_0_pndm.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |
| def test_depth2img_ddim(self): | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth") | |
| pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs() | |
| image = pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_depth2img/stable_diffusion_2_0_ddim.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |
| def test_img2img_lms(self): | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth") | |
| pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs() | |
| image = pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_depth2img/stable_diffusion_2_0_lms.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |
| def test_img2img_dpm(self): | |
| pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth") | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs() | |
| inputs["num_inference_steps"] = 30 | |
| image = pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_depth2img/stable_diffusion_2_0_dpm_multi.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |