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ldm3d-inpainting
/
diffuserslocal
/tests
/pipelines
/stable_diffusion_2
/test_stable_diffusion_depth.py
# 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 is_accelerate_available, is_accelerate_version | |
from diffusers.utils.testing_utils import ( | |
enable_full_determinism, | |
floats_tensor, | |
load_image, | |
load_numpy, | |
nightly, | |
require_torch_gpu, | |
skip_mps, | |
slow, | |
torch_device, | |
) | |
from ..pipeline_params import ( | |
IMAGE_TO_IMAGE_IMAGE_PARAMS, | |
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, | |
TEXT_GUIDED_IMAGE_VARIATION_PARAMS, | |
TEXT_TO_IMAGE_IMAGE_PARAMS, | |
) | |
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin | |
enable_full_determinism() | |
class StableDiffusionDepth2ImgPipelineFastTests( | |
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, 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 | |
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS | |
image_latents_params = TEXT_TO_IMAGE_IMAGE_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).eval() | |
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([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655]) | |
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([0.6296, 0.5125, 0.3890, 0.4456, 0.5955, 0.4621, 0.3810, 0.5310, 0.4626]) | |
else: | |
expected_slice = np.array([0.6012, 0.4507, 0.3769, 0.4121, 0.5566, 0.4585, 0.3803, 0.5045, 0.4631]) | |
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([0.6557, 0.6214, 0.6254, 0.5775, 0.4785, 0.5949, 0.5904, 0.4785, 0.4730]) | |
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([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655]) | |
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() | |
def test_inference_batch_single_identical(self): | |
super().test_inference_batch_single_identical(expected_max_diff=7e-3) | |
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([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655]) | |
assert np.abs(expected_slice - image_slice).max() < 6e-1 | |
def test_stable_diffusion_depth2img_pipeline_k_lms(self): | |
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-2-depth", safety_checker=None | |
) | |
pipe.unet.set_default_attn_processor() | |
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() < 8e-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() < 5e-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 | |