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# Copyright 2024 Marigold authors, PRS ETH Zurich. All rights reserved.
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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.
# --------------------------------------------------------------------------
# More information and citation instructions are available on the
# Marigold project website: https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
AutoencoderTiny,
LCMScheduler,
MarigoldDepthPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
require_torch_gpu,
slow,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class MarigoldDepthPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = MarigoldDepthPipeline
params = frozenset(["image"])
batch_params = frozenset(["image"])
image_params = frozenset(["image"])
image_latents_params = frozenset(["latents"])
callback_cfg_params = frozenset([])
test_xformers_attention = False
required_optional_params = frozenset(
[
"num_inference_steps",
"generator",
"output_type",
]
)
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
time_cond_proj_dim=time_cond_proj_dim,
sample_size=32,
in_channels=8,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = LCMScheduler(
beta_start=0.00085,
beta_end=0.012,
prediction_type="v_prediction",
set_alpha_to_one=False,
steps_offset=1,
beta_schedule="scaled_linear",
clip_sample=False,
thresholding=False,
)
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")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"prediction_type": "depth",
"scale_invariant": True,
"shift_invariant": True,
}
return components
def get_dummy_tiny_autoencoder(self):
return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"image": image,
"num_inference_steps": 1,
"processing_resolution": 0,
"generator": generator,
"output_type": "np",
}
return inputs
def _test_marigold_depth(
self,
generator_seed: int = 0,
expected_slice: np.ndarray = None,
atol: float = 1e-4,
**pipe_kwargs,
):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
pipe_inputs = self.get_dummy_inputs(device, seed=generator_seed)
pipe_inputs.update(**pipe_kwargs)
prediction = pipe(**pipe_inputs).prediction
prediction_slice = prediction[0, -3:, -3:, -1].flatten()
if pipe_inputs.get("match_input_resolution", True):
self.assertEqual(prediction.shape, (1, 32, 32, 1), "Unexpected output resolution")
else:
self.assertTrue(prediction.shape[0] == 1 and prediction.shape[3] == 1, "Unexpected output dimensions")
self.assertEqual(
max(prediction.shape[1:3]),
pipe_inputs.get("processing_resolution", 768),
"Unexpected output resolution",
)
self.assertTrue(np.allclose(prediction_slice, expected_slice, atol=atol))
def test_marigold_depth_dummy_defaults(self):
self._test_marigold_depth(
expected_slice=np.array([0.4529, 0.5184, 0.4985, 0.4355, 0.4273, 0.4153, 0.5229, 0.4818, 0.4627]),
)
def test_marigold_depth_dummy_G0_S1_P32_E1_B1_M1(self):
self._test_marigold_depth(
generator_seed=0,
expected_slice=np.array([0.4529, 0.5184, 0.4985, 0.4355, 0.4273, 0.4153, 0.5229, 0.4818, 0.4627]),
num_inference_steps=1,
processing_resolution=32,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_dummy_G0_S1_P16_E1_B1_M1(self):
self._test_marigold_depth(
generator_seed=0,
expected_slice=np.array([0.4511, 0.4531, 0.4542, 0.5024, 0.4987, 0.4969, 0.5281, 0.5215, 0.5182]),
num_inference_steps=1,
processing_resolution=16,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_dummy_G2024_S1_P32_E1_B1_M1(self):
self._test_marigold_depth(
generator_seed=2024,
expected_slice=np.array([0.4671, 0.4739, 0.5130, 0.4308, 0.4411, 0.4720, 0.5064, 0.4796, 0.4795]),
num_inference_steps=1,
processing_resolution=32,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_dummy_G0_S2_P32_E1_B1_M1(self):
self._test_marigold_depth(
generator_seed=0,
expected_slice=np.array([0.4165, 0.4485, 0.4647, 0.4003, 0.4577, 0.5074, 0.5106, 0.5077, 0.5042]),
num_inference_steps=2,
processing_resolution=32,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_dummy_G0_S1_P64_E1_B1_M1(self):
self._test_marigold_depth(
generator_seed=0,
expected_slice=np.array([0.4817, 0.5425, 0.5146, 0.5367, 0.5034, 0.4743, 0.4395, 0.4734, 0.4399]),
num_inference_steps=1,
processing_resolution=64,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_dummy_G0_S1_P32_E3_B1_M1(self):
self._test_marigold_depth(
generator_seed=0,
expected_slice=np.array([0.3260, 0.3591, 0.2837, 0.2971, 0.2750, 0.2426, 0.4200, 0.3588, 0.3254]),
num_inference_steps=1,
processing_resolution=32,
ensemble_size=3,
ensembling_kwargs={"reduction": "mean"},
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_dummy_G0_S1_P32_E4_B2_M1(self):
self._test_marigold_depth(
generator_seed=0,
expected_slice=np.array([0.3180, 0.4194, 0.3013, 0.2902, 0.3245, 0.2897, 0.4718, 0.4174, 0.3705]),
num_inference_steps=1,
processing_resolution=32,
ensemble_size=4,
ensembling_kwargs={"reduction": "mean"},
batch_size=2,
match_input_resolution=True,
)
def test_marigold_depth_dummy_G0_S1_P16_E1_B1_M0(self):
self._test_marigold_depth(
generator_seed=0,
expected_slice=np.array([0.5515, 0.4588, 0.4197, 0.4741, 0.4229, 0.4328, 0.5333, 0.5314, 0.5182]),
num_inference_steps=1,
processing_resolution=16,
ensemble_size=1,
batch_size=1,
match_input_resolution=False,
)
def test_marigold_depth_dummy_no_num_inference_steps(self):
with self.assertRaises(ValueError) as e:
self._test_marigold_depth(
num_inference_steps=None,
expected_slice=np.array([0.0]),
)
self.assertIn("num_inference_steps", str(e))
def test_marigold_depth_dummy_no_processing_resolution(self):
with self.assertRaises(ValueError) as e:
self._test_marigold_depth(
processing_resolution=None,
expected_slice=np.array([0.0]),
)
self.assertIn("processing_resolution", str(e))
@slow
@require_torch_gpu
class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _test_marigold_depth(
self,
is_fp16: bool = True,
device: str = "cuda",
generator_seed: int = 0,
expected_slice: np.ndarray = None,
model_id: str = "prs-eth/marigold-lcm-v1-0",
image_url: str = "https://marigoldmonodepth.github.io/images/einstein.jpg",
atol: float = 1e-4,
**pipe_kwargs,
):
from_pretrained_kwargs = {}
if is_fp16:
from_pretrained_kwargs["variant"] = "fp16"
from_pretrained_kwargs["torch_dtype"] = torch.float16
pipe = MarigoldDepthPipeline.from_pretrained(model_id, **from_pretrained_kwargs)
if device == "cuda":
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(generator_seed)
image = load_image(image_url)
width, height = image.size
prediction = pipe(image, generator=generator, **pipe_kwargs).prediction
prediction_slice = prediction[0, -3:, -3:, -1].flatten()
if pipe_kwargs.get("match_input_resolution", True):
self.assertEqual(prediction.shape, (1, height, width, 1), "Unexpected output resolution")
else:
self.assertTrue(prediction.shape[0] == 1 and prediction.shape[3] == 1, "Unexpected output dimensions")
self.assertEqual(
max(prediction.shape[1:3]),
pipe_kwargs.get("processing_resolution", 768),
"Unexpected output resolution",
)
self.assertTrue(np.allclose(prediction_slice, expected_slice, atol=atol))
def test_marigold_depth_einstein_f32_cpu_G0_S1_P32_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=False,
device="cpu",
generator_seed=0,
expected_slice=np.array([0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323]),
num_inference_steps=1,
processing_resolution=32,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_einstein_f32_cuda_G0_S1_P768_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=False,
device="cuda",
generator_seed=0,
expected_slice=np.array([0.1244, 0.1265, 0.1292, 0.1240, 0.1252, 0.1266, 0.1246, 0.1226, 0.1180]),
num_inference_steps=1,
processing_resolution=768,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device="cuda",
generator_seed=0,
expected_slice=np.array([0.1241, 0.1262, 0.1290, 0.1238, 0.1250, 0.1265, 0.1244, 0.1225, 0.1179]),
num_inference_steps=1,
processing_resolution=768,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G2024_S1_P768_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device="cuda",
generator_seed=2024,
expected_slice=np.array([0.1710, 0.1725, 0.1738, 0.1700, 0.1700, 0.1696, 0.1698, 0.1663, 0.1592]),
num_inference_steps=1,
processing_resolution=768,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S2_P768_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device="cuda",
generator_seed=0,
expected_slice=np.array([0.1085, 0.1098, 0.1110, 0.1081, 0.1085, 0.1082, 0.1085, 0.1057, 0.0996]),
num_inference_steps=2,
processing_resolution=768,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P512_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device="cuda",
generator_seed=0,
expected_slice=np.array([0.2683, 0.2693, 0.2698, 0.2666, 0.2632, 0.2615, 0.2656, 0.2603, 0.2573]),
num_inference_steps=1,
processing_resolution=512,
ensemble_size=1,
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E3_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device="cuda",
generator_seed=0,
expected_slice=np.array([0.1200, 0.1215, 0.1237, 0.1193, 0.1197, 0.1202, 0.1196, 0.1166, 0.1109]),
num_inference_steps=1,
processing_resolution=768,
ensemble_size=3,
ensembling_kwargs={"reduction": "mean"},
batch_size=1,
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E4_B2_M1(self):
self._test_marigold_depth(
is_fp16=True,
device="cuda",
generator_seed=0,
expected_slice=np.array([0.1121, 0.1135, 0.1155, 0.1111, 0.1115, 0.1118, 0.1111, 0.1079, 0.1019]),
num_inference_steps=1,
processing_resolution=768,
ensemble_size=4,
ensembling_kwargs={"reduction": "mean"},
batch_size=2,
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P512_E1_B1_M0(self):
self._test_marigold_depth(
is_fp16=True,
device="cuda",
generator_seed=0,
expected_slice=np.array([0.2671, 0.2690, 0.2720, 0.2659, 0.2676, 0.2739, 0.2664, 0.2686, 0.2573]),
num_inference_steps=1,
processing_resolution=512,
ensemble_size=1,
batch_size=1,
match_input_resolution=False,
)
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