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Reorganize pipeline tests (#963) 

* Reorganize pipeline tests

* fix vq
This commit is contained in:
Anton Lozhkov 2022-10-24 16:34:01 +02:00 committed by GitHub
parent 2d35f6733a
commit 2c82e0c4eb
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27 changed files with 993 additions and 495 deletions
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23
src/diffusers/utils/testing_utils.py
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@ -14,7 +14,7 @@ import PIL.ImageOps
import requests
from packaging import version
from .import_utils import is_flax_available, is_torch_available
from .import_utils import is_flax_available, is_onnx_available, is_torch_available
global_rng = random.Random()
@ -100,6 +100,20 @@ def slow(test_case):
return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
def require_torch(test_case):
"""
Decorator marking a test that requires PyTorch. These tests are skipped when PyTorch isn't installed.
"""
return unittest.skipUnless(is_torch_available(), "test requires PyTorch")(test_case)
def require_torch_gpu(test_case):
"""Decorator marking a test that requires CUDA and PyTorch."""
return unittest.skipUnless(is_torch_available() and torch_device == "cuda", "test requires PyTorch+CUDA")(
test_case
)
def require_flax(test_case):
"""
Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed
@ -107,6 +121,13 @@ def require_flax(test_case):
return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case)
def require_onnxruntime(test_case):
"""
Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed.
"""
return unittest.skipUnless(is_onnx_available(), "test requires onnxruntime")(test_case)
def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:
"""
Args:

0
tests/pipelines/__init__.py Normal file
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tests/pipelines/ddim/__init__.py Normal file
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114
tests/pipelines/ddim/test_ddim.py Normal file
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@ -0,0 +1,114 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNet2DModel
from diffusers.utils.testing_utils import require_torch, slow, torch_device
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class DDIMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@property
def dummy_uncond_unet(self):
torch.manual_seed(0)
model = UNet2DModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=3,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
return model
def test_inference(self):
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
# Warmup pass when using mps (see #372)
if torch_device == "mps":
_ = ddpm(num_inference_steps=1)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array(
[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]
)
tolerance = 1e-2 if torch_device != "mps" else 3e-2
assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance
@slow
@require_torch
class DDIMPipelineIntegrationTests(unittest.TestCase):
def test_inference_ema_bedroom(self):
model_id = "google/ddpm-ema-bedroom-256"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDIMScheduler.from_config(model_id)
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_inference_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDIMScheduler()
ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
ddim.to(torch_device)
ddim.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddim(generator=generator, eta=0.0, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

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tests/pipelines/ddpm/__init__.py Normal file
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55
tests/pipelines/ddpm/test_ddpm.py Normal file
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@ -0,0 +1,55 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
import torch
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
from diffusers.utils.testing_utils import require_torch, slow, torch_device
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class DDPMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
# FIXME: add fast tests
pass
@slow
@require_torch
class DDPMPipelineIntegrationTests(unittest.TestCase):
def test_inference_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDPMScheduler.from_config(model_id)
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

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tests/pipelines/karras_ve/__init__.py Normal file
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tests/pipelines/karras_ve/test_karras_ve.py Normal file
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@ -0,0 +1,86 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNet2DModel
from diffusers.utils.testing_utils import require_torch, slow, torch_device
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class KarrasVePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@property
def dummy_uncond_unet(self):
torch.manual_seed(0)
model = UNet2DModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=3,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
return model
def test_inference(self):
unet = self.dummy_uncond_unet
scheduler = KarrasVeScheduler()
pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch
class KarrasVePipelineIntegrationTests(unittest.TestCase):
def test_inference(self):
model_id = "google/ncsnpp-celebahq-256"
model = UNet2DModel.from_pretrained(model_id)
scheduler = KarrasVeScheduler()
pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

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tests/pipelines/latent_diffusion/__init__.py Normal file
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tests/pipelines/latent_diffusion/test_latent_diffusion.py Normal file
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@ -0,0 +1,153 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
from diffusers.utils.testing_utils import require_torch, slow, torch_device
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@property
def dummy_cond_unet(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
return model
@property
def dummy_vae(self):
torch.manual_seed(0)
model = 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,
)
return model
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
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,
)
return CLIPTextModel(config)
def test_inference_text2img(self):
unet = self.dummy_cond_unet
scheduler = DDIMScheduler()
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
# Warmup pass when using mps (see #372)
if torch_device == "mps":
generator = torch.manual_seed(0)
_ = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy"
).images
generator = torch.manual_seed(0)
image = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy"
).images
generator = torch.manual_seed(0)
image_from_tuple = ldm(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="numpy",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch
class LDMTextToImagePipelineIntegrationTests(unittest.TestCase):
def test_inference_text2img(self):
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy"
).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_inference_text2img_fast(self):
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

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tests/pipelines/latent_diffusion/test_latent_diffusion_uncond.py Normal file
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@ -0,0 +1,119 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNet2DModel, VQModel
from diffusers.utils.testing_utils import require_torch, slow, torch_device
from transformers import CLIPTextConfig, CLIPTextModel
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class LDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@property
def dummy_uncond_unet(self):
torch.manual_seed(0)
model = UNet2DModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=3,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
return model
@property
def dummy_vq_model(self):
torch.manual_seed(0)
model = VQModel(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=3,
)
return model
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
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,
)
return CLIPTextModel(config)
def test_inference_uncond(self):
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
vae = self.dummy_vq_model
ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler)
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
# Warmup pass when using mps (see #372)
if torch_device == "mps":
generator = torch.manual_seed(0)
_ = ldm(generator=generator, num_inference_steps=1, output_type="numpy").images
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=2, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = ldm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch
class LDMPipelineIntegrationTests(unittest.TestCase):
def test_inference_uncond(self):
ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

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tests/pipelines/pndm/__init__.py Normal file
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tests/pipelines/pndm/test_pndm.py Normal file
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# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNet2DModel
from diffusers.utils.testing_utils import require_torch, slow, torch_device
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class PNDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@property
def dummy_uncond_unet(self):
torch.manual_seed(0)
model = UNet2DModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=3,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
return model
def test_inference(self):
unet = self.dummy_uncond_unet
scheduler = PNDMScheduler()
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
pndm.to(torch_device)
pndm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch
class PNDMPipelineIntegrationTests(unittest.TestCase):
def test_inference_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = PNDMScheduler()
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
pndm.to(torch_device)
pndm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pndm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

0
tests/pipelines/score_sde_ve/__init__.py Normal file
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91
tests/pipelines/score_sde_ve/test_score_sde_ve.py Normal file
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@ -0,0 +1,91 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel
from diffusers.utils.testing_utils import require_torch, slow, torch_device
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class ScoreSdeVeipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@property
def dummy_uncond_unet(self):
torch.manual_seed(0)
model = UNet2DModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=3,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
return model
def test_inference(self):
unet = self.dummy_uncond_unet
scheduler = ScoreSdeVeScheduler()
sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
sde_ve.to(torch_device)
sde_ve.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images
generator = torch.manual_seed(0)
image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, return_dict=False)[
0
]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch
class ScoreSdeVePipelineIntegrationTests(unittest.TestCase):
def test_inference(self):
model_id = "google/ncsnpp-church-256"
model = UNet2DModel.from_pretrained(model_id)
scheduler = ScoreSdeVeScheduler.from_config(model_id)
sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
sde_ve.to(torch_device)
sde_ve.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

0
tests/pipelines/stable_diffusion/__init__.py Normal file
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84
tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py Normal file
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@ -0,0 +1,84 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
from diffusers import OnnxStableDiffusionPipeline
from diffusers.utils.testing_utils import require_onnxruntime, slow
from ...test_pipelines_onnx_common import OnnxPipelineTesterMixin
class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
# FIXME: add fast tests
pass
@slow
@require_onnxruntime
class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
def test_inference(self):
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
prompt = "A painting of a squirrel eating a burger"
np.random.seed(0)
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=5, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3602, 0.3688, 0.3652, 0.3895, 0.3782, 0.3747, 0.3927, 0.4241, 0.4327])
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(
[-0.5950, -0.3039, -1.1672, 0.1594, -1.1572, 0.6719, -1.9712, -0.0403, 0.9592]
)
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(
[-0.4776, -0.0119, -0.8519, -0.0275, -0.9764, 0.9820, -0.3843, 0.3788, 1.2264]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
test_callback_fn.has_been_called = False
pipe = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "Andromeda galaxy in a bottle"
np.random.seed(0)
pipe(prompt=prompt, num_inference_steps=5, guidance_scale=7.5, callback=test_callback_fn, callback_steps=1)
assert test_callback_fn.has_been_called
assert number_of_steps == 6

62
tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py Normal file
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@ -0,0 +1,62 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
from diffusers import OnnxStableDiffusionImg2ImgPipeline
from diffusers.utils.testing_utils import load_image, require_onnxruntime, slow
from ...test_pipelines_onnx_common import OnnxPipelineTesterMixin
class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
# FIXME: add fast tests
pass
@slow
@require_onnxruntime
class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
def test_inference(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))
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
np.random.seed(0)
output = pipe(
prompt=prompt,
init_image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=8,
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([0.4830, 0.5242, 0.5603, 0.5016, 0.5131, 0.5111, 0.4928, 0.5025, 0.5055])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2

65
tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py Normal file
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@ -0,0 +1,65 @@
# coding=utf-8
# Copyright 2022 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 unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import load_image, require_onnxruntime, slow
from ...test_pipelines_onnx_common import OnnxPipelineTesterMixin
class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
# FIXME: add fast tests
pass
@slow
@require_onnxruntime
class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
def test_stable_diffusion_inpaint_onnx(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
)
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "A red cat sitting on a park bench"
np.random.seed(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
guidance_scale=7.5,
num_inference_steps=8,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array([0.2951, 0.2955, 0.2922, 0.2036, 0.1977, 0.2279, 0.1716, 0.1641, 0.1799])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

9
tests/pipelines/stable_diffusion/test_stable_diffusion.py
View File

@ -31,13 +31,16 @@ from diffusers import (
VQModel,
)
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineFastTests(unittest.TestCase):
class StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
@ -514,8 +517,8 @@ class PipelineFastTests(unittest.TestCase):
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
class PipelineIntegrationTests(unittest.TestCase):
@require_torch_gpu
class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()

9
tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py
View File

@ -30,13 +30,16 @@ from diffusers import (
VQModel,
)
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineFastTests(unittest.TestCase):
class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
@ -461,8 +464,8 @@ class PipelineFastTests(unittest.TestCase):
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
class PipelineIntegrationTests(unittest.TestCase):
@require_torch_gpu
class StableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()

9
tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py
View File

@ -29,14 +29,17 @@ from diffusers import (
VQModel,
)
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineFastTests(unittest.TestCase):
class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
@ -258,8 +261,8 @@ class PipelineFastTests(unittest.TestCase):
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
class PipelineIntegrationTests(unittest.TestCase):
@require_torch_gpu
class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()

9
tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint_legacy.py
View File

@ -31,14 +31,17 @@ from diffusers import (
VQModel,
)
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineFastTests(unittest.TestCase):
class StableDiffusionInpaintLegacyPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
@ -338,8 +341,8 @@ class PipelineFastTests(unittest.TestCase):
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
class PipelineIntegrationTests(unittest.TestCase):
@require_torch_gpu
class StableDiffusionInpaintLegacyPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()

487
tests/test_pipelines.py
View File

@ -32,17 +32,7 @@ from diffusers import (
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
KarrasVePipeline,
KarrasVeScheduler,
LDMPipeline,
LDMTextToImagePipeline,
OnnxStableDiffusionImg2ImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionPipeline,
PNDMPipeline,
PNDMScheduler,
ScoreSdeVePipeline,
ScoreSdeVeScheduler,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionPipeline,
@ -53,8 +43,8 @@ from diffusers import (
)
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir
from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
from packaging import version
from PIL import Image
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
@ -145,12 +135,6 @@ class CustomPipelineTests(unittest.TestCase):
class PipelineFastTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def dummy_image(self):
batch_size = 1
@ -261,174 +245,6 @@ class PipelineFastTests(unittest.TestCase):
return extract
def test_ddim(self):
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
# Warmup pass when using mps (see #372)
if torch_device == "mps":
_ = ddpm(num_inference_steps=1)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array(
[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]
)
tolerance = 1e-2 if torch_device != "mps" else 3e-2
assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance
def test_pndm_cifar10(self):
unet = self.dummy_uncond_unet
scheduler = PNDMScheduler()
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
pndm.to(torch_device)
pndm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_ldm_text2img(self):
unet = self.dummy_cond_unet
scheduler = DDIMScheduler()
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
# Warmup pass when using mps (see #372)
if torch_device == "mps":
generator = torch.manual_seed(0)
_ = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy"
).images
generator = torch.manual_seed(0)
image = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy"
).images
generator = torch.manual_seed(0)
image_from_tuple = ldm(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="numpy",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_score_sde_ve_pipeline(self):
unet = self.dummy_uncond_unet
scheduler = ScoreSdeVeScheduler()
sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
sde_ve.to(torch_device)
sde_ve.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images
generator = torch.manual_seed(0)
image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, return_dict=False)[
0
]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_ldm_uncond(self):
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
vae = self.dummy_vq_model
ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler)
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
# Warmup pass when using mps (see #372)
if torch_device == "mps":
generator = torch.manual_seed(0)
_ = ldm(generator=generator, num_inference_steps=1, output_type="numpy").images
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=2, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = ldm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_karras_ve_pipeline(self):
unet = self.dummy_uncond_unet
scheduler = KarrasVeScheduler()
pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_components(self):
"""Test that components property works correctly"""
unet = self.dummy_cond_unet
@ -489,7 +305,8 @@ class PipelineFastTests(unittest.TestCase):
assert image_text2img.shape == (1, 128, 128, 3)
class PipelineTesterMixin(unittest.TestCase):
@slow
class PipelineSlowTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
@ -556,7 +373,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_from_pretrained_hub(self):
model_path = "google/ddpm-cifar10-32"
@ -577,7 +393,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_from_pretrained_hub_pass_model(self):
model_path = "google/ddpm-cifar10-32"
@ -601,7 +416,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_output_format(self):
model_path = "google/ddpm-cifar10-32"
@ -624,156 +438,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert isinstance(images, list)
assert isinstance(images[0], PIL.Image.Image)
@slow
def test_ddpm_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDPMScheduler.from_config(model_id)
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ddim_lsun(self):
model_id = "google/ddpm-ema-bedroom-256"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDIMScheduler.from_config(model_id)
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ddim_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDIMScheduler()
ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
ddim.to(torch_device)
ddim.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddim(generator=generator, eta=0.0, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_pndm_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = PNDMScheduler()
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
pndm.to(torch_device)
pndm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pndm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ldm_text2img(self):
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy"
).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ldm_text2img_fast(self):
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_score_sde_ve_pipeline(self):
model_id = "google/ncsnpp-church-256"
model = UNet2DModel.from_pretrained(model_id)
scheduler = ScoreSdeVeScheduler.from_config(model_id)
sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
sde_ve.to(torch_device)
sde_ve.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ldm_uncond(self):
ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ddpm_ddim_equality(self):
model_id = "google/ddpm-cifar10-32"
@ -824,145 +488,7 @@ class PipelineTesterMixin(unittest.TestCase):
# the values aren't exactly equal, but the images look the same visually
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
@slow
def test_karras_ve_pipeline(self):
model_id = "google/ncsnpp-celebahq-256"
model = UNet2DModel.from_pretrained(model_id)
scheduler = KarrasVeScheduler()
pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_stable_diffusion_onnx(self):
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
prompt = "A painting of a squirrel eating a burger"
np.random.seed(0)
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=5, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3602, 0.3688, 0.3652, 0.3895, 0.3782, 0.3747, 0.3927, 0.4241, 0.4327])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@slow
def test_stable_diffusion_img2img_onnx(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))
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
np.random.seed(0)
output = pipe(
prompt=prompt,
init_image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=8,
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([0.4830, 0.5242, 0.5603, 0.5016, 0.5131, 0.5111, 0.4928, 0.5025, 0.5055])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
@slow
def test_stable_diffusion_inpaint_onnx(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
)
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "A red cat sitting on a park bench"
np.random.seed(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
guidance_scale=7.5,
num_inference_steps=8,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array([0.2951, 0.2955, 0.2922, 0.2036, 0.1977, 0.2279, 0.1716, 0.1641, 0.1799])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@slow
def test_stable_diffusion_onnx_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(
[-0.5950, -0.3039, -1.1672, 0.1594, -1.1572, 0.6719, -1.9712, -0.0403, 0.9592]
)
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(
[-0.4776, -0.0119, -0.8519, -0.0275, -0.9764, 0.9820, -0.3843, 0.3788, 1.2264]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
test_callback_fn.has_been_called = False
pipe = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "Andromeda galaxy in a bottle"
np.random.seed(0)
pipe(prompt=prompt, num_inference_steps=5, guidance_scale=7.5, callback=test_callback_fn, callback_steps=1)
assert test_callback_fn.has_been_called
assert number_of_steps == 6
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
@require_torch_gpu
def test_stable_diffusion_accelerate_load_works(self):
if version.parse(version.parse(transformers.__version__).base_version) < version.parse("4.23"):
return
@ -975,8 +501,7 @@ class PipelineTesterMixin(unittest.TestCase):
model_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, device_map="auto"
).to(torch_device)
@slow
@unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
@require_torch_gpu
def test_stable_diffusion_accelerate_load_reduces_memory_footprint(self):
if version.parse(version.parse(transformers.__version__).base_version) < version.parse("4.23"):
return

12
tests/test_pipelines_common.py Normal file
View File

@ -0,0 +1,12 @@
from diffusers.utils.testing_utils import require_torch
@require_torch
class PipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline,
equivalence of dict and tuple outputs, etc.
"""
pass

2
tests/test_pipelines_flax.py
View File

@ -30,8 +30,8 @@ if is_flax_available():
from jax import pmap
@require_flax
@slow
@require_flax
class FlaxPipelineTests(unittest.TestCase):
def test_dummy_all_tpus(self):
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(

12
tests/test_pipelines_onnx_common.py Normal file
View File

@ -0,0 +1,12 @@
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class OnnxPipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline,
equivalence of dict and tuple outputs, etc.
"""
pass