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Unverified Commit 102cabeb authored by Kashif Rasul's avatar Kashif Rasul Committed by GitHub
Browse files

split tests_modeling_utils (#223) 



* split tests_modeling_utils

* Fix SD tests .to(device)

* fix merge

* Fix style
Co-authored-by: default avataranton-l <anton@huggingface.co>
parent 511bd3aa
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Showing with 1026 additions and 921 deletions
tests/test_modeling_common.py 0 → 100644
View file @ 102cabeb
# 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 inspect
import tempfile
import numpy as np
import torch
from diffusers.testing_utils import torch_device
from diffusers.training_utils import EMAModel
class ModelTesterMixin:
def test_from_pretrained_save_pretrained(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
new_model = self.model_class.from_pretrained(tmpdirname)
new_model.to(torch_device)
with torch.no_grad():
image = model(**inputs_dict)
if isinstance(image, dict):
image = image["sample"]
new_image = new_model(**inputs_dict)
if isinstance(new_image, dict):
new_image = new_image["sample"]
max_diff = (image - new_image).abs().sum().item()
self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
def test_determinism(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
first = model(**inputs_dict)
if isinstance(first, dict):
first = first["sample"]
second = model(**inputs_dict)
if isinstance(second, dict):
second = second["sample"]
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_output(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output["sample"]
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_forward_signature(self):
init_dict, _ = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["sample", "timestep"]
self.assertListEqual(arg_names[:2], expected_arg_names)
def test_model_from_config(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
# test if the model can be loaded from the config
# and has all the expected shape
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_config(tmpdirname)
new_model = self.model_class.from_config(tmpdirname)
new_model.to(torch_device)
new_model.eval()
# check if all paramters shape are the same
for param_name in model.state_dict().keys():
param_1 = model.state_dict()[param_name]
param_2 = new_model.state_dict()[param_name]
self.assertEqual(param_1.shape, param_2.shape)
with torch.no_grad():
output_1 = model(**inputs_dict)
if isinstance(output_1, dict):
output_1 = output_1["sample"]
output_2 = new_model(**inputs_dict)
if isinstance(output_2, dict):
output_2 = output_2["sample"]
self.assertEqual(output_1.shape, output_2.shape)
def test_training(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
output = model(**inputs_dict)
if isinstance(output, dict):
output = output["sample"]
noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
def test_ema_training(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
ema_model = EMAModel(model, device=torch_device)
output = model(**inputs_dict)
if isinstance(output, dict):
output = output["sample"]
noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
ema_model.step(model)
tests/test_modeling_utils.py
View file @ 102cabeb
...@@ -13,42 +13,10 @@ ...@@ -13,42 +13,10 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import inspect
import math
import tempfile import tempfile
import unittest import unittest
import numpy as np
import torch
import PIL
from diffusers import UNet2DConditionModel # noqa: F401 TODO(Patrick) - need to write tests with it
from diffusers import (
AutoencoderKL,
DDIMPipeline,
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
KarrasVePipeline,
KarrasVeScheduler,
LDMPipeline,
LDMTextToImagePipeline,
LMSDiscreteScheduler,
PNDMPipeline,
PNDMScheduler,
ScoreSdeVePipeline,
ScoreSdeVeScheduler,
StableDiffusionPipeline,
UNet2DModel,
VQModel,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.testing_utils import floats_tensor, slow, torch_device
from diffusers.training_utils import EMAModel
torch.backends.cuda.matmul.allow_tf32 = False
class SampleObject(ConfigMixin): class SampleObject(ConfigMixin):
...@@ -129,892 +97,3 @@ class ConfigTester(unittest.TestCase): ...@@ -129,892 +97,3 @@ class ConfigTester(unittest.TestCase):
assert config.pop("c") == (2, 5) # instantiated as tuple assert config.pop("c") == (2, 5) # instantiated as tuple
assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json
assert config == new_config assert config == new_config
class ModelTesterMixin:
def test_from_pretrained_save_pretrained(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
new_model = self.model_class.from_pretrained(tmpdirname)
new_model.to(torch_device)
with torch.no_grad():
image = model(**inputs_dict)
if isinstance(image, dict):
image = image["sample"]
new_image = new_model(**inputs_dict)
if isinstance(new_image, dict):
new_image = new_image["sample"]
max_diff = (image - new_image).abs().sum().item()
self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
def test_determinism(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
first = model(**inputs_dict)
if isinstance(first, dict):
first = first["sample"]
second = model(**inputs_dict)
if isinstance(second, dict):
second = second["sample"]
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_output(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output["sample"]
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_forward_signature(self):
init_dict, _ = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["sample", "timestep"]
self.assertListEqual(arg_names[:2], expected_arg_names)
def test_model_from_config(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
# test if the model can be loaded from the config
# and has all the expected shape
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_config(tmpdirname)
new_model = self.model_class.from_config(tmpdirname)
new_model.to(torch_device)
new_model.eval()
# check if all paramters shape are the same
for param_name in model.state_dict().keys():
param_1 = model.state_dict()[param_name]
param_2 = new_model.state_dict()[param_name]
self.assertEqual(param_1.shape, param_2.shape)
with torch.no_grad():
output_1 = model(**inputs_dict)
if isinstance(output_1, dict):
output_1 = output_1["sample"]
output_2 = new_model(**inputs_dict)
if isinstance(output_2, dict):
output_2 = output_2["sample"]
self.assertEqual(output_1.shape, output_2.shape)
def test_training(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
output = model(**inputs_dict)
if isinstance(output, dict):
output = output["sample"]
noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
def test_ema_training(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
ema_model = EMAModel(model, device=torch_device)
output = model(**inputs_dict)
if isinstance(output, dict):
output = output["sample"]
noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
ema_model.step(model)
class UnetModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNet2DModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": (32, 64),
"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
"attention_head_dim": None,
"out_channels": 3,
"in_channels": 3,
"layers_per_block": 2,
"sample_size": 32,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
# TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
# def test_output_pretrained(self):
# model = UNet2DModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
# model.eval()
#
# torch.manual_seed(0)
# if torch.cuda.is_available():
# torch.cuda.manual_seed_all(0)
#
# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
# time_step = torch.tensor([10])
#
# with torch.no_grad():
# output = model(noise, time_step)["sample"]
#
# output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
# expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
# fmt: on
# self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNet2DModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 4
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (4, 32, 32)
@property
def output_shape(self):
return (4, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"sample_size": 32,
"in_channels": 4,
"out_channels": 4,
"layers_per_block": 2,
"block_out_channels": (32, 64),
"attention_head_dim": 32,
"down_block_types": ("DownBlock2D", "DownBlock2D"),
"up_block_types": ("UpBlock2D", "UpBlock2D"),
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)["sample"]
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
time_step = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
output = model(noise, time_step)["sample"]
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
# TODO(Patrick) - Re-add this test after having cleaned up LDM
# def test_output_pretrained_spatial_transformer(self):
# model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
# model.eval()
#
# torch.manual_seed(0)
# if torch.cuda.is_available():
# torch.cuda.manual_seed_all(0)
#
# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
# context = torch.ones((1, 16, 64), dtype=torch.float32)
# time_step = torch.tensor([10] * noise.shape[0])
#
# with torch.no_grad():
# output = model(noise, time_step, context=context)
#
# output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
# expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
# fmt: on
#
# self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
#
class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNet2DModel
@property
def dummy_input(self, sizes=(32, 32)):
batch_size = 4
num_channels = 3
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": [32, 64, 64, 64],
"in_channels": 3,
"layers_per_block": 1,
"out_channels": 3,
"time_embedding_type": "fourier",
"norm_eps": 1e-6,
"mid_block_scale_factor": math.sqrt(2.0),
"norm_num_groups": None,
"down_block_types": [
"SkipDownBlock2D",
"AttnSkipDownBlock2D",
"SkipDownBlock2D",
"SkipDownBlock2D",
],
"up_block_types": [
"SkipUpBlock2D",
"SkipUpBlock2D",
"AttnSkipUpBlock2D",
"SkipUpBlock2D",
],
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
inputs = self.dummy_input
noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
inputs["sample"] = noise
image = model(**inputs)
assert image is not None, "Make sure output is not None"
def test_output_pretrained_ve_mid(self):
model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (256, 256)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)["sample"]
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([-4836.2231, -6487.1387, -3816.7969, -7964.9253, -10966.2842, -20043.6016, 8137.0571, 2340.3499, 544.6114])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_ve_large(self):
model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)["sample"]
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class VQModelTests(ModelTesterMixin, unittest.TestCase):
model_class = VQModel
@property
def dummy_input(self, sizes=(32, 32)):
batch_size = 4
num_channels = 3
image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
return {"sample": image}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 3,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_forward_signature(self):
pass
def test_training(self):
pass
def test_from_pretrained_hub(self):
model, loading_info = VQModel.from_pretrained("fusing/vqgan-dummy", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = VQModel.from_pretrained("fusing/vqgan-dummy")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
with torch.no_grad():
output = model(image)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
model_class = AutoencoderKL
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
return {"sample": image}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_forward_signature(self):
pass
def test_training(self):
pass
def test_from_pretrained_hub(self):
model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
with torch.no_grad():
output = model(image, sample_posterior=True)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-4.0078e-01, -3.8304e-04, -1.2681e-01, -1.1462e-01, 2.0095e-01, 1.0893e-01, -8.8248e-02, -3.0361e-01, -9.8646e-03])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class PipelineTesterMixin(unittest.TestCase):
def test_from_pretrained_save_pretrained(self):
# 1. Load models
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"),
)
schedular = DDPMScheduler(num_train_timesteps=10)
ddpm = DDPMPipeline(model, schedular)
with tempfile.TemporaryDirectory() as tmpdirname:
ddpm.save_pretrained(tmpdirname)
new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy")["sample"]
generator = generator.manual_seed(0)
new_image = new_ddpm(generator=generator, output_type="numpy")["sample"]
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"
ddpm = DDPMPipeline.from_pretrained(model_path)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
ddpm.scheduler.num_timesteps = 10
ddpm_from_hub.scheduler.num_timesteps = 10
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy")["sample"]
generator = generator.manual_seed(0)
new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
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"
# pass unet into DiffusionPipeline
unet = UNet2DModel.from_pretrained(model_path)
ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
ddpm_from_hub_custom_model.scheduler.num_timesteps = 10
ddpm_from_hub.scheduler.num_timesteps = 10
generator = torch.manual_seed(0)
image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy")["sample"]
generator = generator.manual_seed(0)
new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
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"
pipe = DDIMPipeline.from_pretrained(model_path)
generator = torch.manual_seed(0)
images = pipe(generator=generator, output_type="numpy")["sample"]
assert images.shape == (1, 32, 32, 3)
assert isinstance(images, np.ndarray)
images = pipe(generator=generator, output_type="pil")["sample"]
assert isinstance(images, list)
assert len(images) == 1
assert isinstance(images[0], PIL.Image.Image)
# use PIL by default
images = pipe(generator=generator)["sample"]
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)
scheduler = scheduler.set_format("pt")
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy")["sample"]
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)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy")["sample"]
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(tensor_format="pt")
ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
generator = torch.manual_seed(0)
image = ddim(generator=generator, eta=0.0, output_type="numpy")["sample"]
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(tensor_format="pt")
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
generator = torch.manual_seed(0)
image = pndm(generator=generator, output_type="numpy")["sample"]
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")
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")[
"sample"
]
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")
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")["sample"]
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
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_stable_diffusion(self):
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1").to(torch_device)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np"
)
image = output["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_stable_diffusion_fast_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1").to(torch_device)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
sd_pipe.scheduler = scheduler
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
image = output["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.8354, 0.83, 0.866, 0.838, 0.8315, 0.867, 0.836, 0.8584, 0.869])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@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)
torch.manual_seed(0)
image = sde_ve(num_inference_steps=300, output_type="numpy")["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.64363, 0.5868, 0.3031, 0.2284, 0.7409, 0.3216, 0.25643, 0.6557, 0.2633])
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")
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=5, output_type="numpy")["sample"]
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"
unet = UNet2DModel.from_pretrained(model_id)
ddpm_scheduler = DDPMScheduler(tensor_format="pt")
ddim_scheduler = DDIMScheduler(tensor_format="pt")
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
generator = torch.manual_seed(0)
ddpm_image = ddpm(generator=generator, output_type="numpy")["sample"]
generator = torch.manual_seed(0)
ddim_image = ddim(generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")["sample"]
# the values aren't exactly equal, but the images look the same visually
assert np.abs(ddpm_image - ddim_image).max() < 1e-1
@unittest.skip("(Anton) The test is failing for large batch sizes, needs investigation")
def test_ddpm_ddim_equality_batched(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
ddpm_scheduler = DDPMScheduler(tensor_format="pt")
ddim_scheduler = DDIMScheduler(tensor_format="pt")
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
generator = torch.manual_seed(0)
ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy")["sample"]
generator = torch.manual_seed(0)
ddim_images = ddim(batch_size=4, generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")[
"sample"
]
# 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(tensor_format="pt")
pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
generator = torch.manual_seed(0)
image = pipe(num_inference_steps=20, generator=generator, output_type="numpy")["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.26815, 0.1581, 0.2658, 0.23248, 0.1550, 0.2539, 0.1131, 0.1024, 0.0837])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_lms_stable_diffusion_pipeline(self):
model_id = "CompVis/stable-diffusion-v1-1"
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True).to(torch_device)
scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler", use_auth_token=True)
pipe.scheduler = scheduler
prompt = "a photograph of an astronaut riding a horse"
generator = torch.Generator(device=torch_device).manual_seed(0)
image = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")[
"sample"
]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
tests/test_models_unet.py 0 → 100644
View file @ 102cabeb
# 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 math
import unittest
import torch
from diffusers import UNet2DModel
from diffusers.testing_utils import floats_tensor, torch_device
from .test_modeling_common import ModelTesterMixin
class UnetModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNet2DModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": (32, 64),
"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
"attention_head_dim": None,
"out_channels": 3,
"in_channels": 3,
"layers_per_block": 2,
"sample_size": 32,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
# TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
# def test_output_pretrained(self):
# model = UNet2DModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
# model.eval()
#
# torch.manual_seed(0)
# if torch.cuda.is_available():
# torch.cuda.manual_seed_all(0)
#
# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
# time_step = torch.tensor([10])
#
# with torch.no_grad():
# output = model(noise, time_step)["sample"]
#
# output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
# expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
# fmt: on
# self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNet2DModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 4
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (4, 32, 32)
@property
def output_shape(self):
return (4, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"sample_size": 32,
"in_channels": 4,
"out_channels": 4,
"layers_per_block": 2,
"block_out_channels": (32, 64),
"attention_head_dim": 32,
"down_block_types": ("DownBlock2D", "DownBlock2D"),
"up_block_types": ("UpBlock2D", "UpBlock2D"),
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)["sample"]
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
time_step = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
output = model(noise, time_step)["sample"]
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
# TODO(Patrick) - Re-add this test after having cleaned up LDM
# def test_output_pretrained_spatial_transformer(self):
# model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
# model.eval()
#
# torch.manual_seed(0)
# if torch.cuda.is_available():
# torch.cuda.manual_seed_all(0)
#
# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
# context = torch.ones((1, 16, 64), dtype=torch.float32)
# time_step = torch.tensor([10] * noise.shape[0])
#
# with torch.no_grad():
# output = model(noise, time_step, context=context)
#
# output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
# expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
# fmt: on
#
# self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
#
class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNet2DModel
@property
def dummy_input(self, sizes=(32, 32)):
batch_size = 4
num_channels = 3
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": [32, 64, 64, 64],
"in_channels": 3,
"layers_per_block": 1,
"out_channels": 3,
"time_embedding_type": "fourier",
"norm_eps": 1e-6,
"mid_block_scale_factor": math.sqrt(2.0),
"norm_num_groups": None,
"down_block_types": [
"SkipDownBlock2D",
"AttnSkipDownBlock2D",
"SkipDownBlock2D",
"SkipDownBlock2D",
],
"up_block_types": [
"SkipUpBlock2D",
"SkipUpBlock2D",
"AttnSkipUpBlock2D",
"SkipUpBlock2D",
],
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
inputs = self.dummy_input
noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
inputs["sample"] = noise
image = model(**inputs)
assert image is not None, "Make sure output is not None"
def test_output_pretrained_ve_mid(self):
model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (256, 256)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)["sample"]
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([-4836.2231, -6487.1387, -3816.7969, -7964.9253, -10966.2842, -20043.6016, 8137.0571, 2340.3499, 544.6114])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_ve_large(self):
model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)["sample"]
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
tests/test_models_vae.py 0 → 100644
View file @ 102cabeb
# 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 torch
from diffusers import AutoencoderKL
from diffusers.testing_utils import floats_tensor, torch_device
from .test_modeling_common import ModelTesterMixin
class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
model_class = AutoencoderKL
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
return {"sample": image}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_forward_signature(self):
pass
def test_training(self):
pass
def test_from_pretrained_hub(self):
model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
with torch.no_grad():
output = model(image, sample_posterior=True)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-4.0078e-01, -3.8304e-04, -1.2681e-01, -1.1462e-01, 2.0095e-01, 1.0893e-01, -8.8248e-02, -3.0361e-01, -9.8646e-03])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
tests/test_models_vq.py 0 → 100644
View file @ 102cabeb
# 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 torch
from diffusers import VQModel
from diffusers.testing_utils import floats_tensor, torch_device
from .test_modeling_common import ModelTesterMixin
class VQModelTests(ModelTesterMixin, unittest.TestCase):
model_class = VQModel
@property
def dummy_input(self, sizes=(32, 32)):
batch_size = 4
num_channels = 3
image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
return {"sample": image}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 3,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_forward_signature(self):
pass
def test_training(self):
pass
def test_from_pretrained_hub(self):
model, loading_info = VQModel.from_pretrained("fusing/vqgan-dummy", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = VQModel.from_pretrained("fusing/vqgan-dummy")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
with torch.no_grad():
output = model(image)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
tests/test_pipelines.py 0 → 100644
View file @ 102cabeb
# 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 tempfile
import unittest
import numpy as np
import torch
import PIL
from diffusers import (
DDIMPipeline,
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
KarrasVePipeline,
KarrasVeScheduler,
LDMPipeline,
LDMTextToImagePipeline,
LMSDiscreteScheduler,
PNDMPipeline,
PNDMScheduler,
ScoreSdeVePipeline,
ScoreSdeVeScheduler,
StableDiffusionPipeline,
UNet2DModel,
)
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.testing_utils import slow, torch_device
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineTesterMixin(unittest.TestCase):
def test_from_pretrained_save_pretrained(self):
# 1. Load models
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"),
)
schedular = DDPMScheduler(num_train_timesteps=10)
ddpm = DDPMPipeline(model, schedular)
with tempfile.TemporaryDirectory() as tmpdirname:
ddpm.save_pretrained(tmpdirname)
new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy")["sample"]
generator = generator.manual_seed(0)
new_image = new_ddpm(generator=generator, output_type="numpy")["sample"]
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"
ddpm = DDPMPipeline.from_pretrained(model_path)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
ddpm.scheduler.num_timesteps = 10
ddpm_from_hub.scheduler.num_timesteps = 10
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy")["sample"]
generator = generator.manual_seed(0)
new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
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"
# pass unet into DiffusionPipeline
unet = UNet2DModel.from_pretrained(model_path)
ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
ddpm_from_hub_custom_model.scheduler.num_timesteps = 10
ddpm_from_hub.scheduler.num_timesteps = 10
generator = torch.manual_seed(0)
image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy")["sample"]
generator = generator.manual_seed(0)
new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
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"
pipe = DDIMPipeline.from_pretrained(model_path)
generator = torch.manual_seed(0)
images = pipe(generator=generator, output_type="numpy")["sample"]
assert images.shape == (1, 32, 32, 3)
assert isinstance(images, np.ndarray)
images = pipe(generator=generator, output_type="pil")["sample"]
assert isinstance(images, list)
assert len(images) == 1
assert isinstance(images[0], PIL.Image.Image)
# use PIL by default
images = pipe(generator=generator)["sample"]
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)
scheduler = scheduler.set_format("pt")
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy")["sample"]
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)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy")["sample"]
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(tensor_format="pt")
ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
generator = torch.manual_seed(0)
image = ddim(generator=generator, eta=0.0, output_type="numpy")["sample"]
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(tensor_format="pt")
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
generator = torch.manual_seed(0)
image = pndm(generator=generator, output_type="numpy")["sample"]
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")
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")[
"sample"
]
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")
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")["sample"]
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
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_stable_diffusion(self):
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1").to(torch_device)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np"
)
image = output["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_stable_diffusion_fast_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1").to(torch_device)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
sd_pipe.scheduler = scheduler
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
image = output["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.8354, 0.83, 0.866, 0.838, 0.8315, 0.867, 0.836, 0.8584, 0.869])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@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)
torch.manual_seed(0)
image = sde_ve(num_inference_steps=300, output_type="numpy")["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.64363, 0.5868, 0.3031, 0.2284, 0.7409, 0.3216, 0.25643, 0.6557, 0.2633])
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")
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=5, output_type="numpy")["sample"]
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"
unet = UNet2DModel.from_pretrained(model_id)
ddpm_scheduler = DDPMScheduler(tensor_format="pt")
ddim_scheduler = DDIMScheduler(tensor_format="pt")
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
generator = torch.manual_seed(0)
ddpm_image = ddpm(generator=generator, output_type="numpy")["sample"]
generator = torch.manual_seed(0)
ddim_image = ddim(generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")["sample"]
# the values aren't exactly equal, but the images look the same visually
assert np.abs(ddpm_image - ddim_image).max() < 1e-1
@unittest.skip("(Anton) The test is failing for large batch sizes, needs investigation")
def test_ddpm_ddim_equality_batched(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
ddpm_scheduler = DDPMScheduler(tensor_format="pt")
ddim_scheduler = DDIMScheduler(tensor_format="pt")
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
generator = torch.manual_seed(0)
ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy")["sample"]
generator = torch.manual_seed(0)
ddim_images = ddim(batch_size=4, generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")[
"sample"
]
# 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(tensor_format="pt")
pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
generator = torch.manual_seed(0)
image = pipe(num_inference_steps=20, generator=generator, output_type="numpy")["sample"]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.26815, 0.1581, 0.2658, 0.23248, 0.1550, 0.2539, 0.1131, 0.1024, 0.0837])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_lms_stable_diffusion_pipeline(self):
model_id = "CompVis/stable-diffusion-v1-1"
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True).to(torch_device)
scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler", use_auth_token=True)
pipe.scheduler = scheduler
prompt = "a photograph of an astronaut riding a horse"
generator = torch.Generator(device=torch_device).manual_seed(0)
image = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")[
"sample"
]
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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