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hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_outputs.py
|
import pickle as pkl
import unittest
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
from diffusers.utils.outputs import BaseOutput
from diffusers.utils.testing_utils import require_torch
@dataclass
class CustomOutput(BaseOutput):
images: Union[List[PIL.Image.Image], np.ndarray]
class ConfigTester(unittest.TestCase):
def test_outputs_single_attribute(self):
outputs = CustomOutput(images=np.random.rand(1, 3, 4, 4))
# check every way of getting the attribute
assert isinstance(outputs.images, np.ndarray)
assert outputs.images.shape == (1, 3, 4, 4)
assert isinstance(outputs["images"], np.ndarray)
assert outputs["images"].shape == (1, 3, 4, 4)
assert isinstance(outputs[0], np.ndarray)
assert outputs[0].shape == (1, 3, 4, 4)
# test with a non-tensor attribute
outputs = CustomOutput(images=[PIL.Image.new("RGB", (4, 4))])
# check every way of getting the attribute
assert isinstance(outputs.images, list)
assert isinstance(outputs.images[0], PIL.Image.Image)
assert isinstance(outputs["images"], list)
assert isinstance(outputs["images"][0], PIL.Image.Image)
assert isinstance(outputs[0], list)
assert isinstance(outputs[0][0], PIL.Image.Image)
def test_outputs_dict_init(self):
# test output reinitialization with a `dict` for compatibility with `accelerate`
outputs = CustomOutput({"images": np.random.rand(1, 3, 4, 4)})
# check every way of getting the attribute
assert isinstance(outputs.images, np.ndarray)
assert outputs.images.shape == (1, 3, 4, 4)
assert isinstance(outputs["images"], np.ndarray)
assert outputs["images"].shape == (1, 3, 4, 4)
assert isinstance(outputs[0], np.ndarray)
assert outputs[0].shape == (1, 3, 4, 4)
# test with a non-tensor attribute
outputs = CustomOutput({"images": [PIL.Image.new("RGB", (4, 4))]})
# check every way of getting the attribute
assert isinstance(outputs.images, list)
assert isinstance(outputs.images[0], PIL.Image.Image)
assert isinstance(outputs["images"], list)
assert isinstance(outputs["images"][0], PIL.Image.Image)
assert isinstance(outputs[0], list)
assert isinstance(outputs[0][0], PIL.Image.Image)
def test_outputs_serialization(self):
outputs_orig = CustomOutput(images=[PIL.Image.new("RGB", (4, 4))])
serialized = pkl.dumps(outputs_orig)
outputs_copy = pkl.loads(serialized)
# Check original and copy are equal
assert dir(outputs_orig) == dir(outputs_copy)
assert dict(outputs_orig) == dict(outputs_copy)
assert vars(outputs_orig) == vars(outputs_copy)
@require_torch
def test_torch_pytree(self):
# ensure torch.utils._pytree treats ModelOutput subclasses as nodes (and not leaves)
# this is important for DistributedDataParallel gradient synchronization with static_graph=True
import torch
import torch.utils._pytree
data = np.random.rand(1, 3, 4, 4)
x = CustomOutput(images=data)
self.assertFalse(torch.utils._pytree._is_leaf(x))
expected_flat_outs = [data]
expected_tree_spec = torch.utils._pytree.TreeSpec(CustomOutput, ["images"], [torch.utils._pytree.LeafSpec()])
actual_flat_outs, actual_tree_spec = torch.utils._pytree.tree_flatten(x)
self.assertEqual(expected_flat_outs, actual_flat_outs)
self.assertEqual(expected_tree_spec, actual_tree_spec)
unflattened_x = torch.utils._pytree.tree_unflatten(actual_flat_outs, actual_tree_spec)
self.assertEqual(x, unflattened_x)
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_check_dummies.py
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import unittest
git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
check_dummies.PATH_TO_DIFFUSERS = os.path.join(git_repo_path, "src", "diffusers")
class CheckDummiesTester(unittest.TestCase):
def test_find_backend(self):
simple_backend = find_backend(" if not is_torch_available():")
self.assertEqual(simple_backend, "torch")
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
double_backend = find_backend(" if not (is_torch_available() and is_transformers_available()):")
self.assertEqual(double_backend, "torch_and_transformers")
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
triple_backend = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"
)
self.assertEqual(triple_backend, "torch_and_transformers_and_onnx")
def test_read_init(self):
objects = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch", objects)
self.assertIn("torch_and_transformers", objects)
self.assertIn("flax_and_transformers", objects)
self.assertIn("torch_and_transformers_and_onnx", objects)
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel", objects["torch"])
self.assertIn("FlaxUNet2DConditionModel", objects["flax"])
self.assertIn("StableDiffusionPipeline", objects["torch_and_transformers"])
self.assertIn("FlaxStableDiffusionPipeline", objects["flax_and_transformers"])
self.assertIn("LMSDiscreteScheduler", objects["torch_and_scipy"])
self.assertIn("OnnxStableDiffusionPipeline", objects["torch_and_transformers_and_onnx"])
def test_create_dummy_object(self):
dummy_constant = create_dummy_object("CONSTANT", "'torch'")
self.assertEqual(dummy_constant, "\nCONSTANT = None\n")
dummy_function = create_dummy_object("function", "'torch'")
self.assertEqual(
dummy_function, "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n"
)
expected_dummy_class = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
dummy_class = create_dummy_object("FakeClass", "'torch'")
self.assertEqual(dummy_class, expected_dummy_class)
def test_create_dummy_files(self):
expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
"""
dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
self.assertEqual(dummy_files["torch"], expected_dummy_pytorch_file)
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_config.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from diffusers import (
DDIMScheduler,
DDPMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
PNDMScheduler,
logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger
class SampleObject(ConfigMixin):
config_name = "config.json"
@register_to_config
def __init__(
self,
a=2,
b=5,
c=(2, 5),
d="for diffusion",
e=[1, 3],
):
pass
class SampleObject2(ConfigMixin):
config_name = "config.json"
@register_to_config
def __init__(
self,
a=2,
b=5,
c=(2, 5),
d="for diffusion",
f=[1, 3],
):
pass
class SampleObject3(ConfigMixin):
config_name = "config.json"
@register_to_config
def __init__(
self,
a=2,
b=5,
c=(2, 5),
d="for diffusion",
e=[1, 3],
f=[1, 3],
):
pass
class SampleObject4(ConfigMixin):
config_name = "config.json"
@register_to_config
def __init__(
self,
a=2,
b=5,
c=(2, 5),
d="for diffusion",
e=[1, 5],
f=[5, 4],
):
pass
class ConfigTester(unittest.TestCase):
def test_load_not_from_mixin(self):
with self.assertRaises(ValueError):
ConfigMixin.load_config("dummy_path")
def test_register_to_config(self):
obj = SampleObject()
config = obj.config
assert config["a"] == 2
assert config["b"] == 5
assert config["c"] == (2, 5)
assert config["d"] == "for diffusion"
assert config["e"] == [1, 3]
# init ignore private arguments
obj = SampleObject(_name_or_path="lalala")
config = obj.config
assert config["a"] == 2
assert config["b"] == 5
assert config["c"] == (2, 5)
assert config["d"] == "for diffusion"
assert config["e"] == [1, 3]
# can override default
obj = SampleObject(c=6)
config = obj.config
assert config["a"] == 2
assert config["b"] == 5
assert config["c"] == 6
assert config["d"] == "for diffusion"
assert config["e"] == [1, 3]
# can use positional arguments.
obj = SampleObject(1, c=6)
config = obj.config
assert config["a"] == 1
assert config["b"] == 5
assert config["c"] == 6
assert config["d"] == "for diffusion"
assert config["e"] == [1, 3]
def test_save_load(self):
obj = SampleObject()
config = obj.config
assert config["a"] == 2
assert config["b"] == 5
assert config["c"] == (2, 5)
assert config["d"] == "for diffusion"
assert config["e"] == [1, 3]
with tempfile.TemporaryDirectory() as tmpdirname:
obj.save_config(tmpdirname)
new_obj = SampleObject.from_config(SampleObject.load_config(tmpdirname))
new_config = new_obj.config
# unfreeze configs
config = dict(config)
new_config = dict(new_config)
assert config.pop("c") == (2, 5) # instantiated as tuple
assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json
config.pop("_use_default_values")
assert config == new_config
def test_load_ddim_from_pndm(self):
logger = logging.get_logger("diffusers.configuration_utils")
# 30 for warning
logger.setLevel(30)
with CaptureLogger(logger) as cap_logger:
ddim = DDIMScheduler.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"
)
assert ddim.__class__ == DDIMScheduler
# no warning should be thrown
assert cap_logger.out == ""
def test_load_euler_from_pndm(self):
logger = logging.get_logger("diffusers.configuration_utils")
# 30 for warning
logger.setLevel(30)
with CaptureLogger(logger) as cap_logger:
euler = EulerDiscreteScheduler.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"
)
assert euler.__class__ == EulerDiscreteScheduler
# no warning should be thrown
assert cap_logger.out == ""
def test_load_euler_ancestral_from_pndm(self):
logger = logging.get_logger("diffusers.configuration_utils")
# 30 for warning
logger.setLevel(30)
with CaptureLogger(logger) as cap_logger:
euler = EulerAncestralDiscreteScheduler.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"
)
assert euler.__class__ == EulerAncestralDiscreteScheduler
# no warning should be thrown
assert cap_logger.out == ""
def test_load_pndm(self):
logger = logging.get_logger("diffusers.configuration_utils")
# 30 for warning
logger.setLevel(30)
with CaptureLogger(logger) as cap_logger:
pndm = PNDMScheduler.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"
)
assert pndm.__class__ == PNDMScheduler
# no warning should be thrown
assert cap_logger.out == ""
def test_overwrite_config_on_load(self):
logger = logging.get_logger("diffusers.configuration_utils")
# 30 for warning
logger.setLevel(30)
with CaptureLogger(logger) as cap_logger:
ddpm = DDPMScheduler.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch",
subfolder="scheduler",
prediction_type="sample",
beta_end=8,
)
with CaptureLogger(logger) as cap_logger_2:
ddpm_2 = DDPMScheduler.from_pretrained("google/ddpm-celebahq-256", beta_start=88)
assert ddpm.__class__ == DDPMScheduler
assert ddpm.config.prediction_type == "sample"
assert ddpm.config.beta_end == 8
assert ddpm_2.config.beta_start == 88
# no warning should be thrown
assert cap_logger.out == ""
assert cap_logger_2.out == ""
def test_load_dpmsolver(self):
logger = logging.get_logger("diffusers.configuration_utils")
# 30 for warning
logger.setLevel(30)
with CaptureLogger(logger) as cap_logger:
dpm = DPMSolverMultistepScheduler.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"
)
assert dpm.__class__ == DPMSolverMultistepScheduler
# no warning should be thrown
assert cap_logger.out == ""
def test_use_default_values(self):
# let's first save a config that should be in the form
# a=2,
# b=5,
# c=(2, 5),
# d="for diffusion",
# e=[1, 3],
config = SampleObject()
config_dict = {k: v for k, v in config.config.items() if not k.startswith("_")}
# make sure that default config has all keys in `_use_default_values`
assert set(config_dict.keys()) == set(config.config._use_default_values)
with tempfile.TemporaryDirectory() as tmpdirname:
config.save_config(tmpdirname)
# now loading it with SampleObject2 should put f into `_use_default_values`
config = SampleObject2.from_config(tmpdirname)
assert "f" in config._use_default_values
assert config.f == [1, 3]
# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
# **BECAUSE** it is part of `config._use_default_values`
new_config = SampleObject4.from_config(config.config)
assert new_config.f == [5, 4]
config.config._use_default_values.pop()
new_config_2 = SampleObject4.from_config(config.config)
assert new_config_2.f == [1, 3]
# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
assert new_config_2.e == [1, 3]
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_utils.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from distutils.util import strtobool
import pytest
from diffusers import __version__
from diffusers.utils import deprecate
# Used to test the hub
USER = "__DUMMY_TRANSFORMERS_USER__"
ENDPOINT_STAGING = "https://hub-ci.huggingface.co"
# Not critical, only usable on the sandboxed CI instance.
TOKEN = "hf_94wBhPGp6KrrTH3KDchhKpRxZwd6dmHWLL"
class DeprecateTester(unittest.TestCase):
higher_version = ".".join([str(int(__version__.split(".")[0]) + 1)] + __version__.split(".")[1:])
lower_version = "0.0.1"
def test_deprecate_function_arg(self):
kwargs = {"deprecated_arg": 4}
with self.assertWarns(FutureWarning) as warning:
output = deprecate("deprecated_arg", self.higher_version, "message", take_from=kwargs)
assert output == 4
assert (
str(warning.warning)
== f"The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}."
" message"
)
def test_deprecate_function_arg_tuple(self):
kwargs = {"deprecated_arg": 4}
with self.assertWarns(FutureWarning) as warning:
output = deprecate(("deprecated_arg", self.higher_version, "message"), take_from=kwargs)
assert output == 4
assert (
str(warning.warning)
== f"The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}."
" message"
)
def test_deprecate_function_args(self):
kwargs = {"deprecated_arg_1": 4, "deprecated_arg_2": 8}
with self.assertWarns(FutureWarning) as warning:
output_1, output_2 = deprecate(
("deprecated_arg_1", self.higher_version, "Hey"),
("deprecated_arg_2", self.higher_version, "Hey"),
take_from=kwargs,
)
assert output_1 == 4
assert output_2 == 8
assert (
str(warning.warnings[0].message)
== "The `deprecated_arg_1` argument is deprecated and will be removed in version"
f" {self.higher_version}. Hey"
)
assert (
str(warning.warnings[1].message)
== "The `deprecated_arg_2` argument is deprecated and will be removed in version"
f" {self.higher_version}. Hey"
)
def test_deprecate_function_incorrect_arg(self):
kwargs = {"deprecated_arg": 4}
with self.assertRaises(TypeError) as error:
deprecate(("wrong_arg", self.higher_version, "message"), take_from=kwargs)
assert "test_deprecate_function_incorrect_arg in" in str(error.exception)
assert "line" in str(error.exception)
assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception)
def test_deprecate_arg_no_kwarg(self):
with self.assertWarns(FutureWarning) as warning:
deprecate(("deprecated_arg", self.higher_version, "message"))
assert (
str(warning.warning)
== f"`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message"
)
def test_deprecate_args_no_kwarg(self):
with self.assertWarns(FutureWarning) as warning:
deprecate(
("deprecated_arg_1", self.higher_version, "Hey"),
("deprecated_arg_2", self.higher_version, "Hey"),
)
assert (
str(warning.warnings[0].message)
== f"`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey"
)
assert (
str(warning.warnings[1].message)
== f"`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey"
)
def test_deprecate_class_obj(self):
class Args:
arg = 5
with self.assertWarns(FutureWarning) as warning:
arg = deprecate(("arg", self.higher_version, "message"), take_from=Args())
assert arg == 5
assert (
str(warning.warning)
== f"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
)
def test_deprecate_class_objs(self):
class Args:
arg = 5
foo = 7
with self.assertWarns(FutureWarning) as warning:
arg_1, arg_2 = deprecate(
("arg", self.higher_version, "message"),
("foo", self.higher_version, "message"),
("does not exist", self.higher_version, "message"),
take_from=Args(),
)
assert arg_1 == 5
assert arg_2 == 7
assert (
str(warning.warning)
== f"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
)
assert (
str(warning.warnings[0].message)
== f"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
)
assert (
str(warning.warnings[1].message)
== f"The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message"
)
def test_deprecate_incorrect_version(self):
kwargs = {"deprecated_arg": 4}
with self.assertRaises(ValueError) as error:
deprecate(("wrong_arg", self.lower_version, "message"), take_from=kwargs)
assert (
str(error.exception)
== "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
f" {__version__} is >= {self.lower_version}"
)
def test_deprecate_incorrect_no_standard_warn(self):
with self.assertWarns(FutureWarning) as warning:
deprecate(("deprecated_arg", self.higher_version, "This message is better!!!"), standard_warn=False)
assert str(warning.warning) == "This message is better!!!"
def test_deprecate_stacklevel(self):
with self.assertWarns(FutureWarning) as warning:
deprecate(("deprecated_arg", self.higher_version, "This message is better!!!"), standard_warn=False)
assert str(warning.warning) == "This message is better!!!"
assert "diffusers/tests/others/test_utils.py" in warning.filename
def parse_flag_from_env(key, default=False):
try:
value = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_value = default
else:
# KEY is set, convert it to True or False.
try:
_value = strtobool(value)
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"If set, {key} must be yes or no.")
return _value
_run_staging = parse_flag_from_env("HUGGINGFACE_CO_STAGING", default=False)
def is_staging_test(test_case):
"""
Decorator marking a test as a staging test.
Those tests will run using the staging environment of huggingface.co instead of the real model hub.
"""
if not _run_staging:
return unittest.skip("test is staging test")(test_case)
else:
return pytest.mark.is_staging_test()(test_case)
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_image_processor.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import PIL.Image
import torch
from diffusers.image_processor import VaeImageProcessor
class ImageProcessorTest(unittest.TestCase):
@property
def dummy_sample(self):
batch_size = 1
num_channels = 3
height = 8
width = 8
sample = torch.rand((batch_size, num_channels, height, width))
return sample
@property
def dummy_mask(self):
batch_size = 1
num_channels = 1
height = 8
width = 8
sample = torch.rand((batch_size, num_channels, height, width))
return sample
def to_np(self, image):
if isinstance(image[0], PIL.Image.Image):
return np.stack([np.array(i) for i in image], axis=0)
elif isinstance(image, torch.Tensor):
return image.cpu().numpy().transpose(0, 2, 3, 1)
return image
def test_vae_image_processor_pt(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
input_pt = self.dummy_sample
input_np = self.to_np(input_pt)
for output_type in ["pt", "np", "pil"]:
out = image_processor.postprocess(
image_processor.preprocess(input_pt),
output_type=output_type,
)
out_np = self.to_np(out)
in_np = (input_np * 255).round() if output_type == "pil" else input_np
assert (
np.abs(in_np - out_np).max() < 1e-6
), f"decoded output does not match input for output_type {output_type}"
def test_vae_image_processor_np(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)
for output_type in ["pt", "np", "pil"]:
out = image_processor.postprocess(image_processor.preprocess(input_np), output_type=output_type)
out_np = self.to_np(out)
in_np = (input_np * 255).round() if output_type == "pil" else input_np
assert (
np.abs(in_np - out_np).max() < 1e-6
), f"decoded output does not match input for output_type {output_type}"
def test_vae_image_processor_pil(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)
input_pil = image_processor.numpy_to_pil(input_np)
for output_type in ["pt", "np", "pil"]:
out = image_processor.postprocess(image_processor.preprocess(input_pil), output_type=output_type)
for i, o in zip(input_pil, out):
in_np = np.array(i)
out_np = self.to_np(out) if output_type == "pil" else (self.to_np(out) * 255).round()
assert (
np.abs(in_np - out_np).max() < 1e-6
), f"decoded output does not match input for output_type {output_type}"
def test_preprocess_input_3d(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
input_pt_4d = self.dummy_sample
input_pt_3d = input_pt_4d.squeeze(0)
out_pt_4d = image_processor.postprocess(
image_processor.preprocess(input_pt_4d),
output_type="np",
)
out_pt_3d = image_processor.postprocess(
image_processor.preprocess(input_pt_3d),
output_type="np",
)
input_np_4d = self.to_np(self.dummy_sample)
input_np_3d = input_np_4d.squeeze(0)
out_np_4d = image_processor.postprocess(
image_processor.preprocess(input_np_4d),
output_type="np",
)
out_np_3d = image_processor.postprocess(
image_processor.preprocess(input_np_3d),
output_type="np",
)
assert np.abs(out_pt_4d - out_pt_3d).max() < 1e-6
assert np.abs(out_np_4d - out_np_3d).max() < 1e-6
def test_preprocess_input_list(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
input_pt_4d = self.dummy_sample
input_pt_list = list(input_pt_4d)
out_pt_4d = image_processor.postprocess(
image_processor.preprocess(input_pt_4d),
output_type="np",
)
out_pt_list = image_processor.postprocess(
image_processor.preprocess(input_pt_list),
output_type="np",
)
input_np_4d = self.to_np(self.dummy_sample)
input_np_list = list(input_np_4d)
out_np_4d = image_processor.postprocess(
image_processor.preprocess(input_np_4d),
output_type="np",
)
out_np_list = image_processor.postprocess(
image_processor.preprocess(input_np_list),
output_type="np",
)
assert np.abs(out_pt_4d - out_pt_list).max() < 1e-6
assert np.abs(out_np_4d - out_np_list).max() < 1e-6
def test_preprocess_input_mask_3d(self):
image_processor = VaeImageProcessor(
do_resize=False, do_normalize=False, do_binarize=True, do_convert_grayscale=True
)
input_pt_4d = self.dummy_mask
input_pt_3d = input_pt_4d.squeeze(0)
input_pt_2d = input_pt_3d.squeeze(0)
out_pt_4d = image_processor.postprocess(
image_processor.preprocess(input_pt_4d),
output_type="np",
)
out_pt_3d = image_processor.postprocess(
image_processor.preprocess(input_pt_3d),
output_type="np",
)
out_pt_2d = image_processor.postprocess(
image_processor.preprocess(input_pt_2d),
output_type="np",
)
input_np_4d = self.to_np(self.dummy_mask)
input_np_3d = input_np_4d.squeeze(0)
input_np_3d_1 = input_np_4d.squeeze(-1)
input_np_2d = input_np_3d.squeeze(-1)
out_np_4d = image_processor.postprocess(
image_processor.preprocess(input_np_4d),
output_type="np",
)
out_np_3d = image_processor.postprocess(
image_processor.preprocess(input_np_3d),
output_type="np",
)
out_np_3d_1 = image_processor.postprocess(
image_processor.preprocess(input_np_3d_1),
output_type="np",
)
out_np_2d = image_processor.postprocess(
image_processor.preprocess(input_np_2d),
output_type="np",
)
assert np.abs(out_pt_4d - out_pt_3d).max() == 0
assert np.abs(out_pt_4d - out_pt_2d).max() == 0
assert np.abs(out_np_4d - out_np_3d).max() == 0
assert np.abs(out_np_4d - out_np_3d_1).max() == 0
assert np.abs(out_np_4d - out_np_2d).max() == 0
def test_preprocess_input_mask_list(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=False, do_convert_grayscale=True)
input_pt_4d = self.dummy_mask
input_pt_3d = input_pt_4d.squeeze(0)
input_pt_2d = input_pt_3d.squeeze(0)
inputs_pt = [input_pt_4d, input_pt_3d, input_pt_2d]
inputs_pt_list = [[input_pt] for input_pt in inputs_pt]
for input_pt, input_pt_list in zip(inputs_pt, inputs_pt_list):
out_pt = image_processor.postprocess(
image_processor.preprocess(input_pt),
output_type="np",
)
out_pt_list = image_processor.postprocess(
image_processor.preprocess(input_pt_list),
output_type="np",
)
assert np.abs(out_pt - out_pt_list).max() < 1e-6
input_np_4d = self.to_np(self.dummy_mask)
input_np_3d = input_np_4d.squeeze(0)
input_np_2d = input_np_3d.squeeze(-1)
inputs_np = [input_np_4d, input_np_3d, input_np_2d]
inputs_np_list = [[input_np] for input_np in inputs_np]
for input_np, input_np_list in zip(inputs_np, inputs_np_list):
out_np = image_processor.postprocess(
image_processor.preprocess(input_np),
output_type="np",
)
out_np_list = image_processor.postprocess(
image_processor.preprocess(input_np_list),
output_type="np",
)
assert np.abs(out_np - out_np_list).max() < 1e-6
def test_preprocess_input_mask_3d_batch(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=False, do_convert_grayscale=True)
# create a dummy mask input with batch_size 2
dummy_mask_batch = torch.cat([self.dummy_mask] * 2, axis=0)
# squeeze out the channel dimension
input_pt_3d = dummy_mask_batch.squeeze(1)
input_np_3d = self.to_np(dummy_mask_batch).squeeze(-1)
input_pt_3d_list = list(input_pt_3d)
input_np_3d_list = list(input_np_3d)
out_pt_3d = image_processor.postprocess(
image_processor.preprocess(input_pt_3d),
output_type="np",
)
out_pt_3d_list = image_processor.postprocess(
image_processor.preprocess(input_pt_3d_list),
output_type="np",
)
assert np.abs(out_pt_3d - out_pt_3d_list).max() < 1e-6
out_np_3d = image_processor.postprocess(
image_processor.preprocess(input_np_3d),
output_type="np",
)
out_np_3d_list = image_processor.postprocess(
image_processor.preprocess(input_np_3d_list),
output_type="np",
)
assert np.abs(out_np_3d - out_np_3d_list).max() < 1e-6
def test_vae_image_processor_resize_pt(self):
image_processor = VaeImageProcessor(do_resize=True, vae_scale_factor=1)
input_pt = self.dummy_sample
b, c, h, w = input_pt.shape
scale = 2
out_pt = image_processor.resize(image=input_pt, height=h // scale, width=w // scale)
exp_pt_shape = (b, c, h // scale, w // scale)
assert (
out_pt.shape == exp_pt_shape
), f"resized image output shape '{out_pt.shape}' didn't match expected shape '{exp_pt_shape}'."
def test_vae_image_processor_resize_np(self):
image_processor = VaeImageProcessor(do_resize=True, vae_scale_factor=1)
input_pt = self.dummy_sample
b, c, h, w = input_pt.shape
scale = 2
input_np = self.to_np(input_pt)
out_np = image_processor.resize(image=input_np, height=h // scale, width=w // scale)
exp_np_shape = (b, h // scale, w // scale, c)
assert (
out_np.shape == exp_np_shape
), f"resized image output shape '{out_np.shape}' didn't match expected shape '{exp_np_shape}'."
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_hub_utils.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch
import diffusers.utils.hub_utils
class CreateModelCardTest(unittest.TestCase):
@patch("diffusers.utils.hub_utils.get_full_repo_name")
def test_create_model_card(self, repo_name_mock: Mock) -> None:
repo_name_mock.return_value = "full_repo_name"
with TemporaryDirectory() as tmpdir:
# Dummy args values
args = Mock()
args.output_dir = tmpdir
args.local_rank = 0
args.hub_token = "hub_token"
args.dataset_name = "dataset_name"
args.learning_rate = 0.01
args.train_batch_size = 100000
args.eval_batch_size = 10000
args.gradient_accumulation_steps = 0.01
args.adam_beta1 = 0.02
args.adam_beta2 = 0.03
args.adam_weight_decay = 0.0005
args.adam_epsilon = 0.000001
args.lr_scheduler = 1
args.lr_warmup_steps = 10
args.ema_inv_gamma = 0.001
args.ema_power = 0.1
args.ema_max_decay = 0.2
args.mixed_precision = True
# Model card mush be rendered and saved
diffusers.utils.hub_utils.create_model_card(args, model_name="model_name")
self.assertTrue((Path(tmpdir) / "README.md").is_file())
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_training.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNet2DModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
torch.backends.cuda.matmul.allow_tf32 = False
class TrainingTests(unittest.TestCase):
def get_model_optimizer(self, resolution=32):
set_seed(0)
model = UNet2DModel(sample_size=resolution, in_channels=3, out_channels=3)
optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)
return model, optimizer
@slow
def test_training_step_equality(self):
device = "cpu" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
ddpm_scheduler = DDPMScheduler(
num_train_timesteps=1000,
beta_start=0.0001,
beta_end=0.02,
beta_schedule="linear",
clip_sample=True,
)
ddim_scheduler = DDIMScheduler(
num_train_timesteps=1000,
beta_start=0.0001,
beta_end=0.02,
beta_schedule="linear",
clip_sample=True,
)
assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
# shared batches for DDPM and DDIM
set_seed(0)
clean_images = [torch.randn((4, 3, 32, 32)).clip(-1, 1).to(device) for _ in range(4)]
noise = [torch.randn((4, 3, 32, 32)).to(device) for _ in range(4)]
timesteps = [torch.randint(0, 1000, (4,)).long().to(device) for _ in range(4)]
# train with a DDPM scheduler
model, optimizer = self.get_model_optimizer(resolution=32)
model.train().to(device)
for i in range(4):
optimizer.zero_grad()
ddpm_noisy_images = ddpm_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
ddpm_noise_pred = model(ddpm_noisy_images, timesteps[i]).sample
loss = torch.nn.functional.mse_loss(ddpm_noise_pred, noise[i])
loss.backward()
optimizer.step()
del model, optimizer
# recreate the model and optimizer, and retry with DDIM
model, optimizer = self.get_model_optimizer(resolution=32)
model.train().to(device)
for i in range(4):
optimizer.zero_grad()
ddim_noisy_images = ddim_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
ddim_noise_pred = model(ddim_noisy_images, timesteps[i]).sample
loss = torch.nn.functional.mse_loss(ddim_noise_pred, noise[i])
loss.backward()
optimizer.step()
del model, optimizer
self.assertTrue(torch.allclose(ddpm_noisy_images, ddim_noisy_images, atol=1e-5))
self.assertTrue(torch.allclose(ddpm_noise_pred, ddim_noise_pred, atol=1e-5))
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_ema.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import torch
from diffusers import UNet2DConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device
enable_full_determinism()
class EMAModelTests(unittest.TestCase):
model_id = "hf-internal-testing/tiny-stable-diffusion-pipe"
batch_size = 1
prompt_length = 77
text_encoder_hidden_dim = 32
num_in_channels = 4
latent_height = latent_width = 64
generator = torch.manual_seed(0)
def get_models(self, decay=0.9999):
unet = UNet2DConditionModel.from_pretrained(self.model_id, subfolder="unet")
unet = unet.to(torch_device)
ema_unet = EMAModel(unet.parameters(), decay=decay, model_cls=UNet2DConditionModel, model_config=unet.config)
return unet, ema_unet
def get_dummy_inputs(self):
noisy_latents = torch.randn(
self.batch_size, self.num_in_channels, self.latent_height, self.latent_width, generator=self.generator
).to(torch_device)
timesteps = torch.randint(0, 1000, size=(self.batch_size,), generator=self.generator).to(torch_device)
encoder_hidden_states = torch.randn(
self.batch_size, self.prompt_length, self.text_encoder_hidden_dim, generator=self.generator
).to(torch_device)
return noisy_latents, timesteps, encoder_hidden_states
def simulate_backprop(self, unet):
updated_state_dict = {}
for k, param in unet.state_dict().items():
updated_param = torch.randn_like(param) + (param * torch.randn_like(param))
updated_state_dict.update({k: updated_param})
unet.load_state_dict(updated_state_dict)
return unet
def test_optimization_steps_updated(self):
unet, ema_unet = self.get_models()
# Take the first (hypothetical) EMA step.
ema_unet.step(unet.parameters())
assert ema_unet.optimization_step == 1
# Take two more.
for _ in range(2):
ema_unet.step(unet.parameters())
assert ema_unet.optimization_step == 3
def test_shadow_params_not_updated(self):
unet, ema_unet = self.get_models()
# Since the `unet` is not being updated (i.e., backprop'd)
# there won't be any difference between the `params` of `unet`
# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
ema_unet.step(unet.parameters())
orig_params = list(unet.parameters())
for s_param, param in zip(ema_unet.shadow_params, orig_params):
assert torch.allclose(s_param, param)
# The above holds true even if we call `ema.step()` multiple times since
# `unet` params are still not being updated.
for _ in range(4):
ema_unet.step(unet.parameters())
for s_param, param in zip(ema_unet.shadow_params, orig_params):
assert torch.allclose(s_param, param)
def test_shadow_params_updated(self):
unet, ema_unet = self.get_models()
# Here we simulate the parameter updates for `unet`. Since there might
# be some parameters which are initialized to zero we take extra care to
# initialize their values to something non-zero before the multiplication.
unet_pseudo_updated_step_one = self.simulate_backprop(unet)
# Take the EMA step.
ema_unet.step(unet_pseudo_updated_step_one.parameters())
# Now the EMA'd parameters won't be equal to the original model parameters.
orig_params = list(unet_pseudo_updated_step_one.parameters())
for s_param, param in zip(ema_unet.shadow_params, orig_params):
assert ~torch.allclose(s_param, param)
# Ensure this is the case when we take multiple EMA steps.
for _ in range(4):
ema_unet.step(unet.parameters())
for s_param, param in zip(ema_unet.shadow_params, orig_params):
assert ~torch.allclose(s_param, param)
def test_consecutive_shadow_params_updated(self):
# If we call EMA step after a backpropagation consecutively for two times,
# the shadow params from those two steps should be different.
unet, ema_unet = self.get_models()
# First backprop + EMA
unet_step_one = self.simulate_backprop(unet)
ema_unet.step(unet_step_one.parameters())
step_one_shadow_params = ema_unet.shadow_params
# Second backprop + EMA
unet_step_two = self.simulate_backprop(unet_step_one)
ema_unet.step(unet_step_two.parameters())
step_two_shadow_params = ema_unet.shadow_params
for step_one, step_two in zip(step_one_shadow_params, step_two_shadow_params):
assert ~torch.allclose(step_one, step_two)
def test_zero_decay(self):
# If there's no decay even if there are backprops, EMA steps
# won't take any effect i.e., the shadow params would remain the
# same.
unet, ema_unet = self.get_models(decay=0.0)
unet_step_one = self.simulate_backprop(unet)
ema_unet.step(unet_step_one.parameters())
step_one_shadow_params = ema_unet.shadow_params
unet_step_two = self.simulate_backprop(unet_step_one)
ema_unet.step(unet_step_two.parameters())
step_two_shadow_params = ema_unet.shadow_params
for step_one, step_two in zip(step_one_shadow_params, step_two_shadow_params):
assert torch.allclose(step_one, step_two)
@skip_mps
def test_serialization(self):
unet, ema_unet = self.get_models()
noisy_latents, timesteps, encoder_hidden_states = self.get_dummy_inputs()
with tempfile.TemporaryDirectory() as tmpdir:
ema_unet.save_pretrained(tmpdir)
loaded_unet = UNet2DConditionModel.from_pretrained(tmpdir, model_cls=UNet2DConditionModel)
loaded_unet = loaded_unet.to(unet.device)
# Since no EMA step has been performed the outputs should match.
output = unet(noisy_latents, timesteps, encoder_hidden_states).sample
output_loaded = loaded_unet(noisy_latents, timesteps, encoder_hidden_states).sample
assert torch.allclose(output, output_loaded, atol=1e-4)
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_check_copies.py
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
import shutil
import sys
import tempfile
import unittest
git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
REFERENCE_CODE = """ \"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
"""
class CopyCheckTester(unittest.TestCase):
def setUp(self):
self.diffusers_dir = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir, "schedulers/"))
check_copies.DIFFUSERS_PATH = self.diffusers_dir
shutil.copy(
os.path.join(git_repo_path, "src/diffusers/schedulers/scheduling_ddpm.py"),
os.path.join(self.diffusers_dir, "schedulers/scheduling_ddpm.py"),
)
def tearDown(self):
check_copies.DIFFUSERS_PATH = "src/diffusers"
shutil.rmtree(self.diffusers_dir)
def check_copy_consistency(self, comment, class_name, class_code, overwrite_result=None):
code = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
expected = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
code = check_copies.run_ruff(code)
fname = os.path.join(self.diffusers_dir, "new_code.py")
with open(fname, "w", newline="\n") as f:
f.write(code)
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(fname)) == 0)
else:
check_copies.is_copy_consistent(f.name, overwrite=True)
with open(fname, "r") as f:
self.assertTrue(f.read(), expected)
def test_find_code_in_diffusers(self):
code = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput")
self.assertEqual(code, REFERENCE_CODE)
def test_is_copy_consistent(self):
# Base copy consistency
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput",
"DDPMSchedulerOutput",
REFERENCE_CODE + "\n",
)
# With no empty line at the end
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput",
"DDPMSchedulerOutput",
REFERENCE_CODE,
)
# Copy consistency with rename
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test",
"TestSchedulerOutput",
re.sub("DDPM", "Test", REFERENCE_CODE),
)
# Copy consistency with a really long name
long_class_name = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
self.check_copy_consistency(
f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}",
f"{long_class_name}SchedulerOutput",
re.sub("Bert", long_class_name, REFERENCE_CODE),
)
# Copy consistency with overwrite
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test",
"TestSchedulerOutput",
REFERENCE_CODE,
overwrite_result=re.sub("DDPM", "Test", REFERENCE_CODE),
)
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/others/test_dependencies.py
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import unittest
from importlib import import_module
class DependencyTester(unittest.TestCase):
def test_diffusers_import(self):
try:
import diffusers # noqa: F401
except ImportError:
assert False
def test_backend_registration(self):
import diffusers
from diffusers.dependency_versions_table import deps
all_classes = inspect.getmembers(diffusers, inspect.isclass)
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
backend = "k-diffusion"
elif backend == "invisible_watermark":
backend = "invisible-watermark"
assert backend in deps, f"{backend} is not in the deps table!"
def test_pipeline_imports(self):
import diffusers
import diffusers.pipelines
all_classes = inspect.getmembers(diffusers, inspect.isclass)
for cls_name, cls_module in all_classes:
if hasattr(diffusers.pipelines, cls_name):
pipeline_folder_module = ".".join(str(cls_module.__module__).split(".")[:3])
_ = import_module(pipeline_folder_module, str(cls_name))
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/pipelines/pipeline_params.py
|
# These are canonical sets of parameters for different types of pipelines.
# They are set on subclasses of `PipelineTesterMixin` as `params` and
# `batch_params`.
#
# If a pipeline's set of arguments has minor changes from one of the common sets
# of arguments, do not make modifications to the existing common sets of arguments.
# I.e. a text to image pipeline with non-configurable height and width arguments
# should set its attribute as `params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`.
TEXT_TO_IMAGE_PARAMS = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
TEXT_TO_IMAGE_BATCH_PARAMS = frozenset(["prompt", "negative_prompt"])
TEXT_TO_IMAGE_IMAGE_PARAMS = frozenset([])
IMAGE_TO_IMAGE_IMAGE_PARAMS = frozenset(["image"])
IMAGE_VARIATION_PARAMS = frozenset(
[
"image",
"height",
"width",
"guidance_scale",
]
)
IMAGE_VARIATION_BATCH_PARAMS = frozenset(["image"])
TEXT_GUIDED_IMAGE_VARIATION_PARAMS = frozenset(
[
"prompt",
"image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS = frozenset(["prompt", "image", "negative_prompt"])
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS = frozenset(
[
# Text guided image variation with an image mask
"prompt",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS = frozenset(["prompt", "image", "mask_image", "negative_prompt"])
IMAGE_INPAINTING_PARAMS = frozenset(
[
# image variation with an image mask
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
IMAGE_INPAINTING_BATCH_PARAMS = frozenset(["image", "mask_image"])
IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS = frozenset(
[
"example_image",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS = frozenset(["example_image", "image", "mask_image"])
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS = frozenset(["class_labels"])
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS = frozenset(["class_labels"])
UNCONDITIONAL_IMAGE_GENERATION_PARAMS = frozenset(["batch_size"])
UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS = frozenset([])
UNCONDITIONAL_AUDIO_GENERATION_PARAMS = frozenset(["batch_size"])
UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS = frozenset([])
TEXT_TO_AUDIO_PARAMS = frozenset(
[
"prompt",
"audio_length_in_s",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
TEXT_TO_AUDIO_BATCH_PARAMS = frozenset(["prompt", "negative_prompt"])
TOKENS_TO_AUDIO_GENERATION_PARAMS = frozenset(["input_tokens"])
TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS = frozenset(["input_tokens"])
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS = frozenset(["prompt_embeds"])
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/pipelines/test_pipelines_combined.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from huggingface_hub import ModelCard
from diffusers import (
DDPMScheduler,
DiffusionPipeline,
KandinskyV22CombinedPipeline,
KandinskyV22Pipeline,
KandinskyV22PriorPipeline,
)
from diffusers.pipelines.pipeline_utils import CONNECTED_PIPES_KEYS
def state_dicts_almost_equal(sd1, sd2):
sd1 = dict(sorted(sd1.items()))
sd2 = dict(sorted(sd2.items()))
models_are_equal = True
for ten1, ten2 in zip(sd1.values(), sd2.values()):
if (ten1 - ten2).abs().sum() > 1e-3:
models_are_equal = False
return models_are_equal
class CombinedPipelineFastTest(unittest.TestCase):
def modelcard_has_connected_pipeline(self, model_id):
modelcard = ModelCard.load(model_id)
connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS}
connected_pipes = {k: v for k, v in connected_pipes.items() if v is not None}
return len(connected_pipes) > 0
def test_correct_modelcard_format(self):
# hf-internal-testing/tiny-random-kandinsky-v22-prior has no metadata
assert not self.modelcard_has_connected_pipeline("hf-internal-testing/tiny-random-kandinsky-v22-prior")
# see https://huggingface.co/hf-internal-testing/tiny-random-kandinsky-v22-decoder/blob/8baff9897c6be017013e21b5c562e5a381646c7e/README.md?code=true#L2
assert self.modelcard_has_connected_pipeline("hf-internal-testing/tiny-random-kandinsky-v22-decoder")
def test_load_connected_checkpoint_when_specified(self):
pipeline_prior = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-prior")
pipeline_prior_connected = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-random-kandinsky-v22-prior", load_connected_pipeline=True
)
# Passing `load_connected_pipeline` to prior is a no-op as the pipeline has no connected pipeline
assert pipeline_prior.__class__ == pipeline_prior_connected.__class__
pipeline = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-decoder")
pipeline_connected = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-random-kandinsky-v22-decoder", load_connected_pipeline=True
)
# Passing `load_connected_pipeline` to decoder loads the combined pipeline
assert pipeline.__class__ != pipeline_connected.__class__
assert pipeline.__class__ == KandinskyV22Pipeline
assert pipeline_connected.__class__ == KandinskyV22CombinedPipeline
# check that loaded components match prior and decoder components
assert set(pipeline_connected.components.keys()) == set(
["prior_" + k for k in pipeline_prior.components.keys()] + list(pipeline.components.keys())
)
def test_load_connected_checkpoint_default(self):
prior = KandinskyV22PriorPipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-prior")
decoder = KandinskyV22Pipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-decoder")
# check that combined pipeline loads both prior & decoder because of
# https://huggingface.co/hf-internal-testing/tiny-random-kandinsky-v22-decoder/blob/8baff9897c6be017013e21b5c562e5a381646c7e/README.md?code=true#L3
assert (
KandinskyV22CombinedPipeline._load_connected_pipes
) # combined pipelines will download more checkpoints that just the one specified
pipeline = KandinskyV22CombinedPipeline.from_pretrained(
"hf-internal-testing/tiny-random-kandinsky-v22-decoder"
)
prior_comps = prior.components
decoder_comps = decoder.components
for k, component in pipeline.components.items():
if k.startswith("prior_"):
k = k[6:]
comp = prior_comps[k]
else:
comp = decoder_comps[k]
if isinstance(component, torch.nn.Module):
assert state_dicts_almost_equal(component.state_dict(), comp.state_dict())
elif hasattr(component, "config"):
assert dict(component.config) == dict(comp.config)
else:
assert component.__class__ == comp.__class__
def test_load_connected_checkpoint_with_passed_obj(self):
pipeline = KandinskyV22CombinedPipeline.from_pretrained(
"hf-internal-testing/tiny-random-kandinsky-v22-decoder"
)
prior_scheduler = DDPMScheduler.from_config(pipeline.prior_scheduler.config)
scheduler = DDPMScheduler.from_config(pipeline.scheduler.config)
# make sure we pass a different scheduler and prior_scheduler
assert pipeline.prior_scheduler.__class__ != prior_scheduler.__class__
assert pipeline.scheduler.__class__ != scheduler.__class__
pipeline_new = KandinskyV22CombinedPipeline.from_pretrained(
"hf-internal-testing/tiny-random-kandinsky-v22-decoder",
prior_scheduler=prior_scheduler,
scheduler=scheduler,
)
assert dict(pipeline_new.prior_scheduler.config) == dict(prior_scheduler.config)
assert dict(pipeline_new.scheduler.config) == dict(scheduler.config)
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/pipelines/test_pipelines_auto.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import os
import shutil
import unittest
from collections import OrderedDict
from pathlib import Path
import torch
from diffusers import (
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image,
ControlNetModel,
DiffusionPipeline,
)
from diffusers.pipelines.auto_pipeline import (
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
AUTO_INPAINT_PIPELINES_MAPPING,
AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
)
from diffusers.utils.testing_utils import slow
PRETRAINED_MODEL_REPO_MAPPING = OrderedDict(
[
("stable-diffusion", "runwayml/stable-diffusion-v1-5"),
("if", "DeepFloyd/IF-I-XL-v1.0"),
("kandinsky", "kandinsky-community/kandinsky-2-1"),
("kandinsky22", "kandinsky-community/kandinsky-2-2-decoder"),
]
)
class AutoPipelineFastTest(unittest.TestCase):
def test_from_pipe_consistent(self):
pipe = AutoPipelineForText2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False
)
original_config = dict(pipe.config)
pipe = AutoPipelineForImage2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
pipe = AutoPipelineForText2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
def test_from_pipe_override(self):
pipe = AutoPipelineForText2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False
)
pipe = AutoPipelineForImage2Image.from_pipe(pipe, requires_safety_checker=True)
assert pipe.config.requires_safety_checker is True
pipe = AutoPipelineForText2Image.from_pipe(pipe, requires_safety_checker=True)
assert pipe.config.requires_safety_checker is True
def test_from_pipe_consistent_sdxl(self):
pipe = AutoPipelineForImage2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-xl-pipe",
requires_aesthetics_score=True,
force_zeros_for_empty_prompt=False,
)
original_config = dict(pipe.config)
pipe = AutoPipelineForText2Image.from_pipe(pipe)
pipe = AutoPipelineForImage2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
def test_kwargs_local_files_only(self):
repo = "hf-internal-testing/tiny-stable-diffusion-torch"
tmpdirname = DiffusionPipeline.download(repo)
tmpdirname = Path(tmpdirname)
# edit commit_id to so that it's not the latest commit
commit_id = tmpdirname.name
new_commit_id = commit_id + "hug"
ref_dir = tmpdirname.parent.parent / "refs/main"
with open(ref_dir, "w") as f:
f.write(new_commit_id)
new_tmpdirname = tmpdirname.parent / new_commit_id
os.rename(tmpdirname, new_tmpdirname)
try:
AutoPipelineForText2Image.from_pretrained(repo, local_files_only=True)
except OSError:
assert False, "not able to load local files"
shutil.rmtree(tmpdirname.parent.parent)
def test_from_pipe_controlnet_text2img(self):
pipe = AutoPipelineForText2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe")
controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet")
pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=controlnet)
assert pipe.__class__.__name__ == "StableDiffusionControlNetPipeline"
assert "controlnet" in pipe.components
pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=None)
assert pipe.__class__.__name__ == "StableDiffusionPipeline"
assert "controlnet" not in pipe.components
def test_from_pipe_controlnet_img2img(self):
pipe = AutoPipelineForImage2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe")
controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet")
pipe = AutoPipelineForImage2Image.from_pipe(pipe, controlnet=controlnet)
assert pipe.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe.components
pipe = AutoPipelineForImage2Image.from_pipe(pipe, controlnet=None)
assert pipe.__class__.__name__ == "StableDiffusionImg2ImgPipeline"
assert "controlnet" not in pipe.components
def test_from_pipe_controlnet_inpaint(self):
pipe = AutoPipelineForInpainting.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet")
pipe = AutoPipelineForInpainting.from_pipe(pipe, controlnet=controlnet)
assert pipe.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline"
assert "controlnet" in pipe.components
pipe = AutoPipelineForInpainting.from_pipe(pipe, controlnet=None)
assert pipe.__class__.__name__ == "StableDiffusionInpaintPipeline"
assert "controlnet" not in pipe.components
def test_from_pipe_controlnet_new_task(self):
pipe_text2img = AutoPipelineForText2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet")
pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_text2img, controlnet=controlnet)
assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe_control_img2img.components
pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img, controlnet=None)
assert pipe_inpaint.__class__.__name__ == "StableDiffusionInpaintPipeline"
assert "controlnet" not in pipe_inpaint.components
# testing `from_pipe` for text2img controlnet
## 1. from a different controlnet pipe, without controlnet argument
pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_img2img)
assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline"
assert "controlnet" in pipe_control_text2img.components
## 2. from a different controlnet pipe, with controlnet argument
pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_img2img, controlnet=controlnet)
assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline"
assert "controlnet" in pipe_control_text2img.components
## 3. from same controlnet pipeline class, with a different controlnet component
pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_text2img, controlnet=controlnet)
assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline"
assert "controlnet" in pipe_control_text2img.components
# testing from_pipe for inpainting
## 1. from a different controlnet pipeline class
pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img)
assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline"
assert "controlnet" in pipe_control_inpaint.components
## from a different controlnet pipe, with a different controlnet
pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img, controlnet=controlnet)
assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline"
assert "controlnet" in pipe_control_inpaint.components
## from same controlnet pipe, with a different controlnet
pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_inpaint, controlnet=controlnet)
assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline"
assert "controlnet" in pipe_control_inpaint.components
# testing from_pipe from img2img controlnet
## from a different controlnet pipe, without controlnet argument
pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_text2img)
assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe_control_img2img.components
# from a different controlnet pipe, with a different controlnet component
pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_text2img, controlnet=controlnet)
assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe_control_img2img.components
# from same controlnet pipeline class, with a different controlnet
pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_img2img, controlnet=controlnet)
assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe_control_img2img.components
@slow
class AutoPipelineIntegrationTest(unittest.TestCase):
def test_pipe_auto(self):
for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():
# test txt2img
pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
if "kandinsky" not in model_name:
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_txt2img, pipe_to
gc.collect()
# test img2img
pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
if "kandinsky" not in model_name:
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_img2img, pipe_to
gc.collect()
# test inpaint
if "kandinsky" not in model_name:
pipe_inpaint = AutoPipelineForInpainting.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_inpaint, pipe_to
gc.collect()
def test_from_pipe_consistent(self):
for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():
if model_name in ["kandinsky", "kandinsky22"]:
auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image]
else:
auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image, AutoPipelineForInpainting]
# test from_pretrained
for pipe_from_class in auto_pipes:
pipe_from = pipe_from_class.from_pretrained(model_repo, variant="fp16", torch_dtype=torch.float16)
pipe_from_config = dict(pipe_from.config)
for pipe_to_class in auto_pipes:
pipe_to = pipe_to_class.from_pipe(pipe_from)
self.assertEqual(dict(pipe_to.config), pipe_from_config)
del pipe_from, pipe_to
gc.collect()
def test_controlnet(self):
# test from_pretrained
model_repo = "runwayml/stable-diffusion-v1-5"
controlnet_repo = "lllyasviel/sd-controlnet-canny"
controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16)
pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
pipe_inpaint = AutoPipelineForInpainting.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"])
# test from_pipe
for pipe_from in [pipe_txt2img, pipe_img2img, pipe_inpaint]:
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_txt2img.config))
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_img2img.config))
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_inpaint.config))
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/pipelines/test_pipelines_common.py
|
import contextlib
import gc
import inspect
import io
import json
import os
import re
import tempfile
import unittest
import uuid
from typing import Callable, Union
import numpy as np
import PIL.Image
import torch
from huggingface_hub import delete_repo
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AsymmetricAutoencoderKL,
AutoencoderKL,
AutoencoderTiny,
ConsistencyDecoderVAE,
DDIMScheduler,
DiffusionPipeline,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import logging
from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available
from diffusers.utils.testing_utils import (
CaptureLogger,
require_torch,
torch_device,
)
from ..models.test_models_vae import (
get_asym_autoencoder_kl_config,
get_autoencoder_kl_config,
get_autoencoder_tiny_config,
get_consistency_vae_config,
)
from ..others.test_utils import TOKEN, USER, is_staging_test
def to_np(tensor):
if isinstance(tensor, torch.Tensor):
tensor = tensor.detach().cpu().numpy()
return tensor
def check_same_shape(tensor_list):
shapes = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:])
class PipelineLatentTesterMixin:
"""
This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes.
It provides a set of common tests for PyTorch pipeline that has vae, e.g.
equivalence of different input and output types, etc.
"""
@property
def image_params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `image_params` in the child test class. "
"`image_params` are tested for if all accepted input image types (i.e. `pt`,`pil`,`np`) are producing same results"
)
@property
def image_latents_params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `image_latents_params` in the child test class. "
"`image_latents_params` are tested for if passing latents directly are producing same results"
)
def get_dummy_inputs_by_type(self, device, seed=0, input_image_type="pt", output_type="np"):
inputs = self.get_dummy_inputs(device, seed)
def convert_to_pt(image):
if isinstance(image, torch.Tensor):
input_image = image
elif isinstance(image, np.ndarray):
input_image = VaeImageProcessor.numpy_to_pt(image)
elif isinstance(image, PIL.Image.Image):
input_image = VaeImageProcessor.pil_to_numpy(image)
input_image = VaeImageProcessor.numpy_to_pt(input_image)
else:
raise ValueError(f"unsupported input_image_type {type(image)}")
return input_image
def convert_pt_to_type(image, input_image_type):
if input_image_type == "pt":
input_image = image
elif input_image_type == "np":
input_image = VaeImageProcessor.pt_to_numpy(image)
elif input_image_type == "pil":
input_image = VaeImageProcessor.pt_to_numpy(image)
input_image = VaeImageProcessor.numpy_to_pil(input_image)
else:
raise ValueError(f"unsupported input_image_type {input_image_type}.")
return input_image
for image_param in self.image_params:
if image_param in inputs.keys():
inputs[image_param] = convert_pt_to_type(
convert_to_pt(inputs[image_param]).to(device), input_image_type
)
inputs["output_type"] = output_type
return inputs
def test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4):
self._test_pt_np_pil_outputs_equivalent(expected_max_diff=expected_max_diff)
def _test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4, input_image_type="pt"):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
output_pt = pipe(
**self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pt")
)[0]
output_np = pipe(
**self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="np")
)[0]
output_pil = pipe(
**self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pil")
)[0]
max_diff = np.abs(output_pt.cpu().numpy().transpose(0, 2, 3, 1) - output_np).max()
self.assertLess(
max_diff, expected_max_diff, "`output_type=='pt'` generate different results from `output_type=='np'`"
)
max_diff = np.abs(np.array(output_pil[0]) - (output_np * 255).round()).max()
self.assertLess(max_diff, 2.0, "`output_type=='pil'` generate different results from `output_type=='np'`")
def test_pt_np_pil_inputs_equivalent(self):
if len(self.image_params) == 0:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
out_input_pt = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0]
out_input_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0]
out_input_pil = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pil"))[0]
max_diff = np.abs(out_input_pt - out_input_np).max()
self.assertLess(max_diff, 1e-4, "`input_type=='pt'` generate different result from `input_type=='np'`")
max_diff = np.abs(out_input_pil - out_input_np).max()
self.assertLess(max_diff, 1e-2, "`input_type=='pt'` generate different result from `input_type=='np'`")
def test_latents_input(self):
if len(self.image_latents_params) == 0:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0]
vae = components["vae"]
inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt")
generator = inputs["generator"]
for image_param in self.image_latents_params:
if image_param in inputs.keys():
inputs[image_param] = (
vae.encode(inputs[image_param]).latent_dist.sample(generator) * vae.config.scaling_factor
)
out_latents_inputs = pipe(**inputs)[0]
max_diff = np.abs(out - out_latents_inputs).max()
self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image")
def test_multi_vae(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
block_out_channels = pipe.vae.config.block_out_channels
norm_num_groups = pipe.vae.config.norm_num_groups
vae_classes = [AutoencoderKL, AsymmetricAutoencoderKL, ConsistencyDecoderVAE, AutoencoderTiny]
configs = [
get_autoencoder_kl_config(block_out_channels, norm_num_groups),
get_asym_autoencoder_kl_config(block_out_channels, norm_num_groups),
get_consistency_vae_config(block_out_channels, norm_num_groups),
get_autoencoder_tiny_config(block_out_channels),
]
out_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0]
for vae_cls, config in zip(vae_classes, configs):
vae = vae_cls(**config)
vae = vae.to(torch_device)
components["vae"] = vae
vae_pipe = self.pipeline_class(**components)
out_vae_np = vae_pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0]
assert out_vae_np.shape == out_np.shape
@require_torch
class PipelineKarrasSchedulerTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each PyTorch pipeline that makes use of KarrasDiffusionSchedulers
equivalence of dict and tuple outputs, etc.
"""
def test_karras_schedulers_shape(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=True)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 2
if "strength" in inputs:
inputs["num_inference_steps"] = 4
inputs["strength"] = 0.5
outputs = []
for scheduler_enum in KarrasDiffusionSchedulers:
if "KDPM2" in scheduler_enum.name:
inputs["num_inference_steps"] = 5
scheduler_cls = getattr(diffusers, scheduler_enum.name)
pipe.scheduler = scheduler_cls.from_config(pipe.scheduler.config)
output = pipe(**inputs)[0]
outputs.append(output)
if "KDPM2" in scheduler_enum.name:
inputs["num_inference_steps"] = 2
assert check_same_shape(outputs)
@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.
"""
# Canonical parameters that are passed to `__call__` regardless
# of the type of pipeline. They are always optional and have common
# sense default values.
required_optional_params = frozenset(
[
"num_inference_steps",
"num_images_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
]
)
# set these parameters to False in the child class if the pipeline does not support the corresponding functionality
test_attention_slicing = True
test_xformers_attention = True
def get_generator(self, seed):
device = torch_device if torch_device != "mps" else "cpu"
generator = torch.Generator(device).manual_seed(seed)
return generator
@property
def pipeline_class(self) -> Union[Callable, DiffusionPipeline]:
raise NotImplementedError(
"You need to set the attribute `pipeline_class = ClassNameOfPipeline` in the child test class. "
"See existing pipeline tests for reference."
)
def get_dummy_components(self):
raise NotImplementedError(
"You need to implement `get_dummy_components(self)` in the child test class. "
"See existing pipeline tests for reference."
)
def get_dummy_inputs(self, device, seed=0):
raise NotImplementedError(
"You need to implement `get_dummy_inputs(self, device, seed)` in the child test class. "
"See existing pipeline tests for reference."
)
@property
def params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `params` in the child test class. "
"`params` are checked for if all values are present in `__call__`'s signature."
" You can set `params` using one of the common set of parameters defined in `pipeline_params.py`"
" e.g., `TEXT_TO_IMAGE_PARAMS` defines the common parameters used in text to "
"image pipelines, including prompts and prompt embedding overrides."
"If your pipeline's set of arguments has minor changes from one of the common sets of arguments, "
"do not make modifications to the existing common sets of arguments. I.e. a text to image pipeline "
"with non-configurable height and width arguments should set the attribute as "
"`params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`. "
"See existing pipeline tests for reference."
)
@property
def batch_params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `batch_params` in the child test class. "
"`batch_params` are the parameters required to be batched when passed to the pipeline's "
"`__call__` method. `pipeline_params.py` provides some common sets of parameters such as "
"`TEXT_TO_IMAGE_BATCH_PARAMS`, `IMAGE_VARIATION_BATCH_PARAMS`, etc... If your pipeline's "
"set of batch arguments has minor changes from one of the common sets of batch arguments, "
"do not make modifications to the existing common sets of batch arguments. I.e. a text to "
"image pipeline `negative_prompt` is not batched should set the attribute as "
"`batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {'negative_prompt'}`. "
"See existing pipeline tests for reference."
)
@property
def callback_cfg_params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `callback_cfg_params` in the child test class that requires to run test_callback_cfg. "
"`callback_cfg_params` are the parameters that needs to be passed to the pipeline's callback "
"function when dynamically adjusting `guidance_scale`. They are variables that require special"
"treatment when `do_classifier_free_guidance` is `True`. `pipeline_params.py` provides some common"
" sets of parameters such as `TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS`. If your pipeline's "
"set of cfg arguments has minor changes from one of the common sets of cfg arguments, "
"do not make modifications to the existing common sets of cfg arguments. I.e. for inpaint pipeine, you "
" need to adjust batch size of `mask` and `masked_image_latents` so should set the attribute as"
"`callback_cfg_params = TEXT_TO_IMAGE_CFG_PARAMS.union({'mask', 'masked_image_latents'})`"
)
def tearDown(self):
# clean up the VRAM after each test in case of CUDA runtime errors
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_save_load_local(self, expected_max_difference=5e-4):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
logger = logging.get_logger("diffusers.pipelines.pipeline_utils")
logger.setLevel(diffusers.logging.INFO)
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir, safe_serialization=False)
with CaptureLogger(logger) as cap_logger:
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
for name in pipe_loaded.components.keys():
if name not in pipe_loaded._optional_components:
assert name in str(cap_logger)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, expected_max_difference)
def test_pipeline_call_signature(self):
self.assertTrue(
hasattr(self.pipeline_class, "__call__"), f"{self.pipeline_class} should have a `__call__` method"
)
parameters = inspect.signature(self.pipeline_class.__call__).parameters
optional_parameters = set()
for k, v in parameters.items():
if v.default != inspect._empty:
optional_parameters.add(k)
parameters = set(parameters.keys())
parameters.remove("self")
parameters.discard("kwargs") # kwargs can be added if arguments of pipeline call function are deprecated
remaining_required_parameters = set()
for param in self.params:
if param not in parameters:
remaining_required_parameters.add(param)
self.assertTrue(
len(remaining_required_parameters) == 0,
f"Required parameters not present: {remaining_required_parameters}",
)
remaining_required_optional_parameters = set()
for param in self.required_optional_params:
if param not in optional_parameters:
remaining_required_optional_parameters.add(param)
self.assertTrue(
len(remaining_required_optional_parameters) == 0,
f"Required optional parameters not present: {remaining_required_optional_parameters}",
)
def test_inference_batch_consistent(self, batch_sizes=[2]):
self._test_inference_batch_consistent(batch_sizes=batch_sizes)
def _test_inference_batch_consistent(
self, batch_sizes=[2], additional_params_copy_to_batched_inputs=["num_inference_steps"]
):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["generator"] = self.get_generator(0)
logger = logging.get_logger(pipe.__module__)
logger.setLevel(level=diffusers.logging.FATAL)
# prepare batched inputs
batched_inputs = []
for batch_size in batch_sizes:
batched_input = {}
batched_input.update(inputs)
for name in self.batch_params:
if name not in inputs:
continue
value = inputs[name]
if name == "prompt":
len_prompt = len(value)
# make unequal batch sizes
batched_input[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
# make last batch super long
batched_input[name][-1] = 100 * "very long"
else:
batched_input[name] = batch_size * [value]
if "generator" in inputs:
batched_input["generator"] = [self.get_generator(i) for i in range(batch_size)]
if "batch_size" in inputs:
batched_input["batch_size"] = batch_size
batched_inputs.append(batched_input)
logger.setLevel(level=diffusers.logging.WARNING)
for batch_size, batched_input in zip(batch_sizes, batched_inputs):
output = pipe(**batched_input)
assert len(output[0]) == batch_size
def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-4):
self._test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff)
def _test_inference_batch_single_identical(
self,
batch_size=2,
expected_max_diff=1e-4,
additional_params_copy_to_batched_inputs=["num_inference_steps"],
):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for components in pipe.components.values():
if hasattr(components, "set_default_attn_processor"):
components.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
# Reset generator in case it is has been used in self.get_dummy_inputs
inputs["generator"] = self.get_generator(0)
logger = logging.get_logger(pipe.__module__)
logger.setLevel(level=diffusers.logging.FATAL)
# batchify inputs
batched_inputs = {}
batched_inputs.update(inputs)
for name in self.batch_params:
if name not in inputs:
continue
value = inputs[name]
if name == "prompt":
len_prompt = len(value)
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
batched_inputs[name][-1] = 100 * "very long"
else:
batched_inputs[name] = batch_size * [value]
if "generator" in inputs:
batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)]
if "batch_size" in inputs:
batched_inputs["batch_size"] = batch_size
for arg in additional_params_copy_to_batched_inputs:
batched_inputs[arg] = inputs[arg]
output = pipe(**inputs)
output_batch = pipe(**batched_inputs)
assert output_batch[0].shape[0] == batch_size
max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()
assert max_diff < expected_max_diff
def test_dict_tuple_outputs_equivalent(self, expected_max_difference=1e-4):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
output = pipe(**self.get_dummy_inputs(generator_device))[0]
output_tuple = pipe(**self.get_dummy_inputs(generator_device), return_dict=False)[0]
max_diff = np.abs(to_np(output) - to_np(output_tuple)).max()
self.assertLess(max_diff, expected_max_difference)
def test_components_function(self):
init_components = self.get_dummy_components()
init_components = {k: v for k, v in init_components.items() if not isinstance(v, (str, int, float))}
pipe = self.pipeline_class(**init_components)
self.assertTrue(hasattr(pipe, "components"))
self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_float16_inference(self, expected_max_diff=5e-2):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
components = self.get_dummy_components()
pipe_fp16 = self.pipeline_class(**components)
for component in pipe_fp16.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_fp16.to(torch_device, torch.float16)
pipe_fp16.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
# Reset generator in case it is used inside dummy inputs
if "generator" in inputs:
inputs["generator"] = self.get_generator(0)
output = pipe(**inputs)[0]
fp16_inputs = self.get_dummy_inputs(torch_device)
# Reset generator in case it is used inside dummy inputs
if "generator" in fp16_inputs:
fp16_inputs["generator"] = self.get_generator(0)
output_fp16 = pipe_fp16(**fp16_inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_fp16)).max()
self.assertLess(max_diff, expected_max_diff, "The outputs of the fp16 and fp32 pipelines are too different.")
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self, expected_max_diff=1e-2):
components = self.get_dummy_components()
for name, module in components.items():
if hasattr(module, "half"):
components[name] = module.to(torch_device).half()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)
for component in pipe_loaded.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for name, component in pipe_loaded.components.items():
if hasattr(component, "dtype"):
self.assertTrue(
component.dtype == torch.float16,
f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(
max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading."
)
def test_save_load_optional_components(self, expected_max_difference=1e-4):
if not hasattr(self.pipeline_class, "_optional_components"):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir, safe_serialization=False)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
for component in pipe_loaded.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(generator_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, expected_max_difference)
@unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices")
def test_to_device(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.set_progress_bar_config(disable=None)
pipe.to("cpu")
model_devices = [component.device.type for component in components.values() if hasattr(component, "device")]
self.assertTrue(all(device == "cpu" for device in model_devices))
output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0]
self.assertTrue(np.isnan(output_cpu).sum() == 0)
pipe.to("cuda")
model_devices = [component.device.type for component in components.values() if hasattr(component, "device")]
self.assertTrue(all(device == "cuda" for device in model_devices))
output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0]
self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)
def test_to_dtype(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.set_progress_bar_config(disable=None)
model_dtypes = [component.dtype for component in components.values() if hasattr(component, "dtype")]
self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))
pipe.to(torch_dtype=torch.float16)
model_dtypes = [component.dtype for component in components.values() if hasattr(component, "dtype")]
self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))
def test_attention_slicing_forward_pass(self, expected_max_diff=1e-3):
self._test_attention_slicing_forward_pass(expected_max_diff=expected_max_diff)
def _test_attention_slicing_forward_pass(
self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
):
if not self.test_attention_slicing:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
output_without_slicing = pipe(**inputs)[0]
pipe.enable_attention_slicing(slice_size=1)
inputs = self.get_dummy_inputs(generator_device)
output_with_slicing = pipe(**inputs)[0]
if test_max_difference:
max_diff = np.abs(to_np(output_with_slicing) - to_np(output_without_slicing)).max()
self.assertLess(max_diff, expected_max_diff, "Attention slicing should not affect the inference results")
if test_mean_pixel_difference:
assert_mean_pixel_difference(to_np(output_with_slicing[0]), to_np(output_without_slicing[0]))
@unittest.skipIf(
torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.14.0"),
reason="CPU offload is only available with CUDA and `accelerate v0.14.0` or higher",
)
def test_sequential_cpu_offload_forward_pass(self, expected_max_diff=1e-4):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
output_without_offload = pipe(**inputs)[0]
pipe.enable_sequential_cpu_offload()
inputs = self.get_dummy_inputs(generator_device)
output_with_offload = pipe(**inputs)[0]
max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results")
@unittest.skipIf(
torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.17.0"),
reason="CPU offload is only available with CUDA and `accelerate v0.17.0` or higher",
)
def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4):
generator_device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(generator_device)
output_without_offload = pipe(**inputs)[0]
pipe.enable_model_cpu_offload()
inputs = self.get_dummy_inputs(generator_device)
output_with_offload = pipe(**inputs)[0]
max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results")
offloaded_modules = [
v
for k, v in pipe.components.items()
if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload
]
(
self.assertTrue(all(v.device.type == "cpu" for v in offloaded_modules)),
f"Not offloaded: {[v for v in offloaded_modules if v.device.type != 'cpu']}",
)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass()
def _test_xformers_attention_forwardGenerator_pass(
self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-4
):
if not self.test_xformers_attention:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_without_offload = pipe(**inputs)[0]
output_without_offload = (
output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload
)
pipe.enable_xformers_memory_efficient_attention()
inputs = self.get_dummy_inputs(torch_device)
output_with_offload = pipe(**inputs)[0]
output_with_offload = (
output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload
)
if test_max_difference:
max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results")
if test_mean_pixel_difference:
assert_mean_pixel_difference(output_with_offload[0], output_without_offload[0])
def test_progress_bar(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):
_ = pipe(**inputs)
stderr = stderr.getvalue()
# we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img,
# so we just match "5" in "#####| 1/5 [00:01<00:00]"
max_steps = re.search("/(.*?) ", stderr).group(1)
self.assertTrue(max_steps is not None and len(max_steps) > 0)
self.assertTrue(
f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step"
)
pipe.set_progress_bar_config(disable=True)
with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):
_ = pipe(**inputs)
self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled")
def test_num_images_per_prompt(self):
sig = inspect.signature(self.pipeline_class.__call__)
if "num_images_per_prompt" not in sig.parameters:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
batch_sizes = [1, 2]
num_images_per_prompts = [1, 2]
for batch_size in batch_sizes:
for num_images_per_prompt in num_images_per_prompts:
inputs = self.get_dummy_inputs(torch_device)
for key in inputs.keys():
if key in self.batch_params:
inputs[key] = batch_size * [inputs[key]]
images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
def test_cfg(self):
sig = inspect.signature(self.pipeline_class.__call__)
if "guidance_scale" not in sig.parameters:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["guidance_scale"] = 1.0
out_no_cfg = pipe(**inputs)[0]
inputs["guidance_scale"] = 7.5
out_cfg = pipe(**inputs)[0]
assert out_cfg.shape == out_no_cfg.shape
def test_callback_inputs(self):
sig = inspect.signature(self.pipeline_class.__call__)
has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
has_callback_step_end = "callback_on_step_end" in sig.parameters
if not (has_callback_tensor_inputs and has_callback_step_end):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_subset(pipe, i, t, callback_kwargs):
# interate over callback args
for tensor_name, tensor_value in callback_kwargs.items():
# check that we're only passing in allowed tensor inputs
assert tensor_name in pipe._callback_tensor_inputs
return callback_kwargs
def callback_inputs_all(pipe, i, t, callback_kwargs):
for tensor_name in pipe._callback_tensor_inputs:
assert tensor_name in callback_kwargs
# interate over callback args
for tensor_name, tensor_value in callback_kwargs.items():
# check that we're only passing in allowed tensor inputs
assert tensor_name in pipe._callback_tensor_inputs
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
# Test passing in a subset
inputs["callback_on_step_end"] = callback_inputs_subset
inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
# Test passing in a everything
inputs["callback_on_step_end"] = callback_inputs_all
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
is_last = i == (pipe.num_timesteps - 1)
if is_last:
callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
return callback_kwargs
inputs["callback_on_step_end"] = callback_inputs_change_tensor
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
assert output.abs().sum() == 0
def test_callback_cfg(self):
sig = inspect.signature(self.pipeline_class.__call__)
has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
has_callback_step_end = "callback_on_step_end" in sig.parameters
if not (has_callback_tensor_inputs and has_callback_step_end):
return
if "guidance_scale" not in sig.parameters:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_increase_guidance(pipe, i, t, callback_kwargs):
pipe._guidance_scale += 1.0
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
# use cfg guidance because some pipelines modify the shape of the latents
# outside of the denoising loop
inputs["guidance_scale"] = 2.0
inputs["callback_on_step_end"] = callback_increase_guidance
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
_ = pipe(**inputs)[0]
# we increase the guidance scale by 1.0 at every step
# check that the guidance scale is increased by the number of scheduler timesteps
# accounts for models that modify the number of inference steps based on strength
assert pipe.guidance_scale == (inputs["guidance_scale"] + pipe.num_timesteps)
@is_staging_test
class PipelinePushToHubTester(unittest.TestCase):
identifier = uuid.uuid4()
repo_id = f"test-pipeline-{identifier}"
org_repo_id = f"valid_org/{repo_id}-org"
def get_pipeline_components(self):
unet = 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,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
with tempfile.TemporaryDirectory() as tmpdir:
dummy_vocab = {"<|startoftext|>": 0, "<|endoftext|>": 1, "!": 2}
vocab_path = os.path.join(tmpdir, "vocab.json")
with open(vocab_path, "w") as f:
json.dump(dummy_vocab, f)
merges = "Ġ t\nĠt h"
merges_path = os.path.join(tmpdir, "merges.txt")
with open(merges_path, "w") as f:
f.writelines(merges)
tokenizer = CLIPTokenizer(vocab_file=vocab_path, merges_file=merges_path)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def test_push_to_hub(self):
components = self.get_pipeline_components()
pipeline = StableDiffusionPipeline(**components)
pipeline.push_to_hub(self.repo_id, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(f"{USER}/{self.repo_id}", subfolder="unet")
unet = components["unet"]
for p1, p2 in zip(unet.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=TOKEN, repo_id=self.repo_id)
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
pipeline.save_pretrained(tmp_dir, repo_id=self.repo_id, push_to_hub=True, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(f"{USER}/{self.repo_id}", subfolder="unet")
for p1, p2 in zip(unet.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(self.repo_id, token=TOKEN)
def test_push_to_hub_in_organization(self):
components = self.get_pipeline_components()
pipeline = StableDiffusionPipeline(**components)
pipeline.push_to_hub(self.org_repo_id, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id, subfolder="unet")
unet = components["unet"]
for p1, p2 in zip(unet.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=TOKEN, repo_id=self.org_repo_id)
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
pipeline.save_pretrained(tmp_dir, push_to_hub=True, token=TOKEN, repo_id=self.org_repo_id)
new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id, subfolder="unet")
for p1, p2 in zip(unet.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(self.org_repo_id, token=TOKEN)
# For SDXL and its derivative pipelines (such as ControlNet), we have the text encoders
# and the tokenizers as optional components. So, we need to override the `test_save_load_optional_components()`
# test for all such pipelines. This requires us to use a custom `encode_prompt()` function.
class SDXLOptionalComponentsTesterMixin:
def encode_prompt(
self, tokenizers, text_encoders, prompt: str, num_images_per_prompt: int = 1, negative_prompt: str = None
):
device = text_encoders[0].device
if isinstance(prompt, str):
prompt = [prompt]
batch_size = len(prompt)
prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
if negative_prompt is None:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
else:
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
uncond_input = tokenizer(
negative_prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds = text_encoder(uncond_input.input_ids.to(device), output_hidden_states=True)
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
negative_prompt_embeds_list.append(negative_prompt_embeds)
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# for classifier-free guidance
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
# for classifier-free guidance
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
def _test_save_load_optional_components(self, expected_max_difference=1e-4):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
tokenizer = components.pop("tokenizer")
tokenizer_2 = components.pop("tokenizer_2")
text_encoder = components.pop("text_encoder")
text_encoder_2 = components.pop("text_encoder_2")
tokenizers = [tokenizer, tokenizer_2] if tokenizer is not None else [tokenizer_2]
text_encoders = [text_encoder, text_encoder_2] if text_encoder is not None else [text_encoder_2]
prompt = inputs.pop("prompt")
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(tokenizers, text_encoders, prompt)
inputs["prompt_embeds"] = prompt_embeds
inputs["negative_prompt_embeds"] = negative_prompt_embeds
inputs["pooled_prompt_embeds"] = pooled_prompt_embeds
inputs["negative_pooled_prompt_embeds"] = negative_pooled_prompt_embeds
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
for component in pipe_loaded.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(generator_device)
_ = inputs.pop("prompt")
inputs["prompt_embeds"] = prompt_embeds
inputs["negative_prompt_embeds"] = negative_prompt_embeds
inputs["pooled_prompt_embeds"] = pooled_prompt_embeds
inputs["negative_pooled_prompt_embeds"] = negative_pooled_prompt_embeds
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, expected_max_difference)
# Some models (e.g. unCLIP) are extremely likely to significantly deviate depending on which hardware is used.
# This helper function is used to check that the image doesn't deviate on average more than 10 pixels from a
# reference image.
def assert_mean_pixel_difference(image, expected_image, expected_max_diff=10):
image = np.asarray(DiffusionPipeline.numpy_to_pil(image)[0], dtype=np.float32)
expected_image = np.asarray(DiffusionPipeline.numpy_to_pil(expected_image)[0], dtype=np.float32)
avg_diff = np.abs(image - expected_image).mean()
assert avg_diff < expected_max_diff, f"Error image deviates {avg_diff} pixels on average"
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/pipelines/test_pipelines.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import json
import os
import random
import shutil
import sys
import tempfile
import traceback
import unittest
import unittest.mock as mock
import numpy as np
import PIL.Image
import requests_mock
import safetensors.torch
import torch
from parameterized import parameterized
from PIL import Image
from requests.exceptions import HTTPError
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ConfigMixin,
DDIMPipeline,
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
ModelMixin,
PNDMScheduler,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionPipeline,
UNet2DConditionModel,
UNet2DModel,
UniPCMultistepScheduler,
logging,
)
from diffusers.pipelines.pipeline_utils import _get_pipeline_class
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import (
CONFIG_NAME,
WEIGHTS_NAME,
)
from diffusers.utils.testing_utils import (
CaptureLogger,
enable_full_determinism,
floats_tensor,
get_tests_dir,
load_numpy,
nightly,
require_compel,
require_flax,
require_onnxruntime,
require_python39_or_higher,
require_torch_2,
require_torch_gpu,
run_test_in_subprocess,
slow,
torch_device,
)
from diffusers.utils.torch_utils import is_compiled_module
enable_full_determinism()
# Will be run via run_test_in_subprocess
def _test_from_save_pretrained_dynamo(in_queue, out_queue, timeout):
error = None
try:
# 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"),
)
model = torch.compile(model)
scheduler = DDPMScheduler(num_train_timesteps=10)
ddpm = DDPMPipeline(model, scheduler)
# previous diffusers versions stripped compilation off
# compiled modules
assert is_compiled_module(ddpm.unet)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
with tempfile.TemporaryDirectory() as tmpdirname:
ddpm.save_pretrained(tmpdirname)
new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
new_ddpm.to(torch_device)
generator = torch.Generator(device=torch_device).manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(0)
new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
assert np.abs(image - new_image).max() < 1e-5, "Models don't give the same forward pass"
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class CustomEncoder(ModelMixin, ConfigMixin):
def __init__(self):
super().__init__()
class CustomPipeline(DiffusionPipeline):
def __init__(self, encoder: CustomEncoder, scheduler: DDIMScheduler):
super().__init__()
self.register_modules(encoder=encoder, scheduler=scheduler)
class DownloadTests(unittest.TestCase):
def test_one_request_upon_cached(self):
# TODO: For some reason this test fails on MPS where no HEAD call is made.
if torch_device == "mps":
return
with tempfile.TemporaryDirectory() as tmpdirname:
with requests_mock.mock(real_http=True) as m:
DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-pipe", cache_dir=tmpdirname)
download_requests = [r.method for r in m.request_history]
assert download_requests.count("HEAD") == 15, "15 calls to files"
assert download_requests.count("GET") == 17, "15 calls to files + model_info + model_index.json"
assert (
len(download_requests) == 32
), "2 calls per file (15 files) + send_telemetry, model_info and model_index.json"
with requests_mock.mock(real_http=True) as m:
DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
)
cache_requests = [r.method for r in m.request_history]
assert cache_requests.count("HEAD") == 1, "model_index.json is only HEAD"
assert cache_requests.count("GET") == 1, "model info is only GET"
assert (
len(cache_requests) == 2
), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
def test_less_downloads_passed_object(self):
with tempfile.TemporaryDirectory() as tmpdirname:
cached_folder = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
)
# make sure safety checker is not downloaded
assert "safety_checker" not in os.listdir(cached_folder)
# make sure rest is downloaded
assert "unet" in os.listdir(cached_folder)
assert "tokenizer" in os.listdir(cached_folder)
assert "vae" in os.listdir(cached_folder)
assert "model_index.json" in os.listdir(cached_folder)
assert "scheduler" in os.listdir(cached_folder)
assert "feature_extractor" in os.listdir(cached_folder)
def test_less_downloads_passed_object_calls(self):
# TODO: For some reason this test fails on MPS where no HEAD call is made.
if torch_device == "mps":
return
with tempfile.TemporaryDirectory() as tmpdirname:
with requests_mock.mock(real_http=True) as m:
DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
)
download_requests = [r.method for r in m.request_history]
# 15 - 2 because no call to config or model file for `safety_checker`
assert download_requests.count("HEAD") == 13, "13 calls to files"
# 17 - 2 because no call to config or model file for `safety_checker`
assert download_requests.count("GET") == 15, "13 calls to files + model_info + model_index.json"
assert (
len(download_requests) == 28
), "2 calls per file (13 files) + send_telemetry, model_info and model_index.json"
with requests_mock.mock(real_http=True) as m:
DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
)
cache_requests = [r.method for r in m.request_history]
assert cache_requests.count("HEAD") == 1, "model_index.json is only HEAD"
assert cache_requests.count("GET") == 1, "model info is only GET"
assert (
len(cache_requests) == 2
), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
def test_download_only_pytorch(self):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a flax file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
assert not any(f.endswith(".msgpack") for f in files)
# We need to never convert this tiny model to safetensors for this test to pass
assert not any(f.endswith(".safetensors") for f in files)
def test_force_safetensors_error(self):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
with self.assertRaises(EnvironmentError):
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe-no-safetensors",
safety_checker=None,
cache_dir=tmpdirname,
use_safetensors=True,
)
def test_download_safetensors(self):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe-safetensors",
safety_checker=None,
cache_dir=tmpdirname,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a pytorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
assert not any(f.endswith(".bin") for f in files)
def test_download_safetensors_index(self):
for variant in ["fp16", None]:
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe-indexes",
cache_dir=tmpdirname,
use_safetensors=True,
variant=variant,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a safetensors file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder
if variant is None:
assert not any("fp16" in f for f in files)
else:
model_files = [f for f in files if "safetensors" in f]
assert all("fp16" in f for f in model_files)
assert len([f for f in files if ".safetensors" in f]) == 8
assert not any(".bin" in f for f in files)
def test_download_bin_index(self):
for variant in ["fp16", None]:
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-pipe-indexes",
cache_dir=tmpdirname,
use_safetensors=False,
variant=variant,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a safetensors file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder
if variant is None:
assert not any("fp16" in f for f in files)
else:
model_files = [f for f in files if "bin" in f]
assert all("fp16" in f for f in model_files)
assert len([f for f in files if ".bin" in f]) == 8
assert not any(".safetensors" in f for f in files)
def test_download_no_openvino_by_default(self):
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-open-vino",
cache_dir=tmpdirname,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# make sure that by default no openvino weights are downloaded
assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
assert not any("openvino_" in f for f in files)
def test_download_no_onnx_by_default(self):
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-stable-diffusion-xl-pipe",
cache_dir=tmpdirname,
use_safetensors=False,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# make sure that by default no onnx weights are downloaded for non-ONNX pipelines
assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
assert not any((f.endswith(".onnx") or f.endswith(".pb")) for f in files)
@require_onnxruntime
def test_download_onnx_by_default_for_onnx_pipelines(self):
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = DiffusionPipeline.download(
"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline",
cache_dir=tmpdirname,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# make sure that by default onnx weights are downloaded for ONNX pipelines
assert any((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
assert any((f.endswith(".onnx")) for f in files)
assert any((f.endswith(".pb")) for f in files)
def test_download_no_safety_checker(self):
prompt = "hello"
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe = pipe.to(torch_device)
generator = torch.manual_seed(0)
out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
pipe_2 = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
pipe_2 = pipe_2.to(torch_device)
generator = torch.manual_seed(0)
out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
assert np.max(np.abs(out - out_2)) < 1e-3
def test_load_no_safety_checker_explicit_locally(self):
prompt = "hello"
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe = pipe.to(torch_device)
generator = torch.manual_seed(0)
out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None)
pipe_2 = pipe_2.to(torch_device)
generator = torch.manual_seed(0)
out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
assert np.max(np.abs(out - out_2)) < 1e-3
def test_load_no_safety_checker_default_locally(self):
prompt = "hello"
pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
pipe = pipe.to(torch_device)
generator = torch.manual_seed(0)
out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname)
pipe_2 = pipe_2.to(torch_device)
generator = torch.manual_seed(0)
out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
assert np.max(np.abs(out - out_2)) < 1e-3
def test_cached_files_are_used_when_no_internet(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
orig_pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, "parameters")}
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.request", return_value=response_mock):
# Download this model to make sure it's in the cache.
pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
comps = {k: v for k, v in pipe.components.items() if hasattr(v, "parameters")}
for m1, m2 in zip(orig_comps.values(), comps.values()):
for p1, p2 in zip(m1.parameters(), m2.parameters()):
if p1.data.ne(p2.data).sum() > 0:
assert False, "Parameters not the same!"
def test_local_files_only_are_used_when_no_internet(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# first check that with local files only the pipeline can only be used if cached
with self.assertRaises(FileNotFoundError):
with tempfile.TemporaryDirectory() as tmpdirname:
orig_pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", local_files_only=True, cache_dir=tmpdirname
)
# now download
orig_pipe = DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-torch")
# make sure it can be loaded with local_files_only
orig_pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", local_files_only=True
)
orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, "parameters")}
# Under the mock environment we get a 500 error when trying to connect to the internet.
# Make sure it works local_files_only only works here!
with mock.patch("requests.request", return_value=response_mock):
# Download this model to make sure it's in the cache.
pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
comps = {k: v for k, v in pipe.components.items() if hasattr(v, "parameters")}
for m1, m2 in zip(orig_comps.values(), comps.values()):
for p1, p2 in zip(m1.parameters(), m2.parameters()):
if p1.data.ne(p2.data).sum() > 0:
assert False, "Parameters not the same!"
def test_download_from_variant_folder(self):
for use_safetensors in [False, True]:
other_format = ".bin" if use_safetensors else ".safetensors"
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = StableDiffusionPipeline.download(
"hf-internal-testing/stable-diffusion-all-variants",
cache_dir=tmpdirname,
use_safetensors=use_safetensors,
)
all_root_files = [t[-1] for t in os.walk(tmpdirname)]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a variant file even if we have some here:
# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
assert not any(f.endswith(other_format) for f in files)
# no variants
assert not any(len(f.split(".")) == 3 for f in files)
def test_download_variant_all(self):
for use_safetensors in [False, True]:
other_format = ".bin" if use_safetensors else ".safetensors"
this_format = ".safetensors" if use_safetensors else ".bin"
variant = "fp16"
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = StableDiffusionPipeline.download(
"hf-internal-testing/stable-diffusion-all-variants",
cache_dir=tmpdirname,
variant=variant,
use_safetensors=use_safetensors,
)
all_root_files = [t[-1] for t in os.walk(tmpdirname)]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a non-variant file even if we have some here:
# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
# unet, vae, text_encoder, safety_checker
assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 4
# all checkpoints should have variant ending
assert not any(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files)
assert not any(f.endswith(other_format) for f in files)
def test_download_variant_partly(self):
for use_safetensors in [False, True]:
other_format = ".bin" if use_safetensors else ".safetensors"
this_format = ".safetensors" if use_safetensors else ".bin"
variant = "no_ema"
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = StableDiffusionPipeline.download(
"hf-internal-testing/stable-diffusion-all-variants",
cache_dir=tmpdirname,
variant=variant,
use_safetensors=use_safetensors,
)
all_root_files = [t[-1] for t in os.walk(tmpdirname)]
files = [item for sublist in all_root_files for item in sublist]
unet_files = os.listdir(os.path.join(tmpdirname, "unet"))
# Some of the downloaded files should be a non-variant file, check:
# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
# only unet has "no_ema" variant
assert f"diffusion_pytorch_model.{variant}{this_format}" in unet_files
assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 1
# vae, safety_checker and text_encoder should have no variant
assert sum(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files) == 3
assert not any(f.endswith(other_format) for f in files)
def test_download_broken_variant(self):
for use_safetensors in [False, True]:
# text encoder is missing no variant and "no_ema" variant weights, so the following can't work
for variant in [None, "no_ema"]:
with self.assertRaises(OSError) as error_context:
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/stable-diffusion-broken-variants",
cache_dir=tmpdirname,
variant=variant,
use_safetensors=use_safetensors,
)
assert "Error no file name" in str(error_context.exception)
# text encoder has fp16 variants so we can load it
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = StableDiffusionPipeline.download(
"hf-internal-testing/stable-diffusion-broken-variants",
use_safetensors=use_safetensors,
cache_dir=tmpdirname,
variant="fp16",
)
all_root_files = [t[-1] for t in os.walk(tmpdirname)]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a non-variant file even if we have some here:
# https://huggingface.co/hf-internal-testing/stable-diffusion-broken-variants/tree/main/unet
assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
# only unet has "no_ema" variant
def test_local_save_load_index(self):
prompt = "hello"
for variant in [None, "fp16"]:
for use_safe in [True, False]:
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe-indexes",
variant=variant,
use_safetensors=use_safe,
safety_checker=None,
)
pipe = pipe.to(torch_device)
generator = torch.manual_seed(0)
out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe_2 = StableDiffusionPipeline.from_pretrained(
tmpdirname, safe_serialization=use_safe, variant=variant
)
pipe_2 = pipe_2.to(torch_device)
generator = torch.manual_seed(0)
out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
assert np.max(np.abs(out - out_2)) < 1e-3
def test_text_inversion_download(self):
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe = pipe.to(torch_device)
num_tokens = len(pipe.tokenizer)
# single token load local
with tempfile.TemporaryDirectory() as tmpdirname:
ten = {"<*>": torch.ones((32,))}
torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin"))
pipe.load_textual_inversion(tmpdirname)
token = pipe.tokenizer.convert_tokens_to_ids("<*>")
assert token == num_tokens, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32
assert pipe._maybe_convert_prompt("<*>", pipe.tokenizer) == "<*>"
prompt = "hey <*>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
# single token load local with weight name
with tempfile.TemporaryDirectory() as tmpdirname:
ten = {"<**>": 2 * torch.ones((1, 32))}
torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin"))
pipe.load_textual_inversion(tmpdirname, weight_name="learned_embeds.bin")
token = pipe.tokenizer.convert_tokens_to_ids("<**>")
assert token == num_tokens + 1, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64
assert pipe._maybe_convert_prompt("<**>", pipe.tokenizer) == "<**>"
prompt = "hey <**>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
# multi token load
with tempfile.TemporaryDirectory() as tmpdirname:
ten = {"<***>": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])}
torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin"))
pipe.load_textual_inversion(tmpdirname)
token = pipe.tokenizer.convert_tokens_to_ids("<***>")
token_1 = pipe.tokenizer.convert_tokens_to_ids("<***>_1")
token_2 = pipe.tokenizer.convert_tokens_to_ids("<***>_2")
assert token == num_tokens + 2, "Added token must be at spot `num_tokens`"
assert token_1 == num_tokens + 3, "Added token must be at spot `num_tokens`"
assert token_2 == num_tokens + 4, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96
assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128
assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160
assert pipe._maybe_convert_prompt("<***>", pipe.tokenizer) == "<***> <***>_1 <***>_2"
prompt = "hey <***>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
# multi token load a1111
with tempfile.TemporaryDirectory() as tmpdirname:
ten = {
"string_to_param": {
"*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])
},
"name": "<****>",
}
torch.save(ten, os.path.join(tmpdirname, "a1111.bin"))
pipe.load_textual_inversion(tmpdirname, weight_name="a1111.bin")
token = pipe.tokenizer.convert_tokens_to_ids("<****>")
token_1 = pipe.tokenizer.convert_tokens_to_ids("<****>_1")
token_2 = pipe.tokenizer.convert_tokens_to_ids("<****>_2")
assert token == num_tokens + 5, "Added token must be at spot `num_tokens`"
assert token_1 == num_tokens + 6, "Added token must be at spot `num_tokens`"
assert token_2 == num_tokens + 7, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96
assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128
assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160
assert pipe._maybe_convert_prompt("<****>", pipe.tokenizer) == "<****> <****>_1 <****>_2"
prompt = "hey <****>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
# multi embedding load
with tempfile.TemporaryDirectory() as tmpdirname1:
with tempfile.TemporaryDirectory() as tmpdirname2:
ten = {"<*****>": torch.ones((32,))}
torch.save(ten, os.path.join(tmpdirname1, "learned_embeds.bin"))
ten = {"<******>": 2 * torch.ones((1, 32))}
torch.save(ten, os.path.join(tmpdirname2, "learned_embeds.bin"))
pipe.load_textual_inversion([tmpdirname1, tmpdirname2])
token = pipe.tokenizer.convert_tokens_to_ids("<*****>")
assert token == num_tokens + 8, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32
assert pipe._maybe_convert_prompt("<*****>", pipe.tokenizer) == "<*****>"
token = pipe.tokenizer.convert_tokens_to_ids("<******>")
assert token == num_tokens + 9, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64
assert pipe._maybe_convert_prompt("<******>", pipe.tokenizer) == "<******>"
prompt = "hey <*****> <******>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
# single token state dict load
ten = {"<x>": torch.ones((32,))}
pipe.load_textual_inversion(ten)
token = pipe.tokenizer.convert_tokens_to_ids("<x>")
assert token == num_tokens + 10, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32
assert pipe._maybe_convert_prompt("<x>", pipe.tokenizer) == "<x>"
prompt = "hey <x>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
# multi embedding state dict load
ten1 = {"<xxxxx>": torch.ones((32,))}
ten2 = {"<xxxxxx>": 2 * torch.ones((1, 32))}
pipe.load_textual_inversion([ten1, ten2])
token = pipe.tokenizer.convert_tokens_to_ids("<xxxxx>")
assert token == num_tokens + 11, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32
assert pipe._maybe_convert_prompt("<xxxxx>", pipe.tokenizer) == "<xxxxx>"
token = pipe.tokenizer.convert_tokens_to_ids("<xxxxxx>")
assert token == num_tokens + 12, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64
assert pipe._maybe_convert_prompt("<xxxxxx>", pipe.tokenizer) == "<xxxxxx>"
prompt = "hey <xxxxx> <xxxxxx>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
# auto1111 multi-token state dict load
ten = {
"string_to_param": {
"*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])
},
"name": "<xxxx>",
}
pipe.load_textual_inversion(ten)
token = pipe.tokenizer.convert_tokens_to_ids("<xxxx>")
token_1 = pipe.tokenizer.convert_tokens_to_ids("<xxxx>_1")
token_2 = pipe.tokenizer.convert_tokens_to_ids("<xxxx>_2")
assert token == num_tokens + 13, "Added token must be at spot `num_tokens`"
assert token_1 == num_tokens + 14, "Added token must be at spot `num_tokens`"
assert token_2 == num_tokens + 15, "Added token must be at spot `num_tokens`"
assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96
assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128
assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160
assert pipe._maybe_convert_prompt("<xxxx>", pipe.tokenizer) == "<xxxx> <xxxx>_1 <xxxx>_2"
prompt = "hey <xxxx>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
# multiple references to multi embedding
ten = {"<cat>": torch.ones(3, 32)}
pipe.load_textual_inversion(ten)
assert (
pipe._maybe_convert_prompt("<cat> <cat>", pipe.tokenizer) == "<cat> <cat>_1 <cat>_2 <cat> <cat>_1 <cat>_2"
)
prompt = "hey <cat> <cat>"
out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
assert out.shape == (1, 128, 128, 3)
def test_text_inversion_multi_tokens(self):
pipe1 = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe1 = pipe1.to(torch_device)
token1, token2 = "<*>", "<**>"
ten1 = torch.ones((32,))
ten2 = torch.ones((32,)) * 2
num_tokens = len(pipe1.tokenizer)
pipe1.load_textual_inversion(ten1, token=token1)
pipe1.load_textual_inversion(ten2, token=token2)
emb1 = pipe1.text_encoder.get_input_embeddings().weight
pipe2 = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe2 = pipe2.to(torch_device)
pipe2.load_textual_inversion([ten1, ten2], token=[token1, token2])
emb2 = pipe2.text_encoder.get_input_embeddings().weight
pipe3 = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe3 = pipe3.to(torch_device)
pipe3.load_textual_inversion(torch.stack([ten1, ten2], dim=0), token=[token1, token2])
emb3 = pipe3.text_encoder.get_input_embeddings().weight
assert len(pipe1.tokenizer) == len(pipe2.tokenizer) == len(pipe3.tokenizer) == num_tokens + 2
assert (
pipe1.tokenizer.convert_tokens_to_ids(token1)
== pipe2.tokenizer.convert_tokens_to_ids(token1)
== pipe3.tokenizer.convert_tokens_to_ids(token1)
== num_tokens
)
assert (
pipe1.tokenizer.convert_tokens_to_ids(token2)
== pipe2.tokenizer.convert_tokens_to_ids(token2)
== pipe3.tokenizer.convert_tokens_to_ids(token2)
== num_tokens + 1
)
assert emb1[num_tokens].sum().item() == emb2[num_tokens].sum().item() == emb3[num_tokens].sum().item()
assert (
emb1[num_tokens + 1].sum().item() == emb2[num_tokens + 1].sum().item() == emb3[num_tokens + 1].sum().item()
)
def test_download_ignore_files(self):
# Check https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files/blob/72f58636e5508a218c6b3f60550dc96445547817/model_index.json#L4
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
tmpdirname = DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files")
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a pytorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
assert not any(f in ["vae/diffusion_pytorch_model.bin", "text_encoder/config.json"] for f in files)
assert len(files) == 14
def test_get_pipeline_class_from_flax(self):
flax_config = {"_class_name": "FlaxStableDiffusionPipeline"}
config = {"_class_name": "StableDiffusionPipeline"}
# when loading a PyTorch Pipeline from a FlaxPipeline `model_index.json`, e.g.: https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-lms-pipe/blob/7a9063578b325779f0f1967874a6771caa973cad/model_index.json#L2
# we need to make sure that we don't load the Flax Pipeline class, but instead the PyTorch pipeline class
assert _get_pipeline_class(DiffusionPipeline, flax_config) == _get_pipeline_class(DiffusionPipeline, config)
class CustomPipelineTests(unittest.TestCase):
def test_load_custom_pipeline(self):
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
)
pipeline = pipeline.to(torch_device)
# NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub
# under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24
assert pipeline.__class__.__name__ == "CustomPipeline"
def test_load_custom_github(self):
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="main"
)
# make sure that on "main" pipeline gives only ones because of: https://github.com/huggingface/diffusers/pull/1690
with torch.no_grad():
output = pipeline()
assert output.numel() == output.sum()
# hack since Python doesn't like overwriting modules: https://stackoverflow.com/questions/3105801/unload-a-module-in-python
# Could in the future work with hashes instead.
del sys.modules["diffusers_modules.git.one_step_unet"]
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="0.10.2"
)
with torch.no_grad():
output = pipeline()
assert output.numel() != output.sum()
assert pipeline.__class__.__name__ == "UnetSchedulerOneForwardPipeline"
def test_run_custom_pipeline(self):
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
)
pipeline = pipeline.to(torch_device)
images, output_str = pipeline(num_inference_steps=2, output_type="np")
assert images[0].shape == (1, 32, 32, 3)
# compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102
assert output_str == "This is a test"
def test_remote_components(self):
# make sure that trust remote code has to be passed
with self.assertRaises(ValueError):
pipeline = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sdxl-custom-components")
# Check that only loading custom componets "my_unet", "my_scheduler" works
pipeline = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-sdxl-custom-components", trust_remote_code=True
)
assert pipeline.config.unet == ("diffusers_modules.local.my_unet_model", "MyUNetModel")
assert pipeline.config.scheduler == ("diffusers_modules.local.my_scheduler", "MyScheduler")
assert pipeline.__class__.__name__ == "StableDiffusionXLPipeline"
pipeline = pipeline.to(torch_device)
images = pipeline("test", num_inference_steps=2, output_type="np")[0]
assert images.shape == (1, 64, 64, 3)
# Check that only loading custom componets "my_unet", "my_scheduler" and explicit custom pipeline works
pipeline = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-sdxl-custom-components", custom_pipeline="my_pipeline", trust_remote_code=True
)
assert pipeline.config.unet == ("diffusers_modules.local.my_unet_model", "MyUNetModel")
assert pipeline.config.scheduler == ("diffusers_modules.local.my_scheduler", "MyScheduler")
assert pipeline.__class__.__name__ == "MyPipeline"
pipeline = pipeline.to(torch_device)
images = pipeline("test", num_inference_steps=2, output_type="np")[0]
assert images.shape == (1, 64, 64, 3)
def test_remote_auto_custom_pipe(self):
# make sure that trust remote code has to be passed
with self.assertRaises(ValueError):
pipeline = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sdxl-custom-all")
# Check that only loading custom componets "my_unet", "my_scheduler" and auto custom pipeline works
pipeline = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-sdxl-custom-all", trust_remote_code=True
)
assert pipeline.config.unet == ("diffusers_modules.local.my_unet_model", "MyUNetModel")
assert pipeline.config.scheduler == ("diffusers_modules.local.my_scheduler", "MyScheduler")
assert pipeline.__class__.__name__ == "MyPipeline"
pipeline = pipeline.to(torch_device)
images = pipeline("test", num_inference_steps=2, output_type="np")[0]
assert images.shape == (1, 64, 64, 3)
def test_local_custom_pipeline_repo(self):
local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
)
pipeline = pipeline.to(torch_device)
images, output_str = pipeline(num_inference_steps=2, output_type="np")
assert pipeline.__class__.__name__ == "CustomLocalPipeline"
assert images[0].shape == (1, 32, 32, 3)
# compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
assert output_str == "This is a local test"
def test_local_custom_pipeline_file(self):
local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
local_custom_pipeline_path = os.path.join(local_custom_pipeline_path, "what_ever.py")
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
)
pipeline = pipeline.to(torch_device)
images, output_str = pipeline(num_inference_steps=2, output_type="np")
assert pipeline.__class__.__name__ == "CustomLocalPipeline"
assert images[0].shape == (1, 32, 32, 3)
# compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
assert output_str == "This is a local test"
def test_custom_model_and_pipeline(self):
pipe = CustomPipeline(
encoder=CustomEncoder(),
scheduler=DDIMScheduler(),
)
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname, safe_serialization=False)
pipe_new = CustomPipeline.from_pretrained(tmpdirname)
pipe_new.save_pretrained(tmpdirname)
conf_1 = dict(pipe.config)
conf_2 = dict(pipe_new.config)
del conf_2["_name_or_path"]
assert conf_1 == conf_2
@slow
@require_torch_gpu
def test_download_from_git(self):
# Because adaptive_avg_pool2d_backward_cuda
# does not have a deterministic implementation.
clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)
clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)
pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
custom_pipeline="clip_guided_stable_diffusion",
clip_model=clip_model,
feature_extractor=feature_extractor,
torch_dtype=torch.float16,
)
pipeline.enable_attention_slicing()
pipeline = pipeline.to(torch_device)
# NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under:
# https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py
assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion"
image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0]
assert image.shape == (512, 512, 3)
def test_save_pipeline_change_config(self):
pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = DiffusionPipeline.from_pretrained(tmpdirname)
assert pipe.scheduler.__class__.__name__ == "PNDMScheduler"
# let's make sure that changing the scheduler is correctly reflected
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.save_pretrained(tmpdirname)
pipe = DiffusionPipeline.from_pretrained(tmpdirname)
assert pipe.scheduler.__class__.__name__ == "DPMSolverMultistepScheduler"
class PipelineFastTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def dummy_image(self):
batch_size = 1
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
def dummy_uncond_unet(self, sample_size=32):
torch.manual_seed(0)
model = UNet2DModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=sample_size,
in_channels=3,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
return model
def dummy_cond_unet(self, sample_size=32):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=sample_size,
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)
@property
def dummy_extractor(self):
def extract(*args, **kwargs):
class Out:
def __init__(self):
self.pixel_values = torch.ones([0])
def to(self, device):
self.pixel_values.to(device)
return self
return Out()
return extract
@parameterized.expand(
[
[DDIMScheduler, DDIMPipeline, 32],
[DDPMScheduler, DDPMPipeline, 32],
[DDIMScheduler, DDIMPipeline, (32, 64)],
[DDPMScheduler, DDPMPipeline, (64, 32)],
]
)
def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32):
unet = self.dummy_uncond_unet(sample_size)
scheduler = scheduler_fn()
pipeline = pipeline_fn(unet, scheduler).to(torch_device)
generator = torch.manual_seed(0)
out_image = pipeline(
generator=generator,
num_inference_steps=2,
output_type="np",
).images
sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size
assert out_image.shape == (1, *sample_size, 3)
def test_stable_diffusion_components(self):
"""Test that components property works correctly"""
unet = self.dummy_cond_unet()
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image().cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32))
# make sure here that pndm scheduler skips prk
inpaint = StableDiffusionInpaintPipelineLegacy(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
).to(torch_device)
img2img = StableDiffusionImg2ImgPipeline(**inpaint.components, image_encoder=None).to(torch_device)
text2img = StableDiffusionPipeline(**inpaint.components, image_encoder=None).to(torch_device)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image_inpaint = inpaint(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
).images
image_img2img = img2img(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
image=init_image,
).images
image_text2img = text2img(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
).images
assert image_inpaint.shape == (1, 32, 32, 3)
assert image_img2img.shape == (1, 32, 32, 3)
assert image_text2img.shape == (1, 64, 64, 3)
@require_torch_gpu
def test_pipe_false_offload_warn(self):
unet = self.dummy_cond_unet()
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
sd = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd.enable_model_cpu_offload()
logger = logging.get_logger("diffusers.pipelines.pipeline_utils")
with CaptureLogger(logger) as cap_logger:
sd.to("cuda")
assert "It is strongly recommended against doing so" in str(cap_logger)
sd = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
def test_set_scheduler(self):
unet = self.dummy_cond_unet()
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
sd = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, DDIMScheduler)
sd.scheduler = DDPMScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, DDPMScheduler)
sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, PNDMScheduler)
sd.scheduler = LMSDiscreteScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, LMSDiscreteScheduler)
sd.scheduler = EulerDiscreteScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, EulerDiscreteScheduler)
sd.scheduler = EulerAncestralDiscreteScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, EulerAncestralDiscreteScheduler)
sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, DPMSolverMultistepScheduler)
def test_set_component_to_none(self):
unet = self.dummy_cond_unet()
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
pipeline = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "This is a flower"
out_image = pipeline(
prompt=prompt,
generator=generator,
num_inference_steps=1,
output_type="np",
).images
pipeline.feature_extractor = None
generator = torch.Generator(device="cpu").manual_seed(0)
out_image_2 = pipeline(
prompt=prompt,
generator=generator,
num_inference_steps=1,
output_type="np",
).images
assert out_image.shape == (1, 64, 64, 3)
assert np.abs(out_image - out_image_2).max() < 1e-3
def test_optional_components_is_none(self):
unet = self.dummy_cond_unet()
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
items = {
"feature_extractor": self.dummy_extractor,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": bert,
"tokenizer": tokenizer,
"safety_checker": None,
# we don't add an image encoder
}
pipeline = StableDiffusionPipeline(**items)
assert sorted(pipeline.components.keys()) == sorted(["image_encoder"] + list(items.keys()))
assert pipeline.image_encoder is None
def test_set_scheduler_consistency(self):
unet = self.dummy_cond_unet()
pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
ddim = DDIMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
sd = StableDiffusionPipeline(
unet=unet,
scheduler=pndm,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
pndm_config = sd.scheduler.config
sd.scheduler = DDPMScheduler.from_config(pndm_config)
sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
pndm_config_2 = sd.scheduler.config
pndm_config_2 = {k: v for k, v in pndm_config_2.items() if k in pndm_config}
assert dict(pndm_config) == dict(pndm_config_2)
sd = StableDiffusionPipeline(
unet=unet,
scheduler=ddim,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
ddim_config = sd.scheduler.config
sd.scheduler = LMSDiscreteScheduler.from_config(ddim_config)
sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
ddim_config_2 = sd.scheduler.config
ddim_config_2 = {k: v for k, v in ddim_config_2.items() if k in ddim_config}
assert dict(ddim_config) == dict(ddim_config_2)
def test_save_safe_serialization(self):
pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
with tempfile.TemporaryDirectory() as tmpdirname:
pipeline.save_pretrained(tmpdirname, safe_serialization=True)
# Validate that the VAE safetensor exists and are of the correct format
vae_path = os.path.join(tmpdirname, "vae", "diffusion_pytorch_model.safetensors")
assert os.path.exists(vae_path), f"Could not find {vae_path}"
_ = safetensors.torch.load_file(vae_path)
# Validate that the UNet safetensor exists and are of the correct format
unet_path = os.path.join(tmpdirname, "unet", "diffusion_pytorch_model.safetensors")
assert os.path.exists(unet_path), f"Could not find {unet_path}"
_ = safetensors.torch.load_file(unet_path)
# Validate that the text encoder safetensor exists and are of the correct format
text_encoder_path = os.path.join(tmpdirname, "text_encoder", "model.safetensors")
assert os.path.exists(text_encoder_path), f"Could not find {text_encoder_path}"
_ = safetensors.torch.load_file(text_encoder_path)
pipeline = StableDiffusionPipeline.from_pretrained(tmpdirname)
assert pipeline.unet is not None
assert pipeline.vae is not None
assert pipeline.text_encoder is not None
assert pipeline.scheduler is not None
assert pipeline.feature_extractor is not None
def test_no_pytorch_download_when_doing_safetensors(self):
# by default we don't download
with tempfile.TemporaryDirectory() as tmpdirname:
_ = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname
)
path = os.path.join(
tmpdirname,
"models--hf-internal-testing--diffusers-stable-diffusion-tiny-all",
"snapshots",
"07838d72e12f9bcec1375b0482b80c1d399be843",
"unet",
)
# safetensors exists
assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors"))
# pytorch does not
assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
def test_no_safetensors_download_when_doing_pytorch(self):
use_safetensors = False
with tempfile.TemporaryDirectory() as tmpdirname:
_ = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
cache_dir=tmpdirname,
use_safetensors=use_safetensors,
)
path = os.path.join(
tmpdirname,
"models--hf-internal-testing--diffusers-stable-diffusion-tiny-all",
"snapshots",
"07838d72e12f9bcec1375b0482b80c1d399be843",
"unet",
)
# safetensors does not exists
assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors"))
# pytorch does
assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
def test_optional_components(self):
unet = self.dummy_cond_unet()
pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
orig_sd = StableDiffusionPipeline(
unet=unet,
scheduler=pndm,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=unet,
feature_extractor=self.dummy_extractor,
)
sd = orig_sd
assert sd.config.requires_safety_checker is True
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
# Test that passing None works
sd = StableDiffusionPipeline.from_pretrained(
tmpdirname, feature_extractor=None, safety_checker=None, requires_safety_checker=False
)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor == (None, None)
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
# Test that loading previous None works
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor == (None, None)
orig_sd.save_pretrained(tmpdirname)
# Test that loading without any directory works
shutil.rmtree(os.path.join(tmpdirname, "safety_checker"))
with open(os.path.join(tmpdirname, sd.config_name)) as f:
config = json.load(f)
config["safety_checker"] = [None, None]
with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
json.dump(config, f)
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, requires_safety_checker=False)
sd.save_pretrained(tmpdirname)
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor == (None, None)
# Test that loading from deleted model index works
with open(os.path.join(tmpdirname, sd.config_name)) as f:
config = json.load(f)
del config["safety_checker"]
del config["feature_extractor"]
with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
json.dump(config, f)
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor == (None, None)
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
# Test that partially loading works
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor != (None, None)
# Test that partially loading works
sd = StableDiffusionPipeline.from_pretrained(
tmpdirname,
feature_extractor=self.dummy_extractor,
safety_checker=unet,
requires_safety_checker=[True, True],
)
assert sd.config.requires_safety_checker == [True, True]
assert sd.config.safety_checker != (None, None)
assert sd.config.feature_extractor != (None, None)
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)
assert sd.config.requires_safety_checker == [True, True]
assert sd.config.safety_checker != (None, None)
assert sd.config.feature_extractor != (None, None)
def test_name_or_path(self):
model_path = "hf-internal-testing/tiny-stable-diffusion-torch"
sd = DiffusionPipeline.from_pretrained(model_path)
assert sd.name_or_path == model_path
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
sd = DiffusionPipeline.from_pretrained(tmpdirname)
assert sd.name_or_path == tmpdirname
def test_error_no_variant_available(self):
variant = "fp16"
with self.assertRaises(ValueError) as error_context:
_ = StableDiffusionPipeline.download(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all", variant=variant
)
assert "but no such modeling files are available" in str(error_context.exception)
assert variant in str(error_context.exception)
def test_pipe_to(self):
unet = self.dummy_cond_unet()
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
sd = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
device_type = torch.device(torch_device).type
sd1 = sd.to(device_type)
sd2 = sd.to(torch.device(device_type))
sd3 = sd.to(device_type, torch.float32)
sd4 = sd.to(device=device_type)
sd5 = sd.to(torch_device=device_type)
sd6 = sd.to(device_type, dtype=torch.float32)
sd7 = sd.to(device_type, torch_dtype=torch.float32)
assert sd1.device.type == device_type
assert sd2.device.type == device_type
assert sd3.device.type == device_type
assert sd4.device.type == device_type
assert sd5.device.type == device_type
assert sd6.device.type == device_type
assert sd7.device.type == device_type
sd1 = sd.to(torch.float16)
sd2 = sd.to(None, torch.float16)
sd3 = sd.to(dtype=torch.float16)
sd4 = sd.to(torch_dtype=torch.float16)
sd5 = sd.to(None, dtype=torch.float16)
sd6 = sd.to(None, torch_dtype=torch.float16)
assert sd1.dtype == torch.float16
assert sd2.dtype == torch.float16
assert sd3.dtype == torch.float16
assert sd4.dtype == torch.float16
assert sd5.dtype == torch.float16
assert sd6.dtype == torch.float16
sd1 = sd.to(device=device_type, dtype=torch.float16)
sd2 = sd.to(torch_device=device_type, torch_dtype=torch.float16)
sd3 = sd.to(device_type, torch.float16)
assert sd1.dtype == torch.float16
assert sd2.dtype == torch.float16
assert sd3.dtype == torch.float16
assert sd1.device.type == device_type
assert sd2.device.type == device_type
assert sd3.device.type == device_type
def test_pipe_same_device_id_offload(self):
unet = self.dummy_cond_unet()
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
sd = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd.enable_model_cpu_offload(gpu_id=5)
assert sd._offload_gpu_id == 5
sd.maybe_free_model_hooks()
assert sd._offload_gpu_id == 5
@slow
@require_torch_gpu
class PipelineSlowTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_smart_download(self):
model_id = "hf-internal-testing/unet-pipeline-dummy"
with tempfile.TemporaryDirectory() as tmpdirname:
_ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True)
local_repo_name = "--".join(["models"] + model_id.split("/"))
snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots")
snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])
# inspect all downloaded files to make sure that everything is included
assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name))
assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME))
assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME))
assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME))
assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME))
assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
# let's make sure the super large numpy file:
# https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy
# is not downloaded, but all the expected ones
assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy"))
def test_warning_unused_kwargs(self):
model_id = "hf-internal-testing/unet-pipeline-dummy"
logger = logging.get_logger("diffusers.pipelines")
with tempfile.TemporaryDirectory() as tmpdirname:
with CaptureLogger(logger) as cap_logger:
DiffusionPipeline.from_pretrained(
model_id,
not_used=True,
cache_dir=tmpdirname,
force_download=True,
)
assert (
cap_logger.out.strip().split("\n")[-1]
== "Keyword arguments {'not_used': True} are not expected by DDPMPipeline and will be ignored."
)
def test_from_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"),
)
scheduler = DDPMScheduler(num_train_timesteps=10)
ddpm = DDPMPipeline(model, scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
with tempfile.TemporaryDirectory() as tmpdirname:
ddpm.save_pretrained(tmpdirname)
new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
new_ddpm.to(torch_device)
generator = torch.Generator(device=torch_device).manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(0)
new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
assert np.abs(image - new_image).max() < 1e-5, "Models don't give the same forward pass"
@require_python39_or_higher
@require_torch_2
def test_from_save_pretrained_dynamo(self):
run_test_in_subprocess(test_case=self, target_func=_test_from_save_pretrained_dynamo, inputs=None)
def test_from_pretrained_hub(self):
model_path = "google/ddpm-cifar10-32"
scheduler = DDPMScheduler(num_train_timesteps=10)
ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler)
ddpm = ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
ddpm_from_hub = ddpm_from_hub.to(torch_device)
ddpm_from_hub.set_progress_bar_config(disable=None)
generator = torch.Generator(device=torch_device).manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(0)
new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="numpy").images
assert np.abs(image - new_image).max() < 1e-5, "Models don't give the same forward pass"
def test_from_pretrained_hub_pass_model(self):
model_path = "google/ddpm-cifar10-32"
scheduler = DDPMScheduler(num_train_timesteps=10)
# pass unet into DiffusionPipeline
unet = UNet2DModel.from_pretrained(model_path)
ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler)
ddpm_from_hub_custom_model = ddpm_from_hub_custom_model.to(torch_device)
ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
ddpm_from_hub = ddpm_from_hub.to(torch_device)
ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
generator = torch.Generator(device=torch_device).manual_seed(0)
image = ddpm_from_hub_custom_model(generator=generator, num_inference_steps=5, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(0)
new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="numpy").images
assert np.abs(image - new_image).max() < 1e-5, "Models don't give the same forward pass"
def test_output_format(self):
model_path = "google/ddpm-cifar10-32"
scheduler = DDIMScheduler.from_pretrained(model_path)
pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
images = pipe(output_type="numpy").images
assert images.shape == (1, 32, 32, 3)
assert isinstance(images, np.ndarray)
images = pipe(output_type="pil", num_inference_steps=4).images
assert isinstance(images, list)
assert len(images) == 1
assert isinstance(images[0], PIL.Image.Image)
# use PIL by default
images = pipe(num_inference_steps=4).images
assert isinstance(images, list)
assert isinstance(images[0], PIL.Image.Image)
@require_flax
def test_from_flax_from_pt(self):
pipe_pt = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe_pt.to(torch_device)
from diffusers import FlaxStableDiffusionPipeline
with tempfile.TemporaryDirectory() as tmpdirname:
pipe_pt.save_pretrained(tmpdirname)
pipe_flax, params = FlaxStableDiffusionPipeline.from_pretrained(
tmpdirname, safety_checker=None, from_pt=True
)
with tempfile.TemporaryDirectory() as tmpdirname:
pipe_flax.save_pretrained(tmpdirname, params=params)
pipe_pt_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None, from_flax=True)
pipe_pt_2.to(torch_device)
prompt = "Hello"
generator = torch.manual_seed(0)
image_0 = pipe_pt(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
).images[0]
generator = torch.manual_seed(0)
image_1 = pipe_pt_2(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
).images[0]
assert np.abs(image_0 - image_1).sum() < 1e-5, "Models don't give the same forward pass"
@require_compel
def test_weighted_prompts_compel(self):
from compel import Compel
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_attention_slicing()
compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
prompt = "a red cat playing with a ball{}"
prompts = [prompt.format(s) for s in ["", "++", "--"]]
prompt_embeds = compel(prompts)
generator = [torch.Generator(device="cpu").manual_seed(33) for _ in range(prompt_embeds.shape[0])]
images = pipe(
prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20, output_type="numpy"
).images
for i, image in enumerate(images):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
f"/compel/forest_{i}.npy"
)
assert np.abs(image - expected_image).max() < 3e-1
@nightly
@require_torch_gpu
class PipelineNightlyTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_ddpm_ddim_equality_batched(self):
seed = 0
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
ddpm_scheduler = DDPMScheduler()
ddim_scheduler = DDIMScheduler()
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
ddim.to(torch_device)
ddim.set_progress_bar_config(disable=None)
generator = torch.Generator(device=torch_device).manual_seed(seed)
ddpm_images = ddpm(batch_size=2, generator=generator, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(seed)
ddim_images = ddim(
batch_size=2,
generator=generator,
num_inference_steps=1000,
eta=1.0,
output_type="numpy",
use_clipped_model_output=True, # Need this to make DDIM match DDPM
).images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/pipelines/test_pipeline_utils.py
|
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class IsSafetensorsCompatibleTests(unittest.TestCase):
def test_all_is_compatible(self):
filenames = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(filenames))
def test_diffusers_model_is_compatible(self):
filenames = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(filenames))
def test_diffusers_model_is_not_compatible(self):
filenames = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(filenames))
def test_transformer_model_is_compatible(self):
filenames = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(filenames))
def test_transformer_model_is_not_compatible(self):
filenames = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(filenames))
def test_all_is_compatible_variant(self):
filenames = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
variant = "fp16"
self.assertTrue(is_safetensors_compatible(filenames, variant=variant))
def test_diffusers_model_is_compatible_variant(self):
filenames = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
variant = "fp16"
self.assertTrue(is_safetensors_compatible(filenames, variant=variant))
def test_diffusers_model_is_compatible_variant_partial(self):
# pass variant but use the non-variant filenames
filenames = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
variant = "fp16"
self.assertTrue(is_safetensors_compatible(filenames, variant=variant))
def test_diffusers_model_is_not_compatible_variant(self):
filenames = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
variant = "fp16"
self.assertFalse(is_safetensors_compatible(filenames, variant=variant))
def test_transformer_model_is_compatible_variant(self):
filenames = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
variant = "fp16"
self.assertTrue(is_safetensors_compatible(filenames, variant=variant))
def test_transformer_model_is_compatible_variant_partial(self):
# pass variant but use the non-variant filenames
filenames = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
variant = "fp16"
self.assertTrue(is_safetensors_compatible(filenames, variant=variant))
def test_transformer_model_is_not_compatible_variant(self):
filenames = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
variant = "fp16"
self.assertFalse(is_safetensors_compatible(filenames, variant=variant))
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/pipelines/test_pipelines_onnx_common.py
|
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
| 0
|
hf_public_repos/diffusers/tests
|
hf_public_repos/diffusers/tests/pipelines/test_pipelines_flax.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class DownloadTests(unittest.TestCase):
def test_download_only_pytorch(self):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
_ = FlaxDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith(".bin") for f in files)
@slow
@require_flax
class FlaxPipelineTests(unittest.TestCase):
def test_dummy_all_tpus(self):
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
)
prompt = (
"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
" field, close up, split lighting, cinematic"
)
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 4
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 4.1514745) < 1e-3
assert np.abs(np.abs(images, dtype=np.float32).sum() - 49947.875) < 5e-1
images_pil = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
assert len(images_pil) == num_samples
def test_stable_diffusion_v1_4(self):
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="flax", safety_checker=None
)
prompt = (
"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
" field, close up, split lighting, cinematic"
)
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.05652401)) < 1e-2
assert np.abs((np.abs(images, dtype=np.float32).sum() - 2383808.2)) < 5e-1
def test_stable_diffusion_v1_4_bfloat_16(self):
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jnp.bfloat16, safety_checker=None
)
prompt = (
"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
" field, close up, split lighting, cinematic"
)
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.04003906)) < 5e-2
assert np.abs((np.abs(images, dtype=np.float32).sum() - 2373516.75)) < 5e-1
def test_stable_diffusion_v1_4_bfloat_16_with_safety(self):
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jnp.bfloat16
)
prompt = (
"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
" field, close up, split lighting, cinematic"
)
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.04003906)) < 5e-2
assert np.abs((np.abs(images, dtype=np.float32).sum() - 2373516.75)) < 5e-1
def test_stable_diffusion_v1_4_bfloat_16_ddim(self):
scheduler = FlaxDDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
set_alpha_to_one=False,
steps_offset=1,
)
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="bf16",
dtype=jnp.bfloat16,
scheduler=scheduler,
safety_checker=None,
)
scheduler_state = scheduler.create_state()
params["scheduler"] = scheduler_state
prompt = (
"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
" field, close up, split lighting, cinematic"
)
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.045043945)) < 5e-2
assert np.abs((np.abs(images, dtype=np.float32).sum() - 2347693.5)) < 5e-1
def test_jax_memory_efficient_attention(self):
prompt = (
"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
" field, close up, split lighting, cinematic"
)
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prng_seed = jax.random.split(jax.random.PRNGKey(0), num_samples)
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="bf16",
dtype=jnp.bfloat16,
safety_checker=None,
)
params = replicate(params)
prompt_ids = pipeline.prepare_inputs(prompt)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, jit=True).images
assert images.shape == (num_samples, 1, 512, 512, 3)
slice = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="bf16",
dtype=jnp.bfloat16,
safety_checker=None,
use_memory_efficient_attention=True,
)
params = replicate(params)
prompt_ids = pipeline.prepare_inputs(prompt)
prompt_ids = shard(prompt_ids)
images_eff = pipeline(prompt_ids, params, prng_seed, jit=True).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
slice_eff = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/pndm/test_pndm.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNet2DModel
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch, torch_device
enable_full_determinism()
class PNDMPipelineFastTests(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
@nightly
@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
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/controlnet/test_controlnet.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import traceback
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
EulerDiscreteScheduler,
LCMScheduler,
StableDiffusionControlNetPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_image,
load_numpy,
require_python39_or_higher,
require_torch_2,
require_torch_gpu,
run_test_in_subprocess,
slow,
torch_device,
)
from diffusers.utils.torch_utils import randn_tensor
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
# Will be run via run_test_in_subprocess
def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.to("cuda")
pipe.set_progress_bar_config(disable=None)
pipe.unet.to(memory_format=torch.channels_last)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe.controlnet.to(memory_format=torch.channels_last)
pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
output = pipe(prompt, image, num_inference_steps=10, generator=generator, output_type="np")
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy"
)
expected_image = np.resize(expected_image, (512, 512, 3))
assert np.abs(expected_image - image).max() < 1.0
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class ControlNetPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionControlNetPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
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,
norm_num_groups=1,
time_cond_proj_dim=time_cond_proj_dim,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
image = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
}
return inputs
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_controlnet_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionControlNetPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.52700454, 0.3930534, 0.25509018, 0.7132304, 0.53696585, 0.46568912, 0.7095368, 0.7059624, 0.4744786]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_controlnet_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionControlNetPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.52700454, 0.3930534, 0.25509018, 0.7132304, 0.53696585, 0.46568912, 0.7095368, 0.7059624, 0.4744786]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
class StableDiffusionMultiControlNetPipelineFastTests(
PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionControlNetPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
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,
norm_num_groups=1,
)
torch.manual_seed(0)
def init_weights(m):
if isinstance(m, torch.nn.Conv2d):
torch.nn.init.normal(m.weight)
m.bias.data.fill_(1.0)
controlnet1 = ControlNetModel(
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
controlnet1.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
controlnet2 = ControlNetModel(
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
controlnet2.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
controlnet = MultiControlNetModel([controlnet1, controlnet2])
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
images = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
]
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": images,
}
return inputs
def test_control_guidance_switch(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
scale = 10.0
steps = 4
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_1 = pipe(**inputs)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_1 - output_2)) > 1e-3
assert np.sum(np.abs(output_1 - output_3)) > 1e-3
assert np.sum(np.abs(output_1 - output_4)) > 1e-3
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_save_pretrained_raise_not_implemented_exception(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(tmpdir)
except NotImplementedError:
pass
class StableDiffusionMultiControlNetOneModelPipelineFastTests(
PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionControlNetPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
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,
norm_num_groups=1,
)
torch.manual_seed(0)
def init_weights(m):
if isinstance(m, torch.nn.Conv2d):
torch.nn.init.normal(m.weight)
m.bias.data.fill_(1.0)
controlnet = ControlNetModel(
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
controlnet.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
controlnet = MultiControlNetModel([controlnet])
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
images = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
]
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": images,
}
return inputs
def test_control_guidance_switch(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
scale = 10.0
steps = 4
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_1 = pipe(**inputs)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_3 = pipe(
**inputs,
control_guidance_start=[0.1],
control_guidance_end=[0.2],
)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5])[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_1 - output_2)) > 1e-3
assert np.sum(np.abs(output_1 - output_3)) > 1e-3
assert np.sum(np.abs(output_1 - output_4)) > 1e-3
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_save_pretrained_raise_not_implemented_exception(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(tmpdir)
except NotImplementedError:
pass
@slow
@require_torch_gpu
class ControlNetPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (768, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out.npy"
)
assert np.abs(expected_image - image).max() < 9e-2
def test_depth(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-depth")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "Stormtrooper's lecture"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth_out.npy"
)
assert np.abs(expected_image - image).max() < 8e-1
def test_hed(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-hed")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "oil painting of handsome old man, masterpiece"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (704, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed_out.npy"
)
assert np.abs(expected_image - image).max() < 8e-2
def test_mlsd(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-mlsd")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "room"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (704, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd_out.npy"
)
assert np.abs(expected_image - image).max() < 5e-2
def test_normal(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-normal")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "cute toy"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal_out.npy"
)
assert np.abs(expected_image - image).max() < 5e-2
def test_openpose(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "Chef in the kitchen"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (768, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/chef_pose_out.npy"
)
assert np.abs(expected_image - image).max() < 8e-2
def test_scribble(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(5)
prompt = "bag"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (640, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble_out.npy"
)
assert np.abs(expected_image - image).max() < 8e-2
def test_seg(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(5)
prompt = "house"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg_out.npy"
)
assert np.abs(expected_image - image).max() < 8e-2
def test_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
prompt = "house"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png"
)
_ = pipe(
prompt,
image,
num_inference_steps=2,
output_type="np",
)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 4 * 10**9
def test_canny_guess_mode(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = ""
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
output = pipe(
prompt,
image,
generator=generator,
output_type="np",
num_inference_steps=3,
guidance_scale=3.0,
guess_mode=True,
)
image = output.images[0]
assert image.shape == (768, 512, 3)
image_slice = image[-3:, -3:, -1]
expected_slice = np.array([0.2724, 0.2846, 0.2724, 0.3843, 0.3682, 0.2736, 0.4675, 0.3862, 0.2887])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_canny_guess_mode_euler(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = ""
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
output = pipe(
prompt,
image,
generator=generator,
output_type="np",
num_inference_steps=3,
guidance_scale=3.0,
guess_mode=True,
)
image = output.images[0]
assert image.shape == (768, 512, 3)
image_slice = image[-3:, -3:, -1]
expected_slice = np.array([0.1655, 0.1721, 0.1623, 0.1685, 0.1711, 0.1646, 0.1651, 0.1631, 0.1494])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@require_python39_or_higher
@require_torch_2
def test_stable_diffusion_compile(self):
run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None)
def test_v11_shuffle_global_pool_conditions(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_shuffle")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "New York"
image = load_image(
"https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"
)
output = pipe(
prompt,
image,
generator=generator,
output_type="np",
num_inference_steps=3,
guidance_scale=7.0,
)
image = output.images[0]
assert image.shape == (512, 640, 3)
image_slice = image[-3:, -3:, -1]
expected_slice = np.array([0.1338, 0.1597, 0.1202, 0.1687, 0.1377, 0.1017, 0.2070, 0.1574, 0.1348])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_load_local(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe_1 = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
controlnet = ControlNetModel.from_single_file(
"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
)
pipe_2 = StableDiffusionControlNetPipeline.from_single_file(
"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
safety_checker=None,
controlnet=controlnet,
)
pipes = [pipe_1, pipe_2]
images = []
for pipe in pipes:
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
images.append(output.images[0])
del pipe
gc.collect()
torch.cuda.empty_cache()
assert np.abs(images[0] - images[1]).max() < 1e-3
@slow
@require_torch_gpu
class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_pose_and_canny(self):
controlnet_canny = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
controlnet_pose = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny]
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird and Chef"
image_canny = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
image_pose = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
)
output = pipe(prompt, [image_pose, image_canny], generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (768, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose_canny_out.npy"
)
assert np.abs(expected_image - image).max() < 5e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/controlnet/test_controlnet_sdxl.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
EulerDiscreteScheduler,
LCMScheduler,
StableDiffusionXLControlNetPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, slow, torch_device
from diffusers.utils.torch_utils import randn_tensor
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
SDXLOptionalComponentsTesterMixin,
)
enable_full_determinism()
class StableDiffusionXLControlNetPipelineFastTests(
PipelineLatentTesterMixin,
PipelineKarrasSchedulerTesterMixin,
PipelineTesterMixin,
SDXLOptionalComponentsTesterMixin,
unittest.TestCase,
):
pipeline_class = StableDiffusionXLControlNetPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
time_cond_proj_dim=time_cond_proj_dim,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
conditioning_embedding_out_channels=(16, 32),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
torch.manual_seed(0)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
image = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
"image": image,
}
return inputs
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_multi_prompts(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
inputs = self.get_dummy_inputs(torch_device)
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# copied from test_stable_diffusion_xl.py
def test_stable_diffusion_xl_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 2 * [inputs["prompt"]]
inputs["num_images_per_prompt"] = 2
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
inputs = self.get_dummy_inputs(torch_device)
prompt = 2 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_controlnet_sdxl_guess(self):
device = "cpu"
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["guess_mode"] = True
output = sd_pipe(**inputs)
image_slice = output.images[0, -3:, -3:, -1]
expected_slice = np.array(
[0.7330834, 0.590667, 0.5667336, 0.6029023, 0.5679491, 0.5968194, 0.4032986, 0.47612396, 0.5089609]
)
# make sure that it's equal
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4
def test_controlnet_sdxl_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLControlNetPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.7799, 0.614, 0.6162, 0.7082, 0.6662, 0.5833, 0.4148, 0.5182, 0.4866])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
class StableDiffusionXLMultiControlNetPipelineFastTests(
PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLControlNetPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
torch.manual_seed(0)
def init_weights(m):
if isinstance(m, torch.nn.Conv2d):
torch.nn.init.normal(m.weight)
m.bias.data.fill_(1.0)
controlnet1 = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
conditioning_embedding_out_channels=(16, 32),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
controlnet1.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
controlnet2 = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
conditioning_embedding_out_channels=(16, 32),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
controlnet2.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
controlnet = MultiControlNetModel([controlnet1, controlnet2])
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
images = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
]
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
"image": images,
}
return inputs
def test_control_guidance_switch(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
scale = 10.0
steps = 4
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_1 = pipe(**inputs)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_1 - output_2)) > 1e-3
assert np.sum(np.abs(output_1 - output_3)) > 1e-3
assert np.sum(np.abs(output_1 - output_4)) > 1e-3
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_save_load_optional_components(self):
return self._test_save_load_optional_components()
class StableDiffusionXLMultiControlNetOneModelPipelineFastTests(
PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLControlNetPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
torch.manual_seed(0)
def init_weights(m):
if isinstance(m, torch.nn.Conv2d):
torch.nn.init.normal(m.weight)
m.bias.data.fill_(1.0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
conditioning_embedding_out_channels=(16, 32),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
controlnet.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
controlnet = MultiControlNetModel([controlnet])
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
images = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
]
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
"image": images,
}
return inputs
def test_control_guidance_switch(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
scale = 10.0
steps = 4
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_1 = pipe(**inputs)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_3 = pipe(
**inputs,
control_guidance_start=[0.1],
control_guidance_end=[0.2],
)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5])[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_1 - output_2)) > 1e-3
assert np.sum(np.abs(output_1 - output_3)) > 1e-3
assert np.sum(np.abs(output_1 - output_4)) > 1e-3
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
def test_negative_conditions(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slice_without_neg_cond = image[0, -3:, -3:, -1]
image = pipe(
**inputs,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(0, 0),
negative_target_size=(1024, 1024),
).images
image_slice_with_neg_cond = image[0, -3:, -3:, -1]
self.assertTrue(np.abs(image_slice_without_neg_cond - image_slice_with_neg_cond).max() > 1e-2)
@slow
@require_torch_gpu
class ControlNetSDXLPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0")
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet
)
pipe.enable_sequential_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
assert images[0].shape == (768, 512, 3)
original_image = images[0, -3:, -3:, -1].flatten()
expected_image = np.array([0.4185, 0.4127, 0.4089, 0.4046, 0.4115, 0.4096, 0.4081, 0.4112, 0.3913])
assert np.allclose(original_image, expected_image, atol=1e-04)
def test_depth(self):
controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0")
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet
)
pipe.enable_sequential_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "Stormtrooper's lecture"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
)
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
assert images[0].shape == (512, 512, 3)
original_image = images[0, -3:, -3:, -1].flatten()
expected_image = np.array([0.4399, 0.5112, 0.5478, 0.4314, 0.472, 0.4823, 0.4647, 0.4957, 0.4853])
assert np.allclose(original_image, expected_image, atol=1e-04)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/controlnet/test_controlnet_inpaint.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This model implementation is heavily based on:
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetInpaintPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
from diffusers.utils import load_image
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_numpy,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from diffusers.utils.torch_utils import randn_tensor
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class ControlNetInpaintPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionControlNetInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset({"control_image"}) # skip `image` and `mask` for now, only test for control_image
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
control_image = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
)
init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
init_image = init_image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(init_image)).convert("RGB").resize((64, 64))
mask_image = Image.fromarray(np.uint8(init_image + 4)).convert("RGB").resize((64, 64))
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"mask_image": mask_image,
"control_image": control_image,
}
return inputs
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
class ControlNetSimpleInpaintPipelineFastTests(ControlNetInpaintPipelineFastTests):
pipeline_class = StableDiffusionControlNetInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset([])
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
class MultiControlNetInpaintPipelineFastTests(
PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionControlNetInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
torch.manual_seed(0)
def init_weights(m):
if isinstance(m, torch.nn.Conv2d):
torch.nn.init.normal(m.weight)
m.bias.data.fill_(1.0)
controlnet1 = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
controlnet1.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
controlnet2 = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
controlnet2.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
controlnet = MultiControlNetModel([controlnet1, controlnet2])
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
control_image = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
]
init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
init_image = init_image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(init_image)).convert("RGB").resize((64, 64))
mask_image = Image.fromarray(np.uint8(init_image + 4)).convert("RGB").resize((64, 64))
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"mask_image": mask_image,
"control_image": control_image,
}
return inputs
def test_control_guidance_switch(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
scale = 10.0
steps = 4
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_1 = pipe(**inputs)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_1 - output_2)) > 1e-3
assert np.sum(np.abs(output_1 - output_3)) > 1e-3
assert np.sum(np.abs(output_1 - output_4)) > 1e-3
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_save_pretrained_raise_not_implemented_exception(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(tmpdir)
except NotImplementedError:
pass
@slow
@require_torch_gpu
class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
image = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
).resize((512, 512))
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
).resize((512, 512))
prompt = "pitch black hole"
control_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
output = pipe(
prompt,
image=image,
mask_image=mask_image,
control_image=control_image,
generator=generator,
output_type="np",
num_inference_steps=3,
)
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"
)
assert np.abs(expected_image - image).max() < 9e-2
def test_inpaint(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint")
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(33)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
)
init_image = init_image.resize((512, 512))
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
)
mask_image = mask_image.resize((512, 512))
prompt = "a handsome man with ray-ban sunglasses"
def make_inpaint_condition(image, image_mask):
image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
image[image_mask > 0.5] = -1.0 # set as masked pixel
image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image
control_image = make_inpaint_condition(init_image, mask_image)
output = pipe(
prompt,
image=init_image,
mask_image=mask_image,
control_image=control_image,
guidance_scale=9.0,
eta=1.0,
generator=generator,
num_inference_steps=20,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"
)
assert numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten()) < 1e-2
def test_load_local(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe_1 = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
controlnet = ControlNetModel.from_single_file(
"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
)
pipe_2 = StableDiffusionControlNetInpaintPipeline.from_single_file(
"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
safety_checker=None,
controlnet=controlnet,
)
control_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
image = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
).resize((512, 512))
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
).resize((512, 512))
pipes = [pipe_1, pipe_2]
images = []
for pipe in pipes:
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
output = pipe(
prompt,
image=image,
control_image=control_image,
mask_image=mask_image,
strength=0.9,
generator=generator,
output_type="np",
num_inference_steps=3,
)
images.append(output.images[0])
del pipe
gc.collect()
torch.cuda.empty_cache()
assert np.abs(images[0] - images[1]).max() < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/controlnet/test_controlnet_blip_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTokenizer
from transformers.models.blip_2.configuration_blip_2 import Blip2Config
from transformers.models.clip.configuration_clip import CLIPTextConfig
from diffusers import (
AutoencoderKL,
BlipDiffusionControlNetPipeline,
ControlNetModel,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism
from src.diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor
from src.diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel
from src.diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class BlipDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = BlipDiffusionControlNetPipeline
params = [
"prompt",
"reference_image",
"source_subject_category",
"target_subject_category",
"condtioning_image",
]
batch_params = [
"prompt",
"reference_image",
"source_subject_category",
"target_subject_category",
"condtioning_image",
]
required_optional_params = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"neg_prompt",
"guidance_scale",
"prompt_strength",
"prompt_reps",
]
def get_dummy_components(self):
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
vocab_size=1000,
hidden_size=16,
intermediate_size=16,
projection_dim=16,
num_hidden_layers=1,
num_attention_heads=1,
max_position_embeddings=77,
)
text_encoder = ContextCLIPTextModel(text_encoder_config)
vae = AutoencoderKL(
in_channels=4,
out_channels=4,
down_block_types=("DownEncoderBlock2D",),
up_block_types=("UpDecoderBlock2D",),
block_out_channels=(32,),
layers_per_block=1,
act_fn="silu",
latent_channels=4,
norm_num_groups=16,
sample_size=16,
)
blip_vision_config = {
"hidden_size": 16,
"intermediate_size": 16,
"num_hidden_layers": 1,
"num_attention_heads": 1,
"image_size": 224,
"patch_size": 14,
"hidden_act": "quick_gelu",
}
blip_qformer_config = {
"vocab_size": 1000,
"hidden_size": 16,
"num_hidden_layers": 1,
"num_attention_heads": 1,
"intermediate_size": 16,
"max_position_embeddings": 512,
"cross_attention_frequency": 1,
"encoder_hidden_size": 16,
}
qformer_config = Blip2Config(
vision_config=blip_vision_config,
qformer_config=blip_qformer_config,
num_query_tokens=16,
tokenizer="hf-internal-testing/tiny-random-bert",
)
qformer = Blip2QFormerModel(qformer_config)
unet = UNet2DConditionModel(
block_out_channels=(4, 16),
layers_per_block=1,
norm_num_groups=4,
sample_size=16,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=16,
)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
scheduler = PNDMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
set_alpha_to_one=False,
skip_prk_steps=True,
)
controlnet = ControlNetModel(
block_out_channels=(4, 16),
layers_per_block=1,
in_channels=4,
norm_num_groups=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=16,
conditioning_embedding_out_channels=(8, 16),
)
vae.eval()
qformer.eval()
text_encoder.eval()
image_processor = BlipImageProcessor()
components = {
"text_encoder": text_encoder,
"vae": vae,
"qformer": qformer,
"unet": unet,
"tokenizer": tokenizer,
"scheduler": scheduler,
"controlnet": controlnet,
"image_processor": image_processor,
}
return components
def get_dummy_inputs(self, device, seed=0):
np.random.seed(seed)
reference_image = np.random.rand(32, 32, 3) * 255
reference_image = Image.fromarray(reference_image.astype("uint8")).convert("RGBA")
cond_image = np.random.rand(32, 32, 3) * 255
cond_image = Image.fromarray(cond_image.astype("uint8")).convert("RGBA")
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "swimming underwater",
"generator": generator,
"reference_image": reference_image,
"condtioning_image": cond_image,
"source_subject_category": "dog",
"target_subject_category": "dog",
"height": 32,
"width": 32,
"guidance_scale": 7.5,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def test_blipdiffusion_controlnet(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
image = pipe(**self.get_dummy_inputs(device))[0]
image_slice = image[0, -3:, -3:, 0]
assert image.shape == (1, 16, 16, 4)
expected_slice = np.array([0.7953, 0.7136, 0.6597, 0.4779, 0.7389, 0.4111, 0.5826, 0.4150, 0.8422])
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/controlnet/test_flax_controlnet.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class FlaxControlNetPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def test_canny(self):
controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.bfloat16
)
pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
)
params["controlnet"] = controlnet_params
prompts = "bird"
num_samples = jax.device_count()
prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
canny_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
processed_image = pipe.prepare_image_inputs([canny_image] * num_samples)
rng = jax.random.PRNGKey(0)
rng = jax.random.split(rng, jax.device_count())
p_params = replicate(params)
prompt_ids = shard(prompt_ids)
processed_image = shard(processed_image)
images = pipe(
prompt_ids=prompt_ids,
image=processed_image,
params=p_params,
prng_seed=rng,
num_inference_steps=50,
jit=True,
).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
image_slice = images[0, 253:256, 253:256, -1]
output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
expected_slice = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]
)
print(f"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
def test_pose(self):
controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose", from_pt=True, dtype=jnp.bfloat16
)
pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
)
params["controlnet"] = controlnet_params
prompts = "Chef in the kitchen"
num_samples = jax.device_count()
prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
pose_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
)
processed_image = pipe.prepare_image_inputs([pose_image] * num_samples)
rng = jax.random.PRNGKey(0)
rng = jax.random.split(rng, jax.device_count())
p_params = replicate(params)
prompt_ids = shard(prompt_ids)
processed_image = shard(processed_image)
images = pipe(
prompt_ids=prompt_ids,
image=processed_image,
params=p_params,
prng_seed=rng,
num_inference_steps=50,
jit=True,
).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
image_slice = images[0, 253:256, 253:256, -1]
output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
expected_slice = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]
)
print(f"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py
|
# coding=utf-8
# Copyright 2023 Harutatsu Akiyama, Jinbin Bai, and 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 random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
EulerDiscreteScheduler,
StableDiffusionXLControlNetInpaintPipeline,
UNet2DConditionModel,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class ControlNetPipelineSDXLFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLControlNetInpaintPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = frozenset(IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"mask_image", "control_image"}))
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
unet = 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"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
conditioning_embedding_out_channels=(16, 32),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
}
return components
def get_dummy_inputs(self, device, seed=0, img_res=64):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
# Get random floats in [0, 1] as image
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
mask_image = torch.ones_like(image)
controlnet_embedder_scale_factor = 2
control_image = (
floats_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
rng=random.Random(seed),
)
.to(device)
.cpu()
)
control_image = control_image.cpu().permute(0, 2, 3, 1)[0]
# Convert image and mask_image to [0, 255]
image = 255 * image
mask_image = 255 * mask_image
control_image = 255 * control_image
# Convert to PIL image
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((img_res, img_res))
mask_image = Image.fromarray(np.uint8(mask_image)).convert("L").resize((img_res, img_res))
control_image = Image.fromarray(np.uint8(control_image)).convert("RGB").resize((img_res, img_res))
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": init_image,
"mask_image": mask_image,
"control_image": control_image,
}
return inputs
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_multi_prompts(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
inputs = self.get_dummy_inputs(torch_device)
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
def test_controlnet_sdxl_guess(self):
device = "cpu"
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["guess_mode"] = True
output = sd_pipe(**inputs)
image_slice = output.images[0, -3:, -3:, -1]
expected_slice = np.array(
[0.5381963, 0.4836803, 0.45821992, 0.5577731, 0.51210403, 0.4794795, 0.59282357, 0.5647199, 0.43100584]
)
# make sure that it's equal
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4
# TODO(Patrick, Sayak) - skip for now as this requires more refiner tests
def test_save_load_optional_components(self):
pass
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=5e-1)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
EulerDiscreteScheduler,
StableDiffusionXLControlNetImg2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class ControlNetPipelineSDXLImg2ImgFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLControlNetImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self, skip_first_text_encoder=False):
torch.manual_seed(0)
unet = 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"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64 if not skip_first_text_encoder else 32,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
conditioning_embedding_out_channels=(16, 32),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
torch.manual_seed(0)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder if not skip_first_text_encoder else None,
"tokenizer": tokenizer if not skip_first_text_encoder else None,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
}
return components
def get_dummy_inputs(self, device, seed=0):
controlnet_embedder_scale_factor = 2
image = floats_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
rng=random.Random(seed),
).to(device)
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": image,
}
return inputs
def test_stable_diffusion_xl_controlnet_img2img(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.5557202, 0.46418434, 0.46983826, 0.623529, 0.5557242, 0.49262643, 0.6070508, 0.5702978, 0.43777135]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_controlnet_img2img_guess(self):
device = "cpu"
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["guess_mode"] = True
output = sd_pipe(**inputs)
image_slice = output.images[0, -3:, -3:, -1]
assert output.images.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.5557202, 0.46418434, 0.46983826, 0.623529, 0.5557242, 0.49262643, 0.6070508, 0.5702978, 0.43777135]
)
# make sure that it's equal
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
# TODO(Patrick, Sayak) - skip for now as this requires more refiner tests
def test_save_load_optional_components(self):
pass
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_multi_prompts(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
inputs = self.get_dummy_inputs(torch_device)
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# copied from test_stable_diffusion_xl.py
def test_stable_diffusion_xl_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 2 * [inputs["prompt"]]
inputs["num_images_per_prompt"] = 2
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
inputs = self.get_dummy_inputs(torch_device)
prompt = 2 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/controlnet/test_controlnet_img2img.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImg2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
from diffusers.utils import load_image
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_numpy,
require_torch_gpu,
slow,
torch_device,
)
from diffusers.utils.torch_utils import randn_tensor
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class ControlNetImg2ImgPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionControlNetImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"})
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
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,
norm_num_groups=1,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
control_image = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
)
image = floats_tensor(control_image.shape, rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": control_image,
}
return inputs
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
class StableDiffusionMultiControlNetPipelineFastTests(
PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionControlNetImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
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,
norm_num_groups=1,
)
torch.manual_seed(0)
def init_weights(m):
if isinstance(m, torch.nn.Conv2d):
torch.nn.init.normal(m.weight)
m.bias.data.fill_(1.0)
controlnet1 = ControlNetModel(
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
controlnet1.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
controlnet2 = ControlNetModel(
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
controlnet2.controlnet_down_blocks.apply(init_weights)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
controlnet = MultiControlNetModel([controlnet1, controlnet2])
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
control_image = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
]
image = floats_tensor(control_image[0].shape, rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": control_image,
}
return inputs
def test_control_guidance_switch(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
scale = 10.0
steps = 4
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_1 = pipe(**inputs)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = steps
inputs["controlnet_conditioning_scale"] = scale
output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_1 - output_2)) > 1e-3
assert np.sum(np.abs(output_1 - output_3)) > 1e-3
assert np.sum(np.abs(output_1 - output_4)) > 1e-3
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_save_pretrained_raise_not_implemented_exception(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(tmpdir)
except NotImplementedError:
pass
@slow
@require_torch_gpu
class ControlNetImg2ImgPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "evil space-punk bird"
control_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
image = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
).resize((512, 512))
output = pipe(
prompt,
image,
control_image=control_image,
generator=generator,
output_type="np",
num_inference_steps=50,
strength=0.6,
)
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"
)
assert np.abs(expected_image - image).max() < 9e-2
def test_load_local(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe_1 = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
controlnet = ControlNetModel.from_single_file(
"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
)
pipe_2 = StableDiffusionControlNetImg2ImgPipeline.from_single_file(
"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
safety_checker=None,
controlnet=controlnet,
)
control_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
image = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
).resize((512, 512))
pipes = [pipe_1, pipe_2]
images = []
for pipe in pipes:
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
output = pipe(
prompt,
image=image,
control_image=control_image,
strength=0.9,
generator=generator,
output_type="np",
num_inference_steps=3,
)
images.append(output.images[0])
del pipe
gc.collect()
torch.cuda.empty_cache()
assert np.abs(images[0] - images[1]).max() < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/audioldm/test_audioldm.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechT5HifiGan,
SpeechT5HifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, nightly, torch_device
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class AudioLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = AudioLDMPipeline
params = TEXT_TO_AUDIO_PARAMS
batch_params = TEXT_TO_AUDIO_BATCH_PARAMS
required_optional_params = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
]
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=(32, 64),
class_embed_type="simple_projection",
projection_class_embeddings_input_dim=32,
class_embeddings_concat=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=1,
out_channels=1,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = ClapTextConfig(
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,
projection_dim=32,
)
text_encoder = ClapTextModelWithProjection(text_encoder_config)
tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
vocoder_config = SpeechT5HifiGanConfig(
model_in_dim=8,
sampling_rate=16000,
upsample_initial_channel=16,
upsample_rates=[2, 2],
upsample_kernel_sizes=[4, 4],
resblock_kernel_sizes=[3, 7],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
normalize_before=False,
)
vocoder = SpeechT5HifiGan(vocoder_config)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"vocoder": vocoder,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
}
return inputs
def test_audioldm_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(**inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-2
def test_audioldm_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = audioldm_pipe.tokenizer(
prompt,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = audioldm_pipe.text_encoder(
text_inputs,
)
prompt_embeds = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
prompt_embeds = F.normalize(prompt_embeds, dim=-1)
inputs["prompt_embeds"] = prompt_embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm_negative_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = audioldm_pipe.tokenizer(
p,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
text_embeds = audioldm_pipe.text_encoder(
text_inputs,
)
text_embeds = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
text_embeds = F.normalize(text_embeds, dim=-1)
embeds.append(text_embeds)
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "egg cracking"
output = audioldm_pipe(**inputs, negative_prompt=negative_prompt)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-2
def test_audioldm_num_waveforms_per_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = "A hammer hitting a wooden surface"
# test num_waveforms_per_prompt=1 (default)
audios = audioldm_pipe(prompt, num_inference_steps=2).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
batch_size = 2
audios = audioldm_pipe([prompt] * batch_size, num_inference_steps=2).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
num_waveforms_per_prompt = 2
audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
batch_size = 2
audios = audioldm_pipe(
[prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt
).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def test_audioldm_audio_length_in_s(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
vocoder_sampling_rate = audioldm_pipe.vocoder.config.sampling_rate
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(audio_length_in_s=0.016, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.016
output = audioldm_pipe(audio_length_in_s=0.032, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.032
def test_audioldm_vocoder_model_in_dim(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = ["hey"]
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
assert audio_shape == (1, 256)
config = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
audioldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical()
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
@nightly
class AudioLDMPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_audioldm(self):
audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81920
audio_slice = audio[77230:77240]
expected_slice = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]
)
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-2
@nightly
class AudioLDMPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_audioldm_lms(self):
audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
audioldm_pipe.scheduler = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81920
audio_slice = audio[27780:27790]
expected_slice = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 3e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/ddim/test_ddim.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNet2DModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class DDIMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = DDIMPipeline
params = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"latents",
"callback",
"callback_steps",
}
batch_params = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = 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"),
)
scheduler = DDIMScheduler()
components = {"unet": unet, "scheduler": scheduler}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(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]
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_dict_tuple_outputs_equivalent(self):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)
def test_save_load_local(self):
super().test_save_load_local(expected_max_difference=3e-3)
def test_save_load_optional_components(self):
super().test_save_load_optional_components(expected_max_difference=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class DDIMPipelineIntegrationTests(unittest.TestCase):
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([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_inference_ema_bedroom(self):
model_id = "google/ddpm-ema-bedroom-256"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDIMScheduler.from_pretrained(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([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/audio_diffusion/test_audio_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNet2DConditionModel,
UNet2DModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
enable_full_determinism()
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_unet(self):
torch.manual_seed(0)
model = UNet2DModel(
sample_size=(32, 64),
in_channels=1,
out_channels=1,
layers_per_block=2,
block_out_channels=(128, 128),
down_block_types=("AttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "AttnUpBlock2D"),
)
return model
@property
def dummy_unet_condition(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
sample_size=(64, 32),
in_channels=1,
out_channels=1,
layers_per_block=2,
block_out_channels=(128, 128),
down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),
cross_attention_dim=10,
)
return model
@property
def dummy_vqvae_and_unet(self):
torch.manual_seed(0)
vqvae = AutoencoderKL(
sample_size=(128, 64),
in_channels=1,
out_channels=1,
latent_channels=1,
layers_per_block=2,
block_out_channels=(128, 128),
down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D"),
up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D"),
)
unet = UNet2DModel(
sample_size=(64, 32),
in_channels=1,
out_channels=1,
layers_per_block=2,
block_out_channels=(128, 128),
down_block_types=("AttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "AttnUpBlock2D"),
)
return vqvae, unet
@nightly
def test_audio_diffusion(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
mel = Mel(
x_res=self.dummy_unet.config.sample_size[1],
y_res=self.dummy_unet.config.sample_size[0],
)
scheduler = DDPMScheduler()
pipe = AudioDiffusionPipeline(vqvae=None, unet=self.dummy_unet, mel=mel, scheduler=scheduler)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(42)
output = pipe(generator=generator, steps=4)
audio = output.audios[0]
image = output.images[0]
generator = torch.Generator(device=device).manual_seed(42)
output = pipe(generator=generator, steps=4, return_dict=False)
image_from_tuple = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
image_from_tuple_slice = np.frombuffer(image_from_tuple.tobytes(), dtype="uint8")[:10]
expected_slice = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127])
assert np.abs(image_slice.flatten() - expected_slice).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() == 0
mel = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1],
y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0],
)
scheduler = DDIMScheduler()
dummy_vqvae_and_unet = self.dummy_vqvae_and_unet
pipe = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0], unet=dummy_vqvae_and_unet[1], mel=mel, scheduler=scheduler
)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
np.random.seed(0)
raw_audio = np.random.uniform(-1, 1, ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,))
generator = torch.Generator(device=device).manual_seed(42)
output = pipe(raw_audio=raw_audio, generator=generator, start_step=5, steps=10)
image = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
expected_slice = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121])
assert np.abs(image_slice.flatten() - expected_slice).max() == 0
dummy_unet_condition = self.dummy_unet_condition
pipe = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0], unet=dummy_unet_condition, mel=mel, scheduler=scheduler
)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
np.random.seed(0)
encoding = torch.rand((1, 1, 10))
output = pipe(generator=generator, encoding=encoding)
image = output.images[0]
image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
expected_slice = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111])
assert np.abs(image_slice.flatten() - expected_slice).max() == 0
@nightly
@require_torch_gpu
class PipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_audio_diffusion(self):
device = torch_device
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256")
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(42)
output = pipe(generator=generator)
audio = output.audios[0]
image = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
expected_slice = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26])
assert np.abs(image_slice.flatten() - expected_slice).max() == 0
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
nightly,
require_torch_gpu,
torch_device,
)
enable_full_determinism()
class SafeDiffusionPipelineFastTests(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
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
@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)
@property
def dummy_extractor(self):
def extract(*args, **kwargs):
class Out:
def __init__(self):
self.pixel_values = torch.ones([0])
def to(self, device):
self.pixel_values.to(device)
return self
return Out()
return extract
def test_semantic_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
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([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867])
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_semantic_diffusion_pndm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
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([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994])
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_semantic_diffusion_no_safety_checker(self):
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
)
assert isinstance(pipe, StableDiffusionPipeline)
assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
assert pipe.safety_checker is None
image = pipe("example prompt", num_inference_steps=2).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)
# sanity check that the pipeline still works
assert pipe.safety_checker is None
image = pipe("example prompt", num_inference_steps=2).images[0]
assert image is not None
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
def test_semantic_diffusion_fp16(self):
"""Test that stable diffusion works with fp16"""
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# put models in fp16
unet = unet.half()
vae = vae.half()
bert = bert.half()
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
image = sd_pipe([prompt], num_inference_steps=2, output_type="np").images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class SemanticDiffusionPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_positive_guidance(self):
torch_device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "a photo of a cat"
edit = {
"editing_prompt": ["sunglasses"],
"reverse_editing_direction": [False],
"edit_warmup_steps": 10,
"edit_guidance_scale": 6,
"edit_threshold": 0.95,
"edit_momentum_scale": 0.5,
"edit_mom_beta": 0.6,
}
seed = 3
guidance_scale = 7
# no sega enabled
generator = torch.Generator(torch_device)
generator.manual_seed(seed)
output = pipe(
[prompt],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=50,
output_type="np",
width=512,
height=512,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
expected_slice = [
0.34673113,
0.38492733,
0.37597352,
0.34086335,
0.35650748,
0.35579205,
0.3384763,
0.34340236,
0.3573271,
]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
# with sega enabled
# generator = torch.manual_seed(seed)
generator.manual_seed(seed)
output = pipe(
[prompt],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=50,
output_type="np",
width=512,
height=512,
**edit,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
expected_slice = [
0.41887826,
0.37728766,
0.30138272,
0.41416335,
0.41664985,
0.36283392,
0.36191246,
0.43364465,
0.43001732,
]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_negative_guidance(self):
torch_device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "an image of a crowded boulevard, realistic, 4k"
edit = {
"editing_prompt": "crowd, crowded, people",
"reverse_editing_direction": True,
"edit_warmup_steps": 10,
"edit_guidance_scale": 8.3,
"edit_threshold": 0.9,
"edit_momentum_scale": 0.5,
"edit_mom_beta": 0.6,
}
seed = 9
guidance_scale = 7
# no sega enabled
generator = torch.Generator(torch_device)
generator.manual_seed(seed)
output = pipe(
[prompt],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=50,
output_type="np",
width=512,
height=512,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
expected_slice = [
0.43497998,
0.91814065,
0.7540739,
0.55580205,
0.8467265,
0.5389691,
0.62574506,
0.58897763,
0.50926757,
]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
# with sega enabled
# generator = torch.manual_seed(seed)
generator.manual_seed(seed)
output = pipe(
[prompt],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=50,
output_type="np",
width=512,
height=512,
**edit,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
expected_slice = [
0.3089719,
0.30500144,
0.29016042,
0.30630964,
0.325687,
0.29419225,
0.2908091,
0.28723598,
0.27696294,
]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_multi_cond_guidance(self):
torch_device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "a castle next to a river"
edit = {
"editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
"reverse_editing_direction": False,
"edit_warmup_steps": [15, 18],
"edit_guidance_scale": 6,
"edit_threshold": [0.9, 0.8],
"edit_momentum_scale": 0.5,
"edit_mom_beta": 0.6,
}
seed = 48
guidance_scale = 7
# no sega enabled
generator = torch.Generator(torch_device)
generator.manual_seed(seed)
output = pipe(
[prompt],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=50,
output_type="np",
width=512,
height=512,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
expected_slice = [
0.75163555,
0.76037145,
0.61785,
0.9189673,
0.8627701,
0.85189694,
0.8512813,
0.87012076,
0.8312857,
]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
# with sega enabled
# generator = torch.manual_seed(seed)
generator.manual_seed(seed)
output = pipe(
[prompt],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=50,
output_type="np",
width=512,
height=512,
**edit,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
expected_slice = [
0.73553365,
0.7537271,
0.74341905,
0.66480356,
0.6472925,
0.63039416,
0.64812905,
0.6749717,
0.6517102,
]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_guidance_fp16(self):
torch_device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "a photo of a cat"
edit = {
"editing_prompt": ["sunglasses"],
"reverse_editing_direction": [False],
"edit_warmup_steps": 10,
"edit_guidance_scale": 6,
"edit_threshold": 0.95,
"edit_momentum_scale": 0.5,
"edit_mom_beta": 0.6,
}
seed = 3
guidance_scale = 7
# no sega enabled
generator = torch.Generator(torch_device)
generator.manual_seed(seed)
output = pipe(
[prompt],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=50,
output_type="np",
width=512,
height=512,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
expected_slice = [
0.34887695,
0.3876953,
0.375,
0.34423828,
0.3581543,
0.35717773,
0.3383789,
0.34570312,
0.359375,
]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
# with sega enabled
# generator = torch.manual_seed(seed)
generator.manual_seed(seed)
output = pipe(
[prompt],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=50,
output_type="np",
width=512,
height=512,
**edit,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
expected_slice = [
0.42285156,
0.36914062,
0.29077148,
0.42041016,
0.41918945,
0.35498047,
0.3618164,
0.4423828,
0.43115234,
]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/ddpm/test_ddpm.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
enable_full_determinism()
class DDPMPipelineFastTests(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_fast_inference(self):
device = "cpu"
unet = self.dummy_uncond_unet
scheduler = DDPMScheduler()
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(device)
ddpm.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
generator = torch.Generator(device=device).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(
[9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]
)
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_inference_predict_sample(self):
unet = self.dummy_uncond_unet
scheduler = DDPMScheduler(prediction_type="sample")
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, num_inference_steps=2, output_type="numpy").images
generator = torch.manual_seed(0)
image_eps = ddpm(generator=generator, num_inference_steps=2, output_type="numpy")[0]
image_slice = image[0, -3:, -3:, -1]
image_eps_slice = image_eps[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
tolerance = 1e-2 if torch_device != "mps" else 3e-2
assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance
@slow
@require_torch_gpu
class DDPMPipelineIntegrationTests(unittest.TestCase):
def test_inference_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDPMScheduler.from_pretrained(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([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/pixart/test_pixart.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PixArtAlphaPipeline,
Transformer2DModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, to_np
enable_full_determinism()
class PixArtAlphaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = PixArtAlphaPipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params
def get_dummy_components(self):
torch.manual_seed(0)
transformer = Transformer2DModel(
sample_size=8,
num_layers=2,
patch_size=2,
attention_head_dim=8,
num_attention_heads=3,
caption_channels=32,
in_channels=4,
cross_attention_dim=24,
out_channels=8,
attention_bias=True,
activation_fn="gelu-approximate",
num_embeds_ada_norm=1000,
norm_type="ada_norm_single",
norm_elementwise_affine=False,
norm_eps=1e-6,
)
vae = AutoencoderKL()
scheduler = DDIMScheduler()
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
components = {
"transformer": transformer.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"use_resolution_binning": False,
"output_type": "np",
}
return inputs
def test_sequential_cpu_offload_forward_pass(self):
# TODO(PVP, Sayak) need to fix later
return
def test_save_load_optional_components(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = pipe.encode_prompt(prompt)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 8, 8, 3))
expected_slice = np.array([0.5303, 0.2658, 0.7979, 0.1182, 0.3304, 0.4608, 0.5195, 0.4261, 0.4675])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_inference_non_square_images(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs, height=32, width=48).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 32, 48, 3))
expected_slice = np.array([0.3859, 0.2987, 0.2333, 0.5243, 0.6721, 0.4436, 0.5292, 0.5373, 0.4416])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_inference_with_embeddings_and_multiple_images(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
prompt_embeds, prompt_attn_mask, negative_prompt_embeds, neg_prompt_attn_mask = pipe.encode_prompt(prompt)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attn_mask,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": neg_prompt_attn_mask,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"num_images_per_prompt": 2,
"use_resolution_binning": False,
}
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attn_mask,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": neg_prompt_attn_mask,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"num_images_per_prompt": 2,
"use_resolution_binning": False,
}
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
def test_inference_with_multiple_images_per_prompt(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["num_images_per_prompt"] = 2
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (2, 8, 8, 3))
expected_slice = np.array([0.5303, 0.2658, 0.7979, 0.1182, 0.3304, 0.4608, 0.5195, 0.4261, 0.4675])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_raises_warning_for_mask_feature(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs.update({"mask_feature": True})
with self.assertWarns(FutureWarning) as warning_ctx:
_ = pipe(**inputs).images
assert "mask_feature" in str(warning_ctx.warning)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=1e-3)
@slow
@require_torch_gpu
class PixArtAlphaPipelineIntegrationTests(unittest.TestCase):
ckpt_id_1024 = "PixArt-alpha/PixArt-XL-2-1024-MS"
ckpt_id_512 = "PixArt-alpha/PixArt-XL-2-512x512"
prompt = "A small cactus with a happy face in the Sahara desert."
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_pixart_1024_fast(self):
generator = torch.manual_seed(0)
pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_pixart_512_fast(self):
generator = torch.manual_seed(0)
pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_pixart_1024(self):
generator = torch.manual_seed(0)
pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
image = pipe(prompt, generator=generator, output_type="np").images
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.1941, 0.2117, 0.2188, 0.1946, 0.218, 0.2124, 0.199, 0.2437, 0.2583])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_pixart_512(self):
generator = torch.manual_seed(0)
pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
image = pipe(prompt, generator=generator, output_type="np").images
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.2637, 0.291, 0.2939, 0.207, 0.2512, 0.2783, 0.2168, 0.2324, 0.2817])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_pixart_1024_without_resolution_binning(self):
generator = torch.manual_seed(0)
pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
height, width = 1024, 768
num_inference_steps = 10
image = pipe(
prompt,
height=height,
width=width,
generator=generator,
num_inference_steps=num_inference_steps,
output_type="np",
).images
image_slice = image[0, -3:, -3:, -1]
generator = torch.manual_seed(0)
no_res_bin_image = pipe(
prompt,
height=height,
width=width,
generator=generator,
num_inference_steps=num_inference_steps,
output_type="np",
use_resolution_binning=False,
).images
no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1]
assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4)
def test_pixart_512_without_resolution_binning(self):
generator = torch.manual_seed(0)
pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
height, width = 512, 768
num_inference_steps = 10
image = pipe(
prompt,
height=height,
width=width,
generator=generator,
num_inference_steps=num_inference_steps,
output_type="np",
).images
image_slice = image[0, -3:, -3:, -1]
generator = torch.manual_seed(0)
no_res_bin_image = pipe(
prompt,
height=height,
width=width,
generator=generator,
num_inference_steps=num_inference_steps,
output_type="np",
use_resolution_binning=False,
).images
no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1]
assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/shap_e/test_shap_e_img2img.py
|
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImg2ImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils.testing_utils import (
floats_tensor,
load_image,
load_numpy,
nightly,
require_torch_gpu,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class ShapEImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = ShapEImg2ImgPipeline
params = ["image"]
batch_params = ["image"]
required_optional_params = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
test_xformers_attention = False
@property
def text_embedder_hidden_size(self):
return 16
@property
def time_input_dim(self):
return 16
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def renderer_dim(self):
return 8
@property
def dummy_image_encoder(self):
torch.manual_seed(0)
config = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size,
image_size=32,
projection_dim=self.text_embedder_hidden_size,
intermediate_size=24,
num_attention_heads=2,
num_channels=3,
num_hidden_layers=5,
patch_size=1,
)
model = CLIPVisionModel(config)
return model
@property
def dummy_image_processor(self):
image_processor = CLIPImageProcessor(
crop_size=224,
do_center_crop=True,
do_normalize=True,
do_resize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
resample=3,
size=224,
)
return image_processor
@property
def dummy_prior(self):
torch.manual_seed(0)
model_kwargs = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"embedding_proj_norm_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
model = PriorTransformer(**model_kwargs)
return model
@property
def dummy_renderer(self):
torch.manual_seed(0)
model_kwargs = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
model = ShapERenderer(**model_kwargs)
return model
def get_dummy_components(self):
prior = self.dummy_prior
image_encoder = self.dummy_image_encoder
image_processor = self.dummy_image_processor
shap_e_renderer = self.dummy_renderer
scheduler = HeunDiscreteScheduler(
beta_schedule="exp",
num_train_timesteps=1024,
prediction_type="sample",
use_karras_sigmas=True,
clip_sample=True,
clip_sample_range=1.0,
)
components = {
"prior": prior,
"image_encoder": image_encoder,
"image_processor": image_processor,
"shap_e_renderer": shap_e_renderer,
"scheduler": scheduler,
}
return components
def get_dummy_inputs(self, device, seed=0):
input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"image": input_image,
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "latent",
}
return inputs
def test_shap_e(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.images[0]
image_slice = image[-3:, -3:].cpu().numpy()
assert image.shape == (32, 16)
expected_slice = np.array(
[-1.0, 0.40668195, 0.57322013, -0.9469888, 0.4283227, 0.30348337, -0.81094897, 0.74555075, 0.15342723]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_inference_batch_consistent(self):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[2])
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
batch_size=2,
expected_max_diff=6e-3,
)
def test_num_images_per_prompt(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
batch_size = 1
num_images_per_prompt = 2
inputs = self.get_dummy_inputs(torch_device)
for key in inputs.keys():
if key in self.batch_params:
inputs[key] = batch_size * [inputs[key]]
images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=1e-1)
def test_save_load_local(self):
super().test_save_load_local(expected_max_difference=5e-3)
@unittest.skip("Key error is raised with accelerate")
def test_sequential_cpu_offload_forward_pass(self):
pass
@nightly
@require_torch_gpu
class ShapEImg2ImgPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_shap_e_img2img(self):
input_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_img2img_out.npy"
)
pipe = ShapEImg2ImgPipeline.from_pretrained("openai/shap-e-img2img")
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=torch_device).manual_seed(0)
images = pipe(
input_image,
generator=generator,
guidance_scale=3.0,
num_inference_steps=64,
frame_size=64,
output_type="np",
).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(images, expected_image)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/shap_e/test_shap_e.py
|
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class ShapEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = ShapEPipeline
params = ["prompt"]
batch_params = ["prompt"]
required_optional_params = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
test_xformers_attention = False
@property
def text_embedder_hidden_size(self):
return 16
@property
def time_input_dim(self):
return 16
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def renderer_dim(self):
return 8
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=self.text_embedder_hidden_size,
projection_dim=self.text_embedder_hidden_size,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
return CLIPTextModelWithProjection(config)
@property
def dummy_prior(self):
torch.manual_seed(0)
model_kwargs = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
model = PriorTransformer(**model_kwargs)
return model
@property
def dummy_renderer(self):
torch.manual_seed(0)
model_kwargs = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
model = ShapERenderer(**model_kwargs)
return model
def get_dummy_components(self):
prior = self.dummy_prior
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
shap_e_renderer = self.dummy_renderer
scheduler = HeunDiscreteScheduler(
beta_schedule="exp",
num_train_timesteps=1024,
prediction_type="sample",
use_karras_sigmas=True,
clip_sample=True,
clip_sample_range=1.0,
)
components = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"shap_e_renderer": shap_e_renderer,
"scheduler": scheduler,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "latent",
}
return inputs
def test_shap_e(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.images[0]
image = image.cpu().numpy()
image_slice = image[-3:, -3:]
assert image.shape == (32, 16)
expected_slice = np.array([-1.0000, -0.6241, 1.0000, -0.8978, -0.6866, 0.7876, -0.7473, -0.2874, 0.6103])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_inference_batch_consistent(self):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=6e-3)
def test_num_images_per_prompt(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
batch_size = 1
num_images_per_prompt = 2
inputs = self.get_dummy_inputs(torch_device)
for key in inputs.keys():
if key in self.batch_params:
inputs[key] = batch_size * [inputs[key]]
images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=5e-1)
def test_save_load_local(self):
super().test_save_load_local(expected_max_difference=5e-3)
@unittest.skip("Key error is raised with accelerate")
def test_sequential_cpu_offload_forward_pass(self):
pass
@nightly
@require_torch_gpu
class ShapEPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_shap_e(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy"
)
pipe = ShapEPipeline.from_pretrained("openai/shap-e")
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=torch_device).manual_seed(0)
images = pipe(
"a shark",
generator=generator,
guidance_scale=15.0,
num_inference_steps=64,
frame_size=64,
output_type="np",
).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(images, expected_image)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/score_sde_ve/test_score_sde_ve.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch, torch_device
enable_full_determinism()
class ScoreSdeVeipelineFastTests(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
@nightly
@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_pretrained(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
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPVisionModelWithProjection,
)
from diffusers import (
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionPipeline,
StableDiffusionXLImg2ImgPipeline,
StableDiffusionXLInpaintPipeline,
StableDiffusionXLPipeline,
)
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
enable_full_determinism,
require_torch_gpu,
slow,
torch_device,
)
enable_full_determinism()
class IPAdapterNightlyTestsMixin(unittest.TestCase):
dtype = torch.float16
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_image_encoder(self, repo_id, subfolder):
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
repo_id, subfolder=subfolder, torch_dtype=self.dtype
).to(torch_device)
return image_encoder
def get_image_processor(self, repo_id):
image_processor = CLIPImageProcessor.from_pretrained(repo_id)
return image_processor
def get_dummy_inputs(self, for_image_to_image=False, for_inpainting=False, for_sdxl=False):
image = load_image(
"https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png"
)
if for_sdxl:
image = image.resize((1024, 1024))
input_kwargs = {
"prompt": "best quality, high quality",
"negative_prompt": "monochrome, lowres, bad anatomy, worst quality, low quality",
"num_inference_steps": 5,
"generator": torch.Generator(device="cpu").manual_seed(33),
"ip_adapter_image": image,
"output_type": "np",
}
if for_image_to_image:
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png")
if for_sdxl:
image = image.resize((1024, 1024))
ip_image = ip_image.resize((1024, 1024))
input_kwargs.update({"image": image, "ip_adapter_image": ip_image})
elif for_inpainting:
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/inpaint_image.png")
mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/mask.png")
ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/girl.png")
if for_sdxl:
image = image.resize((1024, 1024))
mask = mask.resize((1024, 1024))
ip_image = ip_image.resize((1024, 1024))
input_kwargs.update({"image": image, "mask_image": mask, "ip_adapter_image": ip_image})
return input_kwargs
@slow
@require_torch_gpu
class IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):
def test_text_to_image(self):
image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
pipeline = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
)
pipeline.to(torch_device)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
inputs = self.get_dummy_inputs()
images = pipeline(**inputs).images
image_slice = images[0, :3, :3, -1].flatten()
expected_slice = np.array([0.8047, 0.8774, 0.9248, 0.9155, 0.9814, 1.0, 0.9678, 1.0, 1.0])
assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)
def test_image_to_image(self):
image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
)
pipeline.to(torch_device)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
inputs = self.get_dummy_inputs(for_image_to_image=True)
images = pipeline(**inputs).images
image_slice = images[0, :3, :3, -1].flatten()
expected_slice = np.array([0.2307, 0.2341, 0.2305, 0.24, 0.2268, 0.25, 0.2322, 0.2588, 0.2935])
assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)
def test_inpainting(self):
image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype
)
pipeline.to(torch_device)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
inputs = self.get_dummy_inputs(for_inpainting=True)
images = pipeline(**inputs).images
image_slice = images[0, :3, :3, -1].flatten()
expected_slice = np.array([0.2705, 0.2395, 0.2209, 0.2312, 0.2102, 0.2104, 0.2178, 0.2065, 0.1997])
assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)
@slow
@require_torch_gpu
class IPAdapterSDXLIntegrationTests(IPAdapterNightlyTestsMixin):
def test_text_to_image_sdxl(self):
image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="sdxl_models/image_encoder")
feature_extractor = self.get_image_processor("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
image_encoder=image_encoder,
feature_extractor=feature_extractor,
torch_dtype=self.dtype,
)
pipeline.to(torch_device)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
inputs = self.get_dummy_inputs()
images = pipeline(**inputs).images
image_slice = images[0, :3, :3, -1].flatten()
expected_slice = np.array([0.0968, 0.0959, 0.0852, 0.0912, 0.0948, 0.093, 0.0893, 0.0932, 0.0923])
assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)
def test_image_to_image_sdxl(self):
image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="sdxl_models/image_encoder")
feature_extractor = self.get_image_processor("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
image_encoder=image_encoder,
feature_extractor=feature_extractor,
torch_dtype=self.dtype,
)
pipeline.to(torch_device)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
inputs = self.get_dummy_inputs(for_image_to_image=True)
images = pipeline(**inputs).images
image_slice = images[0, :3, :3, -1].flatten()
expected_slice = np.array([0.0653, 0.0704, 0.0725, 0.0741, 0.0702, 0.0647, 0.0782, 0.0799, 0.0752])
assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)
def test_inpainting_sdxl(self):
image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="sdxl_models/image_encoder")
feature_extractor = self.get_image_processor("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
image_encoder=image_encoder,
feature_extractor=feature_extractor,
torch_dtype=self.dtype,
)
pipeline.to(torch_device)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
inputs = self.get_dummy_inputs(for_inpainting=True)
images = pipeline(**inputs).images
image_slice = images[0, :3, :3, -1].flatten()
image_slice.tolist()
expected_slice = np.array([0.1418, 0.1493, 0.1428, 0.146, 0.1491, 0.1501, 0.1473, 0.1501, 0.1516])
assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/vq_diffusion/test_vq_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, torch_device
torch.backends.cuda.matmul.allow_tf32 = False
class VQDiffusionPipelineFastTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def num_embed(self):
return 12
@property
def num_embeds_ada_norm(self):
return 12
@property
def text_embedder_hidden_size(self):
return 32
@property
def dummy_vqvae(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,
num_vq_embeddings=self.num_embed,
vq_embed_dim=3,
)
return model
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=self.text_embedder_hidden_size,
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)
@property
def dummy_transformer(self):
torch.manual_seed(0)
height = 12
width = 12
model_kwargs = {
"attention_bias": True,
"cross_attention_dim": 32,
"attention_head_dim": height * width,
"num_attention_heads": 1,
"num_vector_embeds": self.num_embed,
"num_embeds_ada_norm": self.num_embeds_ada_norm,
"norm_num_groups": 32,
"sample_size": width,
"activation_fn": "geglu-approximate",
}
model = Transformer2DModel(**model_kwargs)
return model
def test_vq_diffusion(self):
device = "cpu"
vqvae = self.dummy_vqvae
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
transformer = self.dummy_transformer
scheduler = VQDiffusionScheduler(self.num_embed)
learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(learnable=False)
pipe = VQDiffusionPipeline(
vqvae=vqvae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
scheduler=scheduler,
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
prompt = "teddy bear playing in the pool"
generator = torch.Generator(device=device).manual_seed(0)
output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = pipe(
[prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
expected_slice = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992])
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_vq_diffusion_classifier_free_sampling(self):
device = "cpu"
vqvae = self.dummy_vqvae
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
transformer = self.dummy_transformer
scheduler = VQDiffusionScheduler(self.num_embed)
learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(
learnable=True, hidden_size=self.text_embedder_hidden_size, length=tokenizer.model_max_length
)
pipe = VQDiffusionPipeline(
vqvae=vqvae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
scheduler=scheduler,
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
prompt = "teddy bear playing in the pool"
generator = torch.Generator(device=device).manual_seed(0)
output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = pipe(
[prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
expected_slice = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988])
assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@nightly
@require_torch_gpu
class VQDiffusionPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_vq_diffusion_classifier_free_sampling(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"
)
pipeline = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq")
pipeline = pipeline.to(torch_device)
pipeline.set_progress_bar_config(disable=None)
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
generator = torch.Generator(device=torch_device).manual_seed(0)
output = pipeline(
"teddy bear playing in the pool",
num_images_per_prompt=1,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image).max() < 2.0
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/dance_diffusion/test_dance_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class DanceDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = DanceDiffusionPipeline
params = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params - {
"callback",
"latents",
"callback_steps",
"output_type",
"num_images_per_prompt",
}
batch_params = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
test_attention_slicing = False
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet1DModel(
block_out_channels=(32, 32, 64),
extra_in_channels=16,
sample_size=512,
sample_rate=16_000,
in_channels=2,
out_channels=2,
flip_sin_to_cos=True,
use_timestep_embedding=False,
time_embedding_type="fourier",
mid_block_type="UNetMidBlock1D",
down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),
up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),
)
scheduler = IPNDMScheduler()
components = {
"unet": unet,
"scheduler": scheduler,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 4,
}
return inputs
def test_dance_diffusion(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = DanceDiffusionPipeline(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = pipe(**inputs)
audio = output.audios
audio_slice = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
expected_slice = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000])
assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local()
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@skip_mps
def test_attention_slicing_forward_pass(self):
return super().test_attention_slicing_forward_pass()
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@nightly
@require_torch_gpu
class PipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_dance_diffusion(self):
device = torch_device
pipe = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k")
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
output = pipe(generator=generator, num_inference_steps=100, audio_length_in_s=4.096)
audio = output.audios
audio_slice = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
expected_slice = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020])
assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
def test_dance_diffusion_fp16(self):
device = torch_device
pipe = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k", torch_dtype=torch.float16)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
output = pipe(generator=generator, num_inference_steps=100, audio_length_in_s=4.096)
audio = output.audios
audio_slice = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
expected_slice = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341])
assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/unclip/test_unclip.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNet2DConditionModel, UNet2DModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
skip_mps,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UnCLIPPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = UnCLIPPipeline
params = TEXT_TO_IMAGE_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
"guidance_scale",
"prompt_embeds",
"cross_attention_kwargs",
}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
required_optional_params = [
"generator",
"return_dict",
"prior_num_inference_steps",
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
test_xformers_attention = False
@property
def text_embedder_hidden_size(self):
return 32
@property
def time_input_dim(self):
return 32
@property
def block_out_channels_0(self):
return self.time_input_dim
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def cross_attention_dim(self):
return 100
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=self.text_embedder_hidden_size,
projection_dim=self.text_embedder_hidden_size,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
return CLIPTextModelWithProjection(config)
@property
def dummy_prior(self):
torch.manual_seed(0)
model_kwargs = {
"num_attention_heads": 2,
"attention_head_dim": 12,
"embedding_dim": self.text_embedder_hidden_size,
"num_layers": 1,
}
model = PriorTransformer(**model_kwargs)
return model
@property
def dummy_text_proj(self):
torch.manual_seed(0)
model_kwargs = {
"clip_embeddings_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"cross_attention_dim": self.cross_attention_dim,
}
model = UnCLIPTextProjModel(**model_kwargs)
return model
@property
def dummy_decoder(self):
torch.manual_seed(0)
model_kwargs = {
"sample_size": 32,
# RGB in channels
"in_channels": 3,
# Out channels is double in channels because predicts mean and variance
"out_channels": 6,
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
"layers_per_block": 1,
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": "identity",
}
model = UNet2DConditionModel(**model_kwargs)
return model
@property
def dummy_super_res_kwargs(self):
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def dummy_super_res_first(self):
torch.manual_seed(0)
model = UNet2DModel(**self.dummy_super_res_kwargs)
return model
@property
def dummy_super_res_last(self):
# seeded differently to get different unet than `self.dummy_super_res_first`
torch.manual_seed(1)
model = UNet2DModel(**self.dummy_super_res_kwargs)
return model
def get_dummy_components(self):
prior = self.dummy_prior
decoder = self.dummy_decoder
text_proj = self.dummy_text_proj
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
super_res_first = self.dummy_super_res_first
super_res_last = self.dummy_super_res_last
prior_scheduler = UnCLIPScheduler(
variance_type="fixed_small_log",
prediction_type="sample",
num_train_timesteps=1000,
clip_sample_range=5.0,
)
decoder_scheduler = UnCLIPScheduler(
variance_type="learned_range",
prediction_type="epsilon",
num_train_timesteps=1000,
)
super_res_scheduler = UnCLIPScheduler(
variance_type="fixed_small_log",
prediction_type="epsilon",
num_train_timesteps=1000,
)
components = {
"prior": prior,
"decoder": decoder,
"text_proj": text_proj,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"prior_scheduler": prior_scheduler,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "horse",
"generator": generator,
"prior_num_inference_steps": 2,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def test_unclip(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.images
image_from_tuple = pipe(
**self.get_dummy_inputs(device),
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(
[
0.9997,
0.9988,
0.0028,
0.9997,
0.9984,
0.9965,
0.0029,
0.9986,
0.0025,
]
)
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_unclip_passed_text_embed(self):
device = torch.device("cpu")
class DummyScheduler:
init_noise_sigma = 1
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
prior = components["prior"]
decoder = components["decoder"]
super_res_first = components["super_res_first"]
tokenizer = components["tokenizer"]
text_encoder = components["text_encoder"]
generator = torch.Generator(device=device).manual_seed(0)
dtype = prior.dtype
batch_size = 1
shape = (batch_size, prior.config.embedding_dim)
prior_latents = pipe.prepare_latents(
shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()
)
shape = (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
decoder_latents = pipe.prepare_latents(
shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()
)
shape = (
batch_size,
super_res_first.config.in_channels // 2,
super_res_first.config.sample_size,
super_res_first.config.sample_size,
)
super_res_latents = pipe.prepare_latents(
shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()
)
pipe.set_progress_bar_config(disable=None)
prompt = "this is a prompt example"
generator = torch.Generator(device=device).manual_seed(0)
output = pipe(
[prompt],
generator=generator,
prior_num_inference_steps=2,
decoder_num_inference_steps=2,
super_res_num_inference_steps=2,
prior_latents=prior_latents,
decoder_latents=decoder_latents,
super_res_latents=super_res_latents,
output_type="np",
)
image = output.images
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
)
text_model_output = text_encoder(text_inputs.input_ids)
text_attention_mask = text_inputs.attention_mask
generator = torch.Generator(device=device).manual_seed(0)
image_from_text = pipe(
generator=generator,
prior_num_inference_steps=2,
decoder_num_inference_steps=2,
super_res_num_inference_steps=2,
prior_latents=prior_latents,
decoder_latents=decoder_latents,
super_res_latents=super_res_latents,
text_model_output=text_model_output,
text_attention_mask=text_attention_mask,
output_type="np",
)[0]
# make sure passing text embeddings manually is identical
assert np.abs(image - image_from_text).max() < 1e-4
# Overriding PipelineTesterMixin::test_attention_slicing_forward_pass
# because UnCLIP GPU undeterminism requires a looser check.
@skip_mps
def test_attention_slicing_forward_pass(self):
test_max_difference = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference, expected_max_diff=0.01)
# Overriding PipelineTesterMixin::test_inference_batch_single_identical
# because UnCLIP undeterminism requires a looser check.
@skip_mps
def test_inference_batch_single_identical(self):
additional_params_copy_to_batched_inputs = [
"prior_num_inference_steps",
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
self._test_inference_batch_single_identical(
additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs, expected_max_diff=5e-3
)
def test_inference_batch_consistent(self):
additional_params_copy_to_batched_inputs = [
"prior_num_inference_steps",
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
batch_sizes = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=batch_sizes,
additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs,
)
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs
)
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local(expected_max_difference=5e-3)
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@unittest.skip("UnCLIP produces very large differences in fp16 vs fp32. Test is not useful.")
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=1.0)
@nightly
class UnCLIPPipelineCPUIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_unclip_karlo_cpu_fp32(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/unclip/karlo_v1_alpha_horse_cpu.npy"
)
pipeline = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha")
pipeline.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
output = pipeline(
"horse",
num_images_per_prompt=1,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image).max() < 1e-1
@nightly
@require_torch_gpu
class UnCLIPPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_unclip_karlo(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/unclip/karlo_v1_alpha_horse_fp16.npy"
)
pipeline = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16)
pipeline = pipeline.to(torch_device)
pipeline.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipeline(
"horse",
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(image, expected_image)
def test_unclip_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_ = pipe(
"horse",
num_images_per_prompt=1,
prior_num_inference_steps=2,
decoder_num_inference_steps=2,
super_res_num_inference_steps=2,
output_type="np",
)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/unclip/test_unclip_image_variation.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNet2DConditionModel,
UNet2DModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
require_torch_gpu,
skip_mps,
torch_device,
)
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UnCLIPImageVariationPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = UnCLIPImageVariationPipeline
params = IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
batch_params = IMAGE_VARIATION_BATCH_PARAMS
required_optional_params = [
"generator",
"return_dict",
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
test_xformers_attention = False
@property
def text_embedder_hidden_size(self):
return 32
@property
def time_input_dim(self):
return 32
@property
def block_out_channels_0(self):
return self.time_input_dim
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def cross_attention_dim(self):
return 100
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=self.text_embedder_hidden_size,
projection_dim=self.text_embedder_hidden_size,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
return CLIPTextModelWithProjection(config)
@property
def dummy_image_encoder(self):
torch.manual_seed(0)
config = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size,
projection_dim=self.text_embedder_hidden_size,
num_hidden_layers=5,
num_attention_heads=4,
image_size=32,
intermediate_size=37,
patch_size=1,
)
return CLIPVisionModelWithProjection(config)
@property
def dummy_text_proj(self):
torch.manual_seed(0)
model_kwargs = {
"clip_embeddings_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"cross_attention_dim": self.cross_attention_dim,
}
model = UnCLIPTextProjModel(**model_kwargs)
return model
@property
def dummy_decoder(self):
torch.manual_seed(0)
model_kwargs = {
"sample_size": 32,
# RGB in channels
"in_channels": 3,
# Out channels is double in channels because predicts mean and variance
"out_channels": 6,
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
"layers_per_block": 1,
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": "identity",
}
model = UNet2DConditionModel(**model_kwargs)
return model
@property
def dummy_super_res_kwargs(self):
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def dummy_super_res_first(self):
torch.manual_seed(0)
model = UNet2DModel(**self.dummy_super_res_kwargs)
return model
@property
def dummy_super_res_last(self):
# seeded differently to get different unet than `self.dummy_super_res_first`
torch.manual_seed(1)
model = UNet2DModel(**self.dummy_super_res_kwargs)
return model
def get_dummy_components(self):
decoder = self.dummy_decoder
text_proj = self.dummy_text_proj
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
super_res_first = self.dummy_super_res_first
super_res_last = self.dummy_super_res_last
decoder_scheduler = UnCLIPScheduler(
variance_type="learned_range",
prediction_type="epsilon",
num_train_timesteps=1000,
)
super_res_scheduler = UnCLIPScheduler(
variance_type="fixed_small_log",
prediction_type="epsilon",
num_train_timesteps=1000,
)
feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
image_encoder = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def get_dummy_inputs(self, device, seed=0, pil_image=True):
input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
if pil_image:
input_image = input_image * 0.5 + 0.5
input_image = input_image.clamp(0, 1)
input_image = input_image.cpu().permute(0, 2, 3, 1).float().numpy()
input_image = DiffusionPipeline.numpy_to_pil(input_image)[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def test_unclip_image_variation_input_tensor(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
pipeline_inputs = self.get_dummy_inputs(device, pil_image=False)
output = pipe(**pipeline_inputs)
image = output.images
tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=False)
image_from_tuple = pipe(
**tuple_pipeline_inputs,
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(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
]
)
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_unclip_image_variation_input_image(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)
output = pipe(**pipeline_inputs)
image = output.images
tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)
image_from_tuple = pipe(
**tuple_pipeline_inputs,
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([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971])
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_unclip_image_variation_input_list_images(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)
pipeline_inputs["image"] = [
pipeline_inputs["image"],
pipeline_inputs["image"],
]
output = pipe(**pipeline_inputs)
image = output.images
tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)
tuple_pipeline_inputs["image"] = [
tuple_pipeline_inputs["image"],
tuple_pipeline_inputs["image"],
]
image_from_tuple = pipe(
**tuple_pipeline_inputs,
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 == (2, 64, 64, 3)
expected_slice = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
]
)
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_unclip_passed_image_embed(self):
device = torch.device("cpu")
class DummyScheduler:
init_noise_sigma = 1
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(0)
dtype = pipe.decoder.dtype
batch_size = 1
shape = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
decoder_latents = pipe.prepare_latents(
shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()
)
shape = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
super_res_latents = pipe.prepare_latents(
shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()
)
pipeline_inputs = self.get_dummy_inputs(device, pil_image=False)
img_out_1 = pipe(
**pipeline_inputs, decoder_latents=decoder_latents, super_res_latents=super_res_latents
).images
pipeline_inputs = self.get_dummy_inputs(device, pil_image=False)
# Don't pass image, instead pass embedding
image = pipeline_inputs.pop("image")
image_embeddings = pipe.image_encoder(image).image_embeds
img_out_2 = pipe(
**pipeline_inputs,
decoder_latents=decoder_latents,
super_res_latents=super_res_latents,
image_embeddings=image_embeddings,
).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_1 - img_out_2).max() < 1e-4
# Overriding PipelineTesterMixin::test_attention_slicing_forward_pass
# because UnCLIP GPU undeterminism requires a looser check.
@skip_mps
def test_attention_slicing_forward_pass(self):
test_max_difference = torch_device == "cpu"
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
expected_max_diff = 1e-2
self._test_attention_slicing_forward_pass(
test_max_difference=test_max_difference, expected_max_diff=expected_max_diff
)
# Overriding PipelineTesterMixin::test_inference_batch_single_identical
# because UnCLIP undeterminism requires a looser check.
@unittest.skip("UnCLIP produces very large differences. Test is not useful.")
@skip_mps
def test_inference_batch_single_identical(self):
additional_params_copy_to_batched_inputs = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
self._test_inference_batch_single_identical(
additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs, expected_max_diff=5e-3
)
def test_inference_batch_consistent(self):
additional_params_copy_to_batched_inputs = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
batch_sizes = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=batch_sizes,
additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs,
)
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs
)
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent()
@unittest.skip("UnCLIP produces very large difference. Test is not useful.")
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local(expected_max_difference=4e-3)
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@unittest.skip("UnCLIP produces very large difference in fp16 vs fp32. Test is not useful.")
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=1.0)
@nightly
@require_torch_gpu
class UnCLIPImageVariationPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_unclip_image_variation_karlo(self):
input_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/unclip/karlo_v1_alpha_cat_variation_fp16.npy"
)
pipeline = UnCLIPImageVariationPipeline.from_pretrained(
"kakaobrain/karlo-v1-alpha-image-variations", torch_dtype=torch.float16
)
pipeline = pipeline.to(torch_device)
pipeline.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipeline(
input_image,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(image, expected_image, 15)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/paint_by_example/test_paint_by_example.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig
from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNet2DConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
nightly,
require_torch_gpu,
torch_device,
)
from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = PaintByExamplePipeline
params = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset([]) # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
config = CLIPVisionConfig(
hidden_size=32,
projection_dim=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
image_size=32,
patch_size=4,
)
image_encoder = PaintByExampleImageEncoder(config, proj_size=32)
feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"image_encoder": image_encoder,
"safety_checker": None,
"feature_extractor": feature_extractor,
}
return components
def convert_to_pt(self, image):
image = np.array(image.convert("RGB"))
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
return image
def get_dummy_inputs(self, device="cpu", seed=0):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64))
example_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32))
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"example_image": example_image,
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_paint_by_example_inpaint(self):
components = self.get_dummy_components()
# make sure here that pndm scheduler skips prk
pipe = PaintByExamplePipeline(**components)
pipe = pipe.to("cpu")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
output = pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_paint_by_example_image_tensor(self):
device = "cpu"
inputs = self.get_dummy_inputs()
inputs.pop("mask_image")
image = self.convert_to_pt(inputs.pop("image"))
mask_image = image.clamp(0, 1) / 2
# make sure here that pndm scheduler skips prk
pipe = PaintByExamplePipeline(**self.get_dummy_components())
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(image=image, mask_image=mask_image[:, 0], **inputs)
out_1 = output.images
image = image.cpu().permute(0, 2, 3, 1)[0]
mask_image = mask_image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB")
mask_image = Image.fromarray(np.uint8(mask_image)).convert("RGB")
output = pipe(**self.get_dummy_inputs())
out_2 = output.images
assert out_1.shape == (1, 64, 64, 3)
assert np.abs(out_1.flatten() - out_2.flatten()).max() < 5e-2
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@nightly
@require_torch_gpu
class PaintByExamplePipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_paint_by_example(self):
# make sure here that pndm scheduler skips prk
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/paint_by_example/dog_in_bucket.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/paint_by_example/mask.png"
)
example_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/paint_by_example/panda.jpg"
)
pipe = PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example")
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(321)
output = pipe(
image=init_image,
mask_image=mask_image,
example_image=example_image,
generator=generator,
guidance_scale=5.0,
num_inference_steps=50,
output_type="np",
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/kandinsky3/test_kandinsky3.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import (
AutoPipelineForImage2Image,
AutoPipelineForText2Image,
Kandinsky3Pipeline,
Kandinsky3UNet,
VQModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_image,
require_torch_gpu,
slow,
)
from ..pipeline_params import (
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class Kandinsky3PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = Kandinsky3Pipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
test_xformers_attention = False
@property
def dummy_movq_kwargs(self):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def dummy_movq(self):
torch.manual_seed(0)
model = VQModel(**self.dummy_movq_kwargs)
return model
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = Kandinsky3UNet(
in_channels=4,
time_embedding_dim=4,
groups=2,
attention_head_dim=4,
layers_per_block=3,
block_out_channels=(32, 64),
cross_attention_dim=4,
encoder_hid_dim=32,
)
scheduler = DDPMScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="squaredcos_cap_v2",
clip_sample=True,
thresholding=False,
)
torch.manual_seed(0)
movq = self.dummy_movq
torch.manual_seed(0)
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
components = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
"width": 16,
"height": 16,
}
return inputs
def test_kandinsky3(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
expected_slice = np.array([0.3768, 0.4373, 0.4865, 0.4890, 0.4299, 0.5122, 0.4921, 0.4924, 0.5599])
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=1e-1)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=1e-2)
def test_model_cpu_offload_forward_pass(self):
# TODO(Yiyi) - this test should work, skipped for time reasons for now
pass
@slow
@require_torch_gpu
class Kandinsky3PipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_kandinskyV3(self):
pipe = AutoPipelineForText2Image.from_pretrained(
"kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background."
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipe(prompt, num_inference_steps=25, generator=generator).images[0]
assert image.size == (1024, 1024)
expected_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png"
)
image_processor = VaeImageProcessor()
image_np = image_processor.pil_to_numpy(image)
expected_image_np = image_processor.pil_to_numpy(expected_image)
self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2))
def test_kandinskyV3_img2img(self):
pipe = AutoPipelineForImage2Image.from_pretrained(
"kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png"
)
w, h = 512, 512
image = image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
prompt = "A painting of the inside of a subway train with tiny raccoons."
image = pipe(prompt, image=image, strength=0.75, num_inference_steps=25, generator=generator).images[0]
assert image.size == (512, 512)
expected_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/i2i.png"
)
image_processor = VaeImageProcessor()
image_np = image_processor.pil_to_numpy(image)
expected_image_np = image_processor.pil_to_numpy(expected_image)
self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2))
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/repaint/test_repaint.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from diffusers import RePaintPipeline, RePaintScheduler, UNet2DModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_image,
load_numpy,
nightly,
require_torch_gpu,
skip_mps,
torch_device,
)
from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class RepaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = RePaintPipeline
params = IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
required_optional_params = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
batch_params = IMAGE_INPAINTING_BATCH_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
torch.manual_seed(0)
unet = 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"),
)
scheduler = RePaintScheduler()
components = {"unet": unet, "scheduler": scheduler}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image = np.random.RandomState(seed).standard_normal((1, 3, 32, 32))
image = torch.from_numpy(image).to(device=device, dtype=torch.float32)
mask = (image > 0).to(device=device, dtype=torch.float32)
inputs = {
"image": image,
"mask_image": mask,
"generator": generator,
"num_inference_steps": 5,
"eta": 0.0,
"jump_length": 2,
"jump_n_sample": 2,
"output_type": "numpy",
}
return inputs
def test_repaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = RePaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local()
# RePaint can hardly be made deterministic since the scheduler is currently always
# nondeterministic
@unittest.skip("non-deterministic pipeline")
def test_inference_batch_single_identical(self):
return super().test_inference_batch_single_identical()
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@skip_mps
def test_attention_slicing_forward_pass(self):
return super().test_attention_slicing_forward_pass()
@nightly
@require_torch_gpu
class RepaintPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_celebahq(self):
original_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
"repaint/celeba_hq_256.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
"repaint/celeba_hq_256_result.npy"
)
model_id = "google/ddpm-ema-celebahq-256"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = RePaintScheduler.from_pretrained(model_id)
repaint = RePaintPipeline(unet=unet, scheduler=scheduler).to(torch_device)
repaint.set_progress_bar_config(disable=None)
repaint.enable_attention_slicing()
generator = torch.manual_seed(0)
output = repaint(
original_image,
mask_image,
num_inference_steps=250,
eta=0.0,
jump_length=10,
jump_n_sample=10,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image).mean() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py
|
import gc
import inspect
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
LatentConsistencyModelImg2ImgPipeline,
LCMScheduler,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class LatentConsistencyModelImg2ImgPipelineFastTests(
PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = LatentConsistencyModelImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "negative_prompt", "negative_prompt_embeds"}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents", "negative_prompt"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
layers_per_block=1,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
norm_num_groups=2,
time_cond_proj_dim=32,
)
scheduler = LCMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=64,
layer_norm_eps=1e-05,
num_attention_heads=8,
num_hidden_layers=3,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
}
return components
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
}
return inputs
def test_lcm_onestep(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["num_inference_steps"] = 1
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 32, 32, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.4388, 0.3717, 0.2202, 0.7213, 0.6370, 0.3664, 0.5815, 0.6080, 0.4977])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_lcm_multistep(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 32, 32, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.4150, 0.3719, 0.2479, 0.6333, 0.6024, 0.3778, 0.5036, 0.5420, 0.4678])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 32, 32, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.3994, 0.3471, 0.2540, 0.7030, 0.6193, 0.3645, 0.5777, 0.5850, 0.4965])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=5e-4)
# override default test because the final latent variable is "denoised" instead of "latents"
def test_callback_inputs(self):
sig = inspect.signature(self.pipeline_class.__call__)
if not ("callback_on_step_end_tensor_inputs" in sig.parameters and "callback_on_step_end" in sig.parameters):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_test(pipe, i, t, callback_kwargs):
missing_callback_inputs = set()
for v in pipe._callback_tensor_inputs:
if v not in callback_kwargs:
missing_callback_inputs.add(v)
self.assertTrue(
len(missing_callback_inputs) == 0, f"Missing callback tensor inputs: {missing_callback_inputs}"
)
last_i = pipe.num_timesteps - 1
if i == last_i:
callback_kwargs["denoised"] = torch.zeros_like(callback_kwargs["denoised"])
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
inputs["callback_on_step_end"] = callback_inputs_test
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
assert output.abs().sum() == 0
@slow
@require_torch_gpu
class LatentConsistencyModelImg2ImgPipelineSlowTests(unittest.TestCase):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/sketch-mountains-input.png"
)
init_image = init_image.resize((512, 512))
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "np",
"image": init_image,
}
return inputs
def test_lcm_onestep(self):
pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7", safety_checker=None
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 1
image = pipe(**inputs).images
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, -3:, -3:, -1].flatten()
expected_slice = np.array([0.1950, 0.1961, 0.2308, 0.1786, 0.1837, 0.2320, 0.1898, 0.1885, 0.2309])
assert np.abs(image_slice - expected_slice).max() < 1e-3
def test_lcm_multistep(self):
pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7", safety_checker=None
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, -3:, -3:, -1].flatten()
expected_slice = np.array([0.3756, 0.3816, 0.3767, 0.3718, 0.3739, 0.3735, 0.3863, 0.3803, 0.3563])
assert np.abs(image_slice - expected_slice).max() < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models.py
|
import gc
import inspect
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
LatentConsistencyModelPipeline,
LCMScheduler,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class LatentConsistencyModelPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = LatentConsistencyModelPipeline
params = TEXT_TO_IMAGE_PARAMS - {"negative_prompt", "negative_prompt_embeds"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {"negative_prompt"}
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
layers_per_block=1,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
norm_num_groups=2,
time_cond_proj_dim=32,
)
scheduler = LCMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=64,
layer_norm_eps=1e-05,
num_attention_heads=8,
num_hidden_layers=3,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
}
return inputs
def test_lcm_onestep(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = LatentConsistencyModelPipeline(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["num_inference_steps"] = 1
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 64, 64, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.1441, 0.5304, 0.5452, 0.1361, 0.4011, 0.4370, 0.5326, 0.3492, 0.3637])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_lcm_multistep(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = LatentConsistencyModelPipeline(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 64, 64, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = LatentConsistencyModelPipeline(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 64, 64, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=5e-4)
# skip because lcm pipeline apply cfg differently
def test_callback_cfg(self):
pass
# override default test because the final latent variable is "denoised" instead of "latents"
def test_callback_inputs(self):
sig = inspect.signature(self.pipeline_class.__call__)
if not ("callback_on_step_end_tensor_inputs" in sig.parameters and "callback_on_step_end" in sig.parameters):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_test(pipe, i, t, callback_kwargs):
missing_callback_inputs = set()
for v in pipe._callback_tensor_inputs:
if v not in callback_kwargs:
missing_callback_inputs.add(v)
self.assertTrue(
len(missing_callback_inputs) == 0, f"Missing callback tensor inputs: {missing_callback_inputs}"
)
last_i = pipe.num_timesteps - 1
if i == last_i:
callback_kwargs["denoised"] = torch.zeros_like(callback_kwargs["denoised"])
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
inputs["callback_on_step_end"] = callback_inputs_test
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
assert output.abs().sum() == 0
@slow
@require_torch_gpu
class LatentConsistencyModelPipelineSlowTests(unittest.TestCase):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_lcm_onestep(self):
pipe = LatentConsistencyModelPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", safety_checker=None)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 1
image = pipe(**inputs).images
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, -3:, -3:, -1].flatten()
expected_slice = np.array([0.1025, 0.0911, 0.0984, 0.0981, 0.0901, 0.0918, 0.1055, 0.0940, 0.0730])
assert np.abs(image_slice - expected_slice).max() < 1e-3
def test_lcm_multistep(self):
pipe = LatentConsistencyModelPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", safety_checker=None)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, -3:, -3:, -1].flatten()
expected_slice = np.array([0.01855, 0.01855, 0.01489, 0.01392, 0.01782, 0.01465, 0.01831, 0.02539, 0.0])
assert np.abs(image_slice - expected_slice).max() < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/deepfloyd_if/test_if_inpainting_superresolution.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class IFInpaintingSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
pipeline_class = IFInpaintingSuperResolutionPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"})
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
def get_dummy_components(self):
return self._get_superresolution_dummy_components()
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image = floats_tensor((1, 3, 16, 16), rng=random.Random(seed)).to(device)
original_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
mask_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_float16(expected_max_diff=1e-1)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
def test_save_load_local(self):
self._test_save_load_local()
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,
)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/deepfloyd_if/test_if.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import torch
from diffusers import (
IFImg2ImgPipeline,
IFImg2ImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class IFPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
pipeline_class = IFPipeline
params = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
def get_dummy_components(self):
return self._get_dummy_components()
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_float16(expected_max_diff=1e-1)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
def test_save_load_local(self):
self._test_save_load_local()
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,
)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
@slow
@require_torch_gpu
class IFPipelineSlowTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_all(self):
# if
pipe_1 = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe_2 = IFSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", variant="fp16", torch_dtype=torch.float16, text_encoder=None, tokenizer=None
)
# pre compute text embeddings and remove T5 to save memory
pipe_1.text_encoder.to("cuda")
prompt_embeds, negative_prompt_embeds = pipe_1.encode_prompt("anime turtle", device="cuda")
del pipe_1.tokenizer
del pipe_1.text_encoder
gc.collect()
pipe_1.tokenizer = None
pipe_1.text_encoder = None
pipe_1.enable_model_cpu_offload()
pipe_2.enable_model_cpu_offload()
pipe_1.unet.set_attn_processor(AttnAddedKVProcessor())
pipe_2.unet.set_attn_processor(AttnAddedKVProcessor())
self._test_if(pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds)
pipe_1.remove_all_hooks()
pipe_2.remove_all_hooks()
# img2img
pipe_1 = IFImg2ImgPipeline(**pipe_1.components)
pipe_2 = IFImg2ImgSuperResolutionPipeline(**pipe_2.components)
pipe_1.enable_model_cpu_offload()
pipe_2.enable_model_cpu_offload()
pipe_1.unet.set_attn_processor(AttnAddedKVProcessor())
pipe_2.unet.set_attn_processor(AttnAddedKVProcessor())
self._test_if_img2img(pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds)
pipe_1.remove_all_hooks()
pipe_2.remove_all_hooks()
# inpainting
pipe_1 = IFInpaintingPipeline(**pipe_1.components)
pipe_2 = IFInpaintingSuperResolutionPipeline(**pipe_2.components)
pipe_1.enable_model_cpu_offload()
pipe_2.enable_model_cpu_offload()
pipe_1.unet.set_attn_processor(AttnAddedKVProcessor())
pipe_2.unet.set_attn_processor(AttnAddedKVProcessor())
self._test_if_inpainting(pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds)
def _test_if(self, pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds):
# pipeline 1
_start_torch_memory_measurement()
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe_1(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
num_inference_steps=2,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (64, 64, 3)
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"
)
assert_mean_pixel_difference(image, expected_image)
# pipeline 2
_start_torch_memory_measurement()
generator = torch.Generator(device="cpu").manual_seed(0)
image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
output = pipe_2(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
image=image,
generator=generator,
num_inference_steps=2,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"
)
assert_mean_pixel_difference(image, expected_image)
def _test_if_img2img(self, pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds):
# pipeline 1
_start_torch_memory_measurement()
image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe_1(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
image=image,
num_inference_steps=2,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (64, 64, 3)
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"
)
assert_mean_pixel_difference(image, expected_image)
# pipeline 2
_start_torch_memory_measurement()
generator = torch.Generator(device="cpu").manual_seed(0)
original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)
image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
output = pipe_2(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
image=image,
original_image=original_image,
generator=generator,
num_inference_steps=2,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"
)
assert_mean_pixel_difference(image, expected_image)
def _test_if_inpainting(self, pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds):
# pipeline 1
_start_torch_memory_measurement()
image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
mask_image = floats_tensor((1, 3, 64, 64), rng=random.Random(1)).to(torch_device)
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe_1(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
image=image,
mask_image=mask_image,
num_inference_steps=2,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (64, 64, 3)
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"
)
assert_mean_pixel_difference(image, expected_image)
# pipeline 2
_start_torch_memory_measurement()
generator = torch.Generator(device="cpu").manual_seed(0)
image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)
mask_image = floats_tensor((1, 3, 256, 256), rng=random.Random(1)).to(torch_device)
output = pipe_2(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
image=image,
mask_image=mask_image,
original_image=original_image,
generator=generator,
num_inference_steps=2,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"
)
assert_mean_pixel_difference(image, expected_image)
def _start_torch_memory_measurement():
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/deepfloyd_if/test_if_superresolution.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import torch
from diffusers import IFSuperResolutionPipeline
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class IFSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
pipeline_class = IFSuperResolutionPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
def get_dummy_components(self):
return self._get_superresolution_dummy_components()
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_float16(expected_max_diff=1e-1)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
def test_save_load_local(self):
self._test_save_load_local()
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,
)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/deepfloyd_if/test_if_inpainting.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class IFInpaintingPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
pipeline_class = IFInpaintingPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
def get_dummy_components(self):
return self._get_dummy_components()
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
mask_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_float16(expected_max_diff=1e-1)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
def test_save_load_local(self):
self._test_save_load_local()
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,
)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import torch
from diffusers import IFImg2ImgPipeline
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class IFImg2ImgPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
pipeline_class = IFImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
def get_dummy_components(self):
return self._get_dummy_components()
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_float16(expected_max_diff=1e-1)
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=1e-1)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
def test_save_load_local(self):
self._test_save_load_local()
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,
)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/deepfloyd_if/__init__.py
|
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import DDPMScheduler, UNet2DConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
# WARN: the hf-internal-testing/tiny-random-t5 text encoder has some non-determinism in the `save_load` tests.
class IFPipelineTesterMixin:
def _get_dummy_components(self):
torch.manual_seed(0)
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
unet = UNet2DConditionModel(
sample_size=32,
layers_per_block=1,
block_out_channels=[32, 64],
down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
],
mid_block_type="UNetMidBlock2DSimpleCrossAttn",
up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"],
in_channels=3,
out_channels=6,
cross_attention_dim=32,
encoder_hid_dim=32,
attention_head_dim=8,
addition_embed_type="text",
addition_embed_type_num_heads=2,
cross_attention_norm="group_norm",
resnet_time_scale_shift="scale_shift",
act_fn="gelu",
)
unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
torch.manual_seed(0)
scheduler = DDPMScheduler(
num_train_timesteps=1000,
beta_schedule="squaredcos_cap_v2",
beta_start=0.0001,
beta_end=0.02,
thresholding=True,
dynamic_thresholding_ratio=0.95,
sample_max_value=1.0,
prediction_type="epsilon",
variance_type="learned_range",
)
torch.manual_seed(0)
watermarker = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _get_superresolution_dummy_components(self):
torch.manual_seed(0)
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
unet = UNet2DConditionModel(
sample_size=32,
layers_per_block=[1, 2],
block_out_channels=[32, 64],
down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
],
mid_block_type="UNetMidBlock2DSimpleCrossAttn",
up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"],
in_channels=6,
out_channels=6,
cross_attention_dim=32,
encoder_hid_dim=32,
attention_head_dim=8,
addition_embed_type="text",
addition_embed_type_num_heads=2,
cross_attention_norm="group_norm",
resnet_time_scale_shift="scale_shift",
act_fn="gelu",
class_embed_type="timestep",
mid_block_scale_factor=1.414,
time_embedding_act_fn="gelu",
time_embedding_dim=32,
)
unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
torch.manual_seed(0)
scheduler = DDPMScheduler(
num_train_timesteps=1000,
beta_schedule="squaredcos_cap_v2",
beta_start=0.0001,
beta_end=0.02,
thresholding=True,
dynamic_thresholding_ratio=0.95,
sample_max_value=1.0,
prediction_type="epsilon",
variance_type="learned_range",
)
torch.manual_seed(0)
image_noising_scheduler = DDPMScheduler(
num_train_timesteps=1000,
beta_schedule="squaredcos_cap_v2",
beta_start=0.0001,
beta_end=0.02,
)
torch.manual_seed(0)
watermarker = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
# this test is modified from the base class because if pipelines set the text encoder
# as optional with the intention that the user is allowed to encode the prompt once
# and then pass the embeddings directly to the pipeline. The base class test uses
# the unmodified arguments from `self.get_dummy_inputs` which will pass the unencoded
# prompt to the pipeline when the text encoder is set to None, throwing an error.
# So we make the test reflect the intended usage of setting the text encoder to None.
def _test_save_load_optional_components(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
if "image" in inputs:
image = inputs["image"]
else:
image = None
if "mask_image" in inputs:
mask_image = inputs["mask_image"]
else:
mask_image = None
if "original_image" in inputs:
original_image = inputs["original_image"]
else:
original_image = None
prompt_embeds, negative_prompt_embeds = pipe.encode_prompt(prompt)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
inputs["image"] = image
if mask_image is not None:
inputs["mask_image"] = mask_image
if original_image is not None:
inputs["original_image"] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
inputs["image"] = image
if mask_image is not None:
inputs["mask_image"] = mask_image
if original_image is not None:
inputs["original_image"] = original_image
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
# Modified from `PipelineTesterMixin` to set the attn processor as it's not serialized.
# This should be handled in the base test and then this method can be removed.
def _test_save_load_local(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import torch
from diffusers import IFImg2ImgSuperResolutionPipeline
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class IFImg2ImgSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
pipeline_class = IFImg2ImgSuperResolutionPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"})
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
def get_dummy_components(self):
return self._get_superresolution_dummy_components()
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
original_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = floats_tensor((1, 3, 16, 16), rng=random.Random(seed)).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_float16(expected_max_diff=1e-1)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
def test_save_load_local(self):
self._test_save_load_local()
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,
)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import (
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
DPTConfig,
DPTFeatureExtractor,
DPTForDepthEstimation,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionDepth2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.utils import is_accelerate_available, is_accelerate_version
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class StableDiffusionDepth2ImgPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionDepth2ImgPipeline
test_save_load_optional_components = False
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"depth_mask"})
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=5,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
attention_head_dim=(2, 4),
use_linear_projection=True,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
backbone_config = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [96, 192, 384, 768],
"num_groups": 2,
}
depth_estimator_config = DPTConfig(
image_size=32,
patch_size=16,
num_channels=3,
hidden_size=32,
num_hidden_layers=4,
backbone_out_indices=(0, 1, 2, 3),
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
is_decoder=False,
initializer_range=0.02,
is_hybrid=True,
backbone_config=backbone_config,
backbone_featmap_shape=[1, 384, 24, 24],
)
depth_estimator = DPTForDepthEstimation(depth_estimator_config).eval()
feature_extractor = DPTFeatureExtractor.from_pretrained(
"hf-internal-testing/tiny-random-DPTForDepthEstimation"
)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"depth_estimator": depth_estimator,
"feature_extractor": feature_extractor,
}
return components
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32))
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_save_load_local(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(output - output_loaded).max()
self.assertLess(max_diff, 1e-4)
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self):
components = self.get_dummy_components()
for name, module in components.items():
if hasattr(module, "half"):
components[name] = module.to(torch_device).half()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for name, component in pipe_loaded.components.items():
if hasattr(component, "dtype"):
self.assertTrue(
component.dtype == torch.float16,
f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(output - output_loaded).max()
self.assertLess(max_diff, 2e-2, "The output of the fp16 pipeline changed after saving and loading.")
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_float16_inference(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
for name, module in components.items():
if hasattr(module, "half"):
components[name] = module.half()
pipe_fp16 = self.pipeline_class(**components)
pipe_fp16.to(torch_device)
pipe_fp16.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(torch_device))[0]
output_fp16 = pipe_fp16(**self.get_dummy_inputs(torch_device))[0]
max_diff = np.abs(output - output_fp16).max()
self.assertLess(max_diff, 1.3e-2, "The outputs of the fp16 and fp32 pipelines are too different.")
@unittest.skipIf(
torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.14.0"),
reason="CPU offload is only available with CUDA and `accelerate v0.14.0` or higher",
)
def test_cpu_offload_forward_pass(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_without_offload = pipe(**inputs)[0]
pipe.enable_sequential_cpu_offload()
inputs = self.get_dummy_inputs(torch_device)
output_with_offload = pipe(**inputs)[0]
max_diff = np.abs(output_with_offload - output_without_offload).max()
self.assertLess(max_diff, 1e-4, "CPU offloading should not affect the inference results")
def test_dict_tuple_outputs_equivalent(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(torch_device))[0]
output_tuple = pipe(**self.get_dummy_inputs(torch_device), return_dict=False)[0]
max_diff = np.abs(output - output_tuple).max()
self.assertLess(max_diff, 1e-4)
def test_progress_bar(self):
super().test_progress_bar()
def test_stable_diffusion_depth2img_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = StableDiffusionDepth2ImgPipeline(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
if torch_device == "mps":
expected_slice = np.array([0.6071, 0.5035, 0.4378, 0.5776, 0.5753, 0.4316, 0.4513, 0.5263, 0.4546])
else:
expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_depth2img_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = StableDiffusionDepth2ImgPipeline(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
if torch_device == "mps":
expected_slice = np.array([0.6296, 0.5125, 0.3890, 0.4456, 0.5955, 0.4621, 0.3810, 0.5310, 0.4626])
else:
expected_slice = np.array([0.6012, 0.4507, 0.3769, 0.4121, 0.5566, 0.4585, 0.3803, 0.5045, 0.4631])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_depth2img_multiple_init_images(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = StableDiffusionDepth2ImgPipeline(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["prompt"] = [inputs["prompt"]] * 2
inputs["image"] = 2 * [inputs["image"]]
image = pipe(**inputs).images
image_slice = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
if torch_device == "mps":
expected_slice = np.array([0.6501, 0.5150, 0.4939, 0.6688, 0.5437, 0.5758, 0.5115, 0.4406, 0.4551])
else:
expected_slice = np.array([0.6557, 0.6214, 0.6254, 0.5775, 0.4785, 0.5949, 0.5904, 0.4785, 0.4730])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_depth2img_pil(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = StableDiffusionDepth2ImgPipeline(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
if torch_device == "mps":
expected_slice = np.array([0.53232, 0.47015, 0.40868, 0.45651, 0.4891, 0.4668, 0.4287, 0.48822, 0.47439])
else:
expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@skip_mps
def test_attention_slicing_forward_pass(self):
return super().test_attention_slicing_forward_pass()
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=7e-3)
@slow
@require_torch_gpu
class StableDiffusionDepth2ImgPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png"
)
inputs = {
"prompt": "two tigers",
"image": init_image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_depth2img_pipeline_default(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 480, 640, 3)
expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])
assert np.abs(expected_slice - image_slice).max() < 6e-1
def test_stable_diffusion_depth2img_pipeline_k_lms(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None
)
pipe.unet.set_default_attn_processor()
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 480, 640, 3)
expected_slice = np.array([0.6363, 0.6274, 0.6309, 0.6370, 0.6226, 0.6286, 0.6213, 0.6453, 0.6306])
assert np.abs(expected_slice - image_slice).max() < 8e-4
def test_stable_diffusion_depth2img_pipeline_ddim(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 480, 640, 3)
expected_slice = np.array([0.6424, 0.6524, 0.6249, 0.6041, 0.6634, 0.6420, 0.6522, 0.6555, 0.6436])
assert np.abs(expected_slice - image_slice).max() < 5e-4
def test_stable_diffusion_depth2img_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 60, 80)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.7168, -1.5137, -0.1418, -2.9219, -2.7266, -2.4414, -2.1035, -3.0078, -1.7051]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 60, 80)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.7109, -1.5068, -0.1403, -2.9160, -2.7207, -2.4414, -2.1035, -3.0059, -1.7090]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 2
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 10**9
@nightly
@require_torch_gpu
class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png"
)
inputs = {
"prompt": "two tigers",
"image": init_image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_depth2img_pndm(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs()
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_depth2img/stable_diffusion_2_0_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_depth2img_ddim(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs()
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_depth2img/stable_diffusion_2_0_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_lms(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs()
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_depth2img/stable_diffusion_2_0_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_dpm(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs()
inputs["num_inference_steps"] = 30
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_depth2img/stable_diffusion_2_0_dpm_multi.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
load_numpy,
nightly,
numpy_cosine_similarity_distance,
require_torch_gpu,
skip_mps,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
@skip_mps
class StableDiffusionAttendAndExcitePipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionAttendAndExcitePipeline
test_attention_slicing = False
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"})
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
# Attend and excite requires being able to run a backward pass at
# inference time. There's no deterministic backward operator for pad
@classmethod
def setUpClass(cls):
super().setUpClass()
torch.use_deterministic_algorithms(False)
@classmethod
def tearDownClass(cls):
super().tearDownClass()
torch.use_deterministic_algorithms(True)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=1,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = inputs = {
"prompt": "a cat and a frog",
"token_indices": [2, 5],
"generator": generator,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
"max_iter_to_alter": 2,
"thresholds": {0: 0.7},
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 64, 64, 3))
expected_slice = np.array(
[0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_sequential_cpu_offload_forward_pass(self):
super().test_sequential_cpu_offload_forward_pass(expected_max_diff=5e-4)
def test_inference_batch_consistent(self):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=7e-4)
def test_dict_tuple_outputs_equivalent(self):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)
def test_pt_np_pil_outputs_equivalent(self):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4)
def test_save_load_local(self):
super().test_save_load_local(expected_max_difference=5e-4)
def test_save_load_optional_components(self):
super().test_save_load_optional_components(expected_max_difference=4e-4)
@require_torch_gpu
@nightly
class StableDiffusionAttendAndExcitePipelineIntegrationTests(unittest.TestCase):
# Attend and excite requires being able to run a backward pass at
# inference time. There's no deterministic backward operator for pad
@classmethod
def setUpClass(cls):
super().setUpClass()
torch.use_deterministic_algorithms(False)
@classmethod
def tearDownClass(cls):
super().tearDownClass()
torch.use_deterministic_algorithms(True)
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_attend_and_excite_fp16(self):
generator = torch.manual_seed(51)
pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe.to("cuda")
prompt = "a painting of an elephant with glasses"
token_indices = [5, 7]
image = pipe(
prompt=prompt,
token_indices=token_indices,
guidance_scale=7.5,
generator=generator,
num_inference_steps=5,
max_iter_to_alter=5,
output_type="numpy",
).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"
)
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
assert max_diff < 5e-1
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_diffedit.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
nightly,
numpy_cosine_similarity_distance,
require_torch_gpu,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class StableDiffusionDiffEditPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionDiffEditPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
image_params = frozenset(
[]
) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
image_latents_params = frozenset([])
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
inverse_scheduler = DDIMInverseScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_zero=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"inverse_scheduler": inverse_scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
mask = floats_tensor((1, 16, 16), rng=random.Random(seed)).to(device)
latents = floats_tensor((1, 2, 4, 16, 16), rng=random.Random(seed)).to(device)
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "a dog and a newt",
"mask_image": mask,
"image_latents": latents,
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def get_dummy_mask_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB")
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"image": image,
"source_prompt": "a cat and a frog",
"target_prompt": "a dog and a newt",
"generator": generator,
"num_inference_steps": 2,
"num_maps_per_mask": 2,
"mask_encode_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def get_dummy_inversion_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB")
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"image": image,
"prompt": "a cat and a frog",
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"decode_latents": True,
"output_type": "numpy",
}
return inputs
def test_save_load_optional_components(self):
if not hasattr(self.pipeline_class, "_optional_components"):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(output - output_loaded).max()
self.assertLess(max_diff, 1e-4)
def test_mask(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_mask_inputs(device)
mask = pipe.generate_mask(**inputs)
mask_slice = mask[0, -3:, -3:]
self.assertEqual(mask.shape, (1, 16, 16))
expected_slice = np.array([0] * 9)
max_diff = np.abs(mask_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
self.assertEqual(mask[0, -3, -4], 0)
def test_inversion(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inversion_inputs(device)
image = pipe.invert(**inputs).images
image_slice = image[0, -1, -3:, -3:]
self.assertEqual(image.shape, (2, 32, 32, 3))
expected_slice = np.array(
[0.5160, 0.5115, 0.5060, 0.5456, 0.4704, 0.5060, 0.5019, 0.4405, 0.4726],
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=5e-3)
def test_inversion_dpm(self):
device = "cpu"
components = self.get_dummy_components()
scheduler_args = {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
components["scheduler"] = DPMSolverMultistepScheduler(**scheduler_args)
components["inverse_scheduler"] = DPMSolverMultistepInverseScheduler(**scheduler_args)
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inversion_inputs(device)
image = pipe.invert(**inputs).images
image_slice = image[0, -1, -3:, -3:]
self.assertEqual(image.shape, (2, 32, 32, 3))
expected_slice = np.array(
[0.5305, 0.4673, 0.5314, 0.5308, 0.4886, 0.5279, 0.5142, 0.4724, 0.4892],
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
@require_torch_gpu
@nightly
class StableDiffusionDiffEditPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def setUpClass(cls):
raw_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"
)
raw_image = raw_image.convert("RGB").resize((256, 256))
cls.raw_image = raw_image
def test_stable_diffusion_diffedit_full(self):
generator = torch.manual_seed(0)
pipe = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-base", safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.scheduler.clip_sample = True
pipe.inverse_scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
source_prompt = "a bowl of fruit"
target_prompt = "a bowl of pears"
mask_image = pipe.generate_mask(
image=self.raw_image,
source_prompt=source_prompt,
target_prompt=target_prompt,
generator=generator,
)
inv_latents = pipe.invert(
prompt=source_prompt,
image=self.raw_image,
inpaint_strength=0.7,
generator=generator,
num_inference_steps=5,
).latents
image = pipe(
prompt=target_prompt,
mask_image=mask_image,
image_latents=inv_latents,
generator=generator,
negative_prompt=source_prompt,
inpaint_strength=0.7,
num_inference_steps=5,
output_type="np",
).images[0]
expected_image = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png"
).resize((256, 256))
)
/ 255
)
assert numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten()) < 2e-1
@nightly
@require_torch_gpu
class StableDiffusionDiffEditPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def setUpClass(cls):
raw_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"
)
raw_image = raw_image.convert("RGB").resize((768, 768))
cls.raw_image = raw_image
def test_stable_diffusion_diffedit_dpm(self):
generator = torch.manual_seed(0)
pipe = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.inverse_scheduler = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
source_prompt = "a bowl of fruit"
target_prompt = "a bowl of pears"
mask_image = pipe.generate_mask(
image=self.raw_image,
source_prompt=source_prompt,
target_prompt=target_prompt,
generator=generator,
)
inv_latents = pipe.invert(
prompt=source_prompt,
image=self.raw_image,
inpaint_strength=0.7,
generator=generator,
num_inference_steps=25,
).latents
image = pipe(
prompt=target_prompt,
mask_image=mask_image,
image_latents=inv_latents,
generator=generator,
negative_prompt=source_prompt,
inpaint_strength=0.7,
num_inference_steps=25,
output_type="numpy",
).images[0]
expected_image = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png"
).resize((768, 768))
)
/ 255
)
assert np.abs((expected_image - image).max()) < 5e-1
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_latent_upscale.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def check_same_shape(tensor_list):
shapes = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:])
class StableDiffusionLatentUpscalePipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionLatentUpscalePipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"height",
"width",
"cross_attention_kwargs",
"negative_prompt_embeds",
"prompt_embeds",
}
required_optional_params = PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = frozenset(
[]
) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
image_latents_params = frozenset([])
@property
def dummy_image(self):
batch_size = 1
num_channels = 4
sizes = (16, 16)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
def get_dummy_components(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
act_fn="gelu",
attention_head_dim=8,
norm_num_groups=None,
block_out_channels=[32, 32, 64, 64],
time_cond_proj_dim=160,
conv_in_kernel=1,
conv_out_kernel=1,
cross_attention_dim=32,
down_block_types=(
"KDownBlock2D",
"KCrossAttnDownBlock2D",
"KCrossAttnDownBlock2D",
"KCrossAttnDownBlock2D",
),
in_channels=8,
mid_block_type=None,
only_cross_attention=False,
out_channels=5,
resnet_time_scale_shift="scale_shift",
time_embedding_type="fourier",
timestep_post_act="gelu",
up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"),
)
vae = AutoencoderKL(
block_out_channels=[32, 32, 64, 64],
in_channels=3,
out_channels=3,
down_block_types=[
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
scheduler = EulerDiscreteScheduler(prediction_type="sample")
text_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,
hidden_act="quick_gelu",
projection_dim=512,
)
text_encoder = CLIPTextModel(text_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": model.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": self.dummy_image.cpu(),
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 256, 256, 3))
expected_slice = np.array(
[0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=7e-3)
def test_sequential_cpu_offload_forward_pass(self):
super().test_sequential_cpu_offload_forward_pass(expected_max_diff=3e-3)
def test_dict_tuple_outputs_equivalent(self):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=7e-3)
def test_pt_np_pil_outputs_equivalent(self):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3)
def test_save_load_local(self):
super().test_save_load_local(expected_max_difference=3e-3)
def test_save_load_optional_components(self):
super().test_save_load_optional_components(expected_max_difference=3e-3)
def test_karras_schedulers_shape(self):
skip_schedulers = [
"DDIMScheduler",
"DDPMScheduler",
"PNDMScheduler",
"HeunDiscreteScheduler",
"EulerAncestralDiscreteScheduler",
"KDPM2DiscreteScheduler",
"KDPM2AncestralDiscreteScheduler",
"DPMSolverSDEScheduler",
]
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=True)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 2
outputs = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
scheduler_cls = getattr(diffusers, scheduler_enum.name)
pipe.scheduler = scheduler_cls.from_config(pipe.scheduler.config)
output = pipe(**inputs)[0]
outputs.append(output)
assert check_same_shape(outputs)
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=5e-1)
@require_torch_gpu
@slow
class StableDiffusionLatentUpscalePipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_latent_upscaler_fp16(self):
generator = torch.manual_seed(33)
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.to("cuda")
upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(
"stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16
)
upscaler.to("cuda")
prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic"
low_res_latents = pipe(prompt, generator=generator, output_type="latent").images
image = upscaler(
prompt=prompt,
image=low_res_latents,
num_inference_steps=20,
guidance_scale=0,
generator=generator,
output_type="np",
).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"
)
assert np.abs((expected_image - image).mean()) < 5e-2
def test_latent_upscaler_fp16_image(self):
generator = torch.manual_seed(33)
upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(
"stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16
)
upscaler.to("cuda")
prompt = "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"
low_res_img = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"
)
image = upscaler(
prompt=prompt,
image=low_res_img,
num_inference_steps=20,
guidance_scale=0,
generator=generator,
output_type="np",
).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"
)
assert np.abs((expected_image - image).max()) < 5e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
enable_full_determinism()
class StableDiffusionUpscalePipelineFastTests(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
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
@property
def dummy_cond_unet_upscale(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 32, 64),
layers_per_block=2,
sample_size=32,
in_channels=7,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=8,
use_linear_projection=True,
only_cross_attention=(True, True, False),
num_class_embeds=100,
)
return model
@property
def dummy_vae(self):
torch.manual_seed(0)
model = AutoencoderKL(
block_out_channels=[32, 32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "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,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
return CLIPTextModel(config)
def test_stable_diffusion_upscale(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet_upscale
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
vae = self.dummy_vae
text_encoder = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
)
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
expected_height_width = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])
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_stable_diffusion_upscale_batch(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet_upscale
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
vae = self.dummy_vae
text_encoder = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
output = sd_pipe(
2 * [prompt],
image=2 * [low_res_image],
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
)
image = output.images
assert image.shape[0] == 2
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
num_images_per_prompt=2,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
)
image = output.images
assert image.shape[0] == 2
def test_stable_diffusion_upscale_prompt_embeds(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet_upscale
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
vae = self.dummy_vae
text_encoder = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
)
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
prompt_embeds, negative_prompt_embeds = sd_pipe.encode_prompt(prompt, device, 1, False)
if negative_prompt_embeds is not None:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
image_from_prompt_embeds = sd_pipe(
prompt_embeds=prompt_embeds,
image=[low_res_image],
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_prompt_embeds_slice = image_from_prompt_embeds[0, -3:, -3:, -1]
expected_height_width = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_prompt_embeds_slice.flatten() - expected_slice).max() < 1e-2
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
def test_stable_diffusion_upscale_fp16(self):
"""Test that stable diffusion upscale works with fp16"""
unet = self.dummy_cond_unet_upscale
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
vae = self.dummy_vae
text_encoder = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# put models in fp16, except vae as it overflows in fp16
unet = unet.half()
text_encoder = text_encoder.half()
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
num_inference_steps=2,
output_type="np",
).images
expected_height_width = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
def test_stable_diffusion_upscale_from_save_pretrained(self):
pipes = []
device = "cpu" # ensure determinism for the device-dependent torch.Generator
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=self.dummy_cond_unet_upscale,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=self.dummy_vae,
text_encoder=self.dummy_text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(device)
pipes.append(sd_pipe)
with tempfile.TemporaryDirectory() as tmpdirname:
sd_pipe.save_pretrained(tmpdirname)
sd_pipe = StableDiffusionUpscalePipeline.from_pretrained(tmpdirname).to(device)
pipes.append(sd_pipe)
prompt = "A painting of a squirrel eating a burger"
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
image_slices = []
for pipe in pipes:
generator = torch.Generator(device=device).manual_seed(0)
image = pipe(
[prompt],
image=low_res_image,
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
@slow
@require_torch_gpu
class StableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_diffusion_upscale_pipeline(self):
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat.npy"
)
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "a cat sitting on a park bench"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
image=image,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 1e-3
def test_stable_diffusion_upscale_pipeline_fp16(self):
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat_fp16.npy"
)
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipe = StableDiffusionUpscalePipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "a cat sitting on a park bench"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
image=image,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 5e-1
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png"
)
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipe = StableDiffusionUpscalePipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
prompt = "a cat sitting on a park bench"
generator = torch.manual_seed(0)
_ = pipe(
prompt=prompt,
image=image,
generator=generator,
num_inference_steps=5,
output_type="np",
)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 10**9
def test_download_ckpt_diff_format_is_same(self):
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png"
)
prompt = "a cat sitting on a park bench"
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id)
pipe.enable_model_cpu_offload()
generator = torch.Generator("cpu").manual_seed(0)
output = pipe(prompt=prompt, image=image, generator=generator, output_type="np", num_inference_steps=3)
image_from_pretrained = output.images[0]
single_file_path = (
"https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/blob/main/x4-upscaler-ema.safetensors"
)
pipe_from_single_file = StableDiffusionUpscalePipeline.from_single_file(single_file_path)
pipe_from_single_file.enable_model_cpu_offload()
generator = torch.Generator("cpu").manual_seed(0)
output_from_single_file = pipe_from_single_file(
prompt=prompt, image=image, generator=generator, output_type="np", num_inference_steps=3
)
image_from_single_file = output_from_single_file.images[0]
assert image_from_pretrained.shape == (512, 512, 3)
assert image_from_single_file.shape == (512, 512, 3)
assert (
numpy_cosine_similarity_distance(image_from_pretrained.flatten(), image_from_single_file.flatten()) < 1e-3
)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class StableDiffusion2InpaintPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset(
[]
) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
image_latents_params = frozenset([])
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"mask", "masked_image_latents"})
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64))
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_inpaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_diffusion_inpaint_pipeline(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench.npy"
)
model_id = "stabilityai/stable-diffusion-2-inpainting"
pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 9e-3
def test_stable_diffusion_inpaint_pipeline_fp16(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench_fp16.npy"
)
model_id = "stabilityai/stable-diffusion-2-inpainting"
pipe = StableDiffusionInpaintPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
safety_checker=None,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 5e-1
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"
)
model_id = "stabilityai/stable-diffusion-2-inpainting"
pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionInpaintPipeline.from_pretrained(
model_id,
safety_checker=None,
scheduler=pndm,
torch_dtype=torch.float16,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
generator = torch.manual_seed(0)
_ = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
generator=generator,
num_inference_steps=2,
output_type="np",
)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
logging,
)
from diffusers.utils.testing_utils import (
CaptureLogger,
enable_full_determinism,
load_numpy,
nightly,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import (
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class StableDiffusion2PipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5753, 0.6113, 0.5005, 0.5036, 0.5464, 0.4725, 0.4982, 0.4865, 0.4861])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_pndm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5121, 0.5714, 0.4827, 0.5057, 0.5646, 0.4766, 0.5189, 0.4895, 0.4990])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_lms(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4865, 0.5439, 0.4840, 0.4995, 0.5543, 0.4846, 0.5199, 0.4942, 0.5061])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_euler_ancestral(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = EulerAncestralDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4864, 0.5440, 0.4842, 0.4994, 0.5543, 0.4846, 0.5196, 0.4942, 0.5063])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = EulerDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4865, 0.5439, 0.4840, 0.4995, 0.5543, 0.4846, 0.5199, 0.4942, 0.5061])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_unflawed(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = DDIMScheduler.from_config(
components["scheduler"].config, timestep_spacing="trailing"
)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["guidance_rescale"] = 0.7
inputs["num_inference_steps"] = 10
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4736, 0.5405, 0.4705, 0.4955, 0.5675, 0.4812, 0.5310, 0.4967, 0.5064])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_long_prompt(self):
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
do_classifier_free_guidance = True
negative_prompt = None
num_images_per_prompt = 1
logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion")
logger.setLevel(logging.WARNING)
prompt = 25 * "@"
with CaptureLogger(logger) as cap_logger_3:
text_embeddings_3, negeative_text_embeddings_3 = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negeative_text_embeddings_3 is not None:
text_embeddings_3 = torch.cat([negeative_text_embeddings_3, text_embeddings_3])
prompt = 100 * "@"
with CaptureLogger(logger) as cap_logger:
text_embeddings, negative_embeddings = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negative_embeddings is not None:
text_embeddings = torch.cat([negative_embeddings, text_embeddings])
negative_prompt = "Hello"
with CaptureLogger(logger) as cap_logger_2:
text_embeddings_2, negative_text_embeddings_2 = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negative_text_embeddings_2 is not None:
text_embeddings_2 = torch.cat([negative_text_embeddings_2, text_embeddings_2])
assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape
assert text_embeddings.shape[1] == 77
assert cap_logger.out == cap_logger_2.out
# 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25
assert cap_logger.out.count("@") == 25
assert cap_logger_3.out == ""
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class StableDiffusion2PipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_default_ddim(self):
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base")
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
assert np.abs(image_slice - expected_slice).max() < 7e-3
def test_stable_diffusion_pndm(self):
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
assert np.abs(image_slice - expected_slice).max() < 7e-3
def test_stable_diffusion_k_lms(self):
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.10440, 0.13115, 0.11100, 0.10141, 0.11440, 0.07215, 0.11332, 0.09693, 0.10006])
assert np.abs(image_slice - expected_slice).max() < 3e-3
def test_stable_diffusion_attention_slicing(self):
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16
)
pipe.unet.set_default_attn_processor()
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
# enable attention slicing
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image_sliced = pipe(**inputs).images
mem_bytes = torch.cuda.max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
# make sure that less than 3.3 GB is allocated
assert mem_bytes < 3.3 * 10**9
# disable slicing
pipe.disable_attention_slicing()
pipe.unet.set_default_attn_processor()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image = pipe(**inputs).images
# make sure that more than 3.3 GB is allocated
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes > 3.3 * 10**9
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_sliced.flatten())
assert max_diff < 5e-3
def test_stable_diffusion_text2img_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.3862, -0.4507, -1.1729, 0.0686, -1.1045, 0.7124, -1.8301, 0.1903, 1.2773]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[0.2720, -0.1863, -0.7383, -0.5029, -0.7534, 0.3970, -0.7646, 0.4468, 1.2686]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == inputs["num_inference_steps"]
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.8 GB is allocated
assert mem_bytes < 2.8 * 10**9
def test_stable_diffusion_pipeline_with_model_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
# Normal inference
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base",
torch_dtype=torch.float16,
)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
outputs = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# With model offloading
# Reload but don't move to cuda
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base",
torch_dtype=torch.float16,
)
pipe.unet.set_default_attn_processor()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device, dtype=torch.float16)
outputs_offloaded = pipe(**inputs)
mem_bytes_offloaded = torch.cuda.max_memory_allocated()
images = outputs.images
images_offloaded = outputs_offloaded.images
max_diff = numpy_cosine_similarity_distance(images.flatten(), images_offloaded.flatten())
assert max_diff < 1e-3
assert mem_bytes_offloaded < mem_bytes
assert mem_bytes_offloaded < 3 * 10**9
for module in pipe.text_encoder, pipe.unet, pipe.vae:
assert module.device == torch.device("cpu")
# With attention slicing
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_attention_slicing()
_ = pipe(**inputs)
mem_bytes_slicing = torch.cuda.max_memory_allocated()
assert mem_bytes_slicing < mem_bytes_offloaded
@nightly
@require_torch_gpu
class StableDiffusion2PipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_2_0_default_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_0_base_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_2_1_default_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_lms(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_euler(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_euler.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_dpm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_dpm_multi.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import time
import unittest
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerDiscreteScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.models.attention_processor import AttnProcessor
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
enable_full_determinism()
class StableDiffusion2VPredictionPipelineFastTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@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,
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
)
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,
sample_size=128,
)
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,
# SD2-specific config below
hidden_act="gelu",
projection_dim=64,
)
return CLIPTextModel(config)
def test_stable_diffusion_v_pred_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
prediction_type="v_prediction",
)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=None,
image_encoder=None,
requires_safety_checker=False,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
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([0.6569, 0.6525, 0.5142, 0.4968, 0.4923, 0.4601, 0.4996, 0.5041, 0.4544])
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_stable_diffusion_v_pred_k_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = EulerDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="v_prediction"
)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=None,
image_encoder=None,
requires_safety_checker=False,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
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([0.5644, 0.6514, 0.5190, 0.5663, 0.5287, 0.4953, 0.5430, 0.5243, 0.4778])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
def test_stable_diffusion_v_pred_fp16(self):
"""Test that stable diffusion v-prediction works with fp16"""
unet = self.dummy_cond_unet
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
prediction_type="v_prediction",
)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# put models in fp16
unet = unet.half()
vae = vae.half()
bert = bert.half()
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=None,
image_encoder=None,
requires_safety_checker=False,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images
assert image.shape == (1, 64, 64, 3)
@slow
@require_torch_gpu
class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_diffusion_v_pred_default(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.enable_attention_slicing()
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type="np")
image = output.images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 768, 768, 3)
expected_slice = np.array([0.1868, 0.1922, 0.1527, 0.1921, 0.1908, 0.1624, 0.1779, 0.1652, 0.1734])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_v_pred_upcast_attention(self):
sd_pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.enable_attention_slicing()
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type="np")
image = output.images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 768, 768, 3)
expected_slice = np.array([0.4209, 0.4087, 0.4097, 0.4209, 0.3860, 0.4329, 0.4280, 0.4324, 0.4187])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
def test_stable_diffusion_v_pred_euler(self):
scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler")
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.enable_attention_slicing()
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type="numpy")
image = output.images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 768, 768, 3)
expected_slice = np.array([0.1781, 0.1695, 0.1661, 0.1705, 0.1588, 0.1699, 0.2005, 0.1589, 0.1677])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_v_pred_dpm(self):
"""
TODO: update this test after making DPM compatible with V-prediction!
"""
scheduler = DPMSolverMultistepScheduler.from_pretrained(
"stabilityai/stable-diffusion-2", subfolder="scheduler"
)
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.enable_attention_slicing()
sd_pipe.set_progress_bar_config(disable=None)
prompt = "a photograph of an astronaut riding a horse"
generator = torch.manual_seed(0)
image = sd_pipe(
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="numpy"
).images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 768, 768, 3)
expected_slice = np.array([0.3303, 0.3184, 0.3291, 0.3300, 0.3256, 0.3113, 0.2965, 0.3134, 0.3192])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_attention_slicing_v_pred(self):
torch.cuda.reset_peak_memory_stats()
model_id = "stabilityai/stable-diffusion-2"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "a photograph of an astronaut riding a horse"
# make attention efficient
pipe.enable_attention_slicing()
generator = torch.manual_seed(0)
output_chunked = pipe(
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
)
image_chunked = output_chunked.images
mem_bytes = torch.cuda.max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
# make sure that less than 5.5 GB is allocated
assert mem_bytes < 5.5 * 10**9
# disable slicing
pipe.disable_attention_slicing()
generator = torch.manual_seed(0)
output = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")
image = output.images
# make sure that more than 3.0 GB is allocated
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes > 3 * 10**9
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_chunked.flatten())
assert max_diff < 1e-3
def test_stable_diffusion_text2img_pipeline_v_pred_default(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
"sd2-text2img/astronaut_riding_a_horse_v_pred.npy"
)
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
pipe.to(torch_device)
pipe.enable_attention_slicing()
pipe.set_progress_bar_config(disable=None)
prompt = "astronaut riding a horse"
generator = torch.manual_seed(0)
output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type="np")
image = output.images[0]
assert image.shape == (768, 768, 3)
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
assert max_diff < 1e-3
def test_stable_diffusion_text2img_pipeline_unflawed(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
"sd2-text2img/lion_galaxy.npy"
)
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
pipe.scheduler = DDIMScheduler.from_config(
pipe.scheduler.config, timestep_spacing="trailing", rescale_betas_zero_snr=True
)
pipe.to(torch_device)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"
generator = torch.Generator("cpu").manual_seed(0)
output = pipe(
prompt=prompt,
guidance_scale=7.5,
num_inference_steps=10,
guidance_rescale=0.7,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (768, 768, 3)
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
assert max_diff < 5e-2
def test_stable_diffusion_text2img_pipeline_v_pred_fp16(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
"sd2-text2img/astronaut_riding_a_horse_v_pred_fp16.npy"
)
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "astronaut riding a horse"
generator = torch.manual_seed(0)
output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type="np")
image = output.images[0]
assert image.shape == (768, 768, 3)
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
assert max_diff < 1e-3
def test_download_local(self):
filename = hf_hub_download("stabilityai/stable-diffusion-2-1", filename="v2-1_768-ema-pruned.safetensors")
pipe = StableDiffusionPipeline.from_single_file(filename, torch_dtype=torch.float16)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
image_out = pipe("test", num_inference_steps=1, output_type="np").images[0]
assert image_out.shape == (768, 768, 3)
def test_download_ckpt_diff_format_is_same(self):
single_file_path = (
"https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors"
)
pipe_single = StableDiffusionPipeline.from_single_file(single_file_path)
pipe_single.scheduler = DDIMScheduler.from_config(pipe_single.scheduler.config)
pipe_single.unet.set_attn_processor(AttnProcessor())
pipe_single.enable_model_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
image_ckpt = pipe_single("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor())
pipe.enable_model_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipe("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_ckpt.flatten())
assert max_diff < 1e-3
def test_stable_diffusion_text2img_intermediate_state_v_pred(self):
number_of_steps = 0
def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
test_callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 96, 96)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.7749, 0.0325, 0.5088, 0.1619, 0.3372, 0.3667, -0.5186, 0.6860, 1.4326])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 19:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 96, 96)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([1.3887, 1.0273, 1.7266, 0.0726, 0.6611, 0.1598, -1.0547, 0.1522, 0.0227])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
test_callback_fn.has_been_called = False
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "Andromeda galaxy in a bottle"
generator = torch.manual_seed(0)
pipe(
prompt=prompt,
num_inference_steps=20,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
assert test_callback_fn.has_been_called
assert number_of_steps == 20
def test_stable_diffusion_low_cpu_mem_usage_v_pred(self):
pipeline_id = "stabilityai/stable-diffusion-2"
start_time = time.time()
pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
pipeline_low_cpu_mem_usage.to(torch_device)
low_cpu_mem_usage_time = time.time() - start_time
start_time = time.time()
_ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False)
normal_load_time = time.time() - start_time
assert 2 * low_cpu_mem_usage_time < normal_load_time
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading_v_pred(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipeline_id = "stabilityai/stable-diffusion-2"
prompt = "Andromeda galaxy in a bottle"
pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
pipeline = pipeline.to(torch_device)
pipeline.enable_attention_slicing(1)
pipeline.enable_sequential_cpu_offload()
generator = torch.manual_seed(0)
_ = pipeline(prompt, generator=generator, num_inference_steps=5)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.8 GB is allocated
assert mem_bytes < 2.8 * 10**9
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import nightly, require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@nightly
@require_flax
class FlaxStableDiffusion2PipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def test_stable_diffusion_flax(self):
sd_pipe, params = FlaxStableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2",
revision="bf16",
dtype=jnp.bfloat16,
)
prompt = "A painting of a squirrel eating a burger"
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = sd_pipe.prepare_inputs(prompt)
params = replicate(params)
prompt_ids = shard(prompt_ids)
prng_seed = jax.random.PRNGKey(0)
prng_seed = jax.random.split(prng_seed, jax.device_count())
images = sd_pipe(prompt_ids, params, prng_seed, num_inference_steps=25, jit=True)[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
image_slice = images[0, 253:256, 253:256, -1]
output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
expected_slice = jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512])
print(f"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
@nightly
@require_flax
class FlaxStableDiffusion2PipelineNightlyTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def test_stable_diffusion_dpm_flax(self):
model_id = "stabilityai/stable-diffusion-2"
scheduler, scheduler_params = FlaxDPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
sd_pipe, params = FlaxStableDiffusionPipeline.from_pretrained(
model_id,
scheduler=scheduler,
revision="bf16",
dtype=jnp.bfloat16,
)
params["scheduler"] = scheduler_params
prompt = "A painting of a squirrel eating a burger"
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = sd_pipe.prepare_inputs(prompt)
params = replicate(params)
prompt_ids = shard(prompt_ids)
prng_seed = jax.random.PRNGKey(0)
prng_seed = jax.random.split(prng_seed, jax.device_count())
images = sd_pipe(prompt_ids, params, prng_seed, num_inference_steps=25, jit=True)[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
image_slice = images[0, 253:256, 253:256, -1]
output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
expected_slice = jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297])
print(f"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax_inpaint.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class FlaxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def test_stable_diffusion_inpaint_pipeline(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"
)
model_id = "xvjiarui/stable-diffusion-2-inpainting"
pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
init_image = num_samples * [init_image]
mask_image = num_samples * [mask_image]
prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
processed_masked_images = shard(processed_masked_images)
processed_masks = shard(processed_masks)
output = pipeline(
prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True
)
images = output.images.reshape(num_samples, 512, 512, 3)
image_slice = images[0, 253:256, 253:256, -1]
output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
expected_slice = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]
)
print(f"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = LDMTextToImagePipeline
params = TEXT_TO_IMAGE_PARAMS - {
"negative_prompt",
"negative_prompt_embeds",
"cross_attention_kwargs",
"prompt_embeds",
}
required_optional_params = PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"callback",
"callback_steps",
}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=(32, 64),
in_channels=3,
out_channels=3,
down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D"),
up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D"),
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vqvae": vae,
"bert": text_encoder,
"tokenizer": tokenizer,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_inference_text2img(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = LDMTextToImagePipeline(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
expected_slice = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@nightly
@require_torch_gpu
class LDMTextToImagePipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, dtype=torch.float32, seed=0):
generator = torch.manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_ldm_default_ddim(self):
pipe = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
max_diff = np.abs(expected_slice - image_slice).max()
assert max_diff < 1e-3
@nightly
@require_torch_gpu
class LDMTextToImagePipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, dtype=torch.float32, seed=0):
generator = torch.manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_ldm_default_ddim(self):
pipe = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_uncond.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNet2DModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch, torch_device
enable_full_determinism()
class LDMPipelineFastTests(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)
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])
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
@nightly
@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])
tolerance = 1e-2 if torch_device != "mps" else 3e-2
assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNet2DModel, VQModel
from diffusers.utils import PIL_INTERPOLATION
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
nightly,
require_torch,
torch_device,
)
enable_full_determinism()
class LDMSuperResolutionPipelineFastTests(unittest.TestCase):
@property
def dummy_image(self):
batch_size = 1
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
@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=6,
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
def test_inference_superresolution(self):
device = "cpu"
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
vqvae = self.dummy_vq_model
ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler)
ldm.to(device)
ldm.set_progress_bar_config(disable=None)
init_image = self.dummy_image.to(device)
generator = torch.Generator(device=device).manual_seed(0)
image = ldm(image=init_image, generator=generator, num_inference_steps=2, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
def test_inference_superresolution_fp16(self):
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
vqvae = self.dummy_vq_model
# put models in fp16
unet = unet.half()
vqvae = vqvae.half()
ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler)
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
init_image = self.dummy_image.to(torch_device)
image = ldm(init_image, num_inference_steps=2, output_type="numpy").images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch
class LDMSuperResolutionPipelineIntegrationTests(unittest.TestCase):
def test_inference_superresolution(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/vq_diffusion/teddy_bear_pool.png"
)
init_image = init_image.resize((64, 64), resample=PIL_INTERPOLATION["lanczos"])
ldm = LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution", device_map="auto")
ldm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ldm(image=init_image, generator=generator, 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([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPix2PixPipeline,
UNet2DConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class StableDiffusionInstructPix2PixPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionInstructPix2PixPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"image_latents"}) - {"negative_prompt_embeds"}
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=8,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB")
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"image_guidance_scale": 1,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_pix2pix_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_multiple_init_images(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["prompt"] = [inputs["prompt"]] * 2
image = np.array(inputs["image"]).astype(np.float32) / 255.0
image = torch.from_numpy(image).unsqueeze(0).to(device)
image = image / 2 + 0.5
image = image.permute(0, 3, 1, 2)
inputs["image"] = image.repeat(2, 1, 1, 1)
image = sd_pipe(**inputs).images
image_slice = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
expected_slice = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = EulerAncestralDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
slice = [round(x, 4) for x in image_slice.flatten().tolist()]
print(",".join([str(x) for x in slice]))
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
# Overwrite the default test_latents_inputs because pix2pix encode the image differently
def test_latents_input(self):
components = self.get_dummy_components()
pipe = StableDiffusionInstructPix2PixPipeline(**components)
pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0]
vae = components["vae"]
inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt")
for image_param in self.image_latents_params:
if image_param in inputs.keys():
inputs[image_param] = vae.encode(inputs[image_param]).latent_dist.mode()
out_latents_inputs = pipe(**inputs)[0]
max_diff = np.abs(out - out_latents_inputs).max()
self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image")
# Override the default test_callback_cfg because pix2pix create inputs for cfg differently
def test_callback_cfg(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
def callback_no_cfg(pipe, i, t, callback_kwargs):
if i == 1:
for k, w in callback_kwargs.items():
if k in self.callback_cfg_params:
callback_kwargs[k] = callback_kwargs[k].chunk(3)[0]
pipe._guidance_scale = 1.0
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
inputs["guidance_scale"] = 1.0
inputs["num_inference_steps"] = 2
out_no_cfg = pipe(**inputs)[0]
inputs["guidance_scale"] = 7.5
inputs["callback_on_step_end"] = callback_no_cfg
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
out_callback_no_cfg = pipe(**inputs)[0]
assert out_no_cfg.shape == out_callback_no_cfg.shape
@slow
@require_torch_gpu
class StableDiffusionInstructPix2PixPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, seed=0):
generator = torch.manual_seed(seed)
image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"
)
inputs = {
"prompt": "turn him into a cyborg",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"image_guidance_scale": 1.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_pix2pix_default(self):
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix", safety_checker=None
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_k_lms(self):
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix", safety_checker=None
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_ddim(self):
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix", safety_checker=None
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 3
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs()
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def test_stable_diffusion_pix2pix_pipeline_multiple_of_8(self):
inputs = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
inputs["image"] = inputs["image"].resize((504, 504))
model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
model_id,
safety_checker=None,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
output = pipe(**inputs)
image = output.images[0]
image_slice = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
expected_slice = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_gligen_text_image.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from transformers import (
CLIPProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
StableDiffusionGLIGENTextImagePipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion import CLIPImageProjection
from diffusers.utils import load_image
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import (
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class GligenTextImagePipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionGLIGENTextImagePipeline
params = TEXT_TO_IMAGE_PARAMS | {"gligen_phrases", "gligen_images", "gligen_boxes"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
attention_type="gated-text-image",
)
# unet.position_net = PositionNet(32,32)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image_encoder_config = CLIPVisionConfig(
hidden_size=32,
projection_dim=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
)
image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
image_project = CLIPImageProjection(hidden_size=32)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": image_encoder,
"image_project": image_project,
"processor": processor,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
gligen_images = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/livingroom_modern.png"
)
inputs = {
"prompt": "A modern livingroom",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"gligen_phrases": ["a birthday cake"],
"gligen_images": [gligen_images],
"gligen_boxes": [[0.2676, 0.6088, 0.4773, 0.7183]],
"output_type": "np",
}
return inputs
def test_stable_diffusion_gligen_text_image_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionGLIGENTextImagePipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5069, 0.5561, 0.4577, 0.4792, 0.5203, 0.4089, 0.5039, 0.4919, 0.4499])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_gligen_k_euler_ancestral(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionGLIGENTextImagePipeline(**components)
sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.425, 0.494, 0.429, 0.469, 0.525, 0.417, 0.533, 0.5, 0.47])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(batch_size=3, expected_max_diff=3e-3)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import traceback
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
AutoencoderTiny,
DDIMScheduler,
DPMSolverMultistepScheduler,
HeunDiscreteScheduler,
LCMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionImg2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
require_python39_or_higher,
require_torch_2,
require_torch_gpu,
run_test_in_subprocess,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
# Will be run via run_test_in_subprocess
def _test_img2img_compile(in_queue, out_queue, timeout):
error = None
try:
inputs = in_queue.get(timeout=timeout)
torch_device = inputs.pop("torch_device")
seed = inputs.pop("seed")
inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.unet.to(memory_format=torch.channels_last)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 768, 3)
expected_slice = np.array([0.0606, 0.0570, 0.0805, 0.0579, 0.0628, 0.0623, 0.0843, 0.1115, 0.0806])
assert np.abs(expected_slice - image_slice).max() < 1e-3
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class StableDiffusionImg2ImgPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
time_cond_proj_dim=time_cond_proj_dim,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_tiny_autoencoder(self):
return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_img2img_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4555, 0.3216, 0.4049, 0.4620, 0.4618, 0.4126, 0.4122, 0.4629, 0.4579])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_default_case_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.5709, 0.4614, 0.4587, 0.5978, 0.5298, 0.6910, 0.6240, 0.5212, 0.5454])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_default_case_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.5709, 0.4614, 0.4587, 0.5978, 0.5298, 0.6910, 0.6240, 0.5212, 0.5454])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4593, 0.3408, 0.4232, 0.4749, 0.4476, 0.4115, 0.4357, 0.4733, 0.4663])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_multiple_init_images(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["prompt"] = [inputs["prompt"]] * 2
inputs["image"] = inputs["image"].repeat(2, 1, 1, 1)
image = sd_pipe(**inputs).images
image_slice = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
expected_slice = np.array([0.4241, 0.5576, 0.5711, 0.4792, 0.4311, 0.5952, 0.5827, 0.5138, 0.5109])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_k_lms(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4398, 0.4949, 0.4337, 0.6580, 0.5555, 0.4338, 0.5769, 0.5955, 0.5175])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_tiny_autoencoder(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe.vae = self.get_dummy_tiny_autoencoder()
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.00669, 0.00669, 0.0, 0.00693, 0.00858, 0.0, 0.00567, 0.00515, 0.00125])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local()
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@skip_mps
def test_attention_slicing_forward_pass(self):
return super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=5e-1)
@slow
@require_torch_gpu
class StableDiffusionImg2ImgPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/sketch-mountains-input.png"
)
inputs = {
"prompt": "a fantasy landscape, concept art, high resolution",
"image": init_image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_stable_diffusion_img2img_default(self):
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 768, 3)
expected_slice = np.array([0.4300, 0.4662, 0.4930, 0.3990, 0.4307, 0.4525, 0.3719, 0.4064, 0.3923])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_img2img_k_lms(self):
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 768, 3)
expected_slice = np.array([0.0389, 0.0346, 0.0415, 0.0290, 0.0218, 0.0210, 0.0408, 0.0567, 0.0271])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_img2img_ddim(self):
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 768, 3)
expected_slice = np.array([0.0593, 0.0607, 0.0851, 0.0582, 0.0636, 0.0721, 0.0751, 0.0981, 0.0781])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_img2img_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 96)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([-0.4958, 0.5107, 1.1045, 2.7539, 4.6680, 3.8320, 1.5049, 1.8633, 2.6523])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 96)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([-0.4956, 0.5078, 1.0918, 2.7520, 4.6484, 3.8125, 1.5146, 1.8633, 2.6367])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 2
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def test_stable_diffusion_pipeline_with_model_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
# Normal inference
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
safety_checker=None,
torch_dtype=torch.float16,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# With model offloading
# Reload but don't move to cuda
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
safety_checker=None,
torch_dtype=torch.float16,
)
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
_ = pipe(**inputs)
mem_bytes_offloaded = torch.cuda.max_memory_allocated()
assert mem_bytes_offloaded < mem_bytes
for module in pipe.text_encoder, pipe.unet, pipe.vae:
assert module.device == torch.device("cpu")
def test_img2img_2nd_order(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = HeunDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 10
inputs["strength"] = 0.75
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/img2img_heun.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 5e-2
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 11
inputs["strength"] = 0.75
image_other = sd_pipe(**inputs).images[0]
mean_diff = np.abs(image - image_other).mean()
# images should be very similar
assert mean_diff < 5e-2
def test_stable_diffusion_img2img_pipeline_multiple_of_8(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
# resize to resolution that is divisible by 8 but not 16 or 32
init_image = init_image.resize((760, 504))
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_id,
safety_checker=None,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "A fantasy landscape, trending on artstation"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
generator=generator,
output_type="np",
)
image = output.images[0]
image_slice = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
expected_slice = np.array([0.9393, 0.9500, 0.9399, 0.9438, 0.9458, 0.9400, 0.9455, 0.9414, 0.9423])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
def test_img2img_safety_checker_works(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 20
# make sure the safety checker is activated
inputs["prompt"] = "naked, sex, porn"
out = sd_pipe(**inputs)
assert out.nsfw_content_detected[0], f"Safety checker should work for prompt: {inputs['prompt']}"
assert np.abs(out.images[0]).sum() < 1e-5 # should be all zeros
@require_python39_or_higher
@require_torch_2
def test_img2img_compile(self):
seed = 0
inputs = self.get_inputs(torch_device, seed=seed)
# Can't pickle a Generator object
del inputs["generator"]
inputs["torch_device"] = torch_device
inputs["seed"] = seed
run_test_in_subprocess(test_case=self, target_func=_test_img2img_compile, inputs=inputs)
@nightly
@require_torch_gpu
class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/sketch-mountains-input.png"
)
inputs = {
"prompt": "a fantasy landscape, concept art, high resolution",
"image": init_image,
"generator": generator,
"num_inference_steps": 50,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_img2img_pndm(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/stable_diffusion_1_5_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_ddim(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/stable_diffusion_1_5_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_lms(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/stable_diffusion_1_5_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_dpm(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 30
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/stable_diffusion_1_5_dpm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def get_dummy_inputs(self, seed=0):
generator = np.random.RandomState(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_pipeline_default_ddim(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_pndm(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_lms(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_euler(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_euler_ancestral(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_pipeline_dpm_multistep(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_prompt_embeds(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs()
prompt = 3 * [inputs.pop("prompt")]
text_inputs = pipe.tokenizer(
prompt,
padding="max_length",
max_length=pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_inputs = text_inputs["input_ids"]
prompt_embeds = pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0]
inputs["prompt_embeds"] = prompt_embeds
# forward
output = pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_negative_prompt_embeds(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs()
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = pipe.tokenizer(
p,
padding="max_length",
max_length=pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_inputs = text_inputs["input_ids"]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0])
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_pndm(self):
# using the PNDM scheduler by default
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
np.random.seed(0)
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=10, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_ddim(self):
ddim_scheduler = DDIMScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
)
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
revision="onnx",
scheduler=ddim_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "open neural network exchange"
generator = np.random.RandomState(0)
output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_k_lms(self):
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
)
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
revision="onnx",
scheduler=lms_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "open neural network exchange"
generator = np.random.RandomState(0)
output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161])
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(
[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]
)
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(
[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
test_callback_fn.has_been_called = False
pipe = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "Andromeda galaxy in a bottle"
generator = np.random.RandomState(0)
pipe(
prompt=prompt,
num_inference_steps=5,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def test_stable_diffusion_no_safety_checker(self):
pipe = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
assert isinstance(pipe, OnnxStableDiffusionPipeline)
assert pipe.safety_checker is None
image = pipe("example prompt", num_inference_steps=2).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = OnnxStableDiffusionPipeline.from_pretrained(tmpdirname)
# sanity check that the pipeline still works
assert pipe.safety_checker is None
image = pipe("example prompt", num_inference_steps=2).images[0]
assert image is not None
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_sag.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class StableDiffusionSAGPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionSAGPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": ".",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 1.0,
"sag_scale": 1.0,
"output_type": "numpy",
}
return inputs
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@nightly
@require_torch_gpu
class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_diffusion_1(self):
sag_pipe = StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
sag_pipe = sag_pipe.to(torch_device)
sag_pipe.set_progress_bar_config(disable=None)
prompt = "."
generator = torch.manual_seed(0)
output = sag_pipe(
[prompt], generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type="np"
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
def test_stable_diffusion_2(self):
sag_pipe = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
sag_pipe = sag_pipe.to(torch_device)
sag_pipe.set_progress_bar_config(disable=None)
prompt = "."
generator = torch.manual_seed(0)
output = sag_pipe(
[prompt], generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type="np"
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
def test_stable_diffusion_2_non_square(self):
sag_pipe = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
sag_pipe = sag_pipe.to(torch_device)
sag_pipe.set_progress_bar_config(disable=None)
prompt = "."
generator = torch.manual_seed(0)
output = sag_pipe(
[prompt],
width=768,
height=512,
generator=generator,
guidance_scale=7.5,
sag_scale=1.0,
num_inference_steps=20,
output_type="np",
)
image = output.images
assert image.shape == (1, 512, 768, 3)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_gligen.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
StableDiffusionGLIGENPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import (
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class GligenPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionGLIGENPipeline
params = TEXT_TO_IMAGE_PARAMS | {"gligen_phrases", "gligen_boxes"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
attention_type="gated",
)
# unet.position_net = PositionNet(32,32)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A modern livingroom",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"gligen_phrases": ["a birthday cake"],
"gligen_boxes": [[0.2676, 0.6088, 0.4773, 0.7183]],
"output_type": "np",
}
return inputs
def test_stable_diffusion_gligen_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionGLIGENPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5069, 0.5561, 0.4577, 0.4792, 0.5203, 0.4089, 0.5039, 0.4919, 0.4499])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_gligen_k_euler_ancestral(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionGLIGENPipeline(**components)
sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.425, 0.494, 0.429, 0.469, 0.525, 0.417, 0.533, 0.5, 0.47])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(batch_size=3, expected_max_diff=3e-3)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint_legacy.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class StableDiffusionOnnxInpaintLegacyPipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference(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"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"
)
# using the PNDM scheduler by default
pipe = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A red cat sitting on a park bench"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=15,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_model_editing.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
PNDMScheduler,
StableDiffusionModelEditingPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class StableDiffusionModelEditingPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionModelEditingPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler()
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
generator = torch.manual_seed(seed)
inputs = {
"prompt": "A field of roses",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_model_editing_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionModelEditingPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_model_editing_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionModelEditingPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_model_editing_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = EulerAncestralDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
sd_pipe = StableDiffusionModelEditingPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_model_editing_pndm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler()
sd_pipe = StableDiffusionModelEditingPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
# the pipeline does not expect pndm so test if it raises error.
with self.assertRaises(ValueError):
_ = sd_pipe(**inputs).images
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=5e-3)
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)
@nightly
@require_torch_gpu
class StableDiffusionModelEditingSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, seed=0):
generator = torch.manual_seed(seed)
inputs = {
"prompt": "A field of roses",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_model_editing_default(self):
model_ckpt = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658]
)
assert np.abs(expected_slice - image_slice).max() < 1e-2
# make sure image changes after editing
pipe.edit_model("A pack of roses", "A pack of blue roses")
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
assert np.abs(expected_slice - image_slice).max() > 1e-1
def test_stable_diffusion_model_editing_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
model_ckpt = "CompVis/stable-diffusion-v1-4"
scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
pipe = StableDiffusionModelEditingPipeline.from_pretrained(
model_ckpt, scheduler=scheduler, safety_checker=None
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs()
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 4.4 GB is allocated
assert mem_bytes < 4.4 * 10**9
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_k_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
enable_full_determinism()
@nightly
@require_torch_gpu
class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_diffusion_1(self):
sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
sd_pipe.set_scheduler("sample_euler")
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_2(self):
sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
sd_pipe.set_scheduler("sample_euler")
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1
def test_stable_diffusion_karras_sigmas(self):
sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
sd_pipe.set_scheduler("sample_dpmpp_2m")
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
output = sd_pipe(
[prompt],
generator=generator,
guidance_scale=7.5,
num_inference_steps=15,
output_type="np",
use_karras_sigmas=True,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_noise_sampler_seed(self):
sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
sd_pipe.set_scheduler("sample_dpmpp_sde")
prompt = "A painting of a squirrel eating a burger"
seed = 0
images1 = sd_pipe(
[prompt],
generator=torch.manual_seed(seed),
noise_sampler_seed=seed,
guidance_scale=9.0,
num_inference_steps=20,
output_type="np",
).images
images2 = sd_pipe(
[prompt],
generator=torch.manual_seed(seed),
noise_sampler_seed=seed,
guidance_scale=9.0,
num_inference_steps=20,
output_type="np",
).images
assert images1.shape == (1, 512, 512, 3)
assert images2.shape == (1, 512, 512, 3)
assert np.abs(images1.flatten() - images2.flatten()).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_paradigms.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMParallelScheduler,
DDPMParallelScheduler,
StableDiffusionParadigmsPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
nightly,
require_torch_gpu,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class StableDiffusionParadigmsPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionParadigmsPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
)
scheduler = DDIMParallelScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"generator": generator,
"num_inference_steps": 10,
"guidance_scale": 6.0,
"output_type": "numpy",
"parallel": 3,
"debug": True,
}
return inputs
def test_stable_diffusion_paradigms_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionParadigmsPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_paradigms_default_case_ddpm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
torch.manual_seed(0)
components["scheduler"] = DDPMParallelScheduler()
torch.manual_seed(0)
sd_pipe = StableDiffusionParadigmsPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
# override to speed the overall test timing up.
def test_inference_batch_consistent(self):
super().test_inference_batch_consistent(batch_sizes=[1, 2])
# override to speed the overall test timing up.
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=3e-3)
def test_stable_diffusion_paradigms_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionParadigmsPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@nightly
@require_torch_gpu
class StableDiffusionParadigmsPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, seed=0):
generator = torch.Generator(device=torch_device).manual_seed(seed)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"generator": generator,
"num_inference_steps": 10,
"guidance_scale": 7.5,
"output_type": "numpy",
"parallel": 3,
"debug": True,
}
return inputs
def test_stable_diffusion_paradigms_default(self):
model_ckpt = "stabilityai/stable-diffusion-2-base"
scheduler = DDIMParallelScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
pipe = StableDiffusionParadigmsPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741])
assert np.abs(expected_slice - image_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_adapter.py
|
# 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 gc
import random
import unittest
import numpy as np
import torch
from parameterized import parameterized
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
LCMScheduler,
MultiAdapter,
PNDMScheduler,
StableDiffusionAdapterPipeline,
T2IAdapter,
UNet2DConditionModel,
)
from diffusers.utils import logging
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class AdapterTests:
pipeline_class = StableDiffusionAdapterPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
def get_dummy_components(self, adapter_type, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
time_cond_proj_dim=time_cond_proj_dim,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
if adapter_type == "full_adapter" or adapter_type == "light_adapter":
adapter = T2IAdapter(
in_channels=3,
channels=[32, 64],
num_res_blocks=2,
downscale_factor=2,
adapter_type=adapter_type,
)
elif adapter_type == "multi_adapter":
adapter = MultiAdapter(
[
T2IAdapter(
in_channels=3,
channels=[32, 64],
num_res_blocks=2,
downscale_factor=2,
adapter_type="full_adapter",
),
T2IAdapter(
in_channels=3,
channels=[32, 64],
num_res_blocks=2,
downscale_factor=2,
adapter_type="full_adapter",
),
]
)
else:
raise ValueError(
f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter', 'light_adapter', or 'multi_adapter''"
)
components = {
"adapter": adapter,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_components_with_full_downscaling(self, adapter_type):
"""Get dummy components with x8 VAE downscaling and 4 UNet down blocks.
These dummy components are intended to fully-exercise the T2I-Adapter
downscaling behavior.
"""
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 32, 32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 32, 32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
if adapter_type == "full_adapter" or adapter_type == "light_adapter":
adapter = T2IAdapter(
in_channels=3,
channels=[32, 32, 32, 64],
num_res_blocks=2,
downscale_factor=8,
adapter_type=adapter_type,
)
elif adapter_type == "multi_adapter":
adapter = MultiAdapter(
[
T2IAdapter(
in_channels=3,
channels=[32, 32, 32, 64],
num_res_blocks=2,
downscale_factor=8,
adapter_type="full_adapter",
),
T2IAdapter(
in_channels=3,
channels=[32, 32, 32, 64],
num_res_blocks=2,
downscale_factor=8,
adapter_type="full_adapter",
),
]
)
else:
raise ValueError(
f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter', 'light_adapter', or 'multi_adapter''"
)
components = {
"adapter": adapter,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0, height=64, width=64, num_images=1):
if num_images == 1:
image = floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device)
else:
image = [
floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device) for _ in range(num_images)
]
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
@parameterized.expand(
[
# (dim=264) The internal feature map will be 33x33 after initial pixel unshuffling (downscaled x8).
(((4 * 8 + 1) * 8),),
# (dim=272) The internal feature map will be 17x17 after the first T2I down block (downscaled x16).
(((4 * 4 + 1) * 16),),
# (dim=288) The internal feature map will be 9x9 after the second T2I down block (downscaled x32).
(((4 * 2 + 1) * 32),),
# (dim=320) The internal feature map will be 5x5 after the third T2I down block (downscaled x64).
(((4 * 1 + 1) * 64),),
]
)
def test_multiple_image_dimensions(self, dim):
"""Test that the T2I-Adapter pipeline supports any input dimension that
is divisible by the adapter's `downscale_factor`. This test was added in
response to an issue where the T2I Adapter's downscaling padding
behavior did not match the UNet's behavior.
Note that we have selected `dim` values to produce odd resolutions at
each downscaling level.
"""
components = self.get_dummy_components_with_full_downscaling()
sd_pipe = StableDiffusionAdapterPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device, height=dim, width=dim)
image = sd_pipe(**inputs).images
assert image.shape == (1, dim, dim, 3)
def test_adapter_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionAdapterPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4535, 0.5493, 0.4359, 0.5452, 0.6086, 0.4441, 0.5544, 0.501, 0.4859])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_adapter_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionAdapterPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4535, 0.5493, 0.4359, 0.5452, 0.6086, 0.4441, 0.5544, 0.501, 0.4859])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
class StableDiffusionFullAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self, time_cond_proj_dim=None):
return super().get_dummy_components("full_adapter", time_cond_proj_dim=time_cond_proj_dim)
def get_dummy_components_with_full_downscaling(self):
return super().get_dummy_components_with_full_downscaling("full_adapter")
def test_stable_diffusion_adapter_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionAdapterPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4858, 0.5500, 0.4278, 0.4669, 0.6184, 0.4322, 0.5010, 0.5033, 0.4746])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
class StableDiffusionLightAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self, time_cond_proj_dim=None):
return super().get_dummy_components("light_adapter", time_cond_proj_dim=time_cond_proj_dim)
def get_dummy_components_with_full_downscaling(self):
return super().get_dummy_components_with_full_downscaling("light_adapter")
def test_stable_diffusion_adapter_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionAdapterPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4965, 0.5548, 0.4330, 0.4771, 0.6226, 0.4382, 0.5037, 0.5071, 0.4782])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
class StableDiffusionMultiAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self, time_cond_proj_dim=None):
return super().get_dummy_components("multi_adapter", time_cond_proj_dim=time_cond_proj_dim)
def get_dummy_components_with_full_downscaling(self):
return super().get_dummy_components_with_full_downscaling("multi_adapter")
def get_dummy_inputs(self, device, height=64, width=64, seed=0):
inputs = super().get_dummy_inputs(device, seed, height=height, width=width, num_images=2)
inputs["adapter_conditioning_scale"] = [0.5, 0.5]
return inputs
def test_stable_diffusion_adapter_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionAdapterPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4902, 0.5539, 0.4317, 0.4682, 0.6190, 0.4351, 0.5018, 0.5046, 0.4772])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
def test_inference_batch_consistent(
self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=["num_inference_steps"]
):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
logger = logging.get_logger(pipe.__module__)
logger.setLevel(level=diffusers.logging.FATAL)
# batchify inputs
for batch_size in batch_sizes:
batched_inputs = {}
for name, value in inputs.items():
if name in self.batch_params:
# prompt is string
if name == "prompt":
len_prompt = len(value)
# make unequal batch sizes
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
# make last batch super long
batched_inputs[name][-1] = 100 * "very long"
elif name == "image":
batched_images = []
for image in value:
batched_images.append(batch_size * [image])
batched_inputs[name] = batched_images
else:
batched_inputs[name] = batch_size * [value]
elif name == "batch_size":
batched_inputs[name] = batch_size
else:
batched_inputs[name] = value
for arg in additional_params_copy_to_batched_inputs:
batched_inputs[arg] = inputs[arg]
batched_inputs["output_type"] = "np"
if self.pipeline_class.__name__ == "DanceDiffusionPipeline":
batched_inputs.pop("output_type")
output = pipe(**batched_inputs)
assert len(output[0]) == batch_size
batched_inputs["output_type"] = "np"
if self.pipeline_class.__name__ == "DanceDiffusionPipeline":
batched_inputs.pop("output_type")
output = pipe(**batched_inputs)[0]
assert output.shape[0] == batch_size
logger.setLevel(level=diffusers.logging.WARNING)
def test_num_images_per_prompt(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
batch_sizes = [1, 2]
num_images_per_prompts = [1, 2]
for batch_size in batch_sizes:
for num_images_per_prompt in num_images_per_prompts:
inputs = self.get_dummy_inputs(torch_device)
for key in inputs.keys():
if key in self.batch_params:
if key == "image":
batched_images = []
for image in inputs[key]:
batched_images.append(batch_size * [image])
inputs[key] = batched_images
else:
inputs[key] = batch_size * [inputs[key]]
images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
def test_inference_batch_single_identical(
self,
batch_size=3,
test_max_difference=None,
test_mean_pixel_difference=None,
relax_max_difference=False,
expected_max_diff=2e-3,
additional_params_copy_to_batched_inputs=["num_inference_steps"],
):
if test_max_difference is None:
# TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems
# make sure that batched and non-batched is identical
test_max_difference = torch_device != "mps"
if test_mean_pixel_difference is None:
# TODO same as above
test_mean_pixel_difference = torch_device != "mps"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
logger = logging.get_logger(pipe.__module__)
logger.setLevel(level=diffusers.logging.FATAL)
# batchify inputs
batched_inputs = {}
batch_size = batch_size
for name, value in inputs.items():
if name in self.batch_params:
# prompt is string
if name == "prompt":
len_prompt = len(value)
# make unequal batch sizes
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
# make last batch super long
batched_inputs[name][-1] = 100 * "very long"
elif name == "image":
batched_images = []
for image in value:
batched_images.append(batch_size * [image])
batched_inputs[name] = batched_images
else:
batched_inputs[name] = batch_size * [value]
elif name == "batch_size":
batched_inputs[name] = batch_size
elif name == "generator":
batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)]
else:
batched_inputs[name] = value
for arg in additional_params_copy_to_batched_inputs:
batched_inputs[arg] = inputs[arg]
if self.pipeline_class.__name__ != "DanceDiffusionPipeline":
batched_inputs["output_type"] = "np"
output_batch = pipe(**batched_inputs)
assert output_batch[0].shape[0] == batch_size
inputs["generator"] = self.get_generator(0)
output = pipe(**inputs)
logger.setLevel(level=diffusers.logging.WARNING)
if test_max_difference:
if relax_max_difference:
# Taking the median of the largest <n> differences
# is resilient to outliers
diff = np.abs(output_batch[0][0] - output[0][0])
diff = diff.flatten()
diff.sort()
max_diff = np.median(diff[-5:])
else:
max_diff = np.abs(output_batch[0][0] - output[0][0]).max()
assert max_diff < expected_max_diff
if test_mean_pixel_difference:
assert_mean_pixel_difference(output_batch[0][0], output[0][0])
@slow
@require_torch_gpu
class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_diffusion_adapter_color(self):
adapter_model = "TencentARC/t2iadapter_color_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "snail"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png"
)
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_depth(self):
adapter_model = "TencentARC/t2iadapter_depth_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "snail"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png"
)
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_depth_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_depth_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "desk"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png"
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_depth_sd_v15(self):
adapter_model = "TencentARC/t2iadapter_depth_sd15v2"
sd_model = "runwayml/stable-diffusion-v1-5"
prompt = "desk"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png"
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd15v2.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_keypose_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_keypose_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "person"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/person_keypose.png"
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_keypose_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_openpose_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_openpose_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "person"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/iron_man_pose.png"
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_openpose_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_seg_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_seg_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "motorcycle"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png"
)
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_seg_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_zoedepth_sd_v15(self):
adapter_model = "TencentARC/t2iadapter_zoedepth_sd15v1"
sd_model = "runwayml/stable-diffusion-v1-5"
prompt = "motorcycle"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motorcycle.png"
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_zoedepth_sd15v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_canny_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_canny_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "toy"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png"
input_channels = 1
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_canny_sd_v15(self):
adapter_model = "TencentARC/t2iadapter_canny_sd15v2"
sd_model = "runwayml/stable-diffusion-v1-5"
prompt = "toy"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png"
input_channels = 1
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd15v2.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_sketch_sd14(self):
adapter_model = "TencentARC/t2iadapter_sketch_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "cat"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png"
)
input_channels = 1
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_sketch_sd15(self):
adapter_model = "TencentARC/t2iadapter_sketch_sd15v2"
sd_model = "runwayml/stable-diffusion-v1-5"
prompt = "cat"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png"
)
input_channels = 1
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd15v2.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_seg_sd14v1")
pipe = StableDiffusionAdapterPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", adapter=adapter, safety_checker=None
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png"
)
pipe(prompt="foo", image=image, num_inference_steps=2)
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes < 5 * 10**9
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_pndm(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",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A red cat sitting on a park bench"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
guidance_scale=7.5,
num_inference_steps=10,
generator=generator,
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([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_k_lms(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"
)
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting", subfolder="scheduler", revision="onnx"
)
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
revision="onnx",
scheduler=lms_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A red cat sitting on a park bench"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
guidance_scale=7.5,
num_inference_steps=20,
generator=generator,
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([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
from diffusers import (
AutoencoderKL,
DPMSolverMultistepScheduler,
PNDMScheduler,
StableDiffusionImageVariationPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class StableDiffusionImageVariationPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionImageVariationPipeline
params = IMAGE_VARIATION_PARAMS
batch_params = IMAGE_VARIATION_BATCH_PARAMS
image_params = frozenset([])
# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
image_latents_params = frozenset([])
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
image_encoder_config = CLIPVisionConfig(
hidden_size=32,
projection_dim=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
image_size=32,
patch_size=4,
)
image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"image_encoder": image_encoder,
"feature_extractor": feature_extractor,
"safety_checker": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32))
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_img_variation_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImageVariationPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img_variation_multiple_images(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImageVariationPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["image"] = 2 * [inputs["image"]]
output = sd_pipe(**inputs)
image = output.images
image_slice = image[-1, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
expected_slice = np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class StableDiffusionImageVariationPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_imgvar/input_image_vermeer.png"
)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"image": init_image,
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_stable_diffusion_img_variation_pipeline_default(self):
sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
"lambdalabs/sd-image-variations-diffusers", safety_checker=None
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
inputs = self.get_inputs(generator_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.8449, 0.9079, 0.7571, 0.7873, 0.8348, 0.7010, 0.6694, 0.6873, 0.6138])
max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)
assert max_diff < 1e-4
def test_stable_diffusion_img_variation_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([-0.7974, -0.4343, -1.087, 0.04785, -1.327, 0.855, -2.148, -0.1725, 1.439])
max_diff = numpy_cosine_similarity_distance(latents_slice.flatten(), expected_slice)
assert max_diff < 1e-3
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.3232, 0.004883, 0.913, -1.084, 0.6143, -1.6875, -2.463, -0.439, -0.419])
max_diff = numpy_cosine_similarity_distance(latents_slice.flatten(), expected_slice)
assert max_diff < 1e-3
callback_fn.has_been_called = False
pipe = StableDiffusionImageVariationPipeline.from_pretrained(
"lambdalabs/sd-image-variations-diffusers",
safety_checker=None,
torch_dtype=torch.float16,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
generator_device = "cpu"
inputs = self.get_inputs(generator_device, dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == inputs["num_inference_steps"]
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionImageVariationPipeline.from_pretrained(
"lambdalabs/sd-image-variations-diffusers", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.6 GB is allocated
assert mem_bytes < 2.6 * 10**9
@nightly
@require_torch_gpu
class StableDiffusionImageVariationPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_imgvar/input_image_vermeer.png"
)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"image": init_image,
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_img_variation_pndm(self):
sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers")
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img_variation_dpm(self):
sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers")
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class StableDiffusionPanoramaPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionPanoramaPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=1,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler()
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
generator = torch.manual_seed(seed)
inputs = {
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_panorama_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPanoramaPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_panorama_circular_padding_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPanoramaPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs, circular_padding=True).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6127, 0.6299, 0.4595, 0.4051, 0.4543, 0.3925, 0.5510, 0.5693, 0.5031])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
# override to speed the overall test timing up.
def test_inference_batch_consistent(self):
super().test_inference_batch_consistent(batch_sizes=[1, 2])
# override to speed the overall test timing up.
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=5.0e-3)
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=1e-1)
def test_stable_diffusion_panorama_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPanoramaPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_panorama_views_batch(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPanoramaPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs, view_batch_size=2)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_panorama_views_batch_circular_padding(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPanoramaPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs, circular_padding=True, view_batch_size=2)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6127, 0.6299, 0.4595, 0.4051, 0.4543, 0.3925, 0.5510, 0.5693, 0.5031])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_panorama_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = EulerAncestralDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
sd_pipe = StableDiffusionPanoramaPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_panorama_pndm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
)
sd_pipe = StableDiffusionPanoramaPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@nightly
@require_torch_gpu
class StableDiffusionPanoramaNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, seed=0):
generator = torch.manual_seed(seed)
inputs = {
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_panorama_default(self):
model_ckpt = "stabilityai/stable-diffusion-2-base"
scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
expected_slice = np.array(
[
0.36968392,
0.27025372,
0.32446766,
0.28379387,
0.36363274,
0.30733347,
0.27100027,
0.27054125,
0.25536096,
]
)
assert np.abs(expected_slice - image_slice).max() < 1e-2
def test_stable_diffusion_panorama_k_lms(self):
pipe = StableDiffusionPanoramaPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base", safety_checker=None
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
expected_slice = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
]
)
assert np.abs(expected_slice - image_slice).max() < 1e-2
def test_stable_diffusion_panorama_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[
0.18681869,
0.33907816,
0.5361276,
0.14432865,
-0.02856611,
-0.73941123,
0.23397987,
0.47322682,
-0.37823164,
]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[
0.18539645,
0.33987248,
0.5378559,
0.14437142,
-0.02455261,
-0.7338317,
0.23990755,
0.47356272,
-0.3786505,
]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
model_ckpt = "stabilityai/stable-diffusion-2-base"
scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 3
def test_stable_diffusion_panorama_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
model_ckpt = "stabilityai/stable-diffusion-2-base"
scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs()
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_ldm3d.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDM3DPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class StableDiffusionLDM3DPipelineFastTests(unittest.TestCase):
pipeline_class = StableDiffusionLDM3DPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=6,
out_channels=6,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)
ldm3d_pipe = ldm3d_pipe.to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = ldm3d_pipe(**inputs)
rgb, depth = output.rgb, output.depth
image_slice_rgb = rgb[0, -3:, -3:, -1]
image_slice_depth = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
expected_slice_rgb = np.array(
[0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]
)
expected_slice_depth = np.array([103.46727, 85.812004, 87.849236])
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1e-2
def test_stable_diffusion_prompt_embeds(self):
components = self.get_dummy_components()
ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)
ldm3d_pipe = ldm3d_pipe.to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = ldm3d_pipe(**inputs)
rgb_slice_1, depth_slice_1 = output.rgb, output.depth
rgb_slice_1 = rgb_slice_1[0, -3:, -3:, -1]
depth_slice_1 = depth_slice_1[0, -3:, -1]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = ldm3d_pipe.tokenizer(
prompt,
padding="max_length",
max_length=ldm3d_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = ldm3d_pipe.text_encoder(text_inputs)[0]
inputs["prompt_embeds"] = prompt_embeds
# forward
output = ldm3d_pipe(**inputs)
rgb_slice_2, depth_slice_2 = output.rgb, output.depth
rgb_slice_2 = rgb_slice_2[0, -3:, -3:, -1]
depth_slice_2 = depth_slice_2[0, -3:, -1]
assert np.abs(rgb_slice_1.flatten() - rgb_slice_2.flatten()).max() < 1e-4
assert np.abs(depth_slice_1.flatten() - depth_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)
ldm3d_pipe = ldm3d_pipe.to(device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = ldm3d_pipe(**inputs, negative_prompt=negative_prompt)
rgb, depth = output.rgb, output.depth
rgb_slice = rgb[0, -3:, -3:, -1]
depth_slice = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
expected_slice_rgb = np.array(
[0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]
)
expected_slice_depth = np.array([107.84738, 84.62802, 89.962135])
assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1e-2
@nightly
@require_torch_gpu
class StableDiffusionLDM3DPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_ldm3d_stable_diffusion(self):
ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d")
ldm3d_pipe = ldm3d_pipe.to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
output = ldm3d_pipe(**inputs)
rgb, depth = output.rgb, output.depth
rgb_slice = rgb[0, -3:, -3:, -1].flatten()
depth_slice = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
expected_slice_rgb = np.array(
[0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]
)
expected_slice_depth = np.array(
[0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]
)
assert np.abs(rgb_slice - expected_slice_rgb).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth).max() < 3e-3
@nightly
@require_torch_gpu
class StableDiffusionPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_ldm3d(self):
ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d").to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
output = ldm3d_pipe(**inputs)
rgb, depth = output.rgb, output.depth
expected_rgb_mean = 0.495586
expected_rgb_std = 0.33795515
expected_depth_mean = 112.48518
expected_depth_std = 98.489746
assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
assert np.abs(expected_depth_std - depth.std()) < 1e-3
def test_ldm3d_v2(self):
ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d-4c").to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
output = ldm3d_pipe(**inputs)
rgb, depth = output.rgb, output.depth
expected_rgb_mean = 0.4194127
expected_rgb_std = 0.35375586
expected_depth_mean = 0.5638502
expected_depth_std = 0.34686103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
assert np.abs(expected_depth_std - depth.std()) < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImg2ImgPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import (
floats_tensor,
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class OnnxStableDiffusionImg2ImgPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def get_dummy_inputs(self, seed=0):
image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed))
generator = np.random.RandomState(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_pipeline_default_ddim(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087])
assert np.abs(image_slice - expected_slice).max() < 1e-1
def test_pipeline_pndm(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_lms(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
# warmup pass to apply optimizations
_ = pipe(**self.get_dummy_inputs())
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler_ancestral(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_dpm_multistep(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_pndm(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))
# using the PNDM scheduler by default
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=10,
generator=generator,
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([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
def test_inference_k_lms(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))
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
)
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
revision="onnx",
scheduler=lms_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=20,
generator=generator,
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([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_cycle_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNet2DConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
require_torch_gpu,
skip_mps,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class CycleDiffusionPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = CycleDiffusionPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"})
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "An astronaut riding an elephant",
"source_prompt": "An astronaut riding a horse",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"eta": 0.1,
"strength": 0.8,
"guidance_scale": 3,
"source_guidance_scale": 1,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_cycle(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = CycleDiffusionPipeline(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = pipe(**inputs)
images = output.images
image_slice = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
def test_stable_diffusion_cycle_fp16(self):
components = self.get_dummy_components()
for name, module in components.items():
if hasattr(module, "half"):
components[name] = module.half()
pipe = CycleDiffusionPipeline(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)
images = output.images
image_slice = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
expected_slice = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local()
@unittest.skip("non-deterministic pipeline")
def test_inference_batch_single_identical(self):
return super().test_inference_batch_single_identical()
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@skip_mps
def test_attention_slicing_forward_pass(self):
return super().test_attention_slicing_forward_pass()
@nightly
@require_torch_gpu
class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_cycle_diffusion_pipeline_fp16(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/cycle-diffusion/black_colored_car.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"
)
init_image = init_image.resize((512, 512))
model_id = "CompVis/stable-diffusion-v1-4"
scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = CycleDiffusionPipeline.from_pretrained(
model_id, scheduler=scheduler, safety_checker=None, torch_dtype=torch.float16, revision="fp16"
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
source_prompt = "A black colored car"
prompt = "A blue colored car"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
source_prompt=source_prompt,
image=init_image,
num_inference_steps=100,
eta=0.1,
strength=0.85,
guidance_scale=3,
source_guidance_scale=1,
generator=generator,
output_type="np",
)
image = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image).max() < 5e-1
def test_cycle_diffusion_pipeline(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/cycle-diffusion/black_colored_car.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"
)
init_image = init_image.resize((512, 512))
model_id = "CompVis/stable-diffusion-v1-4"
scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = CycleDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
source_prompt = "A black colored car"
prompt = "A blue colored car"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
source_prompt=source_prompt,
image=init_image,
num_inference_steps=100,
eta=0.1,
strength=0.85,
guidance_scale=3,
source_guidance_scale=1,
generator=generator,
output_type="np",
)
image = output.images
assert np.abs(image - expected_image).max() < 2e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DDPMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
StableDiffusionPix2PixZeroPipeline,
UNet2DConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
load_pt,
nightly,
require_torch_gpu,
skip_mps,
torch_device,
)
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
@skip_mps
class StableDiffusionPix2PixZeroPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionPix2PixZeroPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"image"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def setUpClass(cls):
cls.source_embeds = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/src_emb_0.pt"
)
cls.target_embeds = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/tgt_emb_0.pt"
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler()
inverse_scheduler = DDIMInverseScheduler()
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"inverse_scheduler": inverse_scheduler,
"caption_generator": None,
"caption_processor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
generator = torch.manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"cross_attention_guidance_amount": 0.15,
"source_embeds": self.source_embeds,
"target_embeds": self.target_embeds,
"output_type": "numpy",
}
return inputs
def get_dummy_inversion_inputs(self, device, seed=0):
dummy_image = floats_tensor((2, 3, 32, 32), rng=random.Random(seed)).to(torch_device)
dummy_image = dummy_image / 2 + 0.5
generator = torch.manual_seed(seed)
inputs = {
"prompt": [
"A painting of a squirrel eating a burger",
"A painting of a burger eating a squirrel",
],
"image": dummy_image.cpu(),
"num_inference_steps": 2,
"guidance_scale": 6.0,
"generator": generator,
"output_type": "numpy",
}
return inputs
def get_dummy_inversion_inputs_by_type(self, device, seed=0, input_image_type="pt", output_type="np"):
inputs = self.get_dummy_inversion_inputs(device, seed)
if input_image_type == "pt":
image = inputs["image"]
elif input_image_type == "np":
image = VaeImageProcessor.pt_to_numpy(inputs["image"])
elif input_image_type == "pil":
image = VaeImageProcessor.pt_to_numpy(inputs["image"])
image = VaeImageProcessor.numpy_to_pil(image)
else:
raise ValueError(f"unsupported input_image_type {input_image_type}")
inputs["image"] = image
inputs["output_type"] = output_type
return inputs
def test_save_load_optional_components(self):
if not hasattr(self.pipeline_class, "_optional_components"):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(output - output_loaded).max()
self.assertLess(max_diff, 1e-4)
def test_stable_diffusion_pix2pix_zero_inversion(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inversion_inputs(device)
inputs["image"] = inputs["image"][:1]
inputs["prompt"] = inputs["prompt"][:1]
image = sd_pipe.invert(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4732, 0.4630, 0.5722, 0.5103, 0.5140, 0.5622, 0.5104, 0.5390, 0.5020])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_zero_inversion_batch(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inversion_inputs(device)
image = sd_pipe.invert(**inputs).images
image_slice = image[1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
expected_slice = np.array([0.6046, 0.5400, 0.4902, 0.4448, 0.4694, 0.5498, 0.4857, 0.5073, 0.5089])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_zero_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4863, 0.5053, 0.5033, 0.4007, 0.3571, 0.4768, 0.5176, 0.5277, 0.4940])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_zero_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5177, 0.5097, 0.5047, 0.4076, 0.3667, 0.4767, 0.5238, 0.5307, 0.4958])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_zero_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = EulerAncestralDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5421, 0.5525, 0.6085, 0.5279, 0.4658, 0.5317, 0.4418, 0.4815, 0.5132])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_zero_ddpm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = DDPMScheduler()
sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4861, 0.5053, 0.5038, 0.3994, 0.3562, 0.4768, 0.5172, 0.5280, 0.4938])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_pix2pix_zero_inversion_pt_np_pil_outputs_equivalent(self):
device = torch_device
components = self.get_dummy_components()
sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
output_pt = sd_pipe.invert(**self.get_dummy_inversion_inputs_by_type(device, output_type="pt")).images
output_np = sd_pipe.invert(**self.get_dummy_inversion_inputs_by_type(device, output_type="np")).images
output_pil = sd_pipe.invert(**self.get_dummy_inversion_inputs_by_type(device, output_type="pil")).images
max_diff = np.abs(output_pt.cpu().numpy().transpose(0, 2, 3, 1) - output_np).max()
self.assertLess(max_diff, 1e-4, "`output_type=='pt'` generate different results from `output_type=='np'`")
max_diff = np.abs(np.array(output_pil[0]) - (output_np[0] * 255).round()).max()
self.assertLess(max_diff, 2.0, "`output_type=='pil'` generate different results from `output_type=='np'`")
def test_stable_diffusion_pix2pix_zero_inversion_pt_np_pil_inputs_equivalent(self):
device = torch_device
components = self.get_dummy_components()
sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
out_input_pt = sd_pipe.invert(**self.get_dummy_inversion_inputs_by_type(device, input_image_type="pt")).images
out_input_np = sd_pipe.invert(**self.get_dummy_inversion_inputs_by_type(device, input_image_type="np")).images
out_input_pil = sd_pipe.invert(
**self.get_dummy_inversion_inputs_by_type(device, input_image_type="pil")
).images
max_diff = np.abs(out_input_pt - out_input_np).max()
self.assertLess(max_diff, 1e-4, "`input_type=='pt'` generate different result from `input_type=='np'`")
assert_mean_pixel_difference(out_input_pil, out_input_np, expected_max_diff=1)
# Non-determinism caused by the scheduler optimizing the latent inputs during inference
@unittest.skip("non-deterministic pipeline")
def test_inference_batch_single_identical(self):
return super().test_inference_batch_single_identical()
@nightly
@require_torch_gpu
class StableDiffusionPix2PixZeroPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def setUpClass(cls):
cls.source_embeds = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat.pt"
)
cls.target_embeds = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/dog.pt"
)
def get_inputs(self, seed=0):
generator = torch.manual_seed(seed)
inputs = {
"prompt": "turn him into a cyborg",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"cross_attention_guidance_amount": 0.15,
"source_embeds": self.source_embeds,
"target_embeds": self.target_embeds,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_pix2pix_zero_default(self):
pipe = StableDiffusionPix2PixZeroPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.5742, 0.5757, 0.5747, 0.5781, 0.5688, 0.5713, 0.5742, 0.5664, 0.5747])
assert np.abs(expected_slice - image_slice).max() < 5e-2
def test_stable_diffusion_pix2pix_zero_k_lms(self):
pipe = StableDiffusionPix2PixZeroPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.6367, 0.5459, 0.5146, 0.5479, 0.4905, 0.4753, 0.4961, 0.4629, 0.4624])
assert np.abs(expected_slice - image_slice).max() < 5e-2
def test_stable_diffusion_pix2pix_zero_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.1345, 0.268, 0.1539, 0.0726, 0.0959, 0.2261, -0.2673, 0.0277, -0.2062])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.1393, 0.2637, 0.1617, 0.0724, 0.0987, 0.2271, -0.2666, 0.0299, -0.2104])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionPix2PixZeroPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 3
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPix2PixZeroPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs()
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 8.2 GB is allocated
assert mem_bytes < 8.2 * 10**9
@nightly
@require_torch_gpu
class InversionPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def setUpClass(cls):
raw_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png"
)
raw_image = raw_image.convert("RGB").resize((512, 512))
cls.raw_image = raw_image
def test_stable_diffusion_pix2pix_inversion(self):
pipe = StableDiffusionPix2PixZeroPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe.inverse_scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config)
caption = "a photography of a cat with flowers"
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
output = pipe.invert(caption, image=self.raw_image, generator=generator, num_inference_steps=10)
inv_latents = output[0]
image_slice = inv_latents[0, -3:, -3:, -1].flatten()
assert inv_latents.shape == (1, 4, 64, 64)
expected_slice = np.array([0.8447, -0.0730, 0.7588, -1.2070, -0.4678, 0.1511, -0.8555, 1.1816, -0.7666])
assert np.abs(expected_slice - image_slice.cpu().numpy()).max() < 5e-2
def test_stable_diffusion_2_pix2pix_inversion(self):
pipe = StableDiffusionPix2PixZeroPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", safety_checker=None, torch_dtype=torch.float16
)
pipe.inverse_scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config)
caption = "a photography of a cat with flowers"
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
output = pipe.invert(caption, image=self.raw_image, generator=generator, num_inference_steps=10)
inv_latents = output[0]
image_slice = inv_latents[0, -3:, -3:, -1].flatten()
assert inv_latents.shape == (1, 4, 64, 64)
expected_slice = np.array([0.8970, -0.1611, 0.4766, -1.1162, -0.5923, 0.1050, -0.9678, 1.0537, -0.6050])
assert np.abs(expected_slice - image_slice.cpu().numpy()).max() < 5e-2
def test_stable_diffusion_2_pix2pix_full(self):
# numpy array of https://huggingface.co/datasets/hf-internal-testing/diffusers-images/blob/main/pix2pix/dog_2.png
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/dog_2.npy"
)
pipe = StableDiffusionPix2PixZeroPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", safety_checker=None, torch_dtype=torch.float16
)
pipe.inverse_scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config)
caption = "a photography of a cat with flowers"
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
output = pipe.invert(caption, image=self.raw_image, generator=generator)
inv_latents = output[0]
source_prompts = 4 * ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]
target_prompts = 4 * ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"]
source_embeds = pipe.get_embeds(source_prompts)
target_embeds = pipe.get_embeds(target_prompts)
image = pipe(
caption,
source_embeds=source_embeds,
target_embeds=target_embeds,
num_inference_steps=125,
cross_attention_guidance_amount=0.015,
generator=generator,
latents=inv_latents,
negative_prompt=caption,
output_type="np",
).images
mean_diff = np.abs(expected_image - image).mean()
assert mean_diff < 0.25
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import traceback
import unittest
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AsymmetricAutoencoderKL,
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
LCMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInpaintPipeline,
UNet2DConditionModel,
)
from diffusers.models.attention_processor import AttnProcessor
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
require_python39_or_higher,
require_torch_2,
require_torch_gpu,
run_test_in_subprocess,
slow,
torch_device,
)
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
# Will be run via run_test_in_subprocess
def _test_inpaint_compile(in_queue, out_queue, timeout):
error = None
try:
inputs = in_queue.get(timeout=timeout)
torch_device = inputs.pop("torch_device")
seed = inputs.pop("seed")
inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.unet.set_default_attn_processor()
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.unet.to(memory_format=torch.channels_last)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0689, 0.0699, 0.0790, 0.0536, 0.0470, 0.0488, 0.041, 0.0508, 0.04179])
assert np.abs(expected_slice - image_slice).max() < 3e-3
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class StableDiffusionInpaintPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset([])
# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
image_latents_params = frozenset([])
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"mask", "masked_image_latents"})
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
time_cond_proj_dim=time_cond_proj_dim,
layers_per_block=2,
sample_size=32,
in_channels=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0, img_res=64, output_pil=True):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
if output_pil:
# Get random floats in [0, 1] as image
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
mask_image = torch.ones_like(image)
# Convert image and mask_image to [0, 255]
image = 255 * image
mask_image = 255 * mask_image
# Convert to PIL image
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((img_res, img_res))
mask_image = Image.fromarray(np.uint8(mask_image)).convert("RGB").resize((img_res, img_res))
else:
# Get random floats in [0, 1] as image with spatial size (img_res, img_res)
image = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed)).to(device)
# Convert image to [-1, 1]
init_image = 2.0 * image - 1.0
mask_image = torch.ones((1, 1, img_res, img_res), device=device)
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_inpaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4703, 0.5697, 0.3879, 0.5470, 0.6042, 0.4413, 0.5078, 0.4728, 0.4469])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4931, 0.5988, 0.4569, 0.5556, 0.6650, 0.5087, 0.5966, 0.5358, 0.5269])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4931, 0.5988, 0.4569, 0.5556, 0.6650, 0.5087, 0.5966, 0.5358, 0.5269])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint_image_tensor(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
out_pil = output.images
inputs = self.get_dummy_inputs(device)
inputs["image"] = torch.tensor(np.array(inputs["image"]) / 127.5 - 1).permute(2, 0, 1).unsqueeze(0)
inputs["mask_image"] = torch.tensor(np.array(inputs["mask_image"]) / 255).permute(2, 0, 1)[:1].unsqueeze(0)
output = sd_pipe(**inputs)
out_tensor = output.images
assert out_pil.shape == (1, 64, 64, 3)
assert np.abs(out_pil.flatten() - out_tensor.flatten()).max() < 5e-2
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def test_stable_diffusion_inpaint_strength_zero_test(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
# check that the pipeline raises value error when num_inference_steps is < 1
inputs["strength"] = 0.01
with self.assertRaises(ValueError):
sd_pipe(**inputs).images
def test_stable_diffusion_inpaint_mask_latents(self):
device = "cpu"
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(device)
sd_pipe.set_progress_bar_config(disable=None)
# normal mask + normal image
## `image`: pil, `mask_image``: pil, `masked_image_latents``: None
inputs = self.get_dummy_inputs(device)
inputs["strength"] = 0.9
out_0 = sd_pipe(**inputs).images
# image latents + mask latents
inputs = self.get_dummy_inputs(device)
image = sd_pipe.image_processor.preprocess(inputs["image"]).to(sd_pipe.device)
mask = sd_pipe.mask_processor.preprocess(inputs["mask_image"]).to(sd_pipe.device)
masked_image = image * (mask < 0.5)
generator = torch.Generator(device=device).manual_seed(0)
image_latents = (
sd_pipe.vae.encode(image).latent_dist.sample(generator=generator) * sd_pipe.vae.config.scaling_factor
)
torch.randn((1, 4, 32, 32), generator=generator)
mask_latents = (
sd_pipe.vae.encode(masked_image).latent_dist.sample(generator=generator)
* sd_pipe.vae.config.scaling_factor
)
inputs["image"] = image_latents
inputs["masked_image_latents"] = mask_latents
inputs["mask_image"] = mask
inputs["strength"] = 0.9
generator = torch.Generator(device=device).manual_seed(0)
torch.randn((1, 4, 32, 32), generator=generator)
inputs["generator"] = generator
out_1 = sd_pipe(**inputs).images
assert np.abs(out_0 - out_1).max() < 1e-2
class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipelineFastTests):
pipeline_class = StableDiffusionInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset([])
# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
time_cond_proj_dim=time_cond_proj_dim,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs_2images(self, device, seed=0, img_res=64):
# Get random floats in [0, 1] as image with spatial size (img_res, img_res)
image1 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed)).to(device)
image2 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed + 22)).to(device)
# Convert images to [-1, 1]
init_image1 = 2.0 * image1 - 1.0
init_image2 = 2.0 * image2 - 1.0
# empty mask
mask_image = torch.zeros((1, 1, img_res, img_res), device=device)
if str(device).startswith("mps"):
generator1 = torch.manual_seed(seed)
generator2 = torch.manual_seed(seed)
else:
generator1 = torch.Generator(device=device).manual_seed(seed)
generator2 = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": ["A painting of a squirrel eating a burger"] * 2,
"image": [init_image1, init_image2],
"mask_image": [mask_image] * 2,
"generator": [generator1, generator2],
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_inpaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6584, 0.5424, 0.5649, 0.5449, 0.5897, 0.6111, 0.5404, 0.5463, 0.5214])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6240, 0.5355, 0.5649, 0.5378, 0.5374, 0.6242, 0.5132, 0.5347, 0.5396])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6240, 0.5355, 0.5649, 0.5378, 0.5374, 0.6242, 0.5132, 0.5347, 0.5396])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint_2_images(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
# test to confirm if we pass two same image, we will get same output
inputs = self.get_dummy_inputs(device)
gen1 = torch.Generator(device=device).manual_seed(0)
gen2 = torch.Generator(device=device).manual_seed(0)
for name in ["prompt", "image", "mask_image"]:
inputs[name] = [inputs[name]] * 2
inputs["generator"] = [gen1, gen2]
images = sd_pipe(**inputs).images
assert images.shape == (2, 64, 64, 3)
image_slice1 = images[0, -3:, -3:, -1]
image_slice2 = images[1, -3:, -3:, -1]
assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() < 1e-4
# test to confirm that if we pass two different images, we will get different output
inputs = self.get_dummy_inputs_2images(device)
images = sd_pipe(**inputs).images
assert images.shape == (2, 64, 64, 3)
image_slice1 = images[0, -3:, -3:, -1]
image_slice2 = images[1, -3:, -3:, -1]
assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() > 1e-2
@slow
@require_torch_gpu
class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
def setUp(self):
super().setUp()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_inpaint_ddim(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0427, 0.0460, 0.0483, 0.0460, 0.0584, 0.0521, 0.1549, 0.1695, 0.1794])
assert np.abs(expected_slice - image_slice).max() < 6e-4
def test_stable_diffusion_inpaint_fp16(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None
)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1509, 0.1245, 0.1672, 0.1655, 0.1519, 0.1226, 0.1462, 0.1567, 0.2451])
assert np.abs(expected_slice - image_slice).max() < 1e-1
def test_stable_diffusion_inpaint_pndm(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0425, 0.0273, 0.0344, 0.1694, 0.1727, 0.1812, 0.3256, 0.3311, 0.3272])
assert np.abs(expected_slice - image_slice).max() < 5e-3
def test_stable_diffusion_inpaint_k_lms(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.9314, 0.7575, 0.9432, 0.8885, 0.9028, 0.7298, 0.9811, 0.9667, 0.7633])
assert np.abs(expected_slice - image_slice).max() < 6e-3
def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
@require_python39_or_higher
@require_torch_2
def test_inpaint_compile(self):
seed = 0
inputs = self.get_inputs(torch_device, seed=seed)
# Can't pickle a Generator object
del inputs["generator"]
inputs["torch_device"] = torch_device
inputs["seed"] = seed
run_test_in_subprocess(test_case=self, target_func=_test_inpaint_compile, inputs=inputs)
def test_stable_diffusion_inpaint_pil_input_resolution_test(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
# change input image to a random size (one that would cause a tensor mismatch error)
inputs["image"] = inputs["image"].resize((127, 127))
inputs["mask_image"] = inputs["mask_image"].resize((127, 127))
inputs["height"] = 128
inputs["width"] = 128
image = pipe(**inputs).images
# verify that the returned image has the same height and width as the input height and width
assert image.shape == (1, inputs["height"], inputs["width"], 3)
def test_stable_diffusion_inpaint_strength_test(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
# change input strength
inputs["strength"] = 0.75
image = pipe(**inputs).images
# verify that the returned image has the same height and width as the input height and width
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, 253:256, 253:256, -1].flatten()
expected_slice = np.array([0.2728, 0.2803, 0.2665, 0.2511, 0.2774, 0.2586, 0.2391, 0.2392, 0.2582])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_simple_inpaint_ddim(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3757, 0.3875, 0.4445, 0.4353, 0.3780, 0.4513, 0.3965, 0.3984, 0.4362])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_download_local(self):
filename = hf_hub_download("runwayml/stable-diffusion-inpainting", filename="sd-v1-5-inpainting.ckpt")
pipe = StableDiffusionInpaintPipeline.from_single_file(filename, torch_dtype=torch.float16)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 1
image_out = pipe(**inputs).images[0]
assert image_out.shape == (512, 512, 3)
def test_download_ckpt_diff_format_is_same(self):
ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-inpainting/blob/main/sd-v1-5-inpainting.ckpt"
pipe = StableDiffusionInpaintPipeline.from_single_file(ckpt_path)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor())
pipe.to("cuda")
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 5
image_ckpt = pipe(**inputs).images[0]
pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor())
pipe.to("cuda")
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 5
image = pipe(**inputs).images[0]
assert np.max(np.abs(image - image_ckpt)) < 5e-4
@slow
@require_torch_gpu
class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.TestCase):
def setUp(self):
super().setUp()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_inpaint_ddim(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.vae = vae
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0522, 0.0604, 0.0596, 0.0449, 0.0493, 0.0427, 0.1186, 0.1289, 0.1442])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_inpaint_fp16(self):
vae = AsymmetricAutoencoderKL.from_pretrained(
"cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
)
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None
)
pipe.unet.set_default_attn_processor()
pipe.vae = vae
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1343, 0.1406, 0.1440, 0.1504, 0.1729, 0.0989, 0.1807, 0.2822, 0.1179])
assert np.abs(expected_slice - image_slice).max() < 5e-2
def test_stable_diffusion_inpaint_pndm(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.unet.set_default_attn_processor()
pipe.vae = vae
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0966, 0.1083, 0.1148, 0.1422, 0.1318, 0.1197, 0.3702, 0.3537, 0.3288])
assert np.abs(expected_slice - image_slice).max() < 5e-3
def test_stable_diffusion_inpaint_k_lms(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.unet.set_default_attn_processor()
pipe.vae = vae
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.8931, 0.8683, 0.8965, 0.8501, 0.8592, 0.9118, 0.8734, 0.7463, 0.8990])
assert np.abs(expected_slice - image_slice).max() < 6e-3
def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
vae = AsymmetricAutoencoderKL.from_pretrained(
"cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
)
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16
)
pipe.vae = vae
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.45 GB is allocated
assert mem_bytes < 2.45 * 10**9
@require_python39_or_higher
@require_torch_2
def test_inpaint_compile(self):
pass
def test_stable_diffusion_inpaint_pil_input_resolution_test(self):
vae = AsymmetricAutoencoderKL.from_pretrained(
"cross-attention/asymmetric-autoencoder-kl-x-1-5",
)
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.vae = vae
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
# change input image to a random size (one that would cause a tensor mismatch error)
inputs["image"] = inputs["image"].resize((127, 127))
inputs["mask_image"] = inputs["mask_image"].resize((127, 127))
inputs["height"] = 128
inputs["width"] = 128
image = pipe(**inputs).images
# verify that the returned image has the same height and width as the input height and width
assert image.shape == (1, inputs["height"], inputs["width"], 3)
def test_stable_diffusion_inpaint_strength_test(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.unet.set_default_attn_processor()
pipe.vae = vae
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
# change input strength
inputs["strength"] = 0.75
image = pipe(**inputs).images
# verify that the returned image has the same height and width as the input height and width
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, 253:256, 253:256, -1].flatten()
expected_slice = np.array([0.2458, 0.2576, 0.3124, 0.2679, 0.2669, 0.2796, 0.2872, 0.2975, 0.2661])
assert np.abs(expected_slice - image_slice).max() < 3e-3
def test_stable_diffusion_simple_inpaint_ddim(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
pipe.vae = vae
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3296, 0.4041, 0.4097, 0.4145, 0.4342, 0.4152, 0.4927, 0.4931, 0.4430])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_download_local(self):
vae = AsymmetricAutoencoderKL.from_pretrained(
"cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
)
filename = hf_hub_download("runwayml/stable-diffusion-inpainting", filename="sd-v1-5-inpainting.ckpt")
pipe = StableDiffusionInpaintPipeline.from_single_file(filename, torch_dtype=torch.float16)
pipe.vae = vae
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 1
image_out = pipe(**inputs).images[0]
assert image_out.shape == (512, 512, 3)
def test_download_ckpt_diff_format_is_same(self):
pass
@nightly
@require_torch_gpu
class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_inpaint_ddim(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/stable_diffusion_inpaint_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_pndm(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/stable_diffusion_inpaint_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_lms(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/stable_diffusion_inpaint_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_dpm(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 30
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/stable_diffusion_inpaint_dpm_multi.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
class StableDiffusionInpaintingPrepareMaskAndMaskedImageTests(unittest.TestCase):
def test_pil_inputs(self):
height, width = 32, 32
im = np.random.randint(0, 255, (height, width, 3), dtype=np.uint8)
im = Image.fromarray(im)
mask = np.random.randint(0, 255, (height, width), dtype=np.uint8) > 127.5
mask = Image.fromarray((mask * 255).astype(np.uint8))
t_mask, t_masked, t_image = prepare_mask_and_masked_image(im, mask, height, width, return_image=True)
self.assertTrue(isinstance(t_mask, torch.Tensor))
self.assertTrue(isinstance(t_masked, torch.Tensor))
self.assertTrue(isinstance(t_image, torch.Tensor))
self.assertEqual(t_mask.ndim, 4)
self.assertEqual(t_masked.ndim, 4)
self.assertEqual(t_image.ndim, 4)
self.assertEqual(t_mask.shape, (1, 1, height, width))
self.assertEqual(t_masked.shape, (1, 3, height, width))
self.assertEqual(t_image.shape, (1, 3, height, width))
self.assertTrue(t_mask.dtype == torch.float32)
self.assertTrue(t_masked.dtype == torch.float32)
self.assertTrue(t_image.dtype == torch.float32)
self.assertTrue(t_mask.min() >= 0.0)
self.assertTrue(t_mask.max() <= 1.0)
self.assertTrue(t_masked.min() >= -1.0)
self.assertTrue(t_masked.min() <= 1.0)
self.assertTrue(t_image.min() >= -1.0)
self.assertTrue(t_image.min() >= -1.0)
self.assertTrue(t_mask.sum() > 0.0)
def test_np_inputs(self):
height, width = 32, 32
im_np = np.random.randint(0, 255, (height, width, 3), dtype=np.uint8)
im_pil = Image.fromarray(im_np)
mask_np = (
np.random.randint(
0,
255,
(
height,
width,
),
dtype=np.uint8,
)
> 127.5
)
mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8))
t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
im_np, mask_np, height, width, return_image=True
)
t_mask_pil, t_masked_pil, t_image_pil = prepare_mask_and_masked_image(
im_pil, mask_pil, height, width, return_image=True
)
self.assertTrue((t_mask_np == t_mask_pil).all())
self.assertTrue((t_masked_np == t_masked_pil).all())
self.assertTrue((t_image_np == t_image_pil).all())
def test_torch_3D_2D_inputs(self):
height, width = 32, 32
im_tensor = torch.randint(
0,
255,
(
3,
height,
width,
),
dtype=torch.uint8,
)
mask_tensor = (
torch.randint(
0,
255,
(
height,
width,
),
dtype=torch.uint8,
)
> 127.5
)
im_np = im_tensor.numpy().transpose(1, 2, 0)
mask_np = mask_tensor.numpy()
t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
)
t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
im_np, mask_np, height, width, return_image=True
)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
self.assertTrue((t_image_tensor == t_image_np).all())
def test_torch_3D_3D_inputs(self):
height, width = 32, 32
im_tensor = torch.randint(
0,
255,
(
3,
height,
width,
),
dtype=torch.uint8,
)
mask_tensor = (
torch.randint(
0,
255,
(
1,
height,
width,
),
dtype=torch.uint8,
)
> 127.5
)
im_np = im_tensor.numpy().transpose(1, 2, 0)
mask_np = mask_tensor.numpy()[0]
t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
)
t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
im_np, mask_np, height, width, return_image=True
)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
self.assertTrue((t_image_tensor == t_image_np).all())
def test_torch_4D_2D_inputs(self):
height, width = 32, 32
im_tensor = torch.randint(
0,
255,
(
1,
3,
height,
width,
),
dtype=torch.uint8,
)
mask_tensor = (
torch.randint(
0,
255,
(
height,
width,
),
dtype=torch.uint8,
)
> 127.5
)
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
mask_np = mask_tensor.numpy()
t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
)
t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
im_np, mask_np, height, width, return_image=True
)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
self.assertTrue((t_image_tensor == t_image_np).all())
def test_torch_4D_3D_inputs(self):
height, width = 32, 32
im_tensor = torch.randint(
0,
255,
(
1,
3,
height,
width,
),
dtype=torch.uint8,
)
mask_tensor = (
torch.randint(
0,
255,
(
1,
height,
width,
),
dtype=torch.uint8,
)
> 127.5
)
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
mask_np = mask_tensor.numpy()[0]
t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
)
t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
im_np, mask_np, height, width, return_image=True
)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
self.assertTrue((t_image_tensor == t_image_np).all())
def test_torch_4D_4D_inputs(self):
height, width = 32, 32
im_tensor = torch.randint(
0,
255,
(
1,
3,
height,
width,
),
dtype=torch.uint8,
)
mask_tensor = (
torch.randint(
0,
255,
(
1,
1,
height,
width,
),
dtype=torch.uint8,
)
> 127.5
)
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
mask_np = mask_tensor.numpy()[0][0]
t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
)
t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
im_np, mask_np, height, width, return_image=True
)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
self.assertTrue((t_image_tensor == t_image_np).all())
def test_torch_batch_4D_3D(self):
height, width = 32, 32
im_tensor = torch.randint(
0,
255,
(
2,
3,
height,
width,
),
dtype=torch.uint8,
)
mask_tensor = (
torch.randint(
0,
255,
(
2,
height,
width,
),
dtype=torch.uint8,
)
> 127.5
)
im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
mask_nps = [mask.numpy() for mask in mask_tensor]
t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
)
nps = [prepare_mask_and_masked_image(i, m, height, width, return_image=True) for i, m in zip(im_nps, mask_nps)]
t_mask_np = torch.cat([n[0] for n in nps])
t_masked_np = torch.cat([n[1] for n in nps])
t_image_np = torch.cat([n[2] for n in nps])
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
self.assertTrue((t_image_tensor == t_image_np).all())
def test_torch_batch_4D_4D(self):
height, width = 32, 32
im_tensor = torch.randint(
0,
255,
(
2,
3,
height,
width,
),
dtype=torch.uint8,
)
mask_tensor = (
torch.randint(
0,
255,
(
2,
1,
height,
width,
),
dtype=torch.uint8,
)
> 127.5
)
im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
mask_nps = [mask.numpy()[0] for mask in mask_tensor]
t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
)
nps = [prepare_mask_and_masked_image(i, m, height, width, return_image=True) for i, m in zip(im_nps, mask_nps)]
t_mask_np = torch.cat([n[0] for n in nps])
t_masked_np = torch.cat([n[1] for n in nps])
t_image_np = torch.cat([n[2] for n in nps])
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
self.assertTrue((t_image_tensor == t_image_np).all())
def test_shape_mismatch(self):
height, width = 32, 32
# test height and width
with self.assertRaises(AssertionError):
prepare_mask_and_masked_image(
torch.randn(
3,
height,
width,
),
torch.randn(64, 64),
height,
width,
return_image=True,
)
# test batch dim
with self.assertRaises(AssertionError):
prepare_mask_and_masked_image(
torch.randn(
2,
3,
height,
width,
),
torch.randn(4, 64, 64),
height,
width,
return_image=True,
)
# test batch dim
with self.assertRaises(AssertionError):
prepare_mask_and_masked_image(
torch.randn(
2,
3,
height,
width,
),
torch.randn(4, 1, 64, 64),
height,
width,
return_image=True,
)
def test_type_mismatch(self):
height, width = 32, 32
# test tensors-only
with self.assertRaises(TypeError):
prepare_mask_and_masked_image(
torch.rand(
3,
height,
width,
),
torch.rand(
3,
height,
width,
).numpy(),
height,
width,
return_image=True,
)
# test tensors-only
with self.assertRaises(TypeError):
prepare_mask_and_masked_image(
torch.rand(
3,
height,
width,
).numpy(),
torch.rand(
3,
height,
width,
),
height,
width,
return_image=True,
)
def test_channels_first(self):
height, width = 32, 32
# test channels first for 3D tensors
with self.assertRaises(AssertionError):
prepare_mask_and_masked_image(
torch.rand(height, width, 3),
torch.rand(
3,
height,
width,
),
height,
width,
return_image=True,
)
def test_tensor_range(self):
height, width = 32, 32
# test im <= 1
with self.assertRaises(ValueError):
prepare_mask_and_masked_image(
torch.ones(
3,
height,
width,
)
* 2,
torch.rand(
height,
width,
),
height,
width,
return_image=True,
)
# test im >= -1
with self.assertRaises(ValueError):
prepare_mask_and_masked_image(
torch.ones(
3,
height,
width,
)
* (-2),
torch.rand(
height,
width,
),
height,
width,
return_image=True,
)
# test mask <= 1
with self.assertRaises(ValueError):
prepare_mask_and_masked_image(
torch.rand(
3,
height,
width,
),
torch.ones(
height,
width,
)
* 2,
height,
width,
return_image=True,
)
# test mask >= 0
with self.assertRaises(ValueError):
prepare_mask_and_masked_image(
torch.rand(
3,
height,
width,
),
torch.ones(
height,
width,
)
* -1,
height,
width,
return_image=True,
)
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py
|
# 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 random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import (
floats_tensor,
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class OnnxStableDiffusionUpscalePipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
# TODO: is there an appropriate internal test set?
hub_checkpoint = "ssube/stable-diffusion-x4-upscaler-onnx"
def get_dummy_inputs(self, seed=0):
image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed))
generator = np.random.RandomState(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_pipeline_default_ddpm(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.6957, 0.7002, 0.7186, 0.6881, 0.6693, 0.6910, 0.7445, 0.7274, 0.7056])
assert np.abs(image_slice - expected_slice).max() < 1e-1
def test_pipeline_pndm(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.7349, 0.7347, 0.7034, 0.7696, 0.7876, 0.7597, 0.7916, 0.8085, 0.8036])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_dpm_multistep(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler_ancestral(self):
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_ddpm(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((128, 128))
# using the PNDM scheduler by default
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx",
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
guidance_scale=7.5,
num_inference_steps=10,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
def test_inference_k_lms(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((128, 128))
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx", subfolder="scheduler"
)
pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx",
scheduler=lms_scheduler,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
guidance_scale=7.5,
num_inference_steps=20,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array(
[0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]
)
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import time
import traceback
import unittest
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LCMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
logging,
)
from diffusers.models.attention_processor import AttnProcessor
from diffusers.utils.testing_utils import (
CaptureLogger,
enable_full_determinism,
load_image,
load_numpy,
nightly,
numpy_cosine_similarity_distance,
require_python39_or_higher,
require_torch_2,
require_torch_gpu,
run_test_in_subprocess,
slow,
torch_device,
)
from ..pipeline_params import (
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
# Will be run via run_test_in_subprocess
def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
error = None
try:
inputs = in_queue.get(timeout=timeout)
torch_device = inputs.pop("torch_device")
seed = inputs.pop("seed")
inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.unet.to(memory_format=torch.channels_last)
sd_pipe.unet = torch.compile(sd_pipe.unet, mode="reduce-overhead", fullgraph=True)
sd_pipe.set_progress_bar_config(disable=None)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.38019, 0.28647, 0.27321, 0.40377, 0.38290, 0.35446, 0.39218, 0.38165, 0.42239])
assert np.abs(image_slice - expected_slice).max() < 5e-3
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class StableDiffusionPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
layers_per_block=1,
sample_size=32,
time_cond_proj_dim=time_cond_proj_dim,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
norm_num_groups=2,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=64,
layer_norm_eps=1e-05,
num_attention_heads=8,
num_hidden_layers=3,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3203, 0.4555, 0.4711, 0.3505, 0.3973, 0.4650, 0.5137, 0.3392, 0.4045])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3454, 0.5349, 0.5185, 0.2808, 0.4509, 0.4612, 0.4655, 0.3601, 0.4315])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3454, 0.5349, 0.5185, 0.2808, 0.4509, 0.4612, 0.4655, 0.3601, 0.4315])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = sd_pipe.tokenizer(
prompt,
padding="max_length",
max_length=sd_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = sd_pipe.text_encoder(text_inputs)[0]
inputs["prompt_embeds"] = prompt_embeds
# forward
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_negative_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = sd_pipe.tokenizer(
p,
padding="max_length",
max_length=sd_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
embeds.append(sd_pipe.text_encoder(text_inputs)[0])
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_prompt_embeds_with_plain_negative_prompt_list(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = negative_prompt
prompt = 3 * [inputs.pop("prompt")]
text_inputs = sd_pipe.tokenizer(
prompt,
padding="max_length",
max_length=sd_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = sd_pipe.text_encoder(text_inputs)[0]
inputs["prompt_embeds"] = prompt_embeds
# forward
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_ddim_factor_8(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs, height=136, width=136)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 136, 136, 3)
expected_slice = np.array([0.4346, 0.5621, 0.5016, 0.3926, 0.4533, 0.4134, 0.5625, 0.5632, 0.5265])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_pndm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = PNDMScheduler(skip_prk_steps=True)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3411, 0.5032, 0.4704, 0.3135, 0.4323, 0.4740, 0.5150, 0.3498, 0.4022])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_no_safety_checker(self):
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
)
assert isinstance(pipe, StableDiffusionPipeline)
assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
assert pipe.safety_checker is None
image = pipe("example prompt", num_inference_steps=2).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)
# sanity check that the pipeline still works
assert pipe.safety_checker is None
image = pipe("example prompt", num_inference_steps=2).images[0]
assert image is not None
def test_stable_diffusion_k_lms(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3149, 0.5246, 0.4796, 0.3218, 0.4469, 0.4729, 0.5151, 0.3597, 0.3954])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_euler_ancestral(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3151, 0.5243, 0.4794, 0.3217, 0.4468, 0.4728, 0.5152, 0.3598, 0.3954])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3149, 0.5246, 0.4796, 0.3218, 0.4469, 0.4729, 0.5151, 0.3597, 0.3954])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_vae_slicing(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
image_count = 4
inputs = self.get_dummy_inputs(device)
inputs["prompt"] = [inputs["prompt"]] * image_count
output_1 = sd_pipe(**inputs)
# make sure sliced vae decode yields the same result
sd_pipe.enable_vae_slicing()
inputs = self.get_dummy_inputs(device)
inputs["prompt"] = [inputs["prompt"]] * image_count
output_2 = sd_pipe(**inputs)
# there is a small discrepancy at image borders vs. full batch decode
assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 3e-3
def test_stable_diffusion_vae_tiling(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
# make sure here that pndm scheduler skips prk
components["safety_checker"] = None
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
# Test that tiled decode at 512x512 yields the same result as the non-tiled decode
generator = torch.Generator(device=device).manual_seed(0)
output_1 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
# make sure tiled vae decode yields the same result
sd_pipe.enable_vae_tiling()
generator = torch.Generator(device=device).manual_seed(0)
output_2 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 5e-1
# test that tiled decode works with various shapes
shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]
for shape in shapes:
zeros = torch.zeros(shape).to(device)
sd_pipe.vae.decode(zeros)
def test_stable_diffusion_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.3458, 0.5120, 0.4800, 0.3116, 0.4348, 0.4802, 0.5237, 0.3467, 0.3991])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_long_prompt(self):
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
do_classifier_free_guidance = True
negative_prompt = None
num_images_per_prompt = 1
logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion")
logger.setLevel(logging.WARNING)
prompt = 100 * "@"
with CaptureLogger(logger) as cap_logger:
negative_text_embeddings, text_embeddings = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negative_text_embeddings is not None:
text_embeddings = torch.cat([negative_text_embeddings, text_embeddings])
# 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25
assert cap_logger.out.count("@") == 25
negative_prompt = "Hello"
with CaptureLogger(logger) as cap_logger_2:
negative_text_embeddings_2, text_embeddings_2 = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negative_text_embeddings_2 is not None:
text_embeddings_2 = torch.cat([negative_text_embeddings_2, text_embeddings_2])
assert cap_logger.out == cap_logger_2.out
prompt = 25 * "@"
with CaptureLogger(logger) as cap_logger_3:
negative_text_embeddings_3, text_embeddings_3 = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negative_text_embeddings_3 is not None:
text_embeddings_3 = torch.cat([negative_text_embeddings_3, text_embeddings_3])
assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape
assert text_embeddings.shape[1] == 77
assert cap_logger_3.out == ""
def test_stable_diffusion_height_width_opt(self):
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "hey"
output = sd_pipe(prompt, num_inference_steps=1, output_type="np")
image_shape = output.images[0].shape[:2]
assert image_shape == (64, 64)
output = sd_pipe(prompt, num_inference_steps=1, height=96, width=96, output_type="np")
image_shape = output.images[0].shape[:2]
assert image_shape == (96, 96)
config = dict(sd_pipe.unet.config)
config["sample_size"] = 96
sd_pipe.unet = UNet2DConditionModel.from_config(config).to(torch_device)
output = sd_pipe(prompt, num_inference_steps=1, output_type="np")
image_shape = output.images[0].shape[:2]
assert image_shape == (192, 192)
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def test_freeu_enabled(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "hey"
output = sd_pipe(prompt, num_inference_steps=1, output_type="np", generator=torch.manual_seed(0)).images
sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
output_freeu = sd_pipe(prompt, num_inference_steps=1, output_type="np", generator=torch.manual_seed(0)).images
assert not np.allclose(
output[0, -3:, -3:, -1], output_freeu[0, -3:, -3:, -1]
), "Enabling of FreeU should lead to different results."
def test_freeu_disabled(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "hey"
output = sd_pipe(prompt, num_inference_steps=1, output_type="np", generator=torch.manual_seed(0)).images
sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
sd_pipe.disable_freeu()
freeu_keys = {"s1", "s2", "b1", "b2"}
for upsample_block in sd_pipe.unet.up_blocks:
for key in freeu_keys:
assert getattr(upsample_block, key) is None, f"Disabling of FreeU should have set {key} to None."
output_no_freeu = sd_pipe(
prompt, num_inference_steps=1, output_type="np", generator=torch.manual_seed(0)
).images
assert np.allclose(
output[0, -3:, -3:, -1], output_no_freeu[0, -3:, -3:, -1]
), "Disabling of FreeU should lead to results similar to the default pipeline results."
@slow
@require_torch_gpu
class StableDiffusionPipelineSlowTests(unittest.TestCase):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_1_1_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1")
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.4363, 0.4355, 0.3667, 0.4066, 0.3970, 0.3866, 0.4394, 0.4356, 0.4059])
assert np.abs(image_slice - expected_slice).max() < 3e-3
def test_stable_diffusion_v1_4_with_freeu(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
image = sd_pipe(**inputs).images
image = image[0, -3:, -3:, -1].flatten()
expected_image = [0.0721, 0.0588, 0.0268, 0.0384, 0.0636, 0.0, 0.0429, 0.0344, 0.0309]
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_1_4_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.5740, 0.4784, 0.3162, 0.6358, 0.5831, 0.5505, 0.5082, 0.5631, 0.5575])
assert np.abs(image_slice - expected_slice).max() < 3e-3
def test_stable_diffusion_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.38019, 0.28647, 0.27321, 0.40377, 0.38290, 0.35446, 0.39218, 0.38165, 0.42239])
assert np.abs(image_slice - expected_slice).max() < 1e-4
def test_stable_diffusion_lms(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.10542, 0.09620, 0.07332, 0.09015, 0.09382, 0.07597, 0.08496, 0.07806, 0.06455])
assert np.abs(image_slice - expected_slice).max() < 3e-3
def test_stable_diffusion_dpm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.03503, 0.03494, 0.01087, 0.03128, 0.02552, 0.00803, 0.00742, 0.00372, 0.00000])
assert np.abs(image_slice - expected_slice).max() < 3e-3
def test_stable_diffusion_attention_slicing(self):
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.unet.set_default_attn_processor()
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
# enable attention slicing
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image_sliced = pipe(**inputs).images
mem_bytes = torch.cuda.max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
# make sure that less than 3.75 GB is allocated
assert mem_bytes < 3.75 * 10**9
# disable slicing
pipe.disable_attention_slicing()
pipe.unet.set_default_attn_processor()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image = pipe(**inputs).images
# make sure that more than 3.75 GB is allocated
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes > 3.75 * 10**9
max_diff = numpy_cosine_similarity_distance(image_sliced.flatten(), image.flatten())
assert max_diff < 1e-3
def test_stable_diffusion_vae_slicing(self):
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
# enable vae slicing
pipe.enable_vae_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
inputs["prompt"] = [inputs["prompt"]] * 4
inputs["latents"] = torch.cat([inputs["latents"]] * 4)
image_sliced = pipe(**inputs).images
mem_bytes = torch.cuda.max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
# make sure that less than 4 GB is allocated
assert mem_bytes < 4e9
# disable vae slicing
pipe.disable_vae_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
inputs["prompt"] = [inputs["prompt"]] * 4
inputs["latents"] = torch.cat([inputs["latents"]] * 4)
image = pipe(**inputs).images
# make sure that more than 4 GB is allocated
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes > 4e9
# There is a small discrepancy at the image borders vs. a fully batched version.
max_diff = numpy_cosine_similarity_distance(image_sliced.flatten(), image.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_vae_tiling(self):
torch.cuda.reset_peak_memory_stats()
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionPipeline.from_pretrained(
model_id, revision="fp16", torch_dtype=torch.float16, safety_checker=None
)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
pipe.unet = pipe.unet.to(memory_format=torch.channels_last)
pipe.vae = pipe.vae.to(memory_format=torch.channels_last)
prompt = "a photograph of an astronaut riding a horse"
# enable vae tiling
pipe.enable_vae_tiling()
pipe.enable_model_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
output_chunked = pipe(
[prompt],
width=1024,
height=1024,
generator=generator,
guidance_scale=7.5,
num_inference_steps=2,
output_type="numpy",
)
image_chunked = output_chunked.images
mem_bytes = torch.cuda.max_memory_allocated()
# disable vae tiling
pipe.disable_vae_tiling()
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe(
[prompt],
width=1024,
height=1024,
generator=generator,
guidance_scale=7.5,
num_inference_steps=2,
output_type="numpy",
)
image = output.images
assert mem_bytes < 1e10
max_diff = numpy_cosine_similarity_distance(image_chunked.flatten(), image.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_fp16_vs_autocast(self):
# this test makes sure that the original model with autocast
# and the new model with fp16 yield the same result
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image_fp16 = pipe(**inputs).images
with torch.autocast(torch_device):
inputs = self.get_inputs(torch_device)
image_autocast = pipe(**inputs).images
# Make sure results are close enough
diff = np.abs(image_fp16.flatten() - image_autocast.flatten())
# They ARE different since ops are not run always at the same precision
# however, they should be extremely close.
assert diff.mean() < 2e-2
def test_stable_diffusion_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.5693, -0.3018, -0.9746, 0.0518, -0.8770, 0.7559, -1.7402, 0.1022, 1.1582]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.1958, -0.2993, -1.0166, -0.5005, -0.4810, 0.6162, -0.9492, 0.6621, 1.4492]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == inputs["num_inference_steps"]
def test_stable_diffusion_low_cpu_mem_usage(self):
pipeline_id = "CompVis/stable-diffusion-v1-4"
start_time = time.time()
pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
pipeline_low_cpu_mem_usage.to(torch_device)
low_cpu_mem_usage_time = time.time() - start_time
start_time = time.time()
_ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False)
normal_load_time = time.time() - start_time
assert 2 * low_cpu_mem_usage_time < normal_load_time
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.8 GB is allocated
assert mem_bytes < 2.8 * 10**9
def test_stable_diffusion_pipeline_with_model_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
# Normal inference
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
outputs = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# With model offloading
# Reload but don't move to cuda
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
)
pipe.unet.set_default_attn_processor()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device, dtype=torch.float16)
outputs_offloaded = pipe(**inputs)
mem_bytes_offloaded = torch.cuda.max_memory_allocated()
images = outputs.images
offloaded_images = outputs_offloaded.images
max_diff = numpy_cosine_similarity_distance(images.flatten(), offloaded_images.flatten())
assert max_diff < 1e-3
assert mem_bytes_offloaded < mem_bytes
assert mem_bytes_offloaded < 3.5 * 10**9
for module in pipe.text_encoder, pipe.unet, pipe.vae:
assert module.device == torch.device("cpu")
# With attention slicing
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_attention_slicing()
_ = pipe(**inputs)
mem_bytes_slicing = torch.cuda.max_memory_allocated()
assert mem_bytes_slicing < mem_bytes_offloaded
assert mem_bytes_slicing < 3 * 10**9
def test_stable_diffusion_textual_inversion(self):
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe.load_textual_inversion("sd-concepts-library/low-poly-hd-logos-icons")
a111_file = hf_hub_download("hf-internal-testing/text_inv_embedding_a1111_format", "winter_style.pt")
a111_file_neg = hf_hub_download(
"hf-internal-testing/text_inv_embedding_a1111_format", "winter_style_negative.pt"
)
pipe.load_textual_inversion(a111_file)
pipe.load_textual_inversion(a111_file_neg)
pipe.to("cuda")
generator = torch.Generator(device="cpu").manual_seed(1)
prompt = "An logo of a turtle in strong Style-Winter with <low-poly-hd-logos-icons>"
neg_prompt = "Style-Winter-neg"
image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type="np").images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 8e-1
def test_stable_diffusion_textual_inversion_with_model_cpu_offload(self):
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe.enable_model_cpu_offload()
pipe.load_textual_inversion("sd-concepts-library/low-poly-hd-logos-icons")
a111_file = hf_hub_download("hf-internal-testing/text_inv_embedding_a1111_format", "winter_style.pt")
a111_file_neg = hf_hub_download(
"hf-internal-testing/text_inv_embedding_a1111_format", "winter_style_negative.pt"
)
pipe.load_textual_inversion(a111_file)
pipe.load_textual_inversion(a111_file_neg)
generator = torch.Generator(device="cpu").manual_seed(1)
prompt = "An logo of a turtle in strong Style-Winter with <low-poly-hd-logos-icons>"
neg_prompt = "Style-Winter-neg"
image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type="np").images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 8e-1
def test_stable_diffusion_textual_inversion_with_sequential_cpu_offload(self):
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe.enable_sequential_cpu_offload()
pipe.load_textual_inversion("sd-concepts-library/low-poly-hd-logos-icons")
a111_file = hf_hub_download("hf-internal-testing/text_inv_embedding_a1111_format", "winter_style.pt")
a111_file_neg = hf_hub_download(
"hf-internal-testing/text_inv_embedding_a1111_format", "winter_style_negative.pt"
)
pipe.load_textual_inversion(a111_file)
pipe.load_textual_inversion(a111_file_neg)
generator = torch.Generator(device="cpu").manual_seed(1)
prompt = "An logo of a turtle in strong Style-Winter with <low-poly-hd-logos-icons>"
neg_prompt = "Style-Winter-neg"
image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type="np").images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 8e-1
@require_python39_or_higher
@require_torch_2
def test_stable_diffusion_compile(self):
seed = 0
inputs = self.get_inputs(torch_device, seed=seed)
# Can't pickle a Generator object
del inputs["generator"]
inputs["torch_device"] = torch_device
inputs["seed"] = seed
run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=inputs)
def test_stable_diffusion_lcm(self):
unet = UNet2DConditionModel.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", subfolder="unet")
sd_pipe = StableDiffusionPipeline.from_pretrained("Lykon/dreamshaper-7", unet=unet).to(torch_device)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 6
inputs["output_type"] = "pil"
image = sd_pipe(**inputs).images[0]
expected_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_full/stable_diffusion_lcm.png"
)
image = sd_pipe.image_processor.pil_to_numpy(image)
expected_image = sd_pipe.image_processor.pil_to_numpy(expected_image)
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
assert max_diff < 1e-2
@slow
@require_torch_gpu
class StableDiffusionPipelineCkptTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_download_from_hub(self):
ckpt_paths = [
"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt",
"https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix_base.ckpt",
]
for ckpt_path in ckpt_paths:
pipe = StableDiffusionPipeline.from_single_file(ckpt_path, torch_dtype=torch.float16)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
image_out = pipe("test", num_inference_steps=1, output_type="np").images[0]
assert image_out.shape == (512, 512, 3)
def test_download_local(self):
filename = hf_hub_download("runwayml/stable-diffusion-v1-5", filename="v1-5-pruned-emaonly.ckpt")
pipe = StableDiffusionPipeline.from_single_file(filename, torch_dtype=torch.float16)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
image_out = pipe("test", num_inference_steps=1, output_type="np").images[0]
assert image_out.shape == (512, 512, 3)
def test_download_ckpt_diff_format_is_same(self):
ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt"
pipe = StableDiffusionPipeline.from_single_file(ckpt_path)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor())
pipe.to("cuda")
generator = torch.Generator(device="cpu").manual_seed(0)
image_ckpt = pipe("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor())
pipe.to("cuda")
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipe("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_ckpt.flatten())
assert max_diff < 1e-3
@nightly
@require_torch_gpu
class StableDiffusionPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_stable_diffusion_1_4_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_1_5_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_5_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 3e-3
def test_stable_diffusion_lms(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_euler(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_euler.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint_legacy.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInpaintPipelineLegacy,
UNet2DConditionModel,
UNet2DModel,
VQModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
preprocess_image,
require_torch_gpu,
slow,
torch_device,
)
enable_full_determinism()
class StableDiffusionInpaintLegacyPipelineFastTests(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
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
@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_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_cond_unet_inpaint(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
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_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)
@property
def dummy_extractor(self):
def extract(*args, **kwargs):
class Out:
def __init__(self):
self.pixel_values = torch.ones([0])
def to(self, device):
self.pixel_values.to(device)
return self
return Out()
return extract
def test_stable_diffusion_inpaint_legacy(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionInpaintPipelineLegacy(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
)
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
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([0.4941, 0.5396, 0.4689, 0.6338, 0.5392, 0.4094, 0.5477, 0.5904, 0.5165])
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_stable_diffusion_inpaint_legacy_batched(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
init_images_tens = preprocess_image(init_image, batch_size=2)
init_masks_tens = init_images_tens + 4
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionInpaintPipelineLegacy(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
images = sd_pipe(
[prompt] * 2,
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
image=init_images_tens,
mask_image=init_masks_tens,
).images
assert images.shape == (2, 32, 32, 3)
image_slice_0 = images[0, -3:, -3:, -1].flatten()
image_slice_1 = images[1, -3:, -3:, -1].flatten()
expected_slice_0 = np.array([0.4697, 0.3770, 0.4096, 0.4653, 0.4497, 0.4183, 0.3950, 0.4668, 0.4672])
expected_slice_1 = np.array([0.4105, 0.4987, 0.5771, 0.4921, 0.4237, 0.5684, 0.5496, 0.4645, 0.5272])
assert np.abs(expected_slice_0 - image_slice_0).max() < 1e-2
assert np.abs(expected_slice_1 - image_slice_1).max() < 1e-2
def test_stable_diffusion_inpaint_legacy_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionInpaintPipelineLegacy(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
negative_prompt = "french fries"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
prompt,
negative_prompt=negative_prompt,
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4941, 0.5396, 0.4689, 0.6338, 0.5392, 0.4094, 0.5477, 0.5904, 0.5165])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint_legacy_num_images_per_prompt(self):
device = "cpu"
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionInpaintPipelineLegacy(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
# test num_images_per_prompt=1 (default)
images = sd_pipe(
prompt,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
).images
assert images.shape == (1, 32, 32, 3)
# test num_images_per_prompt=1 (default) for batch of prompts
batch_size = 2
images = sd_pipe(
[prompt] * batch_size,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
).images
assert images.shape == (batch_size, 32, 32, 3)
# test num_images_per_prompt for single prompt
num_images_per_prompt = 2
images = sd_pipe(
prompt,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
num_images_per_prompt=num_images_per_prompt,
).images
assert images.shape == (num_images_per_prompt, 32, 32, 3)
# test num_images_per_prompt for batch of prompts
batch_size = 2
images = sd_pipe(
[prompt] * batch_size,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
num_images_per_prompt=num_images_per_prompt,
).images
assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
@slow
@require_torch_gpu
class StableDiffusionInpaintLegacyPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, generator_device="cpu", seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "A red cat sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_inpaint_legacy_pndm(self):
pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.5665, 0.6117, 0.6430, 0.4057, 0.4594, 0.5658, 0.1596, 0.3106, 0.4305])
assert np.abs(expected_slice - image_slice).max() < 3e-3
def test_stable_diffusion_inpaint_legacy_batched(self):
pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
inputs["prompt"] = [inputs["prompt"]] * 2
inputs["image"] = preprocess_image(inputs["image"], batch_size=2)
mask = inputs["mask_image"].convert("L")
mask = np.array(mask).astype(np.float32) / 255.0
mask = torch.from_numpy(1 - mask)
masks = torch.vstack([mask[None][None]] * 2)
inputs["mask_image"] = masks
image = pipe(**inputs).images
assert image.shape == (2, 512, 512, 3)
image_slice_0 = image[0, 253:256, 253:256, -1].flatten()
image_slice_1 = image[1, 253:256, 253:256, -1].flatten()
expected_slice_0 = np.array(
[0.52093095, 0.4176447, 0.32752383, 0.6175223, 0.50563973, 0.36470804, 0.65460044, 0.5775188, 0.44332123]
)
expected_slice_1 = np.array(
[0.3592432, 0.4233033, 0.3914635, 0.31014425, 0.3702293, 0.39412856, 0.17526966, 0.2642669, 0.37480092]
)
assert np.abs(expected_slice_0 - image_slice_0).max() < 3e-3
assert np.abs(expected_slice_1 - image_slice_1).max() < 3e-3
def test_stable_diffusion_inpaint_legacy_k_lms(self):
pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.4534, 0.4467, 0.4329, 0.4329, 0.4339, 0.4220, 0.4244, 0.4332, 0.4426])
assert np.abs(expected_slice - image_slice).max() < 3e-3
def test_stable_diffusion_inpaint_legacy_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.5977, 1.5449, 1.0586, -0.3250, 0.7383, -0.0862, 0.4631, -0.2571, -1.1289])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.5190, 1.1621, 0.6885, 0.2424, 0.3337, -0.1617, 0.6914, -0.1957, -0.5474])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
callback_fn.has_been_called = False
pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 2
@nightly
@require_torch_gpu
class StableDiffusionInpaintLegacyPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "A red cat sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 50,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_inpaint_pndm(self):
sd_pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint_legacy/stable_diffusion_1_5_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_ddim(self):
sd_pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint_legacy/stable_diffusion_1_5_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_lms(self):
sd_pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint_legacy/stable_diffusion_1_5_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_dpm(self):
sd_pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 30
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint_legacy/stable_diffusion_1_5_dpm_multi.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/audioldm2/test_audioldm2.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import (
ClapAudioConfig,
ClapConfig,
ClapFeatureExtractor,
ClapModel,
ClapTextConfig,
GPT2Config,
GPT2Model,
RobertaTokenizer,
SpeechT5HifiGan,
SpeechT5HifiGanConfig,
T5Config,
T5EncoderModel,
T5Tokenizer,
)
from diffusers import (
AudioLDM2Pipeline,
AudioLDM2ProjectionModel,
AudioLDM2UNet2DConditionModel,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from diffusers.utils.testing_utils import enable_full_determinism, nightly, torch_device
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class AudioLDM2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = AudioLDM2Pipeline
params = TEXT_TO_AUDIO_PARAMS
batch_params = TEXT_TO_AUDIO_BATCH_PARAMS
required_optional_params = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
]
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = AudioLDM2UNet2DConditionModel(
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=([None, 16, 32], [None, 16, 32]),
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=1,
out_channels=1,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_branch_config = ClapTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=16,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=2,
num_hidden_layers=2,
pad_token_id=1,
vocab_size=1000,
projection_dim=16,
)
audio_branch_config = ClapAudioConfig(
spec_size=64,
window_size=4,
num_mel_bins=64,
intermediate_size=37,
layer_norm_eps=1e-05,
depths=[2, 2],
num_attention_heads=[2, 2],
num_hidden_layers=2,
hidden_size=192,
projection_dim=16,
patch_size=2,
patch_stride=2,
patch_embed_input_channels=4,
)
text_encoder_config = ClapConfig.from_text_audio_configs(
text_config=text_branch_config, audio_config=audio_branch_config, projection_dim=16
)
text_encoder = ClapModel(text_encoder_config)
tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
feature_extractor = ClapFeatureExtractor.from_pretrained(
"hf-internal-testing/tiny-random-ClapModel", hop_length=7900
)
torch.manual_seed(0)
text_encoder_2_config = T5Config(
vocab_size=32100,
d_model=32,
d_ff=37,
d_kv=8,
num_heads=2,
num_layers=2,
)
text_encoder_2 = T5EncoderModel(text_encoder_2_config)
tokenizer_2 = T5Tokenizer.from_pretrained("hf-internal-testing/tiny-random-T5Model", model_max_length=77)
torch.manual_seed(0)
language_model_config = GPT2Config(
n_embd=16,
n_head=2,
n_layer=2,
vocab_size=1000,
n_ctx=99,
n_positions=99,
)
language_model = GPT2Model(language_model_config)
language_model.config.max_new_tokens = 8
torch.manual_seed(0)
projection_model = AudioLDM2ProjectionModel(text_encoder_dim=16, text_encoder_1_dim=32, langauge_model_dim=16)
vocoder_config = SpeechT5HifiGanConfig(
model_in_dim=8,
sampling_rate=16000,
upsample_initial_channel=16,
upsample_rates=[2, 2],
upsample_kernel_sizes=[4, 4],
resblock_kernel_sizes=[3, 7],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
normalize_before=False,
)
vocoder = SpeechT5HifiGan(vocoder_config)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"text_encoder_2": text_encoder_2,
"tokenizer": tokenizer,
"tokenizer_2": tokenizer_2,
"feature_extractor": feature_extractor,
"language_model": language_model,
"projection_model": projection_model,
"vocoder": vocoder,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
}
return inputs
def test_audioldm2_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDM2Pipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(**inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[0.0025, 0.0018, 0.0018, -0.0023, -0.0026, -0.0020, -0.0026, -0.0021, -0.0027, -0.0020]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-4
def test_audioldm2_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDM2Pipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = audioldm_pipe.tokenizer(
prompt,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
clap_prompt_embeds = audioldm_pipe.text_encoder.get_text_features(text_inputs)
clap_prompt_embeds = clap_prompt_embeds[:, None, :]
text_inputs = audioldm_pipe.tokenizer_2(
prompt,
padding="max_length",
max_length=True,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
t5_prompt_embeds = audioldm_pipe.text_encoder_2(
text_inputs,
)
t5_prompt_embeds = t5_prompt_embeds[0]
projection_embeds = audioldm_pipe.projection_model(clap_prompt_embeds, t5_prompt_embeds)[0]
generated_prompt_embeds = audioldm_pipe.generate_language_model(projection_embeds, max_new_tokens=8)
inputs["prompt_embeds"] = t5_prompt_embeds
inputs["generated_prompt_embeds"] = generated_prompt_embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm2_negative_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDM2Pipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
embeds = []
generated_embeds = []
for p in [prompt, negative_prompt]:
text_inputs = audioldm_pipe.tokenizer(
p,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
clap_prompt_embeds = audioldm_pipe.text_encoder.get_text_features(text_inputs)
clap_prompt_embeds = clap_prompt_embeds[:, None, :]
text_inputs = audioldm_pipe.tokenizer_2(
prompt,
padding="max_length",
max_length=True if len(embeds) == 0 else embeds[0].shape[1],
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
t5_prompt_embeds = audioldm_pipe.text_encoder_2(
text_inputs,
)
t5_prompt_embeds = t5_prompt_embeds[0]
projection_embeds = audioldm_pipe.projection_model(clap_prompt_embeds, t5_prompt_embeds)[0]
generated_prompt_embeds = audioldm_pipe.generate_language_model(projection_embeds, max_new_tokens=8)
embeds.append(t5_prompt_embeds)
generated_embeds.append(generated_prompt_embeds)
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
inputs["generated_prompt_embeds"], inputs["negative_generated_prompt_embeds"] = generated_embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm2_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDM2Pipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "egg cracking"
output = audioldm_pipe(**inputs, negative_prompt=negative_prompt)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[0.0025, 0.0018, 0.0018, -0.0023, -0.0026, -0.0020, -0.0026, -0.0021, -0.0027, -0.0020]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-4
def test_audioldm2_num_waveforms_per_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDM2Pipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = "A hammer hitting a wooden surface"
# test num_waveforms_per_prompt=1 (default)
audios = audioldm_pipe(prompt, num_inference_steps=2).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
batch_size = 2
audios = audioldm_pipe([prompt] * batch_size, num_inference_steps=2).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
num_waveforms_per_prompt = 2
audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
batch_size = 2
audios = audioldm_pipe(
[prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt
).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def test_audioldm2_audio_length_in_s(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDM2Pipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
vocoder_sampling_rate = audioldm_pipe.vocoder.config.sampling_rate
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(audio_length_in_s=0.016, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.016
output = audioldm_pipe(audio_length_in_s=0.032, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.032
def test_audioldm2_vocoder_model_in_dim(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDM2Pipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = ["hey"]
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
assert audio_shape == (1, 256)
config = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
audioldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)
@unittest.skip("Raises a not implemented error in AudioLDM2")
def test_xformers_attention_forwardGenerator_pass(self):
pass
def test_dict_tuple_outputs_equivalent(self):
# increase tolerance from 1e-4 -> 2e-4 to account for large composite model
super().test_dict_tuple_outputs_equivalent(expected_max_difference=2e-4)
def test_inference_batch_single_identical(self):
# increase tolerance from 1e-4 -> 2e-4 to account for large composite model
self._test_inference_batch_single_identical(expected_max_diff=2e-4)
def test_save_load_local(self):
# increase tolerance from 1e-4 -> 2e-4 to account for large composite model
super().test_save_load_local(expected_max_difference=2e-4)
def test_save_load_optional_components(self):
# increase tolerance from 1e-4 -> 2e-4 to account for large composite model
super().test_save_load_optional_components(expected_max_difference=2e-4)
def test_to_dtype(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.set_progress_bar_config(disable=None)
# The method component.dtype returns the dtype of the first parameter registered in the model, not the
# dtype of the entire model. In the case of CLAP, the first parameter is a float64 constant (logit scale)
model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, "dtype")}
# Without the logit scale parameters, everything is float32
model_dtypes.pop("text_encoder")
self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))
# the CLAP sub-models are float32
model_dtypes["clap_text_branch"] = components["text_encoder"].text_model.dtype
self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))
# Once we send to fp16, all params are in half-precision, including the logit scale
pipe.to(torch_dtype=torch.float16)
model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, "dtype")}
self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes.values()))
def test_sequential_cpu_offload_forward_pass(self):
pass
@nightly
class AudioLDM2PipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_audioldm2(self):
audioldm_pipe = AudioLDM2Pipeline.from_pretrained("cvssp/audioldm2")
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81952
# check the portion of the generated audio with the largest dynamic range (reduces flakiness)
audio_slice = audio[17275:17285]
expected_slice = np.array([0.0791, 0.0666, 0.1158, 0.1227, 0.1171, -0.2880, -0.1940, -0.0283, -0.0126, 0.1127])
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-3
def test_audioldm2_lms(self):
audioldm_pipe = AudioLDM2Pipeline.from_pretrained("cvssp/audioldm2")
audioldm_pipe.scheduler = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81952
# check the portion of the generated audio with the largest dynamic range (reduces flakiness)
audio_slice = audio[31390:31400]
expected_slice = np.array(
[-0.1318, -0.0577, 0.0446, -0.0573, 0.0659, 0.1074, -0.2600, 0.0080, -0.2190, -0.4301]
)
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-3
def test_audioldm2_large(self):
audioldm_pipe = AudioLDM2Pipeline.from_pretrained("cvssp/audioldm2-large")
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81952
# check the portion of the generated audio with the largest dynamic range (reduces flakiness)
audio_slice = audio[8825:8835]
expected_slice = np.array(
[-0.1829, -0.1461, 0.0759, -0.1493, -0.1396, 0.5783, 0.3001, -0.3038, -0.0639, -0.2244]
)
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/spectrogram_diffusion/test_spectrogram_diffusion.py
|
# 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 gc
import unittest
import numpy as np
import torch
from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder
from diffusers.utils.testing_utils import (
enable_full_determinism,
nightly,
require_note_seq,
require_onnxruntime,
require_torch_gpu,
skip_mps,
torch_device,
)
from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
MIDI_FILE = "./tests/fixtures/elise_format0.mid"
# The note-seq package throws an error on import because the default installed version of Ipython
# is not compatible with python 3.8 which we run in the CI.
# https://github.com/huggingface/diffusers/actions/runs/4830121056/jobs/8605954838#step:7:98
@unittest.skip("The note-seq package currently throws an error on import")
class SpectrogramDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = SpectrogramDiffusionPipeline
required_optional_params = PipelineTesterMixin.required_optional_params - {
"callback",
"latents",
"callback_steps",
"output_type",
"num_images_per_prompt",
}
test_attention_slicing = False
batch_params = TOKENS_TO_AUDIO_GENERATION_PARAMS
params = TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
notes_encoder = SpectrogramNotesEncoder(
max_length=2048,
vocab_size=1536,
d_model=768,
dropout_rate=0.1,
num_layers=1,
num_heads=1,
d_kv=4,
d_ff=2048,
feed_forward_proj="gated-gelu",
)
continuous_encoder = SpectrogramContEncoder(
input_dims=128,
targets_context_length=256,
d_model=768,
dropout_rate=0.1,
num_layers=1,
num_heads=1,
d_kv=4,
d_ff=2048,
feed_forward_proj="gated-gelu",
)
decoder = T5FilmDecoder(
input_dims=128,
targets_length=256,
max_decoder_noise_time=20000.0,
d_model=768,
num_layers=1,
num_heads=1,
d_kv=4,
d_ff=2048,
dropout_rate=0.1,
)
scheduler = DDPMScheduler()
components = {
"notes_encoder": notes_encoder.eval(),
"continuous_encoder": continuous_encoder.eval(),
"decoder": decoder.eval(),
"scheduler": scheduler,
"melgan": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"input_tokens": [
[1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
],
"generator": generator,
"num_inference_steps": 4,
"output_type": "mel",
}
return inputs
def test_spectrogram_diffusion(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = SpectrogramDiffusionPipeline(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = pipe(**inputs)
mel = output.audios
mel_slice = mel[0, -3:, -3:]
assert mel_slice.shape == (3, 3)
expected_slice = np.array(
[-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0]
)
assert np.abs(mel_slice.flatten() - expected_slice).max() < 1e-2
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local()
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@skip_mps
def test_attention_slicing_forward_pass(self):
return super().test_attention_slicing_forward_pass()
def test_inference_batch_single_identical(self):
pass
def test_inference_batch_consistent(self):
pass
@skip_mps
def test_progress_bar(self):
return super().test_progress_bar()
@nightly
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class PipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_callback(self):
# TODO - test that pipeline can decode tokens in a callback
# so that music can be played live
device = torch_device
pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
melgan = pipe.melgan
pipe.melgan = None
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
def callback(step, mel_output):
# decode mel to audio
audio = melgan(input_features=mel_output.astype(np.float32))[0]
assert len(audio[0]) == 81920 * (step + 1)
# simulate that audio is played
return audio
processor = MidiProcessor()
input_tokens = processor(MIDI_FILE)
input_tokens = input_tokens[:3]
generator = torch.manual_seed(0)
pipe(input_tokens, num_inference_steps=5, generator=generator, callback=callback, output_type="mel")
def test_spectrogram_fast(self):
device = torch_device
pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
processor = MidiProcessor()
input_tokens = processor(MIDI_FILE)
# just run two denoising loops
input_tokens = input_tokens[:2]
generator = torch.manual_seed(0)
output = pipe(input_tokens, num_inference_steps=2, generator=generator)
audio = output.audios[0]
assert abs(np.abs(audio).sum() - 3612.841) < 1e-1
def test_spectrogram(self):
device = torch_device
pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
processor = MidiProcessor()
input_tokens = processor(MIDI_FILE)
# just run 4 denoising loops
input_tokens = input_tokens[:4]
generator = torch.manual_seed(0)
output = pipe(input_tokens, num_inference_steps=100, generator=generator)
audio = output.audios[0]
assert abs(np.abs(audio).sum() - 9389.1111) < 5e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/musicldm/test_musicldm.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
from transformers import (
ClapAudioConfig,
ClapConfig,
ClapFeatureExtractor,
ClapModel,
ClapTextConfig,
RobertaTokenizer,
SpeechT5HifiGan,
SpeechT5HifiGanConfig,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
MusicLDMPipeline,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class MusicLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = MusicLDMPipeline
params = TEXT_TO_AUDIO_PARAMS
batch_params = TEXT_TO_AUDIO_BATCH_PARAMS
required_optional_params = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
]
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=(32, 64),
class_embed_type="simple_projection",
projection_class_embeddings_input_dim=32,
class_embeddings_concat=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=1,
out_channels=1,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_branch_config = ClapTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=16,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=2,
num_hidden_layers=2,
pad_token_id=1,
vocab_size=1000,
)
audio_branch_config = ClapAudioConfig(
spec_size=64,
window_size=4,
num_mel_bins=64,
intermediate_size=37,
layer_norm_eps=1e-05,
depths=[2, 2],
num_attention_heads=[2, 2],
num_hidden_layers=2,
hidden_size=192,
patch_size=2,
patch_stride=2,
patch_embed_input_channels=4,
)
text_encoder_config = ClapConfig.from_text_audio_configs(
text_config=text_branch_config, audio_config=audio_branch_config, projection_dim=32
)
text_encoder = ClapModel(text_encoder_config)
tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
feature_extractor = ClapFeatureExtractor.from_pretrained(
"hf-internal-testing/tiny-random-ClapModel", hop_length=7900
)
torch.manual_seed(0)
vocoder_config = SpeechT5HifiGanConfig(
model_in_dim=8,
sampling_rate=16000,
upsample_initial_channel=16,
upsample_rates=[2, 2],
upsample_kernel_sizes=[4, 4],
resblock_kernel_sizes=[3, 7],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
normalize_before=False,
)
vocoder = SpeechT5HifiGan(vocoder_config)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"feature_extractor": feature_extractor,
"vocoder": vocoder,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
}
return inputs
def test_musicldm_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
musicldm_pipe = MusicLDMPipeline(**components)
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = musicldm_pipe(**inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0027, -0.0036, -0.0037, -0.0020, -0.0035, -0.0019, -0.0037, -0.0020, -0.0038, -0.0019]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-4
def test_musicldm_prompt_embeds(self):
components = self.get_dummy_components()
musicldm_pipe = MusicLDMPipeline(**components)
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = musicldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = musicldm_pipe.tokenizer(
prompt,
padding="max_length",
max_length=musicldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = musicldm_pipe.text_encoder.get_text_features(text_inputs)
inputs["prompt_embeds"] = prompt_embeds
# forward
output = musicldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_musicldm_negative_prompt_embeds(self):
components = self.get_dummy_components()
musicldm_pipe = MusicLDMPipeline(**components)
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = musicldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = musicldm_pipe.tokenizer(
p,
padding="max_length",
max_length=musicldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
text_embeds = musicldm_pipe.text_encoder.get_text_features(
text_inputs,
)
embeds.append(text_embeds)
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = musicldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_musicldm_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
musicldm_pipe = MusicLDMPipeline(**components)
musicldm_pipe = musicldm_pipe.to(device)
musicldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "egg cracking"
output = musicldm_pipe(**inputs, negative_prompt=negative_prompt)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0027, -0.0036, -0.0037, -0.0019, -0.0035, -0.0018, -0.0037, -0.0021, -0.0038, -0.0018]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-4
def test_musicldm_num_waveforms_per_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
musicldm_pipe = MusicLDMPipeline(**components)
musicldm_pipe = musicldm_pipe.to(device)
musicldm_pipe.set_progress_bar_config(disable=None)
prompt = "A hammer hitting a wooden surface"
# test num_waveforms_per_prompt=1 (default)
audios = musicldm_pipe(prompt, num_inference_steps=2).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
batch_size = 2
audios = musicldm_pipe([prompt] * batch_size, num_inference_steps=2).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
num_waveforms_per_prompt = 2
audios = musicldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
batch_size = 2
audios = musicldm_pipe(
[prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt
).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def test_musicldm_audio_length_in_s(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
musicldm_pipe = MusicLDMPipeline(**components)
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe.set_progress_bar_config(disable=None)
vocoder_sampling_rate = musicldm_pipe.vocoder.config.sampling_rate
inputs = self.get_dummy_inputs(device)
output = musicldm_pipe(audio_length_in_s=0.016, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.016
output = musicldm_pipe(audio_length_in_s=0.032, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.032
def test_musicldm_vocoder_model_in_dim(self):
components = self.get_dummy_components()
musicldm_pipe = MusicLDMPipeline(**components)
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe.set_progress_bar_config(disable=None)
prompt = ["hey"]
output = musicldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
assert audio_shape == (1, 256)
config = musicldm_pipe.vocoder.config
config.model_in_dim *= 2
musicldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)
output = musicldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical()
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
def test_to_dtype(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.set_progress_bar_config(disable=None)
# The method component.dtype returns the dtype of the first parameter registered in the model, not the
# dtype of the entire model. In the case of CLAP, the first parameter is a float64 constant (logit scale)
model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, "dtype")}
# Without the logit scale parameters, everything is float32
model_dtypes.pop("text_encoder")
self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))
# the CLAP sub-models are float32
model_dtypes["clap_text_branch"] = components["text_encoder"].text_model.dtype
self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))
# Once we send to fp16, all params are in half-precision, including the logit scale
pipe.to(torch_dtype=torch.float16)
model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, "dtype")}
self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes.values()))
@nightly
@require_torch_gpu
class MusicLDMPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_musicldm(self):
musicldm_pipe = MusicLDMPipeline.from_pretrained("cvssp/musicldm")
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
audio = musicldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81952
# check the portion of the generated audio with the largest dynamic range (reduces flakiness)
audio_slice = audio[8680:8690]
expected_slice = np.array(
[-0.1042, -0.1068, -0.1235, -0.1387, -0.1428, -0.136, -0.1213, -0.1097, -0.0967, -0.0945]
)
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-3
def test_musicldm_lms(self):
musicldm_pipe = MusicLDMPipeline.from_pretrained("cvssp/musicldm")
musicldm_pipe.scheduler = LMSDiscreteScheduler.from_config(musicldm_pipe.scheduler.config)
musicldm_pipe = musicldm_pipe.to(torch_device)
musicldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
audio = musicldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81952
# check the portion of the generated audio with the largest dynamic range (reduces flakiness)
audio_slice = audio[58020:58030]
expected_slice = np.array([0.3592, 0.3477, 0.4084, 0.4665, 0.5048, 0.5891, 0.6461, 0.5579, 0.4595, 0.4403])
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-3
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from parameterized import parameterized
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
LCMScheduler,
MultiAdapter,
StableDiffusionXLAdapterPipeline,
T2IAdapter,
UNet2DConditionModel,
)
from diffusers.utils import load_image, logging
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineTesterMixin,
SDXLOptionalComponentsTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class StableDiffusionXLAdapterPipelineFastTests(
PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLAdapterPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
def get_dummy_components(self, adapter_type="full_adapter_xl", time_cond_proj_dim=None):
torch.manual_seed(0)
unet = 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"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
time_cond_proj_dim=time_cond_proj_dim,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
if adapter_type == "full_adapter_xl":
adapter = T2IAdapter(
in_channels=3,
channels=[32, 64],
num_res_blocks=2,
downscale_factor=4,
adapter_type=adapter_type,
)
elif adapter_type == "multi_adapter":
adapter = MultiAdapter(
[
T2IAdapter(
in_channels=3,
channels=[32, 64],
num_res_blocks=2,
downscale_factor=4,
adapter_type="full_adapter_xl",
),
T2IAdapter(
in_channels=3,
channels=[32, 64],
num_res_blocks=2,
downscale_factor=4,
adapter_type="full_adapter_xl",
),
]
)
else:
raise ValueError(
f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter_xl', or 'multi_adapter''"
)
components = {
"adapter": adapter,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def get_dummy_components_with_full_downscaling(self, adapter_type="full_adapter_xl"):
"""Get dummy components with x8 VAE downscaling and 3 UNet down blocks.
These dummy components are intended to fully-exercise the T2I-Adapter
downscaling behavior.
"""
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=2,
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=1,
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 32, 32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
if adapter_type == "full_adapter_xl":
adapter = T2IAdapter(
in_channels=3,
channels=[32, 32, 64],
num_res_blocks=2,
downscale_factor=16,
adapter_type=adapter_type,
)
elif adapter_type == "multi_adapter":
adapter = MultiAdapter(
[
T2IAdapter(
in_channels=3,
channels=[32, 32, 64],
num_res_blocks=2,
downscale_factor=16,
adapter_type="full_adapter_xl",
),
T2IAdapter(
in_channels=3,
channels=[32, 32, 64],
num_res_blocks=2,
downscale_factor=16,
adapter_type="full_adapter_xl",
),
]
)
else:
raise ValueError(
f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter_xl', or 'multi_adapter''"
)
components = {
"adapter": adapter,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0, height=64, width=64, num_images=1):
if num_images == 1:
image = floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device)
else:
image = [
floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device) for _ in range(num_images)
]
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_adapter_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLAdapterPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.5752919, 0.6022097, 0.4728038, 0.49861962, 0.57084894, 0.4644975, 0.5193715, 0.5133664, 0.4729858]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
@parameterized.expand(
[
# (dim=144) The internal feature map will be 9x9 after initial pixel unshuffling (downscaled x16).
(((4 * 2 + 1) * 16),),
# (dim=160) The internal feature map will be 5x5 after the first T2I down block (downscaled x32).
(((4 * 1 + 1) * 32),),
]
)
def test_multiple_image_dimensions(self, dim):
"""Test that the T2I-Adapter pipeline supports any input dimension that
is divisible by the adapter's `downscale_factor`. This test was added in
response to an issue where the T2I Adapter's downscaling padding
behavior did not match the UNet's behavior.
Note that we have selected `dim` values to produce odd resolutions at
each downscaling level.
"""
components = self.get_dummy_components_with_full_downscaling()
sd_pipe = StableDiffusionXLAdapterPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device, height=dim, width=dim)
image = sd_pipe(**inputs).images
assert image.shape == (1, dim, dim, 3)
@parameterized.expand(["full_adapter", "full_adapter_xl", "light_adapter"])
def test_total_downscale_factor(self, adapter_type):
"""Test that the T2IAdapter correctly reports its total_downscale_factor."""
batch_size = 1
in_channels = 3
out_channels = [320, 640, 1280, 1280]
in_image_size = 512
adapter = T2IAdapter(
in_channels=in_channels,
channels=out_channels,
num_res_blocks=2,
downscale_factor=8,
adapter_type=adapter_type,
)
adapter.to(torch_device)
in_image = floats_tensor((batch_size, in_channels, in_image_size, in_image_size)).to(torch_device)
adapter_state = adapter(in_image)
# Assume that the last element in `adapter_state` has been downsampled the most, and check
# that it matches the `total_downscale_factor`.
expected_out_image_size = in_image_size // adapter.total_downscale_factor
assert adapter_state[-1].shape == (
batch_size,
out_channels[-1],
expected_out_image_size,
expected_out_image_size,
)
def test_save_load_optional_components(self):
return self._test_save_load_optional_components()
def test_adapter_sdxl_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLAdapterPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5425, 0.5385, 0.4964, 0.5045, 0.6149, 0.4974, 0.5469, 0.5332, 0.5426])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_adapter_sdxl_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLAdapterPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5425, 0.5385, 0.4964, 0.5045, 0.6149, 0.4974, 0.5469, 0.5332, 0.5426])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
class StableDiffusionXLMultiAdapterPipelineFastTests(
StableDiffusionXLAdapterPipelineFastTests, PipelineTesterMixin, unittest.TestCase
):
def get_dummy_components(self, time_cond_proj_dim=None):
return super().get_dummy_components("multi_adapter", time_cond_proj_dim=time_cond_proj_dim)
def get_dummy_components_with_full_downscaling(self):
return super().get_dummy_components_with_full_downscaling("multi_adapter")
def get_dummy_inputs(self, device, seed=0, height=64, width=64):
inputs = super().get_dummy_inputs(device, seed, height, width, num_images=2)
inputs["adapter_conditioning_scale"] = [0.5, 0.5]
return inputs
def test_stable_diffusion_adapter_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLAdapterPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.5813032, 0.60995954, 0.47563356, 0.5056669, 0.57199144, 0.4631841, 0.5176794, 0.51252556, 0.47183886]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
def test_inference_batch_consistent(
self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=["num_inference_steps"]
):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
logger = logging.get_logger(pipe.__module__)
logger.setLevel(level=diffusers.logging.FATAL)
# batchify inputs
for batch_size in batch_sizes:
batched_inputs = {}
for name, value in inputs.items():
if name in self.batch_params:
# prompt is string
if name == "prompt":
len_prompt = len(value)
# make unequal batch sizes
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
# make last batch super long
batched_inputs[name][-1] = 100 * "very long"
elif name == "image":
batched_images = []
for image in value:
batched_images.append(batch_size * [image])
batched_inputs[name] = batched_images
else:
batched_inputs[name] = batch_size * [value]
elif name == "batch_size":
batched_inputs[name] = batch_size
else:
batched_inputs[name] = value
for arg in additional_params_copy_to_batched_inputs:
batched_inputs[arg] = inputs[arg]
batched_inputs["output_type"] = "np"
output = pipe(**batched_inputs)
assert len(output[0]) == batch_size
batched_inputs["output_type"] = "np"
output = pipe(**batched_inputs)[0]
assert output.shape[0] == batch_size
logger.setLevel(level=diffusers.logging.WARNING)
def test_num_images_per_prompt(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
batch_sizes = [1, 2]
num_images_per_prompts = [1, 2]
for batch_size in batch_sizes:
for num_images_per_prompt in num_images_per_prompts:
inputs = self.get_dummy_inputs(torch_device)
for key in inputs.keys():
if key in self.batch_params:
if key == "image":
batched_images = []
for image in inputs[key]:
batched_images.append(batch_size * [image])
inputs[key] = batched_images
else:
inputs[key] = batch_size * [inputs[key]]
images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
def test_inference_batch_single_identical(
self,
batch_size=3,
test_max_difference=None,
test_mean_pixel_difference=None,
relax_max_difference=False,
expected_max_diff=2e-3,
additional_params_copy_to_batched_inputs=["num_inference_steps"],
):
if test_max_difference is None:
# TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems
# make sure that batched and non-batched is identical
test_max_difference = torch_device != "mps"
if test_mean_pixel_difference is None:
# TODO same as above
test_mean_pixel_difference = torch_device != "mps"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
logger = logging.get_logger(pipe.__module__)
logger.setLevel(level=diffusers.logging.FATAL)
# batchify inputs
batched_inputs = {}
batch_size = batch_size
for name, value in inputs.items():
if name in self.batch_params:
# prompt is string
if name == "prompt":
len_prompt = len(value)
# make unequal batch sizes
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
# make last batch super long
batched_inputs[name][-1] = 100 * "very long"
elif name == "image":
batched_images = []
for image in value:
batched_images.append(batch_size * [image])
batched_inputs[name] = batched_images
else:
batched_inputs[name] = batch_size * [value]
elif name == "batch_size":
batched_inputs[name] = batch_size
elif name == "generator":
batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)]
else:
batched_inputs[name] = value
for arg in additional_params_copy_to_batched_inputs:
batched_inputs[arg] = inputs[arg]
output_batch = pipe(**batched_inputs)
assert output_batch[0].shape[0] == batch_size
inputs["generator"] = self.get_generator(0)
output = pipe(**inputs)
logger.setLevel(level=diffusers.logging.WARNING)
if test_max_difference:
if relax_max_difference:
# Taking the median of the largest <n> differences
# is resilient to outliers
diff = np.abs(output_batch[0][0] - output[0][0])
diff = diff.flatten()
diff.sort()
max_diff = np.median(diff[-5:])
else:
max_diff = np.abs(output_batch[0][0] - output[0][0]).max()
assert max_diff < expected_max_diff
if test_mean_pixel_difference:
assert_mean_pixel_difference(output_batch[0][0], output[0][0])
def test_adapter_sdxl_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLAdapterPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5313, 0.5375, 0.4942, 0.5021, 0.6142, 0.4968, 0.5434, 0.5311, 0.5448])
debug = [str(round(i, 4)) for i in image_slice.flatten().tolist()]
print(",".join(debug))
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_adapter_sdxl_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLAdapterPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5313, 0.5375, 0.4942, 0.5021, 0.6142, 0.4968, 0.5434, 0.5311, 0.5448])
debug = [str(round(i, 4)) for i in image_slice.flatten().tolist()]
print(",".join(debug))
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch_gpu
class AdapterSDXLPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny_lora(self):
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16).to(
"cpu"
)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
adapter=adapter,
torch_dtype=torch.float16,
variant="fp16",
)
pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors")
pipe.enable_sequential_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "toy"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png"
)
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
assert images[0].shape == (768, 512, 3)
original_image = images[0, -3:, -3:, -1].flatten()
expected_image = np.array(
[0.50346327, 0.50708383, 0.50719553, 0.5135172, 0.5155377, 0.5066059, 0.49680984, 0.5005894, 0.48509413]
)
assert numpy_cosine_similarity_distance(original_image, expected_image) < 1e-4
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
AutoencoderKL,
AutoencoderTiny,
EulerDiscreteScheduler,
LCMScheduler,
StableDiffusionXLImg2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
require_torch_gpu,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin
enable_full_determinism()
class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionXLImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(
{"add_text_embeds", "add_time_ids", "add_neg_time_ids"}
)
def get_dummy_components(self, skip_first_text_encoder=False, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
time_cond_proj_dim=time_cond_proj_dim,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=72, # 5 * 8 + 32
cross_attention_dim=64 if not skip_first_text_encoder else 32,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
image_encoder_config = CLIPVisionConfig(
hidden_size=32,
image_size=224,
projection_dim=32,
intermediate_size=37,
num_attention_heads=4,
num_channels=3,
num_hidden_layers=5,
patch_size=14,
)
image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
feature_extractor = CLIPImageProcessor(
crop_size=224,
do_center_crop=True,
do_normalize=True,
do_resize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
resample=3,
size=224,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder if not skip_first_text_encoder else None,
"tokenizer": tokenizer if not skip_first_text_encoder else None,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"requires_aesthetics_score": True,
"image_encoder": image_encoder,
"feature_extractor": feature_extractor,
}
return components
def get_dummy_tiny_autoencoder(self):
return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)
def test_components_function(self):
init_components = self.get_dummy_components()
init_components.pop("requires_aesthetics_score")
pipe = self.pipeline_class(**init_components)
self.assertTrue(hasattr(pipe, "components"))
self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
"strength": 0.8,
}
return inputs
def test_stable_diffusion_xl_img2img_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4664, 0.4886, 0.4403, 0.6902, 0.5592, 0.4534, 0.5931, 0.5951, 0.5224])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_img2img_euler_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.5604, 0.4352, 0.4717, 0.5844, 0.5101, 0.6704, 0.6290, 0.5460, 0.5286])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_img2img_euler_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.5604, 0.4352, 0.4717, 0.5844, 0.5101, 0.6704, 0.6290, 0.5460, 0.5286])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
# TODO(Patrick, Sayak) - skip for now as this requires more refiner tests
def test_save_load_optional_components(self):
pass
def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
negative_prompt = 3 * ["this is a negative prompt"]
prompt = 3 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_xl_img2img_tiny_autoencoder(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.vae = self.get_dummy_tiny_autoencoder()
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.0, 0.0, 0.0106, 0.0, 0.0, 0.0087, 0.0052, 0.0062, 0.0177])
assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_multi_prompts(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
def test_stable_diffusion_xl_img2img_negative_conditions(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]
image = sd_pipe(
**inputs,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(
0,
0,
),
negative_target_size=(1024, 1024),
).images
image_slice_with_neg_conditions = image[0, -3:, -3:, -1]
assert (
np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
> 1e-4
)
class StableDiffusionXLImg2ImgRefinerOnlyPipelineFastTests(
PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=72, # 5 * 8 + 32
cross_attention_dim=32,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"tokenizer": None,
"text_encoder": None,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"requires_aesthetics_score": True,
"image_encoder": None,
"feature_extractor": None,
}
return components
def test_components_function(self):
init_components = self.get_dummy_components()
init_components.pop("requires_aesthetics_score")
pipe = self.pipeline_class(**init_components)
self.assertTrue(hasattr(pipe, "components"))
self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
"strength": 0.8,
}
return inputs
def test_stable_diffusion_xl_img2img_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4745, 0.4924, 0.4338, 0.6468, 0.5547, 0.4419, 0.5646, 0.5897, 0.5146])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_img2img_negative_conditions(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]
image = sd_pipe(
**inputs,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(
0,
0,
),
negative_target_size=(1024, 1024),
).images
image_slice_with_neg_conditions = image[0, -3:, -3:, -1]
assert (
np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
> 1e-4
)
def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
negative_prompt = 3 * ["this is a negative prompt"]
prompt = 3 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_xl_img2img_prompt_embeds_only(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
prompt = 3 * [inputs.pop("prompt")]
(
prompt_embeds,
_,
pooled_prompt_embeds,
_,
) = sd_pipe.encode_prompt(prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_instruction_pix2pix.py
|
# coding=utf-8
# Copyright 2023 Harutatsu Akiyama and 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 random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
UNet2DConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_instruct_pix2pix import (
StableDiffusionXLInstructPix2PixPipeline,
)
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, torch_device
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
SDXLOptionalComponentsTesterMixin,
)
enable_full_determinism()
class StableDiffusionXLInstructPix2PixPipelineFastTests(
PipelineLatentTesterMixin,
PipelineKarrasSchedulerTesterMixin,
PipelineTesterMixin,
SDXLOptionalComponentsTesterMixin,
unittest.TestCase,
):
pipeline_class = StableDiffusionXLInstructPix2PixPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=8,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 5 * 8 + 32
cross_attention_dim=64,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
}
return components
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"image_guidance_scale": 1,
"output_type": "numpy",
}
return inputs
def test_components_function(self):
init_components = self.get_dummy_components()
pipe = self.pipeline_class(**init_components)
self.assertTrue(hasattr(pipe, "components"))
self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=2e-3)
# Overwrite the default test_latents_inputs because pix2pix encode the image differently
def test_latents_input(self):
components = self.get_dummy_components()
pipe = StableDiffusionXLInstructPix2PixPipeline(**components)
pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0]
vae = components["vae"]
inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt")
for image_param in self.image_latents_params:
if image_param in inputs.keys():
inputs[image_param] = vae.encode(inputs[image_param]).latent_dist.mode()
out_latents_inputs = pipe(**inputs)[0]
max_diff = np.abs(out - out_latents_inputs).max()
self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image")
def test_cfg(self):
pass
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
LCMScheduler,
StableDiffusionXLImg2ImgPipeline,
StableDiffusionXLPipeline,
UNet2DConditionModel,
UniPCMultistepScheduler,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_image,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import (
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin
enable_full_determinism()
class StableDiffusionXLPipelineFastTests(
PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"add_text_embeds", "add_time_ids"})
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(2, 4),
layers_per_block=2,
time_cond_proj_dim=time_cond_proj_dim,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
norm_num_groups=1,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"image_encoder": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
}
return inputs
def test_stable_diffusion_xl_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5552, 0.5569, 0.4725, 0.4348, 0.4994, 0.4632, 0.5142, 0.5012, 0.47])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_euler_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4917, 0.6555, 0.4348, 0.5219, 0.7324, 0.4855, 0.5168, 0.5447, 0.5156])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_euler_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4917, 0.6555, 0.4348, 0.5219, 0.7324, 0.4855, 0.5168, 0.5447, 0.5156])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 2 * [inputs["prompt"]]
inputs["num_images_per_prompt"] = 2
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
inputs = self.get_dummy_inputs(torch_device)
prompt = 2 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_xl_negative_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
prompt = 3 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_img2img_prompt_embeds_only(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
prompt = 3 * [inputs.pop("prompt")]
(
prompt_embeds,
_,
pooled_prompt_embeds,
_,
) = sd_pipe.encode_prompt(prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_two_xl_mixture_of_denoiser_fast(self):
components = self.get_dummy_components()
pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)
pipe_1.unet.set_default_attn_processor()
pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
pipe_2.unet.set_default_attn_processor()
def assert_run_mixture(
num_steps,
split,
scheduler_cls_orig,
expected_tss,
num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,
):
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = num_steps
class scheduler_cls(scheduler_cls_orig):
pass
pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
# Let's retrieve the number of timesteps we want to use
pipe_1.scheduler.set_timesteps(num_steps)
expected_steps = pipe_1.scheduler.timesteps.tolist()
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))
expected_steps = expected_steps_1 + expected_steps_2
else:
expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))
# now we monkey patch step `done_steps`
# list into the step function for testing
done_steps = []
old_step = copy.copy(scheduler_cls.step)
def new_step(self, *args, **kwargs):
done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
return old_step(self, *args, **kwargs)
scheduler_cls.step = new_step
inputs_1 = {
**inputs,
**{
"denoising_end": 1.0 - (split / num_train_timesteps),
"output_type": "latent",
},
}
latents = pipe_1(**inputs_1).images[0]
assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
inputs_2 = {
**inputs,
**{
"denoising_start": 1.0 - (split / num_train_timesteps),
"image": latents,
},
}
pipe_2(**inputs_2).images[0]
assert expected_steps_2 == done_steps[len(expected_steps_1) :]
assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
steps = 10
for split in [300, 700]:
for scheduler_cls_timesteps in [
(EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),
(
HeunDiscreteScheduler,
[
901.0,
801.0,
801.0,
701.0,
701.0,
601.0,
601.0,
501.0,
501.0,
401.0,
401.0,
301.0,
301.0,
201.0,
201.0,
101.0,
101.0,
1.0,
1.0,
],
),
]:
assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])
@slow
def test_stable_diffusion_two_xl_mixture_of_denoiser(self):
components = self.get_dummy_components()
pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)
pipe_1.unet.set_default_attn_processor()
pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
pipe_2.unet.set_default_attn_processor()
def assert_run_mixture(
num_steps,
split,
scheduler_cls_orig,
expected_tss,
num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,
):
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = num_steps
class scheduler_cls(scheduler_cls_orig):
pass
pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
# Let's retrieve the number of timesteps we want to use
pipe_1.scheduler.set_timesteps(num_steps)
expected_steps = pipe_1.scheduler.timesteps.tolist()
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))
expected_steps = expected_steps_1 + expected_steps_2
else:
expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))
# now we monkey patch step `done_steps`
# list into the step function for testing
done_steps = []
old_step = copy.copy(scheduler_cls.step)
def new_step(self, *args, **kwargs):
done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
return old_step(self, *args, **kwargs)
scheduler_cls.step = new_step
inputs_1 = {
**inputs,
**{
"denoising_end": 1.0 - (split / num_train_timesteps),
"output_type": "latent",
},
}
latents = pipe_1(**inputs_1).images[0]
assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
inputs_2 = {
**inputs,
**{
"denoising_start": 1.0 - (split / num_train_timesteps),
"image": latents,
},
}
pipe_2(**inputs_2).images[0]
assert expected_steps_2 == done_steps[len(expected_steps_1) :]
assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
steps = 10
for split in [300, 500, 700]:
for scheduler_cls_timesteps in [
(DDIMScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),
(EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),
(DPMSolverMultistepScheduler, [901, 811, 721, 631, 541, 451, 361, 271, 181, 91]),
(UniPCMultistepScheduler, [901, 811, 721, 631, 541, 451, 361, 271, 181, 91]),
(
HeunDiscreteScheduler,
[
901.0,
801.0,
801.0,
701.0,
701.0,
601.0,
601.0,
501.0,
501.0,
401.0,
401.0,
301.0,
301.0,
201.0,
201.0,
101.0,
101.0,
1.0,
1.0,
],
),
]:
assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])
steps = 25
for split in [300, 500, 700]:
for scheduler_cls_timesteps in [
(
DDIMScheduler,
[
961,
921,
881,
841,
801,
761,
721,
681,
641,
601,
561,
521,
481,
441,
401,
361,
321,
281,
241,
201,
161,
121,
81,
41,
1,
],
),
(
EulerDiscreteScheduler,
[
961.0,
921.0,
881.0,
841.0,
801.0,
761.0,
721.0,
681.0,
641.0,
601.0,
561.0,
521.0,
481.0,
441.0,
401.0,
361.0,
321.0,
281.0,
241.0,
201.0,
161.0,
121.0,
81.0,
41.0,
1.0,
],
),
(
DPMSolverMultistepScheduler,
[
951,
913,
875,
837,
799,
761,
723,
685,
647,
609,
571,
533,
495,
457,
419,
381,
343,
305,
267,
229,
191,
153,
115,
77,
39,
],
),
(
UniPCMultistepScheduler,
[
951,
913,
875,
837,
799,
761,
723,
685,
647,
609,
571,
533,
495,
457,
419,
381,
343,
305,
267,
229,
191,
153,
115,
77,
39,
],
),
(
HeunDiscreteScheduler,
[
961.0,
921.0,
921.0,
881.0,
881.0,
841.0,
841.0,
801.0,
801.0,
761.0,
761.0,
721.0,
721.0,
681.0,
681.0,
641.0,
641.0,
601.0,
601.0,
561.0,
561.0,
521.0,
521.0,
481.0,
481.0,
441.0,
441.0,
401.0,
401.0,
361.0,
361.0,
321.0,
321.0,
281.0,
281.0,
241.0,
241.0,
201.0,
201.0,
161.0,
161.0,
121.0,
121.0,
81.0,
81.0,
41.0,
41.0,
1.0,
1.0,
],
),
]:
assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])
@slow
def test_stable_diffusion_three_xl_mixture_of_denoiser(self):
components = self.get_dummy_components()
pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)
pipe_1.unet.set_default_attn_processor()
pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
pipe_2.unet.set_default_attn_processor()
pipe_3 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
pipe_3.unet.set_default_attn_processor()
def assert_run_mixture(
num_steps,
split_1,
split_2,
scheduler_cls_orig,
num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,
):
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = num_steps
class scheduler_cls(scheduler_cls_orig):
pass
pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
pipe_3.scheduler = scheduler_cls.from_config(pipe_3.scheduler.config)
# Let's retrieve the number of timesteps we want to use
pipe_1.scheduler.set_timesteps(num_steps)
expected_steps = pipe_1.scheduler.timesteps.tolist()
split_1_ts = num_train_timesteps - int(round(num_train_timesteps * split_1))
split_2_ts = num_train_timesteps - int(round(num_train_timesteps * split_2))
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = expected_steps_1[-1:] + list(
filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps)
)
expected_steps_3 = expected_steps_2[-1:] + list(filter(lambda ts: ts < split_2_ts, expected_steps))
expected_steps = expected_steps_1 + expected_steps_2 + expected_steps_3
else:
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))
expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))
# now we monkey patch step `done_steps`
# list into the step function for testing
done_steps = []
old_step = copy.copy(scheduler_cls.step)
def new_step(self, *args, **kwargs):
done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
return old_step(self, *args, **kwargs)
scheduler_cls.step = new_step
inputs_1 = {**inputs, **{"denoising_end": split_1, "output_type": "latent"}}
latents = pipe_1(**inputs_1).images[0]
assert (
expected_steps_1 == done_steps
), f"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}"
with self.assertRaises(ValueError) as cm:
inputs_2 = {
**inputs,
**{
"denoising_start": split_2,
"denoising_end": split_1,
"image": latents,
"output_type": "latent",
},
}
pipe_2(**inputs_2).images[0]
assert "cannot be larger than or equal to `denoising_end`" in str(cm.exception)
inputs_2 = {
**inputs,
**{"denoising_start": split_1, "denoising_end": split_2, "image": latents, "output_type": "latent"},
}
pipe_2(**inputs_2).images[0]
assert expected_steps_2 == done_steps[len(expected_steps_1) :]
inputs_3 = {**inputs, **{"denoising_start": split_2, "image": latents}}
pipe_3(**inputs_3).images[0]
assert expected_steps_3 == done_steps[len(expected_steps_1) + len(expected_steps_2) :]
assert (
expected_steps == done_steps
), f"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}"
for steps in [7, 11, 20]:
for split_1, split_2 in zip([0.19, 0.32], [0.81, 0.68]):
for scheduler_cls in [
DDIMScheduler,
EulerDiscreteScheduler,
DPMSolverMultistepScheduler,
UniPCMultistepScheduler,
HeunDiscreteScheduler,
]:
assert_run_mixture(steps, split_1, split_2, scheduler_cls)
def test_stable_diffusion_xl_multi_prompts(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
inputs = self.get_dummy_inputs(torch_device)
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
def test_stable_diffusion_xl_negative_conditions(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice_with_no_neg_cond = image[0, -3:, -3:, -1]
image = sd_pipe(
**inputs,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(0, 0),
negative_target_size=(1024, 1024),
).images
image_slice_with_neg_cond = image[0, -3:, -3:, -1]
self.assertTrue(np.abs(image_slice_with_no_neg_cond - image_slice_with_neg_cond).max() > 1e-2)
def test_stable_diffusion_xl_save_from_pretrained(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLPipeline(**components).to(torch_device)
pipes.append(sd_pipe)
with tempfile.TemporaryDirectory() as tmpdirname:
sd_pipe.save_pretrained(tmpdirname)
sd_pipe = StableDiffusionXLPipeline.from_pretrained(tmpdirname).to(torch_device)
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
@slow
class StableDiffusionXLPipelineIntegrationTests(unittest.TestCase):
def test_stable_diffusion_lcm(self):
torch.manual_seed(0)
unet = UNet2DConditionModel.from_pretrained(
"latent-consistency/lcm-ssd-1b", torch_dtype=torch.float16, variant="fp16"
)
sd_pipe = StableDiffusionXLPipeline.from_pretrained(
"segmind/SSD-1B", unet=unet, torch_dtype=torch.float16, variant="fp16"
).to(torch_device)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "a red car standing on the side of the street"
image = sd_pipe(prompt, num_inference_steps=4, guidance_scale=8.0).images[0]
expected_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_full/stable_diffusion_ssd_1b_lcm.png"
)
image = sd_pipe.image_processor.pil_to_numpy(image)
expected_image = sd_pipe.image_processor.pil_to_numpy(expected_image)
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
assert max_diff < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
LCMScheduler,
StableDiffusionXLInpaintPipeline,
UNet2DConditionModel,
UniPCMultistepScheduler,
)
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class StableDiffusionXLInpaintPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionXLInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset([])
# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
image_latents_params = frozenset([])
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(
{
"add_text_embeds",
"add_time_ids",
"mask",
"masked_image_latents",
}
)
def get_dummy_components(self, skip_first_text_encoder=False, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
time_cond_proj_dim=time_cond_proj_dim,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=72, # 5 * 8 + 32
cross_attention_dim=64 if not skip_first_text_encoder else 32,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
image_encoder_config = CLIPVisionConfig(
hidden_size=32,
image_size=224,
projection_dim=32,
intermediate_size=37,
num_attention_heads=4,
num_channels=3,
num_hidden_layers=5,
patch_size=14,
)
image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
feature_extractor = CLIPImageProcessor(
crop_size=224,
do_center_crop=True,
do_normalize=True,
do_resize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
resample=3,
size=224,
)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder if not skip_first_text_encoder else None,
"tokenizer": tokenizer if not skip_first_text_encoder else None,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"image_encoder": image_encoder,
"feature_extractor": feature_extractor,
"requires_aesthetics_score": True,
}
return components
def get_dummy_inputs(self, device, seed=0):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# create mask
image[8:, 8:, :] = 255
mask_image = Image.fromarray(np.uint8(image)).convert("L").resize((64, 64))
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"strength": 1.0,
"output_type": "np",
}
return inputs
def get_dummy_inputs_2images(self, device, seed=0, img_res=64):
# Get random floats in [0, 1] as image with spatial size (img_res, img_res)
image1 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed)).to(device)
image2 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed + 22)).to(device)
# Convert images to [-1, 1]
init_image1 = 2.0 * image1 - 1.0
init_image2 = 2.0 * image2 - 1.0
# empty mask
mask_image = torch.zeros((1, 1, img_res, img_res), device=device)
if str(device).startswith("mps"):
generator1 = torch.manual_seed(seed)
generator2 = torch.manual_seed(seed)
else:
generator1 = torch.Generator(device=device).manual_seed(seed)
generator2 = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": ["A painting of a squirrel eating a burger"] * 2,
"image": [init_image1, init_image2],
"mask_image": [mask_image] * 2,
"generator": [generator1, generator2],
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
}
return inputs
def test_components_function(self):
init_components = self.get_dummy_components()
init_components.pop("requires_aesthetics_score")
pipe = self.pipeline_class(**init_components)
self.assertTrue(hasattr(pipe, "components"))
self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
def test_stable_diffusion_xl_inpaint_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLInpaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.8029, 0.5523, 0.5825, 0.6003, 0.6702, 0.7018, 0.6369, 0.5955, 0.5123])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_inpaint_euler_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLInpaintPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6611, 0.5569, 0.5531, 0.5471, 0.5918, 0.6393, 0.5074, 0.5468, 0.5185])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_inpaint_euler_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionXLInpaintPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.6611, 0.5569, 0.5531, 0.5471, 0.5918, 0.6393, 0.5074, 0.5468, 0.5185])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
# TODO(Patrick, Sayak) - skip for now as this requires more refiner tests
def test_save_load_optional_components(self):
pass
def test_stable_diffusion_xl_inpaint_negative_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLInpaintPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
prompt = 3 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLInpaintPipeline(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLInpaintPipeline(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLInpaintPipeline(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_refiner(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(skip_first_text_encoder=True)
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.7045, 0.4838, 0.5454, 0.6270, 0.6168, 0.6717, 0.6484, 0.5681, 0.4922])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_two_xl_mixture_of_denoiser_fast(self):
components = self.get_dummy_components()
pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)
pipe_1.unet.set_default_attn_processor()
pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)
pipe_2.unet.set_default_attn_processor()
def assert_run_mixture(
num_steps, split, scheduler_cls_orig, num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps
):
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = num_steps
class scheduler_cls(scheduler_cls_orig):
pass
pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
# Let's retrieve the number of timesteps we want to use
pipe_1.scheduler.set_timesteps(num_steps)
expected_steps = pipe_1.scheduler.timesteps.tolist()
split_ts = num_train_timesteps - int(round(num_train_timesteps * split))
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))
expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split_ts, expected_steps))
expected_steps = expected_steps_1 + expected_steps_2
else:
expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts < split_ts, expected_steps))
# now we monkey patch step `done_steps`
# list into the step function for testing
done_steps = []
old_step = copy.copy(scheduler_cls.step)
def new_step(self, *args, **kwargs):
done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
return old_step(self, *args, **kwargs)
scheduler_cls.step = new_step
inputs_1 = {**inputs, **{"denoising_end": split, "output_type": "latent"}}
latents = pipe_1(**inputs_1).images[0]
assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
inputs_2 = {**inputs, **{"denoising_start": split, "image": latents}}
pipe_2(**inputs_2).images[0]
assert expected_steps_2 == done_steps[len(expected_steps_1) :]
assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
for steps in [7, 20]:
assert_run_mixture(steps, 0.33, EulerDiscreteScheduler)
assert_run_mixture(steps, 0.33, HeunDiscreteScheduler)
@slow
def test_stable_diffusion_two_xl_mixture_of_denoiser(self):
components = self.get_dummy_components()
pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)
pipe_1.unet.set_default_attn_processor()
pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)
pipe_2.unet.set_default_attn_processor()
def assert_run_mixture(
num_steps, split, scheduler_cls_orig, num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps
):
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = num_steps
class scheduler_cls(scheduler_cls_orig):
pass
pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
# Let's retrieve the number of timesteps we want to use
pipe_1.scheduler.set_timesteps(num_steps)
expected_steps = pipe_1.scheduler.timesteps.tolist()
split_ts = num_train_timesteps - int(round(num_train_timesteps * split))
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))
expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split_ts, expected_steps))
expected_steps = expected_steps_1 + expected_steps_2
else:
expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts < split_ts, expected_steps))
# now we monkey patch step `done_steps`
# list into the step function for testing
done_steps = []
old_step = copy.copy(scheduler_cls.step)
def new_step(self, *args, **kwargs):
done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
return old_step(self, *args, **kwargs)
scheduler_cls.step = new_step
inputs_1 = {**inputs, **{"denoising_end": split, "output_type": "latent"}}
latents = pipe_1(**inputs_1).images[0]
assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
inputs_2 = {**inputs, **{"denoising_start": split, "image": latents}}
pipe_2(**inputs_2).images[0]
assert expected_steps_2 == done_steps[len(expected_steps_1) :]
assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
for steps in [5, 8, 20]:
for split in [0.33, 0.49, 0.71]:
for scheduler_cls in [
DDIMScheduler,
EulerDiscreteScheduler,
DPMSolverMultistepScheduler,
UniPCMultistepScheduler,
HeunDiscreteScheduler,
]:
assert_run_mixture(steps, split, scheduler_cls)
@slow
def test_stable_diffusion_three_xl_mixture_of_denoiser(self):
components = self.get_dummy_components()
pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)
pipe_1.unet.set_default_attn_processor()
pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)
pipe_2.unet.set_default_attn_processor()
pipe_3 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)
pipe_3.unet.set_default_attn_processor()
def assert_run_mixture(
num_steps,
split_1,
split_2,
scheduler_cls_orig,
num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,
):
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = num_steps
class scheduler_cls(scheduler_cls_orig):
pass
pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
pipe_3.scheduler = scheduler_cls.from_config(pipe_3.scheduler.config)
# Let's retrieve the number of timesteps we want to use
pipe_1.scheduler.set_timesteps(num_steps)
expected_steps = pipe_1.scheduler.timesteps.tolist()
split_1_ts = num_train_timesteps - int(round(num_train_timesteps * split_1))
split_2_ts = num_train_timesteps - int(round(num_train_timesteps * split_2))
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = expected_steps_1[-1:] + list(
filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps)
)
expected_steps_3 = expected_steps_2[-1:] + list(filter(lambda ts: ts < split_2_ts, expected_steps))
expected_steps = expected_steps_1 + expected_steps_2 + expected_steps_3
else:
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))
expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))
# now we monkey patch step `done_steps`
# list into the step function for testing
done_steps = []
old_step = copy.copy(scheduler_cls.step)
def new_step(self, *args, **kwargs):
done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
return old_step(self, *args, **kwargs)
scheduler_cls.step = new_step
inputs_1 = {**inputs, **{"denoising_end": split_1, "output_type": "latent"}}
latents = pipe_1(**inputs_1).images[0]
assert (
expected_steps_1 == done_steps
), f"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}"
inputs_2 = {
**inputs,
**{"denoising_start": split_1, "denoising_end": split_2, "image": latents, "output_type": "latent"},
}
pipe_2(**inputs_2).images[0]
assert expected_steps_2 == done_steps[len(expected_steps_1) :]
inputs_3 = {**inputs, **{"denoising_start": split_2, "image": latents}}
pipe_3(**inputs_3).images[0]
assert expected_steps_3 == done_steps[len(expected_steps_1) + len(expected_steps_2) :]
assert (
expected_steps == done_steps
), f"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}"
for steps in [7, 11, 20]:
for split_1, split_2 in zip([0.19, 0.32], [0.81, 0.68]):
for scheduler_cls in [
DDIMScheduler,
EulerDiscreteScheduler,
DPMSolverMultistepScheduler,
UniPCMultistepScheduler,
HeunDiscreteScheduler,
]:
assert_run_mixture(steps, split_1, split_2, scheduler_cls)
def test_stable_diffusion_xl_multi_prompts(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 5
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 5
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 5
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
def test_stable_diffusion_xl_img2img_negative_conditions(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]
image = sd_pipe(
**inputs,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(
0,
0,
),
negative_target_size=(1024, 1024),
).images
image_slice_with_neg_conditions = image[0, -3:, -3:, -1]
assert (
np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
> 1e-4
)
def test_stable_diffusion_xl_inpaint_mask_latents(self):
device = "cpu"
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(device)
sd_pipe.set_progress_bar_config(disable=None)
# normal mask + normal image
## `image`: pil, `mask_image``: pil, `masked_image_latents``: None
inputs = self.get_dummy_inputs(device)
inputs["strength"] = 0.9
out_0 = sd_pipe(**inputs).images
# image latents + mask latents
inputs = self.get_dummy_inputs(device)
image = sd_pipe.image_processor.preprocess(inputs["image"]).to(sd_pipe.device)
mask = sd_pipe.mask_processor.preprocess(inputs["mask_image"]).to(sd_pipe.device)
masked_image = image * (mask < 0.5)
generator = torch.Generator(device=device).manual_seed(0)
image_latents = sd_pipe._encode_vae_image(image, generator=generator)
torch.randn((1, 4, 32, 32), generator=generator)
mask_latents = sd_pipe._encode_vae_image(masked_image, generator=generator)
inputs["image"] = image_latents
inputs["masked_image_latents"] = mask_latents
inputs["mask_image"] = mask
inputs["strength"] = 0.9
generator = torch.Generator(device=device).manual_seed(0)
torch.randn((1, 4, 32, 32), generator=generator)
inputs["generator"] = generator
out_1 = sd_pipe(**inputs).images
assert np.abs(out_0 - out_1).max() < 1e-2
def test_stable_diffusion_xl_inpaint_2_images(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
# test to confirm if we pass two same image, we will get same output
inputs = self.get_dummy_inputs(device)
gen1 = torch.Generator(device=device).manual_seed(0)
gen2 = torch.Generator(device=device).manual_seed(0)
for name in ["prompt", "image", "mask_image"]:
inputs[name] = [inputs[name]] * 2
inputs["generator"] = [gen1, gen2]
images = sd_pipe(**inputs).images
assert images.shape == (2, 64, 64, 3)
image_slice1 = images[0, -3:, -3:, -1]
image_slice2 = images[1, -3:, -3:, -1]
assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() < 1e-4
# test to confirm that if we pass two different images, we will get different output
inputs = self.get_dummy_inputs_2images(device)
images = sd_pipe(**inputs).images
assert images.shape == (2, 64, 64, 3)
image_slice1 = images[0, -3:, -3:, -1]
image_slice2 = images[1, -3:, -3:, -1]
assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() > 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_text_to_image.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
torch.backends.cuda.matmul.allow_tf32 = False
class VersatileDiffusionTextToImagePipelineFastTests(unittest.TestCase):
pass
@nightly
@require_torch_gpu
class VersatileDiffusionTextToImagePipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_remove_unused_weights_save_load(self):
pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion")
# remove text_unet
pipe.remove_unused_weights()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger "
generator = torch.manual_seed(0)
image = pipe(
prompt=prompt, generator=generator, guidance_scale=7.5, num_inference_steps=2, output_type="numpy"
).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = VersatileDiffusionTextToImagePipeline.from_pretrained(tmpdirname)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = generator.manual_seed(0)
new_image = pipe(
prompt=prompt, generator=generator, guidance_scale=7.5, num_inference_steps=2, output_type="numpy"
).images
assert np.abs(image - new_image).max() < 1e-5, "Models don't have the same forward pass"
def test_inference_text2img(self):
pipe = VersatileDiffusionTextToImagePipeline.from_pretrained(
"shi-labs/versatile-diffusion", torch_dtype=torch.float16
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger "
generator = torch.manual_seed(0)
image = pipe(
prompt=prompt, generator=generator, guidance_scale=7.5, num_inference_steps=50, output_type="numpy"
).images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_image_variation.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
torch.backends.cuda.matmul.allow_tf32 = False
class VersatileDiffusionImageVariationPipelineFastTests(unittest.TestCase):
pass
@nightly
@require_torch_gpu
class VersatileDiffusionImageVariationPipelineIntegrationTests(unittest.TestCase):
def test_inference_image_variations(self):
pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion")
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
image_prompt = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
)
generator = torch.manual_seed(0)
image = pipe(
image=image_prompt,
generator=generator,
guidance_scale=7.5,
num_inference_steps=50,
output_type="numpy",
).images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_dual_guided.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
torch.backends.cuda.matmul.allow_tf32 = False
@nightly
@require_torch_gpu
class VersatileDiffusionDualGuidedPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_remove_unused_weights_save_load(self):
pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion")
# remove text_unet
pipe.remove_unused_weights()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
second_prompt = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
)
generator = torch.manual_seed(0)
image = pipe(
prompt="first prompt",
image=second_prompt,
text_to_image_strength=0.75,
generator=generator,
guidance_scale=7.5,
num_inference_steps=2,
output_type="numpy",
).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(tmpdirname)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = generator.manual_seed(0)
new_image = pipe(
prompt="first prompt",
image=second_prompt,
text_to_image_strength=0.75,
generator=generator,
guidance_scale=7.5,
num_inference_steps=2,
output_type="numpy",
).images
assert np.abs(image - new_image).max() < 1e-5, "Models don't have the same forward pass"
def test_inference_dual_guided(self):
pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion")
pipe.remove_unused_weights()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
first_prompt = "cyberpunk 2077"
second_prompt = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
)
generator = torch.manual_seed(0)
image = pipe(
prompt=first_prompt,
image=second_prompt,
text_to_image_strength=0.75,
generator=generator,
guidance_scale=7.5,
num_inference_steps=50,
output_type="numpy",
).images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 0
|
hf_public_repos/diffusers/tests/pipelines
|
hf_public_repos/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_mega.py
|
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
torch.backends.cuda.matmul.allow_tf32 = False
class VersatileDiffusionMegaPipelineFastTests(unittest.TestCase):
pass
@nightly
@require_torch_gpu
class VersatileDiffusionMegaPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_from_save_pretrained(self):
pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
)
generator = torch.manual_seed(0)
image = pipe.dual_guided(
prompt="first prompt",
image=prompt_image,
text_to_image_strength=0.75,
generator=generator,
guidance_scale=7.5,
num_inference_steps=2,
output_type="numpy",
).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = VersatileDiffusionPipeline.from_pretrained(tmpdirname, torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = generator.manual_seed(0)
new_image = pipe.dual_guided(
prompt="first prompt",
image=prompt_image,
text_to_image_strength=0.75,
generator=generator,
guidance_scale=7.5,
num_inference_steps=2,
output_type="numpy",
).images
assert np.abs(image - new_image).max() < 1e-5, "Models don't have the same forward pass"
def test_inference_dual_guided_then_text_to_image(self):
pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
prompt = "cyberpunk 2077"
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
)
generator = torch.manual_seed(0)
image = pipe.dual_guided(
prompt=prompt,
image=init_image,
text_to_image_strength=0.75,
generator=generator,
guidance_scale=7.5,
num_inference_steps=50,
output_type="numpy",
).images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
prompt = "A painting of a squirrel eating a burger "
generator = torch.manual_seed(0)
image = pipe.text_to_image(
prompt=prompt, generator=generator, guidance_scale=7.5, num_inference_steps=50, output_type="numpy"
).images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
image = pipe.image_variation(init_image, generator=generator, output_type="numpy").images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
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