killabee777 / tests /test_config.py

Add stable diffusion weights

af7c068 over 1 year ago

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	# coding=utf-8
	# Copyright 2022 HuggingFace Inc.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import 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 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
	assert config == new_config

	def test_load_ddim_from_pndm(self):
	logger = logging.get_logger("diffusers.configuration_utils")

	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")

	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")

	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")

	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")

	with CaptureLogger(logger) as cap_logger:
	ddpm = DDPMScheduler.from_pretrained(
	"hf-internal-testing/tiny-stable-diffusion-torch",
	subfolder="scheduler",
	predict_epsilon=False,
	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.predict_epsilon is False
	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")

	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 == ""