declare-lab/tango2
92
1# coding=utf-82# Copyright 2023 HuggingFace Inc.3#4# Licensed under the Apache License, Version 2.0 (the "License");5# you may not use this file except in compliance with the License.6# You may obtain a copy of the License at7#8# http://www.apache.org/licenses/LICENSE-2.09#10# Unless required by applicable law or agreed to in writing, software11# distributed under the License is distributed on an "AS IS" BASIS,12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.13# See the License for the specific language governing permissions and14# limitations under the License.15 16import tempfile17import unittest18 19from diffusers import (20 DDIMScheduler,21 DDPMScheduler,22 DPMSolverMultistepScheduler,23 EulerAncestralDiscreteScheduler,24 EulerDiscreteScheduler,25 PNDMScheduler,26 logging,27)28from diffusers.configuration_utils import ConfigMixin, register_to_config29from diffusers.utils.testing_utils import CaptureLogger30 31 32class SampleObject(ConfigMixin):33 config_name = "config.json"34 35 @register_to_config36 def __init__(37 self,38 a=2,39 b=5,40 c=(2, 5),41 d="for diffusion",42 e=[1, 3],43 ):44 pass45 46 47class SampleObject2(ConfigMixin):48 config_name = "config.json"49 50 @register_to_config51 def __init__(52 self,53 a=2,54 b=5,55 c=(2, 5),56 d="for diffusion",57 f=[1, 3],58 ):59 pass60 61 62class SampleObject3(ConfigMixin):63 config_name = "config.json"64 65 @register_to_config66 def __init__(67 self,68 a=2,69 b=5,70 c=(2, 5),71 d="for diffusion",72 e=[1, 3],73 f=[1, 3],74 ):75 pass76 77 78class ConfigTester(unittest.TestCase):79 def test_load_not_from_mixin(self):80 with self.assertRaises(ValueError):81 ConfigMixin.load_config("dummy_path")82 83 def test_register_to_config(self):84 obj = SampleObject()85 config = obj.config86 assert config["a"] == 287 assert config["b"] == 588 assert config["c"] == (2, 5)89 assert config["d"] == "for diffusion"90 assert config["e"] == [1, 3]91 92 # init ignore private arguments93 obj = SampleObject(_name_or_path="lalala")94 config = obj.config95 assert config["a"] == 296 assert config["b"] == 597 assert config["c"] == (2, 5)98 assert config["d"] == "for diffusion"99 assert config["e"] == [1, 3]100 101 # can override default102 obj = SampleObject(c=6)103 config = obj.config104 assert config["a"] == 2105 assert config["b"] == 5106 assert config["c"] == 6107 assert config["d"] == "for diffusion"108 assert config["e"] == [1, 3]109 110 # can use positional arguments.111 obj = SampleObject(1, c=6)112 config = obj.config113 assert config["a"] == 1114 assert config["b"] == 5115 assert config["c"] == 6116 assert config["d"] == "for diffusion"117 assert config["e"] == [1, 3]118 119 def test_save_load(self):120 obj = SampleObject()121 config = obj.config122 123 assert config["a"] == 2124 assert config["b"] == 5125 assert config["c"] == (2, 5)126 assert config["d"] == "for diffusion"127 assert config["e"] == [1, 3]128 129 with tempfile.TemporaryDirectory() as tmpdirname:130 obj.save_config(tmpdirname)131 new_obj = SampleObject.from_config(SampleObject.load_config(tmpdirname))132 new_config = new_obj.config133 134 # unfreeze configs135 config = dict(config)136 new_config = dict(new_config)137 138 assert config.pop("c") == (2, 5) # instantiated as tuple139 assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json140 assert config == new_config141 142 def test_load_ddim_from_pndm(self):143 logger = logging.get_logger("diffusers.configuration_utils")144 145 with CaptureLogger(logger) as cap_logger:146 ddim = DDIMScheduler.from_pretrained(147 "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"148 )149 150 assert ddim.__class__ == DDIMScheduler151 # no warning should be thrown152 assert cap_logger.out == ""153 154 def test_load_euler_from_pndm(self):155 logger = logging.get_logger("diffusers.configuration_utils")156 157 with CaptureLogger(logger) as cap_logger:158 euler = EulerDiscreteScheduler.from_pretrained(159 "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"160 )161 162 assert euler.__class__ == EulerDiscreteScheduler163 # no warning should be thrown164 assert cap_logger.out == ""165 166 def test_load_euler_ancestral_from_pndm(self):167 logger = logging.get_logger("diffusers.configuration_utils")168 169 with CaptureLogger(logger) as cap_logger:170 euler = EulerAncestralDiscreteScheduler.from_pretrained(171 "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"172 )173 174 assert euler.__class__ == EulerAncestralDiscreteScheduler175 # no warning should be thrown176 assert cap_logger.out == ""177 178 def test_load_pndm(self):179 logger = logging.get_logger("diffusers.configuration_utils")180 181 with CaptureLogger(logger) as cap_logger:182 pndm = PNDMScheduler.from_pretrained(183 "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"184 )185 186 assert pndm.__class__ == PNDMScheduler187 # no warning should be thrown188 assert cap_logger.out == ""189 190 def test_overwrite_config_on_load(self):191 logger = logging.get_logger("diffusers.configuration_utils")192 193 with CaptureLogger(logger) as cap_logger:194 ddpm = DDPMScheduler.from_pretrained(195 "hf-internal-testing/tiny-stable-diffusion-torch",196 subfolder="scheduler",197 prediction_type="sample",198 beta_end=8,199 )200 201 with CaptureLogger(logger) as cap_logger_2:202 ddpm_2 = DDPMScheduler.from_pretrained("google/ddpm-celebahq-256", beta_start=88)203 204 assert ddpm.__class__ == DDPMScheduler205 assert ddpm.config.prediction_type == "sample"206 assert ddpm.config.beta_end == 8207 assert ddpm_2.config.beta_start == 88208 209 # no warning should be thrown210 assert cap_logger.out == ""211 assert cap_logger_2.out == ""212 213 def test_load_dpmsolver(self):214 logger = logging.get_logger("diffusers.configuration_utils")215 216 with CaptureLogger(logger) as cap_logger:217 dpm = DPMSolverMultistepScheduler.from_pretrained(218 "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler"219 )220 221 assert dpm.__class__ == DPMSolverMultistepScheduler222 # no warning should be thrown223 assert cap_logger.out == ""224