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9d6cb8e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 | # Copyright 2023-present the HuggingFace Inc. team.
#
# 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.
from dataclasses import asdict, replace
from unittest import TestCase
import numpy as np
from diffusers import StableDiffusionPipeline
from parameterized import parameterized
from peft import BOFTConfig, LoHaConfig, LoraConfig, OFTConfig, get_peft_model
from .testing_common import ClassInstantier, PeftCommonTester
from .testing_utils import temp_seed
PEFT_DIFFUSERS_SD_MODELS_TO_TEST = ["hf-internal-testing/tiny-stable-diffusion-torch"]
CONFIG_TESTING_KWARGS = (
{
"text_encoder": {
"r": 8,
"lora_alpha": 32,
"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
"lora_dropout": 0.0,
"bias": "none",
},
"unet": {
"r": 8,
"lora_alpha": 32,
"target_modules": ["proj_in", "proj_out", "to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2"],
"lora_dropout": 0.0,
"bias": "none",
},
},
{
"text_encoder": {
"r": 8,
"alpha": 32,
"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
"rank_dropout": 0.0,
"module_dropout": 0.0,
},
"unet": {
"r": 8,
"alpha": 32,
"target_modules": ["proj_in", "proj_out", "to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2"],
"rank_dropout": 0.0,
"module_dropout": 0.0,
},
},
{
"text_encoder": {
"r": 8,
"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
"module_dropout": 0.0,
},
"unet": {
"r": 8,
"target_modules": ["proj_in", "proj_out", "to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2"],
"module_dropout": 0.0,
},
},
{
"text_encoder": {
"boft_block_num": 1,
"boft_block_size": 0,
"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
"boft_dropout": 0.0,
},
"unet": {
"boft_block_num": 1,
"boft_block_size": 0,
"target_modules": ["proj_in", "proj_out", "to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2"],
"boft_dropout": 0.0,
},
},
)
CLASSES_MAPPING = {
"lora": (LoraConfig, CONFIG_TESTING_KWARGS[0]),
"loha": (LoHaConfig, CONFIG_TESTING_KWARGS[1]),
"lokr": (LoHaConfig, CONFIG_TESTING_KWARGS[1]),
"oft": (OFTConfig, CONFIG_TESTING_KWARGS[2]),
"boft": (BOFTConfig, CONFIG_TESTING_KWARGS[3]),
}
PeftStableDiffusionTestConfigManager = ClassInstantier(CLASSES_MAPPING)
class StableDiffusionModelTester(TestCase, PeftCommonTester):
r"""
Tests that diffusers StableDiffusion model works with PEFT as expected.
"""
transformers_class = StableDiffusionPipeline
def instantiate_sd_peft(self, model_id, config_cls, config_kwargs):
# Instantiate StableDiffusionPipeline
model = self.transformers_class.from_pretrained(model_id)
config_kwargs = config_kwargs.copy()
text_encoder_kwargs = config_kwargs.pop("text_encoder")
unet_kwargs = config_kwargs.pop("unet")
# the remaining config kwargs should be applied to both configs
for key, val in config_kwargs.items():
text_encoder_kwargs[key] = val
unet_kwargs[key] = val
# Instantiate text_encoder adapter
config_text_encoder = config_cls(**text_encoder_kwargs)
model.text_encoder = get_peft_model(model.text_encoder, config_text_encoder)
# Instantiate unet adapter
config_unet = config_cls(**unet_kwargs)
model.unet = get_peft_model(model.unet, config_unet)
# Move model to device
model = model.to(self.torch_device)
return model
def prepare_inputs_for_testing(self):
return {
"prompt": "a high quality digital photo of a cute corgi",
"num_inference_steps": 20,
}
@parameterized.expand(
PeftStableDiffusionTestConfigManager.get_grid_parameters(
{
"model_ids": PEFT_DIFFUSERS_SD_MODELS_TO_TEST,
"lora_kwargs": {"init_lora_weights": [False]},
"loha_kwargs": {"init_weights": [False]},
"oft_kwargs": {"init_weights": [False]},
"boft_kwargs": {"init_weights": [False]},
},
)
)
def test_merge_layers(self, test_name, model_id, config_cls, config_kwargs):
# Instantiate model & adapters
model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)
# Generate output for peft modified StableDiffusion
dummy_input = self.prepare_inputs_for_testing()
with temp_seed(seed=42):
peft_output = np.array(model(**dummy_input).images[0]).astype(np.float32)
# Merge adapter and model
if config_cls not in [LoHaConfig, OFTConfig]:
# TODO: Merging the text_encoder is leading to issues on CPU with PyTorch 2.1
model.text_encoder = model.text_encoder.merge_and_unload()
model.unet = model.unet.merge_and_unload()
# Generate output for peft merged StableDiffusion
with temp_seed(seed=42):
merged_output = np.array(model(**dummy_input).images[0]).astype(np.float32)
# Images are in uint8 drange, so use large atol
assert np.allclose(peft_output, merged_output, atol=1.0)
@parameterized.expand(
PeftStableDiffusionTestConfigManager.get_grid_parameters(
{
"model_ids": PEFT_DIFFUSERS_SD_MODELS_TO_TEST,
"lora_kwargs": {"init_lora_weights": [False]},
"loha_kwargs": {"init_weights": [False]},
"oft_kwargs": {"init_weights": [False]},
"boft_kwargs": {"init_weights": [False]},
},
)
)
def test_merge_layers_safe_merge(self, test_name, model_id, config_cls, config_kwargs):
# Instantiate model & adapters
model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)
# Generate output for peft modified StableDiffusion
dummy_input = self.prepare_inputs_for_testing()
with temp_seed(seed=42):
peft_output = np.array(model(**dummy_input).images[0]).astype(np.float32)
# Merge adapter and model
if config_cls not in [LoHaConfig, OFTConfig]:
# TODO: Merging the text_encoder is leading to issues on CPU with PyTorch 2.1
model.text_encoder = model.text_encoder.merge_and_unload(safe_merge=True)
model.unet = model.unet.merge_and_unload(safe_merge=True)
# Generate output for peft merged StableDiffusion
with temp_seed(seed=42):
merged_output = np.array(model(**dummy_input).images[0]).astype(np.float32)
# Images are in uint8 drange, so use large atol
assert np.allclose(peft_output, merged_output, atol=1.0)
@parameterized.expand(
PeftStableDiffusionTestConfigManager.get_grid_parameters(
{
"model_ids": PEFT_DIFFUSERS_SD_MODELS_TO_TEST,
"lora_kwargs": {"init_lora_weights": [False]},
},
filter_params_func=lambda tests: [x for x in tests if all(s not in x[0] for s in ["loha", "lokr", "oft"])],
)
)
def test_add_weighted_adapter_base_unchanged(self, test_name, model_id, config_cls, config_kwargs):
# Instantiate model & adapters
model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)
# Get current available adapter config
text_encoder_adapter_name = next(iter(model.text_encoder.peft_config.keys()))
unet_adapter_name = next(iter(model.unet.peft_config.keys()))
text_encoder_adapter_config = replace(model.text_encoder.peft_config[text_encoder_adapter_name])
unet_adapter_config = replace(model.unet.peft_config[unet_adapter_name])
# Create weighted adapters
model.text_encoder.add_weighted_adapter([unet_adapter_name], [0.5], "weighted_adapter_test")
model.unet.add_weighted_adapter([unet_adapter_name], [0.5], "weighted_adapter_test")
# Assert that base adapters config did not change
assert asdict(text_encoder_adapter_config) == asdict(model.text_encoder.peft_config[text_encoder_adapter_name])
assert asdict(unet_adapter_config) == asdict(model.unet.peft_config[unet_adapter_name])
@parameterized.expand(
PeftStableDiffusionTestConfigManager.get_grid_parameters(
{
"model_ids": PEFT_DIFFUSERS_SD_MODELS_TO_TEST,
"lora_kwargs": {"init_lora_weights": [False]},
"loha_kwargs": {"init_weights": [False]},
"lokr_kwargs": {"init_weights": [False]},
"oft_kwargs": {"init_weights": [False]},
"boft_kwargs": {"init_weights": [False]},
},
)
)
def test_disable_adapter(self, test_name, model_id, config_cls, config_kwargs):
self._test_disable_adapter(model_id, config_cls, config_kwargs)
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