# 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)