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| # 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 os | |
| import tempfile | |
| import time | |
| import unittest | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from huggingface_hub import hf_hub_download | |
| from huggingface_hub.repocard import RepoCard | |
| from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer | |
| from diffusers import ( | |
| AutoencoderKL, | |
| AutoPipelineForImage2Image, | |
| ControlNetModel, | |
| DDIMScheduler, | |
| DiffusionPipeline, | |
| EulerDiscreteScheduler, | |
| LCMScheduler, | |
| StableDiffusionPipeline, | |
| StableDiffusionXLControlNetPipeline, | |
| StableDiffusionXLPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.loaders import AttnProcsLayers | |
| from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProcessor2_0 | |
| from diffusers.utils.import_utils import is_accelerate_available, is_peft_available | |
| from diffusers.utils.testing_utils import ( | |
| floats_tensor, | |
| load_image, | |
| nightly, | |
| require_peft_backend, | |
| require_torch_gpu, | |
| slow, | |
| torch_device, | |
| ) | |
| if is_accelerate_available(): | |
| from accelerate.utils import release_memory | |
| if is_peft_available(): | |
| from peft import LoraConfig | |
| from peft.tuners.tuners_utils import BaseTunerLayer | |
| from peft.utils import get_peft_model_state_dict | |
| 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().max() > 1e-3: | |
| models_are_equal = False | |
| return models_are_equal | |
| def create_unet_lora_layers(unet: nn.Module): | |
| lora_attn_procs = {} | |
| for name in unet.attn_processors.keys(): | |
| cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim | |
| if name.startswith("mid_block"): | |
| hidden_size = unet.config.block_out_channels[-1] | |
| elif name.startswith("up_blocks"): | |
| block_id = int(name[len("up_blocks.")]) | |
| hidden_size = list(reversed(unet.config.block_out_channels))[block_id] | |
| elif name.startswith("down_blocks"): | |
| block_id = int(name[len("down_blocks.")]) | |
| hidden_size = unet.config.block_out_channels[block_id] | |
| lora_attn_processor_class = ( | |
| LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor | |
| ) | |
| lora_attn_procs[name] = lora_attn_processor_class( | |
| hidden_size=hidden_size, cross_attention_dim=cross_attention_dim | |
| ) | |
| unet_lora_layers = AttnProcsLayers(lora_attn_procs) | |
| return lora_attn_procs, unet_lora_layers | |
| class PeftLoraLoaderMixinTests: | |
| torch_device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipeline_class = None | |
| scheduler_cls = None | |
| scheduler_kwargs = None | |
| has_two_text_encoders = False | |
| unet_kwargs = None | |
| vae_kwargs = None | |
| def get_dummy_components(self, scheduler_cls=None): | |
| scheduler_cls = self.scheduler_cls if scheduler_cls is None else LCMScheduler | |
| torch.manual_seed(0) | |
| unet = UNet2DConditionModel(**self.unet_kwargs) | |
| scheduler = scheduler_cls(**self.scheduler_kwargs) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL(**self.vae_kwargs) | |
| text_encoder = CLIPTextModel.from_pretrained("peft-internal-testing/tiny-clip-text-2") | |
| tokenizer = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2") | |
| if self.has_two_text_encoders: | |
| text_encoder_2 = CLIPTextModelWithProjection.from_pretrained("peft-internal-testing/tiny-clip-text-2") | |
| tokenizer_2 = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2") | |
| text_lora_config = LoraConfig( | |
| r=4, lora_alpha=4, target_modules=["q_proj", "k_proj", "v_proj", "out_proj"], init_lora_weights=False | |
| ) | |
| unet_lora_config = LoraConfig( | |
| r=4, lora_alpha=4, target_modules=["to_q", "to_k", "to_v", "to_out.0"], init_lora_weights=False | |
| ) | |
| unet_lora_attn_procs, unet_lora_layers = create_unet_lora_layers(unet) | |
| if self.has_two_text_encoders: | |
| pipeline_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, | |
| } | |
| else: | |
| pipeline_components = { | |
| "unet": unet, | |
| "scheduler": scheduler, | |
| "vae": vae, | |
| "text_encoder": text_encoder, | |
| "tokenizer": tokenizer, | |
| "safety_checker": None, | |
| "feature_extractor": None, | |
| "image_encoder": None, | |
| } | |
| lora_components = { | |
| "unet_lora_layers": unet_lora_layers, | |
| "unet_lora_attn_procs": unet_lora_attn_procs, | |
| } | |
| return pipeline_components, lora_components, text_lora_config, unet_lora_config | |
| def get_dummy_inputs(self, with_generator=True): | |
| batch_size = 1 | |
| sequence_length = 10 | |
| num_channels = 4 | |
| sizes = (32, 32) | |
| generator = torch.manual_seed(0) | |
| noise = floats_tensor((batch_size, num_channels) + sizes) | |
| input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator) | |
| pipeline_inputs = { | |
| "prompt": "A painting of a squirrel eating a burger", | |
| "num_inference_steps": 2, | |
| "guidance_scale": 6.0, | |
| "output_type": "np", | |
| } | |
| if with_generator: | |
| pipeline_inputs.update({"generator": generator}) | |
| return noise, input_ids, pipeline_inputs | |
| # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb | |
| def get_dummy_tokens(self): | |
| max_seq_length = 77 | |
| inputs = torch.randint(2, 56, size=(1, max_seq_length), generator=torch.manual_seed(0)) | |
| prepared_inputs = {} | |
| prepared_inputs["input_ids"] = inputs | |
| return prepared_inputs | |
| def check_if_lora_correctly_set(self, model) -> bool: | |
| """ | |
| Checks if the LoRA layers are correctly set with peft | |
| """ | |
| for module in model.modules(): | |
| if isinstance(module, BaseTunerLayer): | |
| return True | |
| return False | |
| def test_simple_inference(self): | |
| """ | |
| Tests a simple inference and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, _ = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs() | |
| output_no_lora = pipe(**inputs).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| def test_simple_inference_with_text_lora(self): | |
| """ | |
| Tests a simple inference with lora attached on the text encoder | |
| and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, _ = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output" | |
| ) | |
| def test_simple_inference_with_text_lora_and_scale(self): | |
| """ | |
| Tests a simple inference with lora attached on the text encoder + scale argument | |
| and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, _ = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output" | |
| ) | |
| output_lora_scale = pipe( | |
| **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5} | |
| ).images | |
| self.assertTrue( | |
| not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3), | |
| "Lora + scale should change the output", | |
| ) | |
| output_lora_0_scale = pipe( | |
| **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0} | |
| ).images | |
| self.assertTrue( | |
| np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3), | |
| "Lora + 0 scale should lead to same result as no LoRA", | |
| ) | |
| def test_simple_inference_with_text_lora_fused(self): | |
| """ | |
| Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model | |
| and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, _ = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.fuse_lora() | |
| # Fusing should still keep the LoRA layers | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| ouput_fused = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertFalse( | |
| np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output" | |
| ) | |
| def test_simple_inference_with_text_lora_unloaded(self): | |
| """ | |
| Tests a simple inference with lora attached to text encoder, then unloads the lora weights | |
| and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, _ = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.unload_lora_weights() | |
| # unloading should remove the LoRA layers | |
| self.assertFalse( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| self.assertFalse( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), | |
| "Lora not correctly unloaded in text encoder 2", | |
| ) | |
| ouput_unloaded = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| np.allclose(ouput_unloaded, output_no_lora, atol=1e-3, rtol=1e-3), | |
| "Fused lora should change the output", | |
| ) | |
| def test_simple_inference_with_text_lora_save_load(self): | |
| """ | |
| Tests a simple usecase where users could use saving utilities for LoRA. | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, _ = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder) | |
| if self.has_two_text_encoders: | |
| text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2) | |
| self.pipeline_class.save_lora_weights( | |
| save_directory=tmpdirname, | |
| text_encoder_lora_layers=text_encoder_state_dict, | |
| text_encoder_2_lora_layers=text_encoder_2_state_dict, | |
| safe_serialization=False, | |
| ) | |
| else: | |
| self.pipeline_class.save_lora_weights( | |
| save_directory=tmpdirname, | |
| text_encoder_lora_layers=text_encoder_state_dict, | |
| safe_serialization=False, | |
| ) | |
| self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin"))) | |
| pipe.unload_lora_weights() | |
| pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.bin")) | |
| images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| self.assertTrue( | |
| np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3), | |
| "Loading from saved checkpoints should give same results.", | |
| ) | |
| def test_simple_inference_save_pretrained(self): | |
| """ | |
| Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, _ = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| pipe.save_pretrained(tmpdirname) | |
| pipe_from_pretrained = self.pipeline_class.from_pretrained(tmpdirname) | |
| pipe_from_pretrained.to(self.torch_device) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe_from_pretrained.text_encoder), | |
| "Lora not correctly set in text encoder", | |
| ) | |
| if self.has_two_text_encoders: | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe_from_pretrained.text_encoder_2), | |
| "Lora not correctly set in text encoder 2", | |
| ) | |
| images_lora_save_pretrained = pipe_from_pretrained(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| np.allclose(images_lora, images_lora_save_pretrained, atol=1e-3, rtol=1e-3), | |
| "Loading from saved checkpoints should give same results.", | |
| ) | |
| def test_simple_inference_with_text_unet_lora_save_load(self): | |
| """ | |
| Tests a simple usecase where users could use saving utilities for LoRA for Unet + text encoder | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| pipe.unet.add_adapter(unet_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder) | |
| unet_state_dict = get_peft_model_state_dict(pipe.unet) | |
| if self.has_two_text_encoders: | |
| text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2) | |
| self.pipeline_class.save_lora_weights( | |
| save_directory=tmpdirname, | |
| text_encoder_lora_layers=text_encoder_state_dict, | |
| text_encoder_2_lora_layers=text_encoder_2_state_dict, | |
| unet_lora_layers=unet_state_dict, | |
| safe_serialization=False, | |
| ) | |
| else: | |
| self.pipeline_class.save_lora_weights( | |
| save_directory=tmpdirname, | |
| text_encoder_lora_layers=text_encoder_state_dict, | |
| unet_lora_layers=unet_state_dict, | |
| safe_serialization=False, | |
| ) | |
| self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin"))) | |
| pipe.unload_lora_weights() | |
| pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.bin")) | |
| images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| self.assertTrue( | |
| np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3), | |
| "Loading from saved checkpoints should give same results.", | |
| ) | |
| def test_simple_inference_with_text_unet_lora_and_scale(self): | |
| """ | |
| Tests a simple inference with lora attached on the text encoder + Unet + scale argument | |
| and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| pipe.unet.add_adapter(unet_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output" | |
| ) | |
| output_lora_scale = pipe( | |
| **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5} | |
| ).images | |
| self.assertTrue( | |
| not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3), | |
| "Lora + scale should change the output", | |
| ) | |
| output_lora_0_scale = pipe( | |
| **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0} | |
| ).images | |
| self.assertTrue( | |
| np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3), | |
| "Lora + 0 scale should lead to same result as no LoRA", | |
| ) | |
| self.assertTrue( | |
| pipe.text_encoder.text_model.encoder.layers[0].self_attn.q_proj.scaling["default"] == 1.0, | |
| "The scaling parameter has not been correctly restored!", | |
| ) | |
| def test_simple_inference_with_text_lora_unet_fused(self): | |
| """ | |
| Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model | |
| and makes sure it works as expected - with unet | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| pipe.unet.add_adapter(unet_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.fuse_lora() | |
| # Fusing should still keep the LoRA layers | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in unet") | |
| if self.has_two_text_encoders: | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| ouput_fused = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertFalse( | |
| np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output" | |
| ) | |
| def test_simple_inference_with_text_unet_lora_unloaded(self): | |
| """ | |
| Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights | |
| and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue(output_no_lora.shape == (1, 64, 64, 3)) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| pipe.unet.add_adapter(unet_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.unload_lora_weights() | |
| # unloading should remove the LoRA layers | |
| self.assertFalse( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder" | |
| ) | |
| self.assertFalse(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly unloaded in Unet") | |
| if self.has_two_text_encoders: | |
| self.assertFalse( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), | |
| "Lora not correctly unloaded in text encoder 2", | |
| ) | |
| ouput_unloaded = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| np.allclose(ouput_unloaded, output_no_lora, atol=1e-3, rtol=1e-3), | |
| "Fused lora should change the output", | |
| ) | |
| def test_simple_inference_with_text_unet_lora_unfused(self): | |
| """ | |
| Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights | |
| and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| pipe.unet.add_adapter(unet_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.fuse_lora() | |
| output_fused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.unfuse_lora() | |
| output_unfused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| # unloading should remove the LoRA layers | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Unfuse should still keep LoRA layers" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Unfuse should still keep LoRA layers") | |
| if self.has_two_text_encoders: | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Unfuse should still keep LoRA layers" | |
| ) | |
| # Fuse and unfuse should lead to the same results | |
| self.assertTrue( | |
| np.allclose(output_fused_lora, output_unfused_lora, atol=1e-3, rtol=1e-3), | |
| "Fused lora should change the output", | |
| ) | |
| def test_simple_inference_with_text_unet_multi_adapter(self): | |
| """ | |
| Tests a simple inference with lora attached to text encoder and unet, attaches | |
| multiple adapters and set them | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-1") | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-2") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-1") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-2") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1") | |
| pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.set_adapters("adapter-1") | |
| output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.set_adapters("adapter-2") | |
| output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.set_adapters(["adapter-1", "adapter-2"]) | |
| output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| # Fuse and unfuse should lead to the same results | |
| self.assertFalse( | |
| np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3), | |
| "Adapter 1 and 2 should give different results", | |
| ) | |
| self.assertFalse( | |
| np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3), | |
| "Adapter 1 and mixed adapters should give different results", | |
| ) | |
| self.assertFalse( | |
| np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3), | |
| "Adapter 2 and mixed adapters should give different results", | |
| ) | |
| pipe.disable_lora() | |
| output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3), | |
| "output with no lora and output with lora disabled should give same results", | |
| ) | |
| def test_simple_inference_with_text_unet_multi_adapter_delete_adapter(self): | |
| """ | |
| Tests a simple inference with lora attached to text encoder and unet, attaches | |
| multiple adapters and set/delete them | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-1") | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-2") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-1") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-2") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1") | |
| pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.set_adapters("adapter-1") | |
| output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.set_adapters("adapter-2") | |
| output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.set_adapters(["adapter-1", "adapter-2"]) | |
| output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertFalse( | |
| np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3), | |
| "Adapter 1 and 2 should give different results", | |
| ) | |
| self.assertFalse( | |
| np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3), | |
| "Adapter 1 and mixed adapters should give different results", | |
| ) | |
| self.assertFalse( | |
| np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3), | |
| "Adapter 2 and mixed adapters should give different results", | |
| ) | |
| pipe.delete_adapters("adapter-1") | |
| output_deleted_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| np.allclose(output_deleted_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3), | |
| "Adapter 1 and 2 should give different results", | |
| ) | |
| pipe.delete_adapters("adapter-2") | |
| output_deleted_adapters = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| np.allclose(output_no_lora, output_deleted_adapters, atol=1e-3, rtol=1e-3), | |
| "output with no lora and output with lora disabled should give same results", | |
| ) | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-1") | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-2") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-1") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-2") | |
| pipe.set_adapters(["adapter-1", "adapter-2"]) | |
| pipe.delete_adapters(["adapter-1", "adapter-2"]) | |
| output_deleted_adapters = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| np.allclose(output_no_lora, output_deleted_adapters, atol=1e-3, rtol=1e-3), | |
| "output with no lora and output with lora disabled should give same results", | |
| ) | |
| def test_simple_inference_with_text_unet_multi_adapter_weighted(self): | |
| """ | |
| Tests a simple inference with lora attached to text encoder and unet, attaches | |
| multiple adapters and set them | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-1") | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-2") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-1") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-2") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1") | |
| pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.set_adapters("adapter-1") | |
| output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.set_adapters("adapter-2") | |
| output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| pipe.set_adapters(["adapter-1", "adapter-2"]) | |
| output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| # Fuse and unfuse should lead to the same results | |
| self.assertFalse( | |
| np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3), | |
| "Adapter 1 and 2 should give different results", | |
| ) | |
| self.assertFalse( | |
| np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3), | |
| "Adapter 1 and mixed adapters should give different results", | |
| ) | |
| self.assertFalse( | |
| np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3), | |
| "Adapter 2 and mixed adapters should give different results", | |
| ) | |
| pipe.set_adapters(["adapter-1", "adapter-2"], [0.5, 0.6]) | |
| output_adapter_mixed_weighted = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertFalse( | |
| np.allclose(output_adapter_mixed_weighted, output_adapter_mixed, atol=1e-3, rtol=1e-3), | |
| "Weighted adapter and mixed adapter should give different results", | |
| ) | |
| pipe.disable_lora() | |
| output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| self.assertTrue( | |
| np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3), | |
| "output with no lora and output with lora disabled should give same results", | |
| ) | |
| def test_lora_fuse_nan(self): | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-1") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-1") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| # corrupt one LoRA weight with `inf` values | |
| with torch.no_grad(): | |
| pipe.unet.mid_block.attentions[0].transformer_blocks[0].attn1.to_q.lora_A["adapter-1"].weight += float( | |
| "inf" | |
| ) | |
| # with `safe_fusing=True` we should see an Error | |
| with self.assertRaises(ValueError): | |
| pipe.fuse_lora(safe_fusing=True) | |
| # without we should not see an error, but every image will be black | |
| pipe.fuse_lora(safe_fusing=False) | |
| out = pipe("test", num_inference_steps=2, output_type="np").images | |
| self.assertTrue(np.isnan(out).all()) | |
| def test_get_adapters(self): | |
| """ | |
| Tests a simple usecase where we attach multiple adapters and check if the results | |
| are the expected results | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-1") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-1") | |
| adapter_names = pipe.get_active_adapters() | |
| self.assertListEqual(adapter_names, ["adapter-1"]) | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-2") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-2") | |
| adapter_names = pipe.get_active_adapters() | |
| self.assertListEqual(adapter_names, ["adapter-2"]) | |
| pipe.set_adapters(["adapter-1", "adapter-2"]) | |
| self.assertListEqual(pipe.get_active_adapters(), ["adapter-1", "adapter-2"]) | |
| def test_get_list_adapters(self): | |
| """ | |
| Tests a simple usecase where we attach multiple adapters and check if the results | |
| are the expected results | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-1") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-1") | |
| adapter_names = pipe.get_list_adapters() | |
| self.assertDictEqual(adapter_names, {"text_encoder": ["adapter-1"], "unet": ["adapter-1"]}) | |
| pipe.text_encoder.add_adapter(text_lora_config, "adapter-2") | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-2") | |
| adapter_names = pipe.get_list_adapters() | |
| self.assertDictEqual( | |
| adapter_names, {"text_encoder": ["adapter-1", "adapter-2"], "unet": ["adapter-1", "adapter-2"]} | |
| ) | |
| pipe.set_adapters(["adapter-1", "adapter-2"]) | |
| self.assertDictEqual( | |
| pipe.get_list_adapters(), | |
| {"unet": ["adapter-1", "adapter-2"], "text_encoder": ["adapter-1", "adapter-2"]}, | |
| ) | |
| pipe.unet.add_adapter(unet_lora_config, "adapter-3") | |
| self.assertDictEqual( | |
| pipe.get_list_adapters(), | |
| {"unet": ["adapter-1", "adapter-2", "adapter-3"], "text_encoder": ["adapter-1", "adapter-2"]}, | |
| ) | |
| def test_simple_inference_with_text_unet_lora_unfused_torch_compile(self): | |
| """ | |
| Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights | |
| and makes sure it works as expected | |
| """ | |
| for scheduler_cls in [DDIMScheduler, LCMScheduler]: | |
| components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls) | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(self.torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| _, _, inputs = self.get_dummy_inputs(with_generator=False) | |
| pipe.text_encoder.add_adapter(text_lora_config) | |
| pipe.unet.add_adapter(unet_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder" | |
| ) | |
| self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet") | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2.add_adapter(text_lora_config) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2" | |
| ) | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| pipe.text_encoder = torch.compile(pipe.text_encoder, mode="reduce-overhead", fullgraph=True) | |
| if self.has_two_text_encoders: | |
| pipe.text_encoder_2 = torch.compile(pipe.text_encoder_2, mode="reduce-overhead", fullgraph=True) | |
| # Just makes sure it works.. | |
| _ = pipe(**inputs, generator=torch.manual_seed(0)).images | |
| class StableDiffusionLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase): | |
| pipeline_class = StableDiffusionPipeline | |
| scheduler_cls = DDIMScheduler | |
| scheduler_kwargs = { | |
| "beta_start": 0.00085, | |
| "beta_end": 0.012, | |
| "beta_schedule": "scaled_linear", | |
| "clip_sample": False, | |
| "set_alpha_to_one": False, | |
| "steps_offset": 1, | |
| } | |
| unet_kwargs = { | |
| "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, | |
| } | |
| vae_kwargs = { | |
| "block_out_channels": [32, 64], | |
| "in_channels": 3, | |
| "out_channels": 3, | |
| "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| "latent_channels": 4, | |
| } | |
| def test_integration_move_lora_cpu(self): | |
| path = "runwayml/stable-diffusion-v1-5" | |
| lora_id = "takuma104/lora-test-text-encoder-lora-target" | |
| pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16) | |
| pipe.load_lora_weights(lora_id, adapter_name="adapter-1") | |
| pipe.load_lora_weights(lora_id, adapter_name="adapter-2") | |
| pipe = pipe.to("cuda") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), | |
| "Lora not correctly set in text encoder", | |
| ) | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.unet), | |
| "Lora not correctly set in text encoder", | |
| ) | |
| # We will offload the first adapter in CPU and check if the offloading | |
| # has been performed correctly | |
| pipe.set_lora_device(["adapter-1"], "cpu") | |
| for name, module in pipe.unet.named_modules(): | |
| if "adapter-1" in name and not isinstance(module, (nn.Dropout, nn.Identity)): | |
| self.assertTrue(module.weight.device == torch.device("cpu")) | |
| elif "adapter-2" in name and not isinstance(module, (nn.Dropout, nn.Identity)): | |
| self.assertTrue(module.weight.device != torch.device("cpu")) | |
| for name, module in pipe.text_encoder.named_modules(): | |
| if "adapter-1" in name and not isinstance(module, (nn.Dropout, nn.Identity)): | |
| self.assertTrue(module.weight.device == torch.device("cpu")) | |
| elif "adapter-2" in name and not isinstance(module, (nn.Dropout, nn.Identity)): | |
| self.assertTrue(module.weight.device != torch.device("cpu")) | |
| pipe.set_lora_device(["adapter-1"], 0) | |
| for n, m in pipe.unet.named_modules(): | |
| if "adapter-1" in n and not isinstance(m, (nn.Dropout, nn.Identity)): | |
| self.assertTrue(m.weight.device != torch.device("cpu")) | |
| for n, m in pipe.text_encoder.named_modules(): | |
| if "adapter-1" in n and not isinstance(m, (nn.Dropout, nn.Identity)): | |
| self.assertTrue(m.weight.device != torch.device("cpu")) | |
| pipe.set_lora_device(["adapter-1", "adapter-2"], "cuda") | |
| for n, m in pipe.unet.named_modules(): | |
| if ("adapter-1" in n or "adapter-2" in n) and not isinstance(m, (nn.Dropout, nn.Identity)): | |
| self.assertTrue(m.weight.device != torch.device("cpu")) | |
| for n, m in pipe.text_encoder.named_modules(): | |
| if ("adapter-1" in n or "adapter-2" in n) and not isinstance(m, (nn.Dropout, nn.Identity)): | |
| self.assertTrue(m.weight.device != torch.device("cpu")) | |
| def test_integration_logits_with_scale(self): | |
| path = "runwayml/stable-diffusion-v1-5" | |
| lora_id = "takuma104/lora-test-text-encoder-lora-target" | |
| pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32) | |
| pipe.load_lora_weights(lora_id) | |
| pipe = pipe.to("cuda") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), | |
| "Lora not correctly set in text encoder 2", | |
| ) | |
| prompt = "a red sks dog" | |
| images = pipe( | |
| prompt=prompt, | |
| num_inference_steps=15, | |
| cross_attention_kwargs={"scale": 0.5}, | |
| generator=torch.manual_seed(0), | |
| output_type="np", | |
| ).images | |
| expected_slice_scale = np.array([0.307, 0.283, 0.310, 0.310, 0.300, 0.314, 0.336, 0.314, 0.321]) | |
| predicted_slice = images[0, -3:, -3:, -1].flatten() | |
| self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3)) | |
| def test_integration_logits_no_scale(self): | |
| path = "runwayml/stable-diffusion-v1-5" | |
| lora_id = "takuma104/lora-test-text-encoder-lora-target" | |
| pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32) | |
| pipe.load_lora_weights(lora_id) | |
| pipe = pipe.to("cuda") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.text_encoder), | |
| "Lora not correctly set in text encoder", | |
| ) | |
| prompt = "a red sks dog" | |
| images = pipe(prompt=prompt, num_inference_steps=30, generator=torch.manual_seed(0), output_type="np").images | |
| expected_slice_scale = np.array([0.074, 0.064, 0.073, 0.0842, 0.069, 0.0641, 0.0794, 0.076, 0.084]) | |
| predicted_slice = images[0, -3:, -3:, -1].flatten() | |
| self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3)) | |
| def test_integration_logits_multi_adapter(self): | |
| path = "stabilityai/stable-diffusion-xl-base-1.0" | |
| lora_id = "CiroN2022/toy-face" | |
| pipe = StableDiffusionXLPipeline.from_pretrained(path, torch_dtype=torch.float16) | |
| pipe.load_lora_weights(lora_id, weight_name="toy_face_sdxl.safetensors", adapter_name="toy") | |
| pipe = pipe.to("cuda") | |
| self.assertTrue( | |
| self.check_if_lora_correctly_set(pipe.unet), | |
| "Lora not correctly set in Unet", | |
| ) | |
| prompt = "toy_face of a hacker with a hoodie" | |
| lora_scale = 0.9 | |
| images = pipe( | |
| prompt=prompt, | |
| num_inference_steps=30, | |
| generator=torch.manual_seed(0), | |
| cross_attention_kwargs={"scale": lora_scale}, | |
| output_type="np", | |
| ).images | |
| expected_slice_scale = np.array([0.538, 0.539, 0.540, 0.540, 0.542, 0.539, 0.538, 0.541, 0.539]) | |
| predicted_slice = images[0, -3:, -3:, -1].flatten() | |
| self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3)) | |
| pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel") | |
| pipe.set_adapters("pixel") | |
| prompt = "pixel art, a hacker with a hoodie, simple, flat colors" | |
| images = pipe( | |
| prompt, | |
| num_inference_steps=30, | |
| guidance_scale=7.5, | |
| cross_attention_kwargs={"scale": lora_scale}, | |
| generator=torch.manual_seed(0), | |
| output_type="np", | |
| ).images | |
| predicted_slice = images[0, -3:, -3:, -1].flatten() | |
| expected_slice_scale = np.array( | |
| [0.61973065, 0.62018543, 0.62181497, 0.61933696, 0.6208608, 0.620576, 0.6200281, 0.62258327, 0.6259889] | |
| ) | |
| self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3)) | |
| # multi-adapter inference | |
| pipe.set_adapters(["pixel", "toy"], adapter_weights=[0.5, 1.0]) | |
| images = pipe( | |
| prompt, | |
| num_inference_steps=30, | |
| guidance_scale=7.5, | |
| cross_attention_kwargs={"scale": 1.0}, | |
| generator=torch.manual_seed(0), | |
| output_type="np", | |
| ).images | |
| predicted_slice = images[0, -3:, -3:, -1].flatten() | |
| expected_slice_scale = np.array([0.5888, 0.5897, 0.5946, 0.5888, 0.5935, 0.5946, 0.5857, 0.5891, 0.5909]) | |
| self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3)) | |
| # Lora disabled | |
| pipe.disable_lora() | |
| images = pipe( | |
| prompt, | |
| num_inference_steps=30, | |
| guidance_scale=7.5, | |
| cross_attention_kwargs={"scale": lora_scale}, | |
| generator=torch.manual_seed(0), | |
| output_type="np", | |
| ).images | |
| predicted_slice = images[0, -3:, -3:, -1].flatten() | |
| expected_slice_scale = np.array([0.5456, 0.5466, 0.5487, 0.5458, 0.5469, 0.5454, 0.5446, 0.5479, 0.5487]) | |
| self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3)) | |
| class StableDiffusionXLLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase): | |
| has_two_text_encoders = True | |
| pipeline_class = StableDiffusionXLPipeline | |
| scheduler_cls = EulerDiscreteScheduler | |
| scheduler_kwargs = { | |
| "beta_start": 0.00085, | |
| "beta_end": 0.012, | |
| "beta_schedule": "scaled_linear", | |
| "timestep_spacing": "leading", | |
| "steps_offset": 1, | |
| } | |
| unet_kwargs = { | |
| "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"), | |
| "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, | |
| } | |
| vae_kwargs = { | |
| "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, | |
| } | |
| class LoraIntegrationTests(unittest.TestCase): | |
| def tearDown(self): | |
| import gc | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| def test_dreambooth_old_format(self): | |
| generator = torch.Generator("cpu").manual_seed(0) | |
| lora_model_id = "hf-internal-testing/lora_dreambooth_dog_example" | |
| card = RepoCard.load(lora_model_id) | |
| base_model_id = card.data.to_dict()["base_model"] | |
| pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None) | |
| pipe = pipe.to(torch_device) | |
| pipe.load_lora_weights(lora_model_id) | |
| images = pipe( | |
| "A photo of a sks dog floating in the river", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.7207, 0.6787, 0.6010, 0.7478, 0.6838, 0.6064, 0.6984, 0.6443, 0.5785]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-4)) | |
| release_memory(pipe) | |
| def test_dreambooth_text_encoder_new_format(self): | |
| generator = torch.Generator().manual_seed(0) | |
| lora_model_id = "hf-internal-testing/lora-trained" | |
| card = RepoCard.load(lora_model_id) | |
| base_model_id = card.data.to_dict()["base_model"] | |
| pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None) | |
| pipe = pipe.to(torch_device) | |
| pipe.load_lora_weights(lora_model_id) | |
| images = pipe("A photo of a sks dog", output_type="np", generator=generator, num_inference_steps=2).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.6628, 0.6138, 0.5390, 0.6625, 0.6130, 0.5463, 0.6166, 0.5788, 0.5359]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-4)) | |
| release_memory(pipe) | |
| def test_a1111(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None).to( | |
| torch_device | |
| ) | |
| lora_model_id = "hf-internal-testing/civitai-light-shadow-lora" | |
| lora_filename = "light_and_shadow.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_lycoris(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| "hf-internal-testing/Amixx", safety_checker=None, use_safetensors=True, variant="fp16" | |
| ).to(torch_device) | |
| lora_model_id = "hf-internal-testing/edgLycorisMugler-light" | |
| lora_filename = "edgLycorisMugler-light.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.6463, 0.658, 0.599, 0.6542, 0.6512, 0.6213, 0.658, 0.6485, 0.6017]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_a1111_with_model_cpu_offload(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None) | |
| pipe.enable_model_cpu_offload() | |
| lora_model_id = "hf-internal-testing/civitai-light-shadow-lora" | |
| lora_filename = "light_and_shadow.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_a1111_with_sequential_cpu_offload(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None) | |
| pipe.enable_sequential_cpu_offload() | |
| lora_model_id = "hf-internal-testing/civitai-light-shadow-lora" | |
| lora_filename = "light_and_shadow.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_kohya_sd_v15_with_higher_dimensions(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to( | |
| torch_device | |
| ) | |
| lora_model_id = "hf-internal-testing/urushisato-lora" | |
| lora_filename = "urushisato_v15.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.7165, 0.6616, 0.5833, 0.7504, 0.6718, 0.587, 0.6871, 0.6361, 0.5694]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_vanilla_funetuning(self): | |
| generator = torch.Generator().manual_seed(0) | |
| lora_model_id = "hf-internal-testing/sd-model-finetuned-lora-t4" | |
| card = RepoCard.load(lora_model_id) | |
| base_model_id = card.data.to_dict()["base_model"] | |
| pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None) | |
| pipe = pipe.to(torch_device) | |
| pipe.load_lora_weights(lora_model_id) | |
| images = pipe("A pokemon with blue eyes.", output_type="np", generator=generator, num_inference_steps=2).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.7406, 0.699, 0.5963, 0.7493, 0.7045, 0.6096, 0.6886, 0.6388, 0.583]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-4)) | |
| release_memory(pipe) | |
| def test_unload_kohya_lora(self): | |
| generator = torch.manual_seed(0) | |
| prompt = "masterpiece, best quality, mountain" | |
| num_inference_steps = 2 | |
| pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to( | |
| torch_device | |
| ) | |
| initial_images = pipe( | |
| prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps | |
| ).images | |
| initial_images = initial_images[0, -3:, -3:, -1].flatten() | |
| lora_model_id = "hf-internal-testing/civitai-colored-icons-lora" | |
| lora_filename = "Colored_Icons_by_vizsumit.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| generator = torch.manual_seed(0) | |
| lora_images = pipe( | |
| prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps | |
| ).images | |
| lora_images = lora_images[0, -3:, -3:, -1].flatten() | |
| pipe.unload_lora_weights() | |
| generator = torch.manual_seed(0) | |
| unloaded_lora_images = pipe( | |
| prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps | |
| ).images | |
| unloaded_lora_images = unloaded_lora_images[0, -3:, -3:, -1].flatten() | |
| self.assertFalse(np.allclose(initial_images, lora_images)) | |
| self.assertTrue(np.allclose(initial_images, unloaded_lora_images, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_load_unload_load_kohya_lora(self): | |
| # This test ensures that a Kohya-style LoRA can be safely unloaded and then loaded | |
| # without introducing any side-effects. Even though the test uses a Kohya-style | |
| # LoRA, the underlying adapter handling mechanism is format-agnostic. | |
| generator = torch.manual_seed(0) | |
| prompt = "masterpiece, best quality, mountain" | |
| num_inference_steps = 2 | |
| pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to( | |
| torch_device | |
| ) | |
| initial_images = pipe( | |
| prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps | |
| ).images | |
| initial_images = initial_images[0, -3:, -3:, -1].flatten() | |
| lora_model_id = "hf-internal-testing/civitai-colored-icons-lora" | |
| lora_filename = "Colored_Icons_by_vizsumit.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| generator = torch.manual_seed(0) | |
| lora_images = pipe( | |
| prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps | |
| ).images | |
| lora_images = lora_images[0, -3:, -3:, -1].flatten() | |
| pipe.unload_lora_weights() | |
| generator = torch.manual_seed(0) | |
| unloaded_lora_images = pipe( | |
| prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps | |
| ).images | |
| unloaded_lora_images = unloaded_lora_images[0, -3:, -3:, -1].flatten() | |
| self.assertFalse(np.allclose(initial_images, lora_images)) | |
| self.assertTrue(np.allclose(initial_images, unloaded_lora_images, atol=1e-3)) | |
| # make sure we can load a LoRA again after unloading and they don't have | |
| # any undesired effects. | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| generator = torch.manual_seed(0) | |
| lora_images_again = pipe( | |
| prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps | |
| ).images | |
| lora_images_again = lora_images_again[0, -3:, -3:, -1].flatten() | |
| self.assertTrue(np.allclose(lora_images, lora_images_again, atol=1e-3)) | |
| release_memory(pipe) | |
| class LoraSDXLIntegrationTests(unittest.TestCase): | |
| def tearDown(self): | |
| import gc | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| def test_sdxl_0_9_lora_one(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9") | |
| lora_model_id = "hf-internal-testing/sdxl-0.9-daiton-lora" | |
| lora_filename = "daiton-xl-lora-test.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| pipe.enable_model_cpu_offload() | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.3838, 0.3482, 0.3588, 0.3162, 0.319, 0.3369, 0.338, 0.3366, 0.3213]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_sdxl_0_9_lora_two(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9") | |
| lora_model_id = "hf-internal-testing/sdxl-0.9-costumes-lora" | |
| lora_filename = "saijo.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| pipe.enable_model_cpu_offload() | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.3137, 0.3269, 0.3355, 0.255, 0.2577, 0.2563, 0.2679, 0.2758, 0.2626]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_sdxl_0_9_lora_three(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9") | |
| lora_model_id = "hf-internal-testing/sdxl-0.9-kamepan-lora" | |
| lora_filename = "kame_sdxl_v2-000020-16rank.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| pipe.enable_model_cpu_offload() | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.4015, 0.3761, 0.3616, 0.3745, 0.3462, 0.3337, 0.3564, 0.3649, 0.3468]) | |
| self.assertTrue(np.allclose(images, expected, atol=5e-3)) | |
| release_memory(pipe) | |
| def test_sdxl_1_0_lora(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0") | |
| pipe.enable_model_cpu_offload() | |
| lora_model_id = "hf-internal-testing/sdxl-1.0-lora" | |
| lora_filename = "sd_xl_offset_example-lora_1.0.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-4)) | |
| release_memory(pipe) | |
| def test_sdxl_lcm_lora(self): | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16) | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
| pipe.enable_model_cpu_offload() | |
| generator = torch.Generator().manual_seed(0) | |
| lora_model_id = "latent-consistency/lcm-lora-sdxl" | |
| pipe.load_lora_weights(lora_model_id) | |
| image = pipe( | |
| "masterpiece, best quality, mountain", generator=generator, num_inference_steps=4, guidance_scale=0.5 | |
| ).images[0] | |
| expected_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_lora/sdxl_lcm_lora.png" | |
| ) | |
| image_np = pipe.image_processor.pil_to_numpy(image) | |
| expected_image_np = pipe.image_processor.pil_to_numpy(expected_image) | |
| self.assertTrue(np.allclose(image_np, expected_image_np, atol=1e-2)) | |
| pipe.unload_lora_weights() | |
| release_memory(pipe) | |
| def test_sdv1_5_lcm_lora(self): | |
| pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) | |
| pipe.to("cuda") | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
| generator = torch.Generator().manual_seed(0) | |
| lora_model_id = "latent-consistency/lcm-lora-sdv1-5" | |
| pipe.load_lora_weights(lora_model_id) | |
| image = pipe( | |
| "masterpiece, best quality, mountain", generator=generator, num_inference_steps=4, guidance_scale=0.5 | |
| ).images[0] | |
| expected_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_lora/sdv15_lcm_lora.png" | |
| ) | |
| image_np = pipe.image_processor.pil_to_numpy(image) | |
| expected_image_np = pipe.image_processor.pil_to_numpy(expected_image) | |
| self.assertTrue(np.allclose(image_np, expected_image_np, atol=1e-2)) | |
| pipe.unload_lora_weights() | |
| release_memory(pipe) | |
| def test_sdv1_5_lcm_lora_img2img(self): | |
| pipe = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) | |
| pipe.to("cuda") | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
| init_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape.png" | |
| ) | |
| generator = torch.Generator().manual_seed(0) | |
| lora_model_id = "latent-consistency/lcm-lora-sdv1-5" | |
| pipe.load_lora_weights(lora_model_id) | |
| image = pipe( | |
| "snowy mountain", | |
| generator=generator, | |
| image=init_image, | |
| strength=0.5, | |
| num_inference_steps=4, | |
| guidance_scale=0.5, | |
| ).images[0] | |
| expected_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_lora/sdv15_lcm_lora_img2img.png" | |
| ) | |
| image_np = pipe.image_processor.pil_to_numpy(image) | |
| expected_image_np = pipe.image_processor.pil_to_numpy(expected_image) | |
| self.assertTrue(np.allclose(image_np, expected_image_np, atol=1e-2)) | |
| pipe.unload_lora_weights() | |
| release_memory(pipe) | |
| def test_sdxl_1_0_lora_fusion(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0") | |
| lora_model_id = "hf-internal-testing/sdxl-1.0-lora" | |
| lora_filename = "sd_xl_offset_example-lora_1.0.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| pipe.fuse_lora() | |
| # We need to unload the lora weights since in the previous API `fuse_lora` led to lora weights being | |
| # silently deleted - otherwise this will CPU OOM | |
| pipe.unload_lora_weights() | |
| pipe.enable_model_cpu_offload() | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| # This way we also test equivalence between LoRA fusion and the non-fusion behaviour. | |
| expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-4)) | |
| release_memory(pipe) | |
| def test_sdxl_1_0_lora_unfusion(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0") | |
| lora_model_id = "hf-internal-testing/sdxl-1.0-lora" | |
| lora_filename = "sd_xl_offset_example-lora_1.0.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| pipe.fuse_lora() | |
| pipe.enable_model_cpu_offload() | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images_with_fusion = images[0, -3:, -3:, -1].flatten() | |
| pipe.unfuse_lora() | |
| generator = torch.Generator().manual_seed(0) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images_without_fusion = images[0, -3:, -3:, -1].flatten() | |
| self.assertTrue(np.allclose(images_with_fusion, images_without_fusion, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_sdxl_1_0_lora_unfusion_effectivity(self): | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0") | |
| pipe.enable_model_cpu_offload() | |
| generator = torch.Generator().manual_seed(0) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| original_image_slice = images[0, -3:, -3:, -1].flatten() | |
| lora_model_id = "hf-internal-testing/sdxl-1.0-lora" | |
| lora_filename = "sd_xl_offset_example-lora_1.0.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| pipe.fuse_lora() | |
| generator = torch.Generator().manual_seed(0) | |
| _ = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| pipe.unfuse_lora() | |
| # We need to unload the lora weights - in the old API unfuse led to unloading the adapter weights | |
| pipe.unload_lora_weights() | |
| generator = torch.Generator().manual_seed(0) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images_without_fusion_slice = images[0, -3:, -3:, -1].flatten() | |
| self.assertTrue(np.allclose(original_image_slice, images_without_fusion_slice, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_sdxl_1_0_lora_fusion_efficiency(self): | |
| generator = torch.Generator().manual_seed(0) | |
| lora_model_id = "hf-internal-testing/sdxl-1.0-lora" | |
| lora_filename = "sd_xl_offset_example-lora_1.0.safetensors" | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16 | |
| ) | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.bfloat16) | |
| pipe.enable_model_cpu_offload() | |
| start_time = time.time() | |
| for _ in range(3): | |
| pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| end_time = time.time() | |
| elapsed_time_non_fusion = end_time - start_time | |
| del pipe | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16 | |
| ) | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.bfloat16) | |
| pipe.fuse_lora() | |
| # We need to unload the lora weights since in the previous API `fuse_lora` led to lora weights being | |
| # silently deleted - otherwise this will CPU OOM | |
| pipe.unload_lora_weights() | |
| pipe.enable_model_cpu_offload() | |
| start_time = time.time() | |
| generator = torch.Generator().manual_seed(0) | |
| for _ in range(3): | |
| pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| end_time = time.time() | |
| elapsed_time_fusion = end_time - start_time | |
| self.assertTrue(elapsed_time_fusion < elapsed_time_non_fusion) | |
| release_memory(pipe) | |
| def test_sdxl_1_0_last_ben(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0") | |
| pipe.enable_model_cpu_offload() | |
| lora_model_id = "TheLastBen/Papercut_SDXL" | |
| lora_filename = "papercut.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| images = pipe("papercut.safetensors", output_type="np", generator=generator, num_inference_steps=2).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.5244, 0.4347, 0.4312, 0.4246, 0.4398, 0.4409, 0.4884, 0.4938, 0.4094]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_sdxl_1_0_fuse_unfuse_all(self): | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16) | |
| text_encoder_1_sd = copy.deepcopy(pipe.text_encoder.state_dict()) | |
| text_encoder_2_sd = copy.deepcopy(pipe.text_encoder_2.state_dict()) | |
| unet_sd = copy.deepcopy(pipe.unet.state_dict()) | |
| pipe.load_lora_weights( | |
| "davizca87/sun-flower", weight_name="snfw3rXL-000004.safetensors", torch_dtype=torch.float16 | |
| ) | |
| fused_te_state_dict = pipe.text_encoder.state_dict() | |
| fused_te_2_state_dict = pipe.text_encoder_2.state_dict() | |
| unet_state_dict = pipe.unet.state_dict() | |
| for key, value in text_encoder_1_sd.items(): | |
| self.assertTrue(torch.allclose(fused_te_state_dict[key], value)) | |
| for key, value in text_encoder_2_sd.items(): | |
| self.assertTrue(torch.allclose(fused_te_2_state_dict[key], value)) | |
| for key, value in unet_state_dict.items(): | |
| self.assertTrue(torch.allclose(unet_state_dict[key], value)) | |
| pipe.fuse_lora() | |
| pipe.unload_lora_weights() | |
| assert not state_dicts_almost_equal(text_encoder_1_sd, pipe.text_encoder.state_dict()) | |
| assert not state_dicts_almost_equal(text_encoder_2_sd, pipe.text_encoder_2.state_dict()) | |
| assert not state_dicts_almost_equal(unet_sd, pipe.unet.state_dict()) | |
| release_memory(pipe) | |
| del unet_sd, text_encoder_1_sd, text_encoder_2_sd | |
| def test_sdxl_1_0_lora_with_sequential_cpu_offloading(self): | |
| generator = torch.Generator().manual_seed(0) | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0") | |
| pipe.enable_sequential_cpu_offload() | |
| lora_model_id = "hf-internal-testing/sdxl-1.0-lora" | |
| lora_filename = "sd_xl_offset_example-lora_1.0.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipe) | |
| def test_sd_load_civitai_empty_network_alpha(self): | |
| """ | |
| This test simply checks that loading a LoRA with an empty network alpha works fine | |
| See: https://github.com/huggingface/diffusers/issues/5606 | |
| """ | |
| pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("cuda") | |
| pipeline.enable_sequential_cpu_offload() | |
| civitai_path = hf_hub_download("ybelkada/test-ahi-civitai", "ahi_lora_weights.safetensors") | |
| pipeline.load_lora_weights(civitai_path, adapter_name="ahri") | |
| images = pipeline( | |
| "ahri, masterpiece, league of legends", | |
| output_type="np", | |
| generator=torch.manual_seed(156), | |
| num_inference_steps=5, | |
| ).images | |
| images = images[0, -3:, -3:, -1].flatten() | |
| expected = np.array([0.0, 0.0, 0.0, 0.002557, 0.020954, 0.001792, 0.006581, 0.00591, 0.002995]) | |
| self.assertTrue(np.allclose(images, expected, atol=1e-3)) | |
| release_memory(pipeline) | |
| def test_canny_lora(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.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors") | |
| pipe.enable_sequential_cpu_offload() | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| prompt = "corgi" | |
| 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([0.4574, 0.4461, 0.4435, 0.4462, 0.4396, 0.439, 0.4474, 0.4486, 0.4333]) | |
| assert np.allclose(original_image, expected_image, atol=1e-04) | |
| release_memory(pipe) | |
| def test_sequential_fuse_unfuse(self): | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16) | |
| # 1. round | |
| pipe.load_lora_weights("Pclanglais/TintinIA", torch_dtype=torch.float16) | |
| pipe.to("cuda") | |
| pipe.fuse_lora() | |
| generator = torch.Generator().manual_seed(0) | |
| images = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| image_slice = images[0, -3:, -3:, -1].flatten() | |
| pipe.unfuse_lora() | |
| # 2. round | |
| pipe.load_lora_weights("ProomptEngineer/pe-balloon-diffusion-style", torch_dtype=torch.float16) | |
| pipe.fuse_lora() | |
| pipe.unfuse_lora() | |
| # 3. round | |
| pipe.load_lora_weights("ostris/crayon_style_lora_sdxl", torch_dtype=torch.float16) | |
| pipe.fuse_lora() | |
| pipe.unfuse_lora() | |
| # 4. back to 1st round | |
| pipe.load_lora_weights("Pclanglais/TintinIA", torch_dtype=torch.float16) | |
| pipe.fuse_lora() | |
| generator = torch.Generator().manual_seed(0) | |
| images_2 = pipe( | |
| "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2 | |
| ).images | |
| image_slice_2 = images_2[0, -3:, -3:, -1].flatten() | |
| self.assertTrue(np.allclose(image_slice, image_slice_2, atol=1e-3)) | |
| release_memory(pipe) | |