mirror of
https://github.com/huggingface/diffusers.git
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d87fe95f90
* introduce to promote reusability. * up * add more tests * up * remove comments. * fix fuse_nan test * clarify the scope of fuse_lora and unfuse_lora * remove space * rewrite fuse_lora a bit. * feedback * copy over load_lora_into_text_encoder. * address dhruv's feedback. * fix-copies * fix issubclass. * num_fused_loras * fix * fix * remove mapping * up * fix * style * fix-copies * change to SD3TransformerLoRALoadersMixin * Apply suggestions from code review Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com> * up * handle wuerstchen * up * move lora to lora_pipeline.py * up * fix-copies * fix documentation. * comment set_adapters(). * fix-copies * fix set_adapters() at the model level. * fix? * fix * loraloadermixin. --------- Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
1787 lines
83 KiB
Python
1787 lines
83 KiB
Python
# coding=utf-8
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# Copyright 2024 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import unittest
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from itertools import product
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import numpy as np
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import torch
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from transformers import AutoTokenizer, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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FlowMatchEulerDiscreteScheduler,
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LCMScheduler,
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SD3Transformer2DModel,
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UNet2DConditionModel,
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)
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from diffusers.utils.import_utils import is_peft_available
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from diffusers.utils.testing_utils import (
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floats_tensor,
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require_peft_backend,
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require_peft_version_greater,
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skip_mps,
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torch_device,
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)
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if is_peft_available():
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from peft import LoraConfig
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from peft.tuners.tuners_utils import BaseTunerLayer
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from peft.utils import get_peft_model_state_dict
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def state_dicts_almost_equal(sd1, sd2):
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sd1 = dict(sorted(sd1.items()))
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sd2 = dict(sorted(sd2.items()))
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models_are_equal = True
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for ten1, ten2 in zip(sd1.values(), sd2.values()):
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if (ten1 - ten2).abs().max() > 1e-3:
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models_are_equal = False
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return models_are_equal
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def check_if_lora_correctly_set(model) -> bool:
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"""
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Checks if the LoRA layers are correctly set with peft
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"""
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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return True
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return False
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@require_peft_backend
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class PeftLoraLoaderMixinTests:
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pipeline_class = None
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scheduler_cls = None
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scheduler_kwargs = None
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has_two_text_encoders = False
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has_three_text_encoders = False
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unet_kwargs = None
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transformer_kwargs = None
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vae_kwargs = None
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def get_dummy_components(self, scheduler_cls=None, use_dora=False):
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if self.unet_kwargs and self.transformer_kwargs:
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raise ValueError("Both `unet_kwargs` and `transformer_kwargs` cannot be specified.")
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if self.has_two_text_encoders and self.has_three_text_encoders:
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raise ValueError("Both `has_two_text_encoders` and `has_three_text_encoders` cannot be True.")
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scheduler_cls = self.scheduler_cls if scheduler_cls is None else scheduler_cls
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rank = 4
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torch.manual_seed(0)
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if self.unet_kwargs is not None:
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unet = UNet2DConditionModel(**self.unet_kwargs)
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else:
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transformer = SD3Transformer2DModel(**self.transformer_kwargs)
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scheduler = scheduler_cls(**self.scheduler_kwargs)
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torch.manual_seed(0)
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vae = AutoencoderKL(**self.vae_kwargs)
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if not self.has_three_text_encoders:
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text_encoder = CLIPTextModel.from_pretrained("peft-internal-testing/tiny-clip-text-2")
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tokenizer = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2")
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if self.has_two_text_encoders:
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text_encoder_2 = CLIPTextModelWithProjection.from_pretrained("peft-internal-testing/tiny-clip-text-2")
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tokenizer_2 = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2")
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if self.has_three_text_encoders:
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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tokenizer_3 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
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text_encoder = CLIPTextModelWithProjection.from_pretrained("hf-internal-testing/tiny-sd3-text_encoder")
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text_encoder_2 = CLIPTextModelWithProjection.from_pretrained("hf-internal-testing/tiny-sd3-text_encoder-2")
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text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
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text_lora_config = LoraConfig(
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r=rank,
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lora_alpha=rank,
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target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
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init_lora_weights=False,
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use_dora=use_dora,
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)
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denoiser_lora_config = LoraConfig(
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r=rank,
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lora_alpha=rank,
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target_modules=["to_q", "to_k", "to_v", "to_out.0"],
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init_lora_weights=False,
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use_dora=use_dora,
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)
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if self.has_two_text_encoders or self.has_three_text_encoders:
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if self.unet_kwargs is not None:
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pipeline_components = {
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"unet": unet,
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"scheduler": scheduler,
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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"text_encoder_2": text_encoder_2,
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"tokenizer_2": tokenizer_2,
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"image_encoder": None,
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"feature_extractor": None,
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}
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elif self.has_three_text_encoders and self.transformer_kwargs is not None:
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pipeline_components = {
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"transformer": transformer,
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"scheduler": scheduler,
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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"text_encoder_2": text_encoder_2,
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"tokenizer_2": tokenizer_2,
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"text_encoder_3": text_encoder_3,
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"tokenizer_3": tokenizer_3,
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}
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else:
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pipeline_components = {
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"unet": unet,
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"scheduler": scheduler,
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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"safety_checker": None,
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"feature_extractor": None,
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"image_encoder": None,
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}
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return pipeline_components, text_lora_config, denoiser_lora_config
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def get_dummy_inputs(self, with_generator=True):
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batch_size = 1
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sequence_length = 10
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num_channels = 4
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sizes = (32, 32)
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generator = torch.manual_seed(0)
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noise = floats_tensor((batch_size, num_channels) + sizes)
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input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
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pipeline_inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"num_inference_steps": 5,
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"guidance_scale": 6.0,
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"output_type": "np",
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}
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if with_generator:
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pipeline_inputs.update({"generator": generator})
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return noise, input_ids, pipeline_inputs
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# Copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
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def get_dummy_tokens(self):
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max_seq_length = 77
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inputs = torch.randint(2, 56, size=(1, max_seq_length), generator=torch.manual_seed(0))
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prepared_inputs = {}
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prepared_inputs["input_ids"] = inputs
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return prepared_inputs
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def test_simple_inference(self):
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"""
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Tests a simple inference and makes sure it works as expected
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"""
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scheduler_classes = (
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[FlowMatchEulerDiscreteScheduler]
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if self.has_three_text_encoders and self.transformer_kwargs
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else [DDIMScheduler, LCMScheduler]
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)
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for scheduler_cls in scheduler_classes:
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components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
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pipe = self.pipeline_class(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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_, _, inputs = self.get_dummy_inputs()
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output_no_lora = pipe(**inputs).images
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shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
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self.assertTrue(output_no_lora.shape == shape_to_be_checked)
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def test_simple_inference_with_text_lora(self):
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"""
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Tests a simple inference with lora attached on the text encoder
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and makes sure it works as expected
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"""
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scheduler_classes = (
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[FlowMatchEulerDiscreteScheduler]
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if self.has_three_text_encoders and self.transformer_kwargs
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else [DDIMScheduler, LCMScheduler]
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)
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for scheduler_cls in scheduler_classes:
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components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
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pipe = self.pipeline_class(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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_, _, inputs = self.get_dummy_inputs(with_generator=False)
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output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
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shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
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self.assertTrue(output_no_lora.shape == shape_to_be_checked)
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pipe.text_encoder.add_adapter(text_lora_config)
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self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
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if self.has_two_text_encoders or self.has_three_text_encoders:
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pipe.text_encoder_2.add_adapter(text_lora_config)
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self.assertTrue(
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check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
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)
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output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
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self.assertTrue(
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not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
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)
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def test_simple_inference_with_text_lora_and_scale(self):
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"""
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Tests a simple inference with lora attached on the text encoder + scale argument
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and makes sure it works as expected
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"""
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scheduler_classes = (
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[FlowMatchEulerDiscreteScheduler]
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if self.has_three_text_encoders and self.transformer_kwargs
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else [DDIMScheduler, LCMScheduler]
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)
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for scheduler_cls in scheduler_classes:
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components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
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pipe = self.pipeline_class(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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_, _, inputs = self.get_dummy_inputs(with_generator=False)
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output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
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shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
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self.assertTrue(output_no_lora.shape == shape_to_be_checked)
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pipe.text_encoder.add_adapter(text_lora_config)
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self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
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if self.has_two_text_encoders or self.has_three_text_encoders:
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pipe.text_encoder_2.add_adapter(text_lora_config)
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self.assertTrue(
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check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
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)
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output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
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self.assertTrue(
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not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
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)
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if self.unet_kwargs is not None:
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output_lora_scale = pipe(
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**inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5}
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).images
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else:
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output_lora_scale = pipe(
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**inputs, generator=torch.manual_seed(0), joint_attention_kwargs={"scale": 0.5}
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).images
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self.assertTrue(
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not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
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"Lora + scale should change the output",
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)
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if self.unet_kwargs is not None:
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output_lora_0_scale = pipe(
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**inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0}
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).images
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else:
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output_lora_0_scale = pipe(
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**inputs, generator=torch.manual_seed(0), joint_attention_kwargs={"scale": 0.0}
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).images
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self.assertTrue(
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np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),
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"Lora + 0 scale should lead to same result as no LoRA",
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)
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def test_simple_inference_with_text_lora_fused(self):
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"""
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Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
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and makes sure it works as expected
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"""
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scheduler_classes = (
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[FlowMatchEulerDiscreteScheduler]
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if self.has_three_text_encoders and self.transformer_kwargs
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else [DDIMScheduler, LCMScheduler]
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)
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for scheduler_cls in scheduler_classes:
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components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
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pipe = self.pipeline_class(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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_, _, inputs = self.get_dummy_inputs(with_generator=False)
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output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
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shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
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self.assertTrue(output_no_lora.shape == shape_to_be_checked)
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pipe.text_encoder.add_adapter(text_lora_config)
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self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
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if self.has_two_text_encoders or self.has_three_text_encoders:
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pipe.text_encoder_2.add_adapter(text_lora_config)
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self.assertTrue(
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check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
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)
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pipe.fuse_lora()
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# Fusing should still keep the LoRA layers
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self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
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if self.has_two_text_encoders or self.has_three_text_encoders:
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self.assertTrue(
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check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
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)
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ouput_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
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self.assertFalse(
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np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
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)
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def test_simple_inference_with_text_lora_unloaded(self):
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"""
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Tests a simple inference with lora attached to text encoder, then unloads the lora weights
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and makes sure it works as expected
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"""
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scheduler_classes = (
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[FlowMatchEulerDiscreteScheduler]
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if self.has_three_text_encoders and self.transformer_kwargs
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else [DDIMScheduler, LCMScheduler]
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)
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for scheduler_cls in scheduler_classes:
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components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
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pipe = self.pipeline_class(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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_, _, inputs = self.get_dummy_inputs(with_generator=False)
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output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
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shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
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self.assertTrue(output_no_lora.shape == shape_to_be_checked)
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pipe.text_encoder.add_adapter(text_lora_config)
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self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
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if self.has_two_text_encoders or self.has_three_text_encoders:
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pipe.text_encoder_2.add_adapter(text_lora_config)
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self.assertTrue(
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check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
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)
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pipe.unload_lora_weights()
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# unloading should remove the LoRA layers
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self.assertFalse(
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check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder"
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)
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if self.has_two_text_encoders or self.has_three_text_encoders:
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self.assertFalse(
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check_if_lora_correctly_set(pipe.text_encoder_2),
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"Lora not correctly unloaded in text encoder 2",
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)
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ouput_unloaded = pipe(**inputs, generator=torch.manual_seed(0)).images
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self.assertTrue(
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np.allclose(ouput_unloaded, output_no_lora, atol=1e-3, rtol=1e-3),
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"Fused lora should change the output",
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)
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def test_simple_inference_with_text_lora_save_load(self):
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"""
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Tests a simple usecase where users could use saving utilities for LoRA.
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"""
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scheduler_classes = (
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[FlowMatchEulerDiscreteScheduler]
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if self.has_three_text_encoders and self.transformer_kwargs
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else [DDIMScheduler, LCMScheduler]
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)
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for scheduler_cls in scheduler_classes:
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|
components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
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 or self.has_three_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(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
self.assertTrue(
|
|
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_partial_text_lora(self):
|
|
"""
|
|
Tests a simple inference with lora attached on the text encoder
|
|
with different ranks and some adapters removed
|
|
and makes sure it works as expected
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, _, _ = self.get_dummy_components(scheduler_cls)
|
|
# Verify `StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder` handles different ranks per module (PR#8324).
|
|
text_lora_config = LoraConfig(
|
|
r=4,
|
|
rank_pattern={"q_proj": 1, "k_proj": 2, "v_proj": 3},
|
|
lora_alpha=4,
|
|
target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
|
|
init_lora_weights=False,
|
|
use_dora=False,
|
|
)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
# Gather the state dict for the PEFT model, excluding `layers.4`, to ensure `load_lora_into_text_encoder`
|
|
# supports missing layers (PR#8324).
|
|
state_dict = {
|
|
f"text_encoder.{module_name}": param
|
|
for module_name, param in get_peft_model_state_dict(pipe.text_encoder).items()
|
|
if "text_model.encoder.layers.4" not in module_name
|
|
}
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
|
|
)
|
|
state_dict.update(
|
|
{
|
|
f"text_encoder_2.{module_name}": param
|
|
for module_name, param in get_peft_model_state_dict(pipe.text_encoder_2).items()
|
|
if "text_model.encoder.layers.4" not in module_name
|
|
}
|
|
)
|
|
|
|
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"
|
|
)
|
|
|
|
# Unload lora and load it back using the pipe.load_lora_weights machinery
|
|
pipe.unload_lora_weights()
|
|
pipe.load_lora_weights(state_dict)
|
|
|
|
output_partial_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
self.assertTrue(
|
|
not np.allclose(output_partial_lora, output_lora, atol=1e-3, rtol=1e-3),
|
|
"Removing adapters should change the output",
|
|
)
|
|
|
|
def test_simple_inference_save_pretrained(self):
|
|
"""
|
|
Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
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(torch_device)
|
|
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe_from_pretrained.text_encoder),
|
|
"Lora not correctly set in text encoder",
|
|
)
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
self.assertTrue(
|
|
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_denoiser_lora_save_load(self):
|
|
"""
|
|
Tests a simple usecase where users could use saving utilities for LoRA for Unet + text encoder
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config)
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config)
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config)
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in Unet")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
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.unet_kwargs is not None:
|
|
denoiser_state_dict = get_peft_model_state_dict(pipe.unet)
|
|
else:
|
|
denoiser_state_dict = get_peft_model_state_dict(pipe.transformer)
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
|
|
|
|
if self.unet_kwargs is not None:
|
|
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=denoiser_state_dict,
|
|
safe_serialization=False,
|
|
)
|
|
else:
|
|
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,
|
|
transformer_lora_layers=denoiser_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=denoiser_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(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
self.assertTrue(
|
|
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_denoiser_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
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config)
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config)
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
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"
|
|
)
|
|
|
|
if self.unet_kwargs is not None:
|
|
output_lora_scale = pipe(
|
|
**inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5}
|
|
).images
|
|
else:
|
|
output_lora_scale = pipe(
|
|
**inputs, generator=torch.manual_seed(0), joint_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",
|
|
)
|
|
|
|
if self.unet_kwargs is not None:
|
|
output_lora_0_scale = pipe(
|
|
**inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0}
|
|
).images
|
|
else:
|
|
output_lora_0_scale = pipe(
|
|
**inputs, generator=torch.manual_seed(0), joint_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_denoiser_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
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config)
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config)
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config)
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
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(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
self.assertTrue(
|
|
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_denoiser_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
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config)
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config)
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
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(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder"
|
|
)
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertFalse(
|
|
check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly unloaded in denoiser"
|
|
)
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
self.assertFalse(
|
|
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_denoiser_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
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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)
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config)
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config)
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
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(check_if_lora_correctly_set(pipe.text_encoder), "Unfuse should still keep LoRA layers")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Unfuse should still keep LoRA layers")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
self.assertTrue(
|
|
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_denoiser_multi_adapter(self):
|
|
"""
|
|
Tests a simple inference with lora attached to text encoder and unet, attaches
|
|
multiple adapters and set them
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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")
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_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(
|
|
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_denoiser_block_scale(self):
|
|
"""
|
|
Tests a simple inference with lora attached to text encoder and unet, attaches
|
|
one adapter and set differnt weights for different blocks (i.e. block lora)
|
|
"""
|
|
if self.pipeline_class.__name__ == "StableDiffusion3Pipeline":
|
|
return
|
|
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
|
|
)
|
|
|
|
weights_1 = {"text_encoder": 2, "unet": {"down": 5}}
|
|
pipe.set_adapters("adapter-1", weights_1)
|
|
output_weights_1 = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
|
|
weights_2 = {"unet": {"up": 5}}
|
|
pipe.set_adapters("adapter-1", weights_2)
|
|
output_weights_2 = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
|
|
self.assertFalse(
|
|
np.allclose(output_weights_1, output_weights_2, atol=1e-3, rtol=1e-3),
|
|
"LoRA weights 1 and 2 should give different results",
|
|
)
|
|
self.assertFalse(
|
|
np.allclose(output_no_lora, output_weights_1, atol=1e-3, rtol=1e-3),
|
|
"No adapter and LoRA weights 1 should give different results",
|
|
)
|
|
self.assertFalse(
|
|
np.allclose(output_no_lora, output_weights_2, atol=1e-3, rtol=1e-3),
|
|
"No adapter and LoRA weights 2 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_denoiser_multi_adapter_block_lora(self):
|
|
"""
|
|
Tests a simple inference with lora attached to text encoder and unet, attaches
|
|
multiple adapters and set differnt weights for different blocks (i.e. block lora)
|
|
"""
|
|
if self.pipeline_class.__name__ == "StableDiffusion3Pipeline":
|
|
return
|
|
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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")
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_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(
|
|
check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
|
|
)
|
|
|
|
scales_1 = {"text_encoder": 2, "unet": {"down": 5}}
|
|
scales_2 = {"unet": {"down": 5, "mid": 5}}
|
|
pipe.set_adapters("adapter-1", scales_1)
|
|
|
|
output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
|
|
pipe.set_adapters("adapter-2", scales_2)
|
|
output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
|
|
pipe.set_adapters(["adapter-1", "adapter-2"], [scales_1, scales_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",
|
|
)
|
|
|
|
# a mismatching number of adapter_names and adapter_weights should raise an error
|
|
with self.assertRaises(ValueError):
|
|
pipe.set_adapters(["adapter-1", "adapter-2"], [scales_1])
|
|
|
|
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
|
|
"""Tests that any valid combination of lora block scales can be used in pipe.set_adapter"""
|
|
if self.pipeline_class.__name__ == "StableDiffusion3Pipeline":
|
|
return
|
|
|
|
def updown_options(blocks_with_tf, layers_per_block, value):
|
|
"""
|
|
Generate every possible combination for how a lora weight dict for the up/down part can be.
|
|
E.g. 2, {"block_1": 2}, {"block_1": [2,2,2]}, {"block_1": 2, "block_2": [2,2,2]}, ...
|
|
"""
|
|
num_val = value
|
|
list_val = [value] * layers_per_block
|
|
|
|
node_opts = [None, num_val, list_val]
|
|
node_opts_foreach_block = [node_opts] * len(blocks_with_tf)
|
|
|
|
updown_opts = [num_val]
|
|
for nodes in product(*node_opts_foreach_block):
|
|
if all(n is None for n in nodes):
|
|
continue
|
|
opt = {}
|
|
for b, n in zip(blocks_with_tf, nodes):
|
|
if n is not None:
|
|
opt["block_" + str(b)] = n
|
|
updown_opts.append(opt)
|
|
return updown_opts
|
|
|
|
def all_possible_dict_opts(unet, value):
|
|
"""
|
|
Generate every possible combination for how a lora weight dict can be.
|
|
E.g. 2, {"unet: {"down": 2}}, {"unet: {"down": [2,2,2]}}, {"unet: {"mid": 2, "up": [2,2,2]}}, ...
|
|
"""
|
|
|
|
down_blocks_with_tf = [i for i, d in enumerate(unet.down_blocks) if hasattr(d, "attentions")]
|
|
up_blocks_with_tf = [i for i, u in enumerate(unet.up_blocks) if hasattr(u, "attentions")]
|
|
|
|
layers_per_block = unet.config.layers_per_block
|
|
|
|
text_encoder_opts = [None, value]
|
|
text_encoder_2_opts = [None, value]
|
|
mid_opts = [None, value]
|
|
down_opts = [None] + updown_options(down_blocks_with_tf, layers_per_block, value)
|
|
up_opts = [None] + updown_options(up_blocks_with_tf, layers_per_block + 1, value)
|
|
|
|
opts = []
|
|
|
|
for t1, t2, d, m, u in product(text_encoder_opts, text_encoder_2_opts, down_opts, mid_opts, up_opts):
|
|
if all(o is None for o in (t1, t2, d, m, u)):
|
|
continue
|
|
opt = {}
|
|
if t1 is not None:
|
|
opt["text_encoder"] = t1
|
|
if t2 is not None:
|
|
opt["text_encoder_2"] = t2
|
|
if all(o is None for o in (d, m, u)):
|
|
# no unet scaling
|
|
continue
|
|
opt["unet"] = {}
|
|
if d is not None:
|
|
opt["unet"]["down"] = d
|
|
if m is not None:
|
|
opt["unet"]["mid"] = m
|
|
if u is not None:
|
|
opt["unet"]["up"] = u
|
|
opts.append(opt)
|
|
|
|
return opts
|
|
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(self.scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
|
|
|
|
for scale_dict in all_possible_dict_opts(pipe.unet, value=1234):
|
|
# test if lora block scales can be set with this scale_dict
|
|
if not self.has_two_text_encoders and "text_encoder_2" in scale_dict:
|
|
del scale_dict["text_encoder_2"]
|
|
|
|
pipe.set_adapters("adapter-1", scale_dict) # test will fail if this line throws an error
|
|
|
|
def test_simple_inference_with_text_denoiser_multi_adapter_delete_adapter(self):
|
|
"""
|
|
Tests a simple inference with lora attached to text encoder and unet, attaches
|
|
multiple adapters and set/delete them
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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")
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_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(
|
|
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")
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_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_denoiser_multi_adapter_weighted(self):
|
|
"""
|
|
Tests a simple inference with lora attached to text encoder and unet, attaches
|
|
multiple adapters and set them
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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")
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_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(
|
|
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",
|
|
)
|
|
|
|
@skip_mps
|
|
def test_lora_fuse_nan(self):
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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")
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
# corrupt one LoRA weight with `inf` values
|
|
with torch.no_grad():
|
|
if self.unet_kwargs:
|
|
pipe.unet.mid_block.attentions[0].transformer_blocks[0].attn1.to_q.lora_A[
|
|
"adapter-1"
|
|
].weight += float("inf")
|
|
else:
|
|
pipe.transformer.transformer_blocks[0].attn.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
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_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")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_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
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
|
|
adapter_names = pipe.get_list_adapters()
|
|
dicts_to_be_checked = {"text_encoder": ["adapter-1"]}
|
|
if self.unet_kwargs is not None:
|
|
dicts_to_be_checked.update({"unet": ["adapter-1"]})
|
|
else:
|
|
dicts_to_be_checked.update({"transformer": ["adapter-1"]})
|
|
self.assertDictEqual(adapter_names, dicts_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
|
|
|
|
adapter_names = pipe.get_list_adapters()
|
|
dicts_to_be_checked = {"text_encoder": ["adapter-1", "adapter-2"]}
|
|
if self.unet_kwargs is not None:
|
|
dicts_to_be_checked.update({"unet": ["adapter-1", "adapter-2"]})
|
|
else:
|
|
dicts_to_be_checked.update({"transformer": ["adapter-1", "adapter-2"]})
|
|
self.assertDictEqual(adapter_names, dicts_to_be_checked)
|
|
|
|
pipe.set_adapters(["adapter-1", "adapter-2"])
|
|
dicts_to_be_checked = {"text_encoder": ["adapter-1", "adapter-2"]}
|
|
if self.unet_kwargs is not None:
|
|
dicts_to_be_checked.update({"unet": ["adapter-1", "adapter-2"]})
|
|
else:
|
|
dicts_to_be_checked.update({"transformer": ["adapter-1", "adapter-2"]})
|
|
self.assertDictEqual(
|
|
pipe.get_list_adapters(),
|
|
dicts_to_be_checked,
|
|
)
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-3")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-3")
|
|
|
|
dicts_to_be_checked = {"text_encoder": ["adapter-1", "adapter-2"]}
|
|
if self.unet_kwargs is not None:
|
|
dicts_to_be_checked.update({"unet": ["adapter-1", "adapter-2", "adapter-3"]})
|
|
else:
|
|
dicts_to_be_checked.update({"transformer": ["adapter-1", "adapter-2", "adapter-3"]})
|
|
self.assertDictEqual(pipe.get_list_adapters(), dicts_to_be_checked)
|
|
|
|
@require_peft_version_greater(peft_version="0.6.2")
|
|
def test_simple_inference_with_text_lora_denoiser_fused_multi(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 and multi-adapter case
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
|
|
# Attach a second adapter
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_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(
|
|
check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
|
|
)
|
|
|
|
# set them to multi-adapter inference mode
|
|
pipe.set_adapters(["adapter-1", "adapter-2"])
|
|
ouputs_all_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
|
|
pipe.set_adapters(["adapter-1"])
|
|
ouputs_lora_1 = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
|
|
pipe.fuse_lora(adapter_names=["adapter-1"])
|
|
|
|
# Fusing should still keep the LoRA layers so outpout should remain the same
|
|
outputs_lora_1_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
|
|
self.assertTrue(
|
|
np.allclose(ouputs_lora_1, outputs_lora_1_fused, atol=1e-3, rtol=1e-3),
|
|
"Fused lora should not change the output",
|
|
)
|
|
|
|
pipe.unfuse_lora()
|
|
pipe.fuse_lora(adapter_names=["adapter-2", "adapter-1"])
|
|
|
|
# Fusing should still keep the LoRA layers
|
|
output_all_lora_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
self.assertTrue(
|
|
np.allclose(output_all_lora_fused, ouputs_all_lora, atol=1e-3, rtol=1e-3),
|
|
"Fused lora should not change the output",
|
|
)
|
|
|
|
@require_peft_version_greater(peft_version="0.9.0")
|
|
def test_simple_inference_with_dora(self):
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(
|
|
scheduler_cls, use_dora=True
|
|
)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
|
|
output_no_dora_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
|
|
self.assertTrue(output_no_dora_lora.shape == shape_to_be_checked)
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config)
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config)
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config)
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
|
|
)
|
|
|
|
output_dora_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
|
|
|
|
self.assertFalse(
|
|
np.allclose(output_dora_lora, output_no_dora_lora, atol=1e-3, rtol=1e-3),
|
|
"DoRA lora should change the output",
|
|
)
|
|
|
|
@unittest.skip("This is failing for now - need to investigate")
|
|
def test_simple_inference_with_text_denoiser_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
|
|
"""
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(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)
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config)
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config)
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
pipe.text_encoder_2.add_adapter(text_lora_config)
|
|
self.assertTrue(
|
|
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 or self.has_three_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
|
|
|
|
def test_modify_padding_mode(self):
|
|
if self.pipeline_class.__name__ == "StableDiffusion3Pipeline":
|
|
return
|
|
|
|
def set_pad_mode(network, mode="circular"):
|
|
for _, module in network.named_modules():
|
|
if isinstance(module, torch.nn.Conv2d):
|
|
module.padding_mode = mode
|
|
|
|
scheduler_classes = (
|
|
[FlowMatchEulerDiscreteScheduler]
|
|
if self.has_three_text_encoders and self.transformer_kwargs
|
|
else [DDIMScheduler, LCMScheduler]
|
|
)
|
|
for scheduler_cls in scheduler_classes:
|
|
components, _, _ = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
_pad_mode = "circular"
|
|
set_pad_mode(pipe.vae, _pad_mode)
|
|
set_pad_mode(pipe.unet, _pad_mode)
|
|
|
|
_, _, inputs = self.get_dummy_inputs()
|
|
_ = pipe(**inputs).images
|