mirror of
https://github.com/huggingface/diffusers.git
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2504 lines
121 KiB
Python
2504 lines
121 KiB
Python
# coding=utf-8
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# Copyright 2025 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 inspect
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import os
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import re
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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 pytest
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import torch
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from parameterized import parameterized
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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LCMScheduler,
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UNet2DConditionModel,
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)
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from diffusers.utils import logging
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from diffusers.utils.import_utils import is_peft_available
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from ..testing_utils import (
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CaptureLogger,
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check_if_dicts_are_equal,
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floats_tensor,
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is_torch_version,
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require_peft_backend,
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require_peft_version_greater,
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require_torch_accelerator,
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require_transformers_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, inject_adapter_in_model, set_peft_model_state_dict
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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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def check_module_lora_metadata(parsed_metadata: dict, lora_metadatas: dict, module_key: str):
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extracted = {
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k.removeprefix(f"{module_key}."): v for k, v in parsed_metadata.items() if k.startswith(f"{module_key}.")
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}
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check_if_dicts_are_equal(extracted, lora_metadatas[f"{module_key}_lora_adapter_metadata"])
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def initialize_dummy_state_dict(state_dict):
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if not all(v.device.type == "meta" for _, v in state_dict.items()):
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raise ValueError("`state_dict` has non-meta values.")
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return {k: torch.randn(v.shape, device=torch_device, dtype=v.dtype) for k, v in state_dict.items()}
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POSSIBLE_ATTENTION_KWARGS_NAMES = ["cross_attention_kwargs", "joint_attention_kwargs", "attention_kwargs"]
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def determine_attention_kwargs_name(pipeline_class):
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call_signature_keys = inspect.signature(pipeline_class.__call__).parameters.keys()
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# TODO(diffusers): Discuss a common naming convention across library for 1.0.0 release
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for possible_attention_kwargs in POSSIBLE_ATTENTION_KWARGS_NAMES:
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if possible_attention_kwargs in call_signature_keys:
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attention_kwargs_name = possible_attention_kwargs
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break
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assert attention_kwargs_name is not None
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return attention_kwargs_name
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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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scheduler_classes = [DDIMScheduler, LCMScheduler]
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has_two_text_encoders = False
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has_three_text_encoders = False
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text_encoder_cls, text_encoder_id, text_encoder_subfolder = None, None, ""
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text_encoder_2_cls, text_encoder_2_id, text_encoder_2_subfolder = None, None, ""
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text_encoder_3_cls, text_encoder_3_id, text_encoder_3_subfolder = None, None, ""
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tokenizer_cls, tokenizer_id, tokenizer_subfolder = None, None, ""
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tokenizer_2_cls, tokenizer_2_id, tokenizer_2_subfolder = None, None, ""
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tokenizer_3_cls, tokenizer_3_id, tokenizer_3_subfolder = None, None, ""
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unet_kwargs = None
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transformer_cls = None
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transformer_kwargs = None
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vae_cls = AutoencoderKL
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vae_kwargs = None
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text_encoder_target_modules = ["q_proj", "k_proj", "v_proj", "out_proj"]
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denoiser_target_modules = ["to_q", "to_k", "to_v", "to_out.0"]
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cached_non_lora_outputs = {}
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@pytest.fixture(scope="class", autouse=True)
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def cache_non_lora_outputs(self):
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"""
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This fixture will be executed once per test class and will populate
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the cached_non_lora_outputs dictionary.
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"""
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for scheduler_cls in self.scheduler_classes:
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# Check if the output for this scheduler is already cached to avoid re-running
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if scheduler_cls.__name__ in self.cached_non_lora_outputs:
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continue
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components, _, _ = 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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# Always ensure the inputs are without the `generator`. Make sure to pass the `generator`
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# explicitly.
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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))[0]
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self.cached_non_lora_outputs[scheduler_cls.__name__] = output_no_lora
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# Ensures that there's no inconsistency when reusing the cache.
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yield
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self.cached_non_lora_outputs.clear()
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def get_dummy_components(self, scheduler_cls=None, use_dora=False, lora_alpha=None):
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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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lora_alpha = rank if lora_alpha is None else lora_alpha
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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 = self.transformer_cls(**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 = self.vae_cls(**self.vae_kwargs)
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text_encoder = self.text_encoder_cls.from_pretrained(
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self.text_encoder_id, subfolder=self.text_encoder_subfolder
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)
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tokenizer = self.tokenizer_cls.from_pretrained(self.tokenizer_id, subfolder=self.tokenizer_subfolder)
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if self.text_encoder_2_cls is not None:
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text_encoder_2 = self.text_encoder_2_cls.from_pretrained(
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self.text_encoder_2_id, subfolder=self.text_encoder_2_subfolder
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)
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tokenizer_2 = self.tokenizer_2_cls.from_pretrained(
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self.tokenizer_2_id, subfolder=self.tokenizer_2_subfolder
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)
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if self.text_encoder_3_cls is not None:
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text_encoder_3 = self.text_encoder_3_cls.from_pretrained(
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self.text_encoder_3_id, subfolder=self.text_encoder_3_subfolder
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)
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tokenizer_3 = self.tokenizer_3_cls.from_pretrained(
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self.tokenizer_3_id, subfolder=self.tokenizer_3_subfolder
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)
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text_lora_config = LoraConfig(
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r=rank,
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lora_alpha=lora_alpha,
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target_modules=self.text_encoder_target_modules,
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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=lora_alpha,
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target_modules=self.denoiser_target_modules,
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init_lora_weights=False,
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use_dora=use_dora,
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)
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pipeline_components = {
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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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}
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# Denoiser
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if self.unet_kwargs is not None:
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pipeline_components.update({"unet": unet})
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elif self.transformer_kwargs is not None:
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pipeline_components.update({"transformer": transformer})
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# Remaining text encoders.
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if self.text_encoder_2_cls is not None:
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pipeline_components.update({"tokenizer_2": tokenizer_2, "text_encoder_2": text_encoder_2})
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if self.text_encoder_3_cls is not None:
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pipeline_components.update({"tokenizer_3": tokenizer_3, "text_encoder_3": text_encoder_3})
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# Remaining stuff
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init_params = inspect.signature(self.pipeline_class.__init__).parameters
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if "safety_checker" in init_params:
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pipeline_components.update({"safety_checker": None})
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if "feature_extractor" in init_params:
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pipeline_components.update({"feature_extractor": None})
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if "image_encoder" in init_params:
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pipeline_components.update({"image_encoder": None})
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return pipeline_components, text_lora_config, denoiser_lora_config
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@property
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def output_shape(self):
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raise NotImplementedError
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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 _get_lora_state_dicts(self, modules_to_save):
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state_dicts = {}
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for module_name, module in modules_to_save.items():
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if module is not None:
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state_dicts[f"{module_name}_lora_layers"] = get_peft_model_state_dict(module)
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return state_dicts
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def _get_lora_adapter_metadata(self, modules_to_save):
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metadatas = {}
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for module_name, module in modules_to_save.items():
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if module is not None:
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metadatas[f"{module_name}_lora_adapter_metadata"] = module.peft_config["default"].to_dict()
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return metadatas
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def _get_modules_to_save(self, pipe, has_denoiser=False):
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modules_to_save = {}
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lora_loadable_modules = self.pipeline_class._lora_loadable_modules
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if (
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"text_encoder" in lora_loadable_modules
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and hasattr(pipe, "text_encoder")
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and getattr(pipe.text_encoder, "peft_config", None) is not None
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):
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modules_to_save["text_encoder"] = pipe.text_encoder
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if (
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"text_encoder_2" in lora_loadable_modules
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and hasattr(pipe, "text_encoder_2")
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and getattr(pipe.text_encoder_2, "peft_config", None) is not None
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):
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modules_to_save["text_encoder_2"] = pipe.text_encoder_2
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if has_denoiser:
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if "unet" in lora_loadable_modules and hasattr(pipe, "unet"):
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modules_to_save["unet"] = pipe.unet
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if "transformer" in lora_loadable_modules and hasattr(pipe, "transformer"):
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modules_to_save["transformer"] = pipe.transformer
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return modules_to_save
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def add_adapters_to_pipeline(self, pipe, text_lora_config=None, denoiser_lora_config=None, adapter_name="default"):
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if text_lora_config is not None:
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if "text_encoder" in self.pipeline_class._lora_loadable_modules:
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pipe.text_encoder.add_adapter(text_lora_config, adapter_name=adapter_name)
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self.assertTrue(
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check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
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)
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if denoiser_lora_config is not None:
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denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
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denoiser.add_adapter(denoiser_lora_config, adapter_name=adapter_name)
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self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
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else:
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denoiser = None
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if text_lora_config is not None and self.has_two_text_encoders or self.has_three_text_encoders:
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if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
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pipe.text_encoder_2.add_adapter(text_lora_config, adapter_name=adapter_name)
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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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return pipe, denoiser
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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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for scheduler_cls in self.scheduler_classes:
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output_no_lora = self.cached_non_lora_outputs[scheduler_cls.__name__]
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self.assertTrue(output_no_lora.shape == self.output_shape)
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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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for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
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self.assertTrue(output_no_lora.shape == self.output_shape)
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pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config=None)
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output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
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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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@require_peft_version_greater("0.13.1")
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def test_low_cpu_mem_usage_with_injection(self):
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"""Tests if we can inject LoRA state dict with low_cpu_mem_usage."""
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for scheduler_cls in self.scheduler_classes:
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components, text_lora_config, denoiser_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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if "text_encoder" in self.pipeline_class._lora_loadable_modules:
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inject_adapter_in_model(text_lora_config, pipe.text_encoder, low_cpu_mem_usage=True)
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self.assertTrue(
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check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder."
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)
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self.assertTrue(
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"meta" in {p.device.type for p in pipe.text_encoder.parameters()},
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"The LoRA params should be on 'meta' device.",
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)
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te_state_dict = initialize_dummy_state_dict(get_peft_model_state_dict(pipe.text_encoder))
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set_peft_model_state_dict(pipe.text_encoder, te_state_dict, low_cpu_mem_usage=True)
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self.assertTrue(
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"meta" not in {p.device.type for p in pipe.text_encoder.parameters()},
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"No param should be on 'meta' device.",
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)
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denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
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inject_adapter_in_model(denoiser_lora_config, denoiser, low_cpu_mem_usage=True)
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self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
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self.assertTrue(
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"meta" in {p.device.type for p in denoiser.parameters()}, "The LoRA params should be on 'meta' device."
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)
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denoiser_state_dict = initialize_dummy_state_dict(get_peft_model_state_dict(denoiser))
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set_peft_model_state_dict(denoiser, denoiser_state_dict, low_cpu_mem_usage=True)
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self.assertTrue(
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"meta" not in {p.device.type for p in denoiser.parameters()}, "No param should be on 'meta' device."
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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 "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
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inject_adapter_in_model(text_lora_config, pipe.text_encoder_2, low_cpu_mem_usage=True)
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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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self.assertTrue(
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"meta" in {p.device.type for p in pipe.text_encoder_2.parameters()},
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"The LoRA params should be on 'meta' device.",
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)
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te2_state_dict = initialize_dummy_state_dict(get_peft_model_state_dict(pipe.text_encoder_2))
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set_peft_model_state_dict(pipe.text_encoder_2, te2_state_dict, low_cpu_mem_usage=True)
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self.assertTrue(
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"meta" not in {p.device.type for p in pipe.text_encoder_2.parameters()},
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"No param should be on 'meta' device.",
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)
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_, _, inputs = self.get_dummy_inputs()
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output_lora = pipe(**inputs)[0]
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|
self.assertTrue(output_lora.shape == self.output_shape)
|
|
|
|
@require_peft_version_greater("0.13.1")
|
|
@require_transformers_version_greater("4.45.2")
|
|
def test_low_cpu_mem_usage_with_loading(self):
|
|
"""Tests if we can load LoRA state dict with low_cpu_mem_usage."""
|
|
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
|
|
)
|
|
|
|
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"), low_cpu_mem_usage=False)
|
|
|
|
for module_name, module in modules_to_save.items():
|
|
self.assertTrue(check_if_lora_correctly_set(module), f"Lora not correctly set in {module_name}")
|
|
|
|
images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(
|
|
np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
|
|
"Loading from saved checkpoints should give same results.",
|
|
)
|
|
|
|
# Now, check for `low_cpu_mem_usage.`
|
|
pipe.unload_lora_weights()
|
|
pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.bin"), low_cpu_mem_usage=True)
|
|
|
|
for module_name, module in modules_to_save.items():
|
|
self.assertTrue(check_if_lora_correctly_set(module), f"Lora not correctly set in {module_name}")
|
|
|
|
images_lora_from_pretrained_low_cpu = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(
|
|
np.allclose(
|
|
images_lora_from_pretrained_low_cpu, images_lora_from_pretrained, atol=1e-3, rtol=1e-3
|
|
),
|
|
"Loading from saved checkpoints with `low_cpu_mem_usage` should give same results.",
|
|
)
|
|
|
|
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
|
|
"""
|
|
attention_kwargs_name = determine_attention_kwargs_name(self.pipeline_class)
|
|
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config=None)
|
|
|
|
output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(
|
|
not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
|
|
)
|
|
|
|
attention_kwargs = {attention_kwargs_name: {"scale": 0.5}}
|
|
output_lora_scale = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
|
|
|
|
self.assertTrue(
|
|
not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
|
|
"Lora + scale should change the output",
|
|
)
|
|
|
|
attention_kwargs = {attention_kwargs_name: {"scale": 0.0}}
|
|
output_lora_0_scale = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
|
|
|
|
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 self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config=None)
|
|
|
|
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")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
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))[0]
|
|
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 self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config=None)
|
|
|
|
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"
|
|
)
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
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))[0]
|
|
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 self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config=None)
|
|
|
|
images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
|
|
)
|
|
|
|
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"))
|
|
|
|
for module_name, module in modules_to_save.items():
|
|
self.assertTrue(check_if_lora_correctly_set(module), f"Lora not correctly set in {module_name}")
|
|
|
|
images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
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
|
|
"""
|
|
for scheduler_cls in self.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={self.text_encoder_target_modules[i]: i + 1 for i in range(3)},
|
|
lora_alpha=4,
|
|
target_modules=self.text_encoder_target_modules,
|
|
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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config=None)
|
|
|
|
state_dict = {}
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
# 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:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
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))[0]
|
|
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))[0]
|
|
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_with_text_lora(self):
|
|
"""
|
|
Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config=None)
|
|
images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
pipe.save_pretrained(tmpdirname)
|
|
|
|
pipe_from_pretrained = self.pipeline_class.from_pretrained(tmpdirname)
|
|
pipe_from_pretrained.to(torch_device)
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
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:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
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))[0]
|
|
|
|
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
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
|
|
)
|
|
|
|
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"))
|
|
|
|
for module_name, module in modules_to_save.items():
|
|
self.assertTrue(check_if_lora_correctly_set(module), f"Lora not correctly set in {module_name}")
|
|
|
|
images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
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
|
|
"""
|
|
attention_kwargs_name = determine_attention_kwargs_name(self.pipeline_class)
|
|
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(
|
|
not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
|
|
)
|
|
|
|
attention_kwargs = {attention_kwargs_name: {"scale": 0.5}}
|
|
output_lora_scale = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
|
|
|
|
self.assertTrue(
|
|
not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
|
|
"Lora + scale should change the output",
|
|
)
|
|
|
|
attention_kwargs = {attention_kwargs_name: {"scale": 0.0}}
|
|
output_lora_0_scale = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
|
|
|
|
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",
|
|
)
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
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
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, denoiser = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules)
|
|
|
|
# Fusing should still keep the LoRA layers
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
|
|
)
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
|
|
)
|
|
|
|
output_fused = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertFalse(
|
|
np.allclose(output_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
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, denoiser = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
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"
|
|
)
|
|
self.assertFalse(check_if_lora_correctly_set(denoiser), "Lora not correctly unloaded in denoiser")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
self.assertFalse(
|
|
check_if_lora_correctly_set(pipe.text_encoder_2),
|
|
"Lora not correctly unloaded in text encoder 2",
|
|
)
|
|
|
|
output_unloaded = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(
|
|
np.allclose(output_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, expected_atol: float = 1e-3, expected_rtol: float = 1e-3
|
|
):
|
|
"""
|
|
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 self.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, denoiser = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules)
|
|
self.assertTrue(pipe.num_fused_loras == 1, f"{pipe.num_fused_loras=}, {pipe.fused_loras=}")
|
|
output_fused_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
pipe.unfuse_lora(components=self.pipeline_class._lora_loadable_modules)
|
|
self.assertTrue(pipe.num_fused_loras == 0, f"{pipe.num_fused_loras=}, {pipe.fused_loras=}")
|
|
output_unfused_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
# unloading should remove the LoRA layers
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Unfuse should still keep LoRA layers")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Unfuse should still keep LoRA layers")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
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=expected_atol, rtol=expected_rtol),
|
|
"Fused lora should not 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
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
|
|
)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-2")
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
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))[0]
|
|
self.assertFalse(
|
|
np.allclose(output_no_lora, output_adapter_1, atol=1e-3, rtol=1e-3),
|
|
"Adapter outputs should be different.",
|
|
)
|
|
|
|
pipe.set_adapters("adapter-2")
|
|
output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertFalse(
|
|
np.allclose(output_no_lora, output_adapter_2, atol=1e-3, rtol=1e-3),
|
|
"Adapter outputs should be different.",
|
|
)
|
|
|
|
pipe.set_adapters(["adapter-1", "adapter-2"])
|
|
output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertFalse(
|
|
np.allclose(output_no_lora, output_adapter_mixed, atol=1e-3, rtol=1e-3),
|
|
"Adapter outputs should be different.",
|
|
)
|
|
|
|
# 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))[0]
|
|
|
|
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_wrong_adapter_name_raises_error(self):
|
|
adapter_name = "adapter-1"
|
|
|
|
scheduler_cls = self.scheduler_classes[0]
|
|
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, _ = self.add_adapters_to_pipeline(
|
|
pipe, text_lora_config, denoiser_lora_config, adapter_name=adapter_name
|
|
)
|
|
|
|
with self.assertRaises(ValueError) as err_context:
|
|
pipe.set_adapters("test")
|
|
|
|
self.assertTrue("not in the list of present adapters" in str(err_context.exception))
|
|
|
|
# test this works.
|
|
pipe.set_adapters(adapter_name)
|
|
_ = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
def test_multiple_wrong_adapter_name_raises_error(self):
|
|
adapter_name = "adapter-1"
|
|
scheduler_cls = self.scheduler_classes[0]
|
|
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, _ = self.add_adapters_to_pipeline(
|
|
pipe, text_lora_config, denoiser_lora_config, adapter_name=adapter_name
|
|
)
|
|
|
|
scale_with_wrong_components = {"foo": 0.0, "bar": 0.0, "tik": 0.0}
|
|
logger = logging.get_logger("diffusers.loaders.lora_base")
|
|
logger.setLevel(30)
|
|
with CaptureLogger(logger) as cap_logger:
|
|
pipe.set_adapters(adapter_name, adapter_weights=scale_with_wrong_components)
|
|
|
|
wrong_components = sorted(set(scale_with_wrong_components.keys()))
|
|
msg = f"The following components in `adapter_weights` are not part of the pipeline: {wrong_components}. "
|
|
self.assertTrue(msg in str(cap_logger.out))
|
|
|
|
# test this works.
|
|
pipe.set_adapters(adapter_name)
|
|
_ = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
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 different weights for different blocks (i.e. block lora)
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
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))[0]
|
|
|
|
weights_2 = {"unet": {"up": 5}}
|
|
pipe.set_adapters("adapter-1", weights_2)
|
|
output_weights_2 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
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))[0]
|
|
|
|
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 different weights for different blocks (i.e. block lora)
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
|
|
)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-2")
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
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))[0]
|
|
|
|
pipe.set_adapters("adapter-2", scales_2)
|
|
output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
pipe.set_adapters(["adapter-1", "adapter-2"], [scales_1, scales_2])
|
|
output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
# 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))[0]
|
|
|
|
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"""
|
|
|
|
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")
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
lora_loadable_components = self.pipeline_class._lora_loadable_modules
|
|
if "text_encoder_2" in lora_loadable_components:
|
|
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
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
|
|
)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-2")
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
lora_loadable_components = self.pipeline_class._lora_loadable_modules
|
|
if "text_encoder_2" in lora_loadable_components:
|
|
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))[0]
|
|
|
|
pipe.set_adapters("adapter-2")
|
|
output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
pipe.set_adapters(["adapter-1", "adapter-2"])
|
|
output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
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))[0]
|
|
|
|
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))[0]
|
|
|
|
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",
|
|
)
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-2")
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
pipe.set_adapters(["adapter-1", "adapter-2"])
|
|
pipe.delete_adapters(["adapter-1", "adapter-2"])
|
|
|
|
output_deleted_adapters = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
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
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
|
|
)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-2")
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
lora_loadable_components = self.pipeline_class._lora_loadable_modules
|
|
if "text_encoder_2" in lora_loadable_components:
|
|
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))[0]
|
|
|
|
pipe.set_adapters("adapter-2")
|
|
output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
pipe.set_adapters(["adapter-1", "adapter-2"])
|
|
output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
# 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))[0]
|
|
|
|
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))[0]
|
|
|
|
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
|
|
@pytest.mark.xfail(
|
|
condition=torch.device(torch_device).type == "cpu" and is_torch_version(">=", "2.5"),
|
|
reason="Test currently fails on CPU and PyTorch 2.5.1 but not on PyTorch 2.4.1.",
|
|
strict=False,
|
|
)
|
|
def test_lora_fuse_nan(self):
|
|
for scheduler_cls in self.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)
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
|
|
)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "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:
|
|
named_modules = [name for name, _ in pipe.transformer.named_modules()]
|
|
possible_tower_names = [
|
|
"transformer_blocks",
|
|
"blocks",
|
|
"joint_transformer_blocks",
|
|
"single_transformer_blocks",
|
|
]
|
|
filtered_tower_names = [
|
|
tower_name for tower_name in possible_tower_names if hasattr(pipe.transformer, tower_name)
|
|
]
|
|
if len(filtered_tower_names) == 0:
|
|
reason = (
|
|
f"`pipe.transformer` didn't have any of the following attributes: {possible_tower_names}."
|
|
)
|
|
raise ValueError(reason)
|
|
for tower_name in filtered_tower_names:
|
|
transformer_tower = getattr(pipe.transformer, tower_name)
|
|
has_attn1 = any("attn1" in name for name in named_modules)
|
|
if has_attn1:
|
|
transformer_tower[0].attn1.to_q.lora_A["adapter-1"].weight += float("inf")
|
|
else:
|
|
transformer_tower[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(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)
|
|
|
|
# without we should not see an error, but every image will be black
|
|
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
|
|
out = pipe(**inputs)[0]
|
|
|
|
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 self.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")
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.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")
|
|
denoiser.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
|
|
"""
|
|
for scheduler_cls in self.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)
|
|
|
|
# 1.
|
|
dicts_to_be_checked = {}
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
dicts_to_be_checked = {"text_encoder": ["adapter-1"]}
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
dicts_to_be_checked.update({"unet": ["adapter-1"]})
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
dicts_to_be_checked.update({"transformer": ["adapter-1"]})
|
|
|
|
self.assertDictEqual(pipe.get_list_adapters(), dicts_to_be_checked)
|
|
|
|
# 2.
|
|
dicts_to_be_checked = {}
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
dicts_to_be_checked = {"text_encoder": ["adapter-1", "adapter-2"]}
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
|
|
dicts_to_be_checked.update({"unet": ["adapter-1", "adapter-2"]})
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
|
|
dicts_to_be_checked.update({"transformer": ["adapter-1", "adapter-2"]})
|
|
|
|
self.assertDictEqual(pipe.get_list_adapters(), dicts_to_be_checked)
|
|
|
|
# 3.
|
|
pipe.set_adapters(["adapter-1", "adapter-2"])
|
|
|
|
dicts_to_be_checked = {}
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
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,
|
|
)
|
|
|
|
# 4.
|
|
dicts_to_be_checked = {}
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
dicts_to_be_checked = {"text_encoder": ["adapter-1", "adapter-2"]}
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-3")
|
|
dicts_to_be_checked.update({"unet": ["adapter-1", "adapter-2", "adapter-3"]})
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-3")
|
|
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, expected_atol: float = 1e-3, expected_rtol: float = 1e-3
|
|
):
|
|
"""
|
|
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
|
|
"""
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
|
|
)
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-2")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
lora_loadable_components = self.pipeline_class._lora_loadable_modules
|
|
if "text_encoder_2" in lora_loadable_components:
|
|
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"
|
|
)
|
|
pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
|
|
|
|
# set them to multi-adapter inference mode
|
|
pipe.set_adapters(["adapter-1", "adapter-2"])
|
|
outputs_all_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
pipe.set_adapters(["adapter-1"])
|
|
outputs_lora_1 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, adapter_names=["adapter-1"])
|
|
self.assertTrue(pipe.num_fused_loras == 1, f"{pipe.num_fused_loras=}, {pipe.fused_loras=}")
|
|
|
|
# Fusing should still keep the LoRA layers so output should remain the same
|
|
outputs_lora_1_fused = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(outputs_lora_1, outputs_lora_1_fused, atol=expected_atol, rtol=expected_rtol),
|
|
"Fused lora should not change the output",
|
|
)
|
|
|
|
pipe.unfuse_lora(components=self.pipeline_class._lora_loadable_modules)
|
|
self.assertTrue(pipe.num_fused_loras == 0, f"{pipe.num_fused_loras=}, {pipe.fused_loras=}")
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Unfuse should still keep LoRA layers")
|
|
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Unfuse should still keep LoRA layers")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder_2), "Unfuse should still keep LoRA layers"
|
|
)
|
|
|
|
pipe.fuse_lora(
|
|
components=self.pipeline_class._lora_loadable_modules, adapter_names=["adapter-2", "adapter-1"]
|
|
)
|
|
self.assertTrue(pipe.num_fused_loras == 2, f"{pipe.num_fused_loras=}, {pipe.fused_loras=}")
|
|
|
|
# Fusing should still keep the LoRA layers
|
|
output_all_lora_fused = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(
|
|
np.allclose(output_all_lora_fused, outputs_all_lora, atol=expected_atol, rtol=expected_rtol),
|
|
"Fused lora should not change the output",
|
|
)
|
|
pipe.unfuse_lora(components=self.pipeline_class._lora_loadable_modules)
|
|
self.assertTrue(pipe.num_fused_loras == 0, f"{pipe.num_fused_loras=}, {pipe.fused_loras=}")
|
|
|
|
def test_lora_scale_kwargs_match_fusion(self, expected_atol: float = 1e-3, expected_rtol: float = 1e-3):
|
|
attention_kwargs_name = determine_attention_kwargs_name(self.pipeline_class)
|
|
|
|
for lora_scale in [1.0, 0.8]:
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
|
|
self.assertTrue(
|
|
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
|
|
)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
lora_loadable_components = self.pipeline_class._lora_loadable_modules
|
|
if "text_encoder_2" in lora_loadable_components:
|
|
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",
|
|
)
|
|
|
|
pipe.set_adapters(["adapter-1"])
|
|
attention_kwargs = {attention_kwargs_name: {"scale": lora_scale}}
|
|
outputs_lora_1 = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
|
|
|
|
pipe.fuse_lora(
|
|
components=self.pipeline_class._lora_loadable_modules,
|
|
adapter_names=["adapter-1"],
|
|
lora_scale=lora_scale,
|
|
)
|
|
self.assertTrue(pipe.num_fused_loras == 1, f"{pipe.num_fused_loras=}, {pipe.fused_loras=}")
|
|
|
|
outputs_lora_1_fused = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(outputs_lora_1, outputs_lora_1_fused, atol=expected_atol, rtol=expected_rtol),
|
|
"Fused lora should not change the output",
|
|
)
|
|
self.assertFalse(
|
|
np.allclose(output_no_lora, outputs_lora_1, atol=expected_atol, rtol=expected_rtol),
|
|
"LoRA should change the output",
|
|
)
|
|
|
|
@require_peft_version_greater(peft_version="0.9.0")
|
|
def test_simple_inference_with_dora(self):
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_dora_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
output_dora_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
self.assertFalse(
|
|
np.allclose(output_dora_lora, output_no_dora_lora, atol=1e-3, rtol=1e-3),
|
|
"DoRA lora should change the output",
|
|
)
|
|
|
|
def test_missing_keys_warning(self):
|
|
scheduler_cls = self.scheduler_classes[0]
|
|
# Skip text encoder check for now as that is handled with `transformers`.
|
|
components, _, 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)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
|
|
)
|
|
pipe.unload_lora_weights()
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
|
|
state_dict = torch.load(os.path.join(tmpdirname, "pytorch_lora_weights.bin"), weights_only=True)
|
|
|
|
# To make things dynamic since we cannot settle with a single key for all the models where we
|
|
# offer PEFT support.
|
|
missing_key = [k for k in state_dict if "lora_A" in k][0]
|
|
del state_dict[missing_key]
|
|
|
|
logger = logging.get_logger("diffusers.utils.peft_utils")
|
|
logger.setLevel(30)
|
|
with CaptureLogger(logger) as cap_logger:
|
|
pipe.load_lora_weights(state_dict)
|
|
|
|
# Since the missing key won't contain the adapter name ("default_0").
|
|
# Also strip out the component prefix (such as "unet." from `missing_key`).
|
|
component = list({k.split(".")[0] for k in state_dict})[0]
|
|
self.assertTrue(missing_key.replace(f"{component}.", "") in cap_logger.out.replace("default_0.", ""))
|
|
|
|
def test_unexpected_keys_warning(self):
|
|
scheduler_cls = self.scheduler_classes[0]
|
|
# Skip text encoder check for now as that is handled with `transformers`.
|
|
components, _, 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)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
|
|
)
|
|
pipe.unload_lora_weights()
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
|
|
state_dict = torch.load(os.path.join(tmpdirname, "pytorch_lora_weights.bin"), weights_only=True)
|
|
|
|
unexpected_key = [k for k in state_dict if "lora_A" in k][0] + ".diffusers_cat"
|
|
state_dict[unexpected_key] = torch.tensor(1.0, device=torch_device)
|
|
|
|
logger = logging.get_logger("diffusers.utils.peft_utils")
|
|
logger.setLevel(30)
|
|
with CaptureLogger(logger) as cap_logger:
|
|
pipe.load_lora_weights(state_dict)
|
|
|
|
self.assertTrue(".diffusers_cat" in cap_logger.out)
|
|
|
|
@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
|
|
"""
|
|
for scheduler_cls in self.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, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
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))[0]
|
|
|
|
def test_modify_padding_mode(self):
|
|
def set_pad_mode(network, mode="circular"):
|
|
for _, module in network.named_modules():
|
|
if isinstance(module, torch.nn.Conv2d):
|
|
module.padding_mode = mode
|
|
|
|
for scheduler_cls in self.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)[0]
|
|
|
|
def test_logs_info_when_no_lora_keys_found(self):
|
|
scheduler_cls = self.scheduler_classes[0]
|
|
# Skip text encoder check for now as that is handled with `transformers`.
|
|
components, _, _ = 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)
|
|
original_out = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
|
|
no_op_state_dict = {"lora_foo": torch.tensor(2.0), "lora_bar": torch.tensor(3.0)}
|
|
logger = logging.get_logger("diffusers.loaders.peft")
|
|
logger.setLevel(logging.WARNING)
|
|
|
|
with CaptureLogger(logger) as cap_logger:
|
|
pipe.load_lora_weights(no_op_state_dict)
|
|
out_after_lora_attempt = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
denoiser = getattr(pipe, "unet") if self.unet_kwargs is not None else getattr(pipe, "transformer")
|
|
self.assertTrue(cap_logger.out.startswith(f"No LoRA keys associated to {denoiser.__class__.__name__}"))
|
|
self.assertTrue(np.allclose(original_out, out_after_lora_attempt, atol=1e-5, rtol=1e-5))
|
|
|
|
# test only for text encoder
|
|
for lora_module in self.pipeline_class._lora_loadable_modules:
|
|
if "text_encoder" in lora_module:
|
|
text_encoder = getattr(pipe, lora_module)
|
|
if lora_module == "text_encoder":
|
|
prefix = "text_encoder"
|
|
elif lora_module == "text_encoder_2":
|
|
prefix = "text_encoder_2"
|
|
|
|
logger = logging.get_logger("diffusers.loaders.lora_base")
|
|
logger.setLevel(logging.WARNING)
|
|
|
|
with CaptureLogger(logger) as cap_logger:
|
|
self.pipeline_class.load_lora_into_text_encoder(
|
|
no_op_state_dict, network_alphas=None, text_encoder=text_encoder, prefix=prefix
|
|
)
|
|
|
|
self.assertTrue(
|
|
cap_logger.out.startswith(f"No LoRA keys associated to {text_encoder.__class__.__name__}")
|
|
)
|
|
|
|
def test_set_adapters_match_attention_kwargs(self):
|
|
"""Test to check if outputs after `set_adapters()` and attention kwargs match."""
|
|
attention_kwargs_name = determine_attention_kwargs_name(self.pipeline_class)
|
|
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
lora_scale = 0.5
|
|
attention_kwargs = {attention_kwargs_name: {"scale": lora_scale}}
|
|
output_lora_scale = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
|
|
self.assertFalse(
|
|
np.allclose(output_no_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
|
|
"Lora + scale should change the output",
|
|
)
|
|
|
|
pipe.set_adapters("default", lora_scale)
|
|
output_lora_scale_wo_kwargs = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(
|
|
not np.allclose(output_no_lora, output_lora_scale_wo_kwargs, atol=1e-3, rtol=1e-3),
|
|
"Lora + scale should change the output",
|
|
)
|
|
self.assertTrue(
|
|
np.allclose(output_lora_scale, output_lora_scale_wo_kwargs, atol=1e-3, rtol=1e-3),
|
|
"Lora + scale should match the output of `set_adapters()`.",
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=True, **lora_state_dicts
|
|
)
|
|
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
|
|
|
|
for module_name, module in modules_to_save.items():
|
|
self.assertTrue(check_if_lora_correctly_set(module), f"Lora not correctly set in {module_name}")
|
|
|
|
output_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
|
|
self.assertTrue(
|
|
not np.allclose(output_no_lora, output_lora_from_pretrained, atol=1e-3, rtol=1e-3),
|
|
"Lora + scale should change the output",
|
|
)
|
|
self.assertTrue(
|
|
np.allclose(output_lora_scale, output_lora_from_pretrained, atol=1e-3, rtol=1e-3),
|
|
"Loading from saved checkpoints should give same results as attention_kwargs.",
|
|
)
|
|
self.assertTrue(
|
|
np.allclose(output_lora_scale_wo_kwargs, output_lora_from_pretrained, atol=1e-3, rtol=1e-3),
|
|
"Loading from saved checkpoints should give same results as set_adapters().",
|
|
)
|
|
|
|
@require_peft_version_greater("0.13.2")
|
|
def test_lora_B_bias(self):
|
|
# Currently, this test is only relevant for Flux Control LoRA as we are not
|
|
# aware of any other LoRA checkpoint that has its `lora_B` biases trained.
|
|
components, _, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
# keep track of the bias values of the base layers to perform checks later.
|
|
bias_values = {}
|
|
denoiser = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
for name, module in denoiser.named_modules():
|
|
if any(k in name for k in self.denoiser_target_modules):
|
|
if module.bias is not None:
|
|
bias_values[name] = module.bias.data.clone()
|
|
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
|
|
original_output = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
denoiser_lora_config.lora_bias = False
|
|
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")
|
|
lora_bias_false_output = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
pipe.delete_adapters("adapter-1")
|
|
|
|
denoiser_lora_config.lora_bias = True
|
|
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")
|
|
lora_bias_true_output = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
self.assertFalse(np.allclose(original_output, lora_bias_false_output, atol=1e-3, rtol=1e-3))
|
|
self.assertFalse(np.allclose(original_output, lora_bias_true_output, atol=1e-3, rtol=1e-3))
|
|
self.assertFalse(np.allclose(lora_bias_false_output, lora_bias_true_output, atol=1e-3, rtol=1e-3))
|
|
|
|
def test_correct_lora_configs_with_different_ranks(self):
|
|
components, _, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
|
|
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)
|
|
|
|
original_output = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
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")
|
|
|
|
lora_output_same_rank = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.delete_adapters("adapter-1")
|
|
else:
|
|
pipe.transformer.delete_adapters("adapter-1")
|
|
|
|
denoiser = pipe.unet if self.unet_kwargs is not None else pipe.transformer
|
|
for name, _ in denoiser.named_modules():
|
|
if "to_k" in name and "attn" in name and "lora" not in name:
|
|
module_name_to_rank_update = name.replace(".base_layer.", ".")
|
|
break
|
|
|
|
# change the rank_pattern
|
|
updated_rank = denoiser_lora_config.r * 2
|
|
denoiser_lora_config.rank_pattern = {module_name_to_rank_update: updated_rank}
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
updated_rank_pattern = pipe.unet.peft_config["adapter-1"].rank_pattern
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
updated_rank_pattern = pipe.transformer.peft_config["adapter-1"].rank_pattern
|
|
|
|
self.assertTrue(updated_rank_pattern == {module_name_to_rank_update: updated_rank})
|
|
|
|
lora_output_diff_rank = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(not np.allclose(original_output, lora_output_same_rank, atol=1e-3, rtol=1e-3))
|
|
self.assertTrue(not np.allclose(lora_output_diff_rank, lora_output_same_rank, atol=1e-3, rtol=1e-3))
|
|
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.delete_adapters("adapter-1")
|
|
else:
|
|
pipe.transformer.delete_adapters("adapter-1")
|
|
|
|
# similarly change the alpha_pattern
|
|
updated_alpha = denoiser_lora_config.lora_alpha * 2
|
|
denoiser_lora_config.alpha_pattern = {module_name_to_rank_update: updated_alpha}
|
|
if self.unet_kwargs is not None:
|
|
pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
|
|
self.assertTrue(
|
|
pipe.unet.peft_config["adapter-1"].alpha_pattern == {module_name_to_rank_update: updated_alpha}
|
|
)
|
|
else:
|
|
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
|
|
self.assertTrue(
|
|
pipe.transformer.peft_config["adapter-1"].alpha_pattern == {module_name_to_rank_update: updated_alpha}
|
|
)
|
|
|
|
lora_output_diff_alpha = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(not np.allclose(original_output, lora_output_diff_alpha, atol=1e-3, rtol=1e-3))
|
|
self.assertTrue(not np.allclose(lora_output_diff_alpha, lora_output_same_rank, atol=1e-3, rtol=1e-3))
|
|
|
|
def test_layerwise_casting_inference_denoiser(self):
|
|
from diffusers.hooks._common import _GO_LC_SUPPORTED_PYTORCH_LAYERS
|
|
from diffusers.hooks.layerwise_casting import DEFAULT_SKIP_MODULES_PATTERN
|
|
|
|
def check_linear_dtype(module, storage_dtype, compute_dtype):
|
|
patterns_to_check = DEFAULT_SKIP_MODULES_PATTERN
|
|
if getattr(module, "_skip_layerwise_casting_patterns", None) is not None:
|
|
patterns_to_check += tuple(module._skip_layerwise_casting_patterns)
|
|
for name, submodule in module.named_modules():
|
|
if not isinstance(submodule, _GO_LC_SUPPORTED_PYTORCH_LAYERS):
|
|
continue
|
|
dtype_to_check = storage_dtype
|
|
if "lora" in name or any(re.search(pattern, name) for pattern in patterns_to_check):
|
|
dtype_to_check = compute_dtype
|
|
if getattr(submodule, "weight", None) is not None:
|
|
self.assertEqual(submodule.weight.dtype, dtype_to_check)
|
|
if getattr(submodule, "bias", None) is not None:
|
|
self.assertEqual(submodule.bias.dtype, dtype_to_check)
|
|
|
|
def initialize_pipeline(storage_dtype=None, compute_dtype=torch.float32):
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device, dtype=compute_dtype)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
pipe, denoiser = self.add_adapters_to_pipeline(pipe, text_lora_config, denoiser_lora_config)
|
|
|
|
if storage_dtype is not None:
|
|
denoiser.enable_layerwise_casting(storage_dtype=storage_dtype, compute_dtype=compute_dtype)
|
|
check_linear_dtype(denoiser, storage_dtype, compute_dtype)
|
|
|
|
return pipe
|
|
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
|
|
pipe_fp32 = initialize_pipeline(storage_dtype=None)
|
|
pipe_fp32(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
pipe_float8_e4m3_fp32 = initialize_pipeline(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.float32)
|
|
pipe_float8_e4m3_fp32(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
pipe_float8_e4m3_bf16 = initialize_pipeline(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16)
|
|
pipe_float8_e4m3_bf16(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
@require_peft_version_greater("0.14.0")
|
|
def test_layerwise_casting_peft_input_autocast_denoiser(self):
|
|
r"""
|
|
A test that checks if layerwise casting works correctly with PEFT layers and forward pass does not fail. This
|
|
is different from `test_layerwise_casting_inference_denoiser` as that disables the application of layerwise
|
|
cast hooks on the PEFT layers (relevant logic in `models.modeling_utils.ModelMixin.enable_layerwise_casting`).
|
|
In this test, we enable the layerwise casting on the PEFT layers as well. If run with PEFT version <= 0.14.0,
|
|
this test will fail with the following error:
|
|
|
|
```
|
|
RuntimeError: expected mat1 and mat2 to have the same dtype, but got: c10::Float8_e4m3fn != float
|
|
```
|
|
|
|
See the docstring of [`hooks.layerwise_casting.PeftInputAutocastDisableHook`] for more details.
|
|
"""
|
|
|
|
from diffusers.hooks._common import _GO_LC_SUPPORTED_PYTORCH_LAYERS
|
|
from diffusers.hooks.layerwise_casting import (
|
|
_PEFT_AUTOCAST_DISABLE_HOOK,
|
|
DEFAULT_SKIP_MODULES_PATTERN,
|
|
apply_layerwise_casting,
|
|
)
|
|
|
|
storage_dtype = torch.float8_e4m3fn
|
|
compute_dtype = torch.float32
|
|
|
|
def check_module(denoiser):
|
|
# This will also check if the peft layers are in torch.float8_e4m3fn dtype (unlike test_layerwise_casting_inference_denoiser)
|
|
for name, module in denoiser.named_modules():
|
|
if not isinstance(module, _GO_LC_SUPPORTED_PYTORCH_LAYERS):
|
|
continue
|
|
dtype_to_check = storage_dtype
|
|
if any(re.search(pattern, name) for pattern in patterns_to_check):
|
|
dtype_to_check = compute_dtype
|
|
if getattr(module, "weight", None) is not None:
|
|
self.assertEqual(module.weight.dtype, dtype_to_check)
|
|
if getattr(module, "bias", None) is not None:
|
|
self.assertEqual(module.bias.dtype, dtype_to_check)
|
|
if isinstance(module, BaseTunerLayer):
|
|
self.assertTrue(getattr(module, "_diffusers_hook", None) is not None)
|
|
self.assertTrue(module._diffusers_hook.get_hook(_PEFT_AUTOCAST_DISABLE_HOOK) is not None)
|
|
|
|
# 1. Test forward with add_adapter
|
|
components, _, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device, dtype=compute_dtype)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
patterns_to_check = DEFAULT_SKIP_MODULES_PATTERN
|
|
if getattr(denoiser, "_skip_layerwise_casting_patterns", None) is not None:
|
|
patterns_to_check += tuple(denoiser._skip_layerwise_casting_patterns)
|
|
|
|
apply_layerwise_casting(
|
|
denoiser, storage_dtype=storage_dtype, compute_dtype=compute_dtype, skip_modules_pattern=patterns_to_check
|
|
)
|
|
check_module(denoiser)
|
|
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
# 2. Test forward with load_lora_weights
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=True, **lora_state_dicts
|
|
)
|
|
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
|
|
components, _, _ = self.get_dummy_components(self.scheduler_classes[0])
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device, dtype=compute_dtype)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
apply_layerwise_casting(
|
|
denoiser,
|
|
storage_dtype=storage_dtype,
|
|
compute_dtype=compute_dtype,
|
|
skip_modules_pattern=patterns_to_check,
|
|
)
|
|
check_module(denoiser)
|
|
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
@parameterized.expand([4, 8, 16])
|
|
def test_lora_adapter_metadata_is_loaded_correctly(self, lora_alpha):
|
|
scheduler_cls = self.scheduler_classes[0]
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(
|
|
scheduler_cls, lora_alpha=lora_alpha
|
|
)
|
|
pipe = self.pipeline_class(**components)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(
|
|
pipe, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
lora_metadatas = self._get_lora_adapter_metadata(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(save_directory=tmpdir, **lora_state_dicts, **lora_metadatas)
|
|
pipe.unload_lora_weights()
|
|
|
|
out = pipe.lora_state_dict(tmpdir, return_lora_metadata=True)
|
|
if len(out) == 3:
|
|
_, _, parsed_metadata = out
|
|
elif len(out) == 2:
|
|
_, parsed_metadata = out
|
|
|
|
denoiser_key = (
|
|
f"{self.pipeline_class.transformer_name}"
|
|
if self.transformer_kwargs is not None
|
|
else f"{self.pipeline_class.unet_name}"
|
|
)
|
|
self.assertTrue(any(k.startswith(f"{denoiser_key}.") for k in parsed_metadata))
|
|
check_module_lora_metadata(
|
|
parsed_metadata=parsed_metadata, lora_metadatas=lora_metadatas, module_key=denoiser_key
|
|
)
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
text_encoder_key = self.pipeline_class.text_encoder_name
|
|
self.assertTrue(any(k.startswith(f"{text_encoder_key}.") for k in parsed_metadata))
|
|
check_module_lora_metadata(
|
|
parsed_metadata=parsed_metadata, lora_metadatas=lora_metadatas, module_key=text_encoder_key
|
|
)
|
|
|
|
if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
|
|
text_encoder_2_key = "text_encoder_2"
|
|
self.assertTrue(any(k.startswith(f"{text_encoder_2_key}.") for k in parsed_metadata))
|
|
check_module_lora_metadata(
|
|
parsed_metadata=parsed_metadata, lora_metadatas=lora_metadatas, module_key=text_encoder_2_key
|
|
)
|
|
|
|
@parameterized.expand([4, 8, 16])
|
|
def test_lora_adapter_metadata_save_load_inference(self, lora_alpha):
|
|
scheduler_cls = self.scheduler_classes[0]
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(
|
|
scheduler_cls, lora_alpha=lora_alpha
|
|
)
|
|
pipe = self.pipeline_class(**components).to(torch_device)
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
|
|
output_no_lora = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
self.assertTrue(output_no_lora.shape == self.output_shape)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(
|
|
pipe, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
|
|
)
|
|
output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
lora_metadatas = self._get_lora_adapter_metadata(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(save_directory=tmpdir, **lora_state_dicts, **lora_metadatas)
|
|
pipe.unload_lora_weights()
|
|
pipe.load_lora_weights(tmpdir)
|
|
|
|
output_lora_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(output_lora, output_lora_pretrained, atol=1e-3, rtol=1e-3), "Lora outputs should match."
|
|
)
|
|
|
|
def test_lora_unload_add_adapter(self):
|
|
"""Tests if `unload_lora_weights()` -> `add_adapter()` works."""
|
|
scheduler_cls = self.scheduler_classes[0]
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
|
|
pipe = self.pipeline_class(**components).to(torch_device)
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
|
|
pipe, _ = self.add_adapters_to_pipeline(
|
|
pipe, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
|
|
)
|
|
_ = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
# unload and then add.
|
|
pipe.unload_lora_weights()
|
|
pipe, _ = self.add_adapters_to_pipeline(
|
|
pipe, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
|
|
)
|
|
_ = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
def test_inference_load_delete_load_adapters(self):
|
|
"Tests if `load_lora_weights()` -> `delete_adapters()` -> `load_lora_weights()` works."
|
|
for scheduler_cls in self.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 = self.cached_non_lora_outputs[scheduler_cls.__name__]
|
|
|
|
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
|
|
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"
|
|
)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
if self.has_two_text_encoders or self.has_three_text_encoders:
|
|
lora_loadable_components = self.pipeline_class._lora_loadable_modules
|
|
if "text_encoder_2" in lora_loadable_components:
|
|
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_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(save_directory=tmpdirname, **lora_state_dicts)
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
|
|
|
|
# First, delete adapter and compare.
|
|
pipe.delete_adapters(pipe.get_active_adapters()[0])
|
|
output_no_adapter = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertFalse(np.allclose(output_adapter_1, output_no_adapter, atol=1e-3, rtol=1e-3))
|
|
self.assertTrue(np.allclose(output_no_lora, output_no_adapter, atol=1e-3, rtol=1e-3))
|
|
|
|
# Then load adapter and compare.
|
|
pipe.load_lora_weights(tmpdirname)
|
|
output_lora_loaded = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(np.allclose(output_adapter_1, output_lora_loaded, atol=1e-3, rtol=1e-3))
|
|
|
|
def _test_group_offloading_inference_denoiser(self, offload_type, use_stream):
|
|
from diffusers.hooks.group_offloading import _get_top_level_group_offload_hook
|
|
|
|
onload_device = torch_device
|
|
offload_device = torch.device("cpu")
|
|
|
|
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=True, **lora_state_dicts
|
|
)
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
|
|
|
|
components, _, _ = self.get_dummy_components(self.scheduler_classes[0])
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
|
|
pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
|
|
check_if_lora_correctly_set(denoiser)
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
|
|
# Test group offloading with load_lora_weights
|
|
denoiser.enable_group_offload(
|
|
onload_device=onload_device,
|
|
offload_device=offload_device,
|
|
offload_type=offload_type,
|
|
num_blocks_per_group=1,
|
|
use_stream=use_stream,
|
|
)
|
|
# Place other model-level components on `torch_device`.
|
|
for _, component in pipe.components.items():
|
|
if isinstance(component, torch.nn.Module):
|
|
component.to(torch_device)
|
|
group_offload_hook_1 = _get_top_level_group_offload_hook(denoiser)
|
|
self.assertTrue(group_offload_hook_1 is not None)
|
|
output_1 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
# Test group offloading after removing the lora
|
|
pipe.unload_lora_weights()
|
|
group_offload_hook_2 = _get_top_level_group_offload_hook(denoiser)
|
|
self.assertTrue(group_offload_hook_2 is not None)
|
|
output_2 = pipe(**inputs, generator=torch.manual_seed(0))[0] # noqa: F841
|
|
|
|
# Add the lora again and check if group offloading works
|
|
pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
|
|
check_if_lora_correctly_set(denoiser)
|
|
group_offload_hook_3 = _get_top_level_group_offload_hook(denoiser)
|
|
self.assertTrue(group_offload_hook_3 is not None)
|
|
output_3 = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
self.assertTrue(np.allclose(output_1, output_3, atol=1e-3, rtol=1e-3))
|
|
|
|
@parameterized.expand([("block_level", True), ("leaf_level", False), ("leaf_level", True)])
|
|
@require_torch_accelerator
|
|
def test_group_offloading_inference_denoiser(self, offload_type, use_stream):
|
|
for cls in inspect.getmro(self.__class__):
|
|
if "test_group_offloading_inference_denoiser" in cls.__dict__ and cls is not PeftLoraLoaderMixinTests:
|
|
# Skip this test if it is overwritten by child class. We need to do this because parameterized
|
|
# materializes the test methods on invocation which cannot be overridden.
|
|
return
|
|
self._test_group_offloading_inference_denoiser(offload_type, use_stream)
|
|
|
|
@require_torch_accelerator
|
|
def test_lora_loading_model_cpu_offload(self):
|
|
components, _, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
|
|
_, _, inputs = self.get_dummy_inputs(with_generator=False)
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
denoiser.add_adapter(denoiser_lora_config)
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
|
|
lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
|
|
self.pipeline_class.save_lora_weights(
|
|
save_directory=tmpdirname, safe_serialization=True, **lora_state_dicts
|
|
)
|
|
# reinitialize the pipeline to mimic the inference workflow.
|
|
components, _, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.enable_model_cpu_offload(device=torch_device)
|
|
pipe.load_lora_weights(tmpdirname)
|
|
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
|
|
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
|
|
|
|
output_lora_loaded = pipe(**inputs, generator=torch.manual_seed(0))[0]
|
|
self.assertTrue(np.allclose(output_lora, output_lora_loaded, atol=1e-3, rtol=1e-3))
|