Merge branch 'main' into feat/ip-adapter

invokeai/backend/__init__.py

@@ -1,5 +1,5 @@
 """
 Initialization file for invokeai.backend
 """
-from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo  # noqa: F401
+from .model_management import BaseModelType, ModelCache, ModelInfo, ModelManager, ModelType, SubModelType  # noqa: F401
 from .model_management.models import SilenceWarnings  # noqa: F401

invokeai/backend/image_util/invisible_watermark.py

@@ -3,12 +3,13 @@ This module defines a singleton object, "invisible_watermark" that
 wraps the invisible watermark model. It respects the global "invisible_watermark"
 configuration variable, that allows the watermarking to be supressed.
 """
-import numpy as np
 import cv2
-from PIL import Image
+import numpy as np
 from imwatermark import WatermarkEncoder
-from invokeai.app.services.config import InvokeAIAppConfig
+from PIL import Image
+
 import invokeai.backend.util.logging as logger
+from invokeai.app.services.config import InvokeAIAppConfig
 
 config = InvokeAIAppConfig.get_config()
 

invokeai/backend/image_util/patchmatch.py

@@ -5,6 +5,7 @@ wraps the actual patchmatch object. It respects the global
 be suppressed or deferred
 """
 import numpy as np
+
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 

invokeai/backend/image_util/safety_checker.py

@@ -5,10 +5,11 @@ configuration variable, that allows the checker to be supressed.
 """
 import numpy as np
 from PIL import Image
-from invokeai.backend import SilenceWarnings
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.util.devices import choose_torch_device
+
 import invokeai.backend.util.logging as logger
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend import SilenceWarnings
+from invokeai.backend.util.devices import choose_torch_device
 
 config = InvokeAIAppConfig.get_config()
 

invokeai/backend/install/check_root.py

@@ -2,9 +2,8 @@
 Check that the invokeai_root is correctly configured and exit if not.
 """
 import sys
-from invokeai.app.services.config import (
-    InvokeAIAppConfig,
-)
+
+from invokeai.app.services.config import InvokeAIAppConfig
 
 
 def check_invokeai_root(config: InvokeAIAppConfig):

invokeai/backend/install/invokeai_configure.py

@@ -6,68 +6,56 @@
 #
 # Coauthor: Kevin Turner http://github.com/keturn
 #
-import sys
 import argparse
 import io
 import os
-import psutil
 import shutil
+import sys
 import textwrap
-import torch
 import traceback
-import yaml
 import warnings
 from argparse import Namespace
 from enum import Enum
 from pathlib import Path
 from shutil import get_terminal_size
-from typing import get_type_hints, get_args, Any
+from typing import Any, get_args, get_type_hints
 from urllib import request
 
 import npyscreen
-import transformers
 import omegaconf
+import psutil
+import torch
+import transformers
+import yaml
 from diffusers import AutoencoderKL
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from huggingface_hub import HfFolder
 from huggingface_hub import login as hf_hub_login
 from omegaconf import OmegaConf
+from pydantic.error_wrappers import ValidationError
 from tqdm import tqdm
-from transformers import (
-    CLIPTextModel,
-    CLIPTextConfig,
-    CLIPTokenizer,
-    AutoFeatureExtractor,
-    BertTokenizerFast,
-)
-import invokeai.configs as configs
+from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
-from invokeai.app.services.config import (
-    InvokeAIAppConfig,
-)
+import invokeai.configs as configs
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.install.legacy_arg_parsing import legacy_parser
+from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, hf_download_from_pretrained
+from invokeai.backend.model_management.model_probe import BaseModelType, ModelType
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
 
 # TO DO - Move all the frontend code into invokeai.frontend.install
 from invokeai.frontend.install.widgets import (
-    SingleSelectColumnsSimple,
-    MultiSelectColumns,
-    CenteredButtonPress,
-    FileBox,
-    set_min_terminal_size,
-    CyclingForm,
     MIN_COLS,
     MIN_LINES,
+    CenteredButtonPress,
+    CyclingForm,
+    FileBox,
+    MultiSelectColumns,
+    SingleSelectColumnsSimple,
     WindowTooSmallException,
+    set_min_terminal_size,
 )
-from invokeai.backend.install.legacy_arg_parsing import legacy_parser
-from invokeai.backend.install.model_install_backend import (
-    hf_download_from_pretrained,
-    InstallSelections,
-    ModelInstall,
-)
-from invokeai.backend.model_management.model_probe import ModelType, BaseModelType
-from pydantic.error_wrappers import ValidationError
 
 warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()
@@ -507,7 +495,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
                 scroll_exit=True,
             )
         else:
-            self.vram_cache_size = DummyWidgetValue.zero
+            self.vram = DummyWidgetValue.zero
         self.nextrely += 1
         self.outdir = self.add_widget_intelligent(
             FileBox,
@@ -605,7 +593,8 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
             "vram",
             "outdir",
         ]:
-            setattr(new_opts, attr, getattr(self, attr).value)
+            if hasattr(self, attr):
+                setattr(new_opts, attr, getattr(self, attr).value)
 
         for attr in self.autoimport_dirs:
             directory = Path(self.autoimport_dirs[attr].value)

invokeai/backend/install/migrate_to_3.py

@@ -3,33 +3,26 @@ Migrate the models directory and models.yaml file from an existing
 InvokeAI 2.3 installation to 3.0.0.
 """
 
-import os
 import argparse
+import os
 import shutil
-import yaml
-
-import transformers
-import diffusers
 import warnings
-
 from dataclasses import dataclass
 from pathlib import Path
-from omegaconf import OmegaConf, DictConfig
 from typing import Union
 
-from diffusers import StableDiffusionPipeline, AutoencoderKL
+import diffusers
+import transformers
+import yaml
+from diffusers import AutoencoderKL, StableDiffusionPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import (
-    CLIPTextModel,
-    CLIPTokenizer,
-    AutoFeatureExtractor,
-    BertTokenizerFast,
-)
+from omegaconf import DictConfig, OmegaConf
+from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_management import ModelManager
-from invokeai.backend.model_management.model_probe import ModelProbe, ModelType, BaseModelType, ModelProbeInfo
+from invokeai.backend.model_management.model_probe import BaseModelType, ModelProbe, ModelProbeInfo, ModelType
 
 warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()

invokeai/backend/install/model_install_backend.py

@@ -19,18 +19,8 @@ from tqdm import tqdm
 
 import invokeai.configs as configs
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_management import (
-    AddModelResult,
-    BaseModelType,
-    ModelManager,
-    ModelType,
-    ModelVariantType,
-)
-from invokeai.backend.model_management.model_probe import (
-    ModelProbe,
-    ModelProbeInfo,
-    SchedulerPredictionType,
-)
+from invokeai.backend.model_management import AddModelResult, BaseModelType, ModelManager, ModelType, ModelVariantType
+from invokeai.backend.model_management.model_probe import ModelProbe, ModelProbeInfo, SchedulerPredictionType
 from invokeai.backend.util import download_with_resume
 from invokeai.backend.util.devices import choose_torch_device, torch_dtype
 

invokeai/backend/model_management/__init__.py

@@ -1,15 +1,19 @@
 """
 Initialization file for invokeai.backend.model_management
 """
-from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType  # noqa: F401
-from .model_cache import ModelCache  # noqa: F401
+# This import must be first
+from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType  # noqa: F401 isort: split
+
 from .lora import ModelPatcher, ONNXModelPatcher  # noqa: F401
+from .model_cache import ModelCache  # noqa: F401
 from .models import (  # noqa: F401
     BaseModelType,
-    ModelType,
-    SubModelType,
-    ModelVariantType,
-    ModelNotFoundException,
     DuplicateModelException,
+    ModelNotFoundException,
+    ModelType,
+    ModelVariantType,
+    SubModelType,
 )
-from .model_merge import ModelMerger, MergeInterpolationMethod  # noqa: F401
+
+# This import must be last
+from .model_merge import ModelMerger, MergeInterpolationMethod  # noqa: F401 isort: split

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -25,12 +25,7 @@ from typing import Optional, Union
 
 import requests
 import torch
-from diffusers.models import (
-    AutoencoderKL,
-    ControlNetModel,
-    PriorTransformer,
-    UNet2DConditionModel,
-)
+from diffusers.models import AutoencoderKL, ControlNetModel, PriorTransformer, UNet2DConditionModel
 from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
 from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
@@ -64,6 +59,7 @@ from transformers import (
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.util.logging import InvokeAILogger
+
 from .models import BaseModelType, ModelVariantType
 
 try:
@@ -1203,8 +1199,8 @@ def download_from_original_stable_diffusion_ckpt(
         StableDiffusionControlNetPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionPipeline,
-        StableDiffusionXLPipeline,
         StableDiffusionXLImg2ImgPipeline,
+        StableDiffusionXLPipeline,
         StableUnCLIPImg2ImgPipeline,
         StableUnCLIPPipeline,
     )

invokeai/backend/model_management/lora.py

@@ -2,8 +2,8 @@ from __future__ import annotations
 
 import copy
 from contextlib import contextmanager
-from typing import Optional, Dict, Tuple, Any, Union, List
 from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -14,7 +14,6 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 from .models.lora import LoRAModel
 
-
 """
 loras = [
     (lora_model1, 0.7),
@@ -307,9 +306,10 @@ class TextualInversionManager(BaseTextualInversionManager):
 
 
 class ONNXModelPatcher:
-    from .models.base import IAIOnnxRuntimeModel
     from diffusers import OnnxRuntimeModel
 
+    from .models.base import IAIOnnxRuntimeModel
+
     @classmethod
     @contextmanager
     def apply_lora_unet(

invokeai/backend/model_management/model_cache.py

@@ -17,18 +17,23 @@ context. Use like this:
 """
 
 import gc
+import hashlib
 import os
 import sys
-import hashlib
 from contextlib import suppress
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Dict, Union, types, Optional, Type, Any
+from typing import Any, Dict, Optional, Type, Union, types
 
 import torch
 
 import invokeai.backend.util.logging as logger
-from .models import BaseModelType, ModelType, SubModelType, ModelBase
+
+from ..util.devices import choose_torch_device
+from .models import BaseModelType, ModelBase, ModelType, SubModelType
+
+if choose_torch_device() == torch.device("mps"):
+    from torch import mps
 
 # Maximum size of the cache, in gigs
 # Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
@@ -405,6 +410,8 @@ class ModelCache(object):
 
         gc.collect()
         torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
 
         self.logger.debug(f"After unloading: cached_models={len(self._cached_models)}")
 
@@ -425,6 +432,8 @@ class ModelCache(object):
 
         gc.collect()
         torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
 
     def _local_model_hash(self, model_path: Union[str, Path]) -> str:
         sha = hashlib.sha256()

invokeai/backend/model_management/model_merge.py

@@ -9,13 +9,14 @@ Copyright (c) 2023 Lincoln Stein and the InvokeAI Development Team
 import warnings
 from enum import Enum
 from pathlib import Path
+from typing import List, Optional, Union
+
 from diffusers import DiffusionPipeline
 from diffusers import logging as dlogging
-from typing import List, Union, Optional
 
 import invokeai.backend.util.logging as logger
 
-from ...backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult
+from ...backend.model_management import AddModelResult, BaseModelType, ModelManager, ModelType, ModelVariantType
 
 
 class MergeInterpolationMethod(str, Enum):

invokeai/backend/model_management/models/base.py

@@ -8,18 +8,7 @@ from abc import ABCMeta, abstractmethod
 from contextlib import suppress
 from enum import Enum
 from pathlib import Path
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Generic,
-    List,
-    Literal,
-    Optional,
-    Type,
-    TypeVar,
-    Union,
-)
+from typing import Any, Callable, Dict, Generic, List, Literal, Optional, Type, TypeVar, Union
 
 import numpy as np
 import onnx

invokeai/backend/model_management/models/controlnet.py

@@ -1,23 +1,26 @@
 import os
-import torch
 from enum import Enum
 from pathlib import Path
-from typing import Optional, Literal
+from typing import Literal, Optional
+
+import torch
+
+import invokeai.backend.util.logging as logger
+from invokeai.app.services.config import InvokeAIAppConfig
+
 from .base import (
+    BaseModelType,
+    EmptyConfigLoader,
+    InvalidModelException,
     ModelBase,
     ModelConfigBase,
-    BaseModelType,
+    ModelNotFoundException,
     ModelType,
     SubModelType,
-    EmptyConfigLoader,
-    calc_model_size_by_fs,
     calc_model_size_by_data,
+    calc_model_size_by_fs,
     classproperty,
-    InvalidModelException,
-    ModelNotFoundException,
 )
-from invokeai.app.services.config import InvokeAIAppConfig
-import invokeai.backend.util.logging as logger
 
 
 class ControlNetModelFormat(str, Enum):

invokeai/backend/model_management/models/sdxl.py

@@ -1,19 +1,21 @@
-import os
 import json
+import os
 from enum import Enum
-from pydantic import Field
 from typing import Literal, Optional
+
+from omegaconf import OmegaConf
+from pydantic import Field
+
 from .base import (
-    ModelConfigBase,
     BaseModelType,
+    DiffusersModel,
+    InvalidModelException,
+    ModelConfigBase,
     ModelType,
     ModelVariantType,
-    DiffusersModel,
-    read_checkpoint_meta,
     classproperty,
-    InvalidModelException,
+    read_checkpoint_meta,
 )
-from omegaconf import OmegaConf
 
 
 class StableDiffusionXLModelFormat(str, Enum):

invokeai/backend/model_management/models/stable_diffusion.py

@@ -1,26 +1,29 @@
-import os
 import json
+import os
 from enum import Enum
-from pydantic import Field
 from pathlib import Path
 from typing import Literal, Optional, Union
+
 from diffusers import StableDiffusionInpaintPipeline, StableDiffusionPipeline
-from .base import (
-    ModelConfigBase,
-    BaseModelType,
-    ModelType,
-    ModelVariantType,
-    DiffusersModel,
-    SilenceWarnings,
-    read_checkpoint_meta,
-    classproperty,
-    InvalidModelException,
-    ModelNotFoundException,
-)
-from .sdxl import StableDiffusionXLModel
+from omegaconf import OmegaConf
+from pydantic import Field
+
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
-from omegaconf import OmegaConf
+
+from .base import (
+    BaseModelType,
+    DiffusersModel,
+    InvalidModelException,
+    ModelConfigBase,
+    ModelNotFoundException,
+    ModelType,
+    ModelVariantType,
+    SilenceWarnings,
+    classproperty,
+    read_checkpoint_meta,
+)
+from .sdxl import StableDiffusionXLModel
 
 
 class StableDiffusion1ModelFormat(str, Enum):
@@ -272,8 +275,8 @@ def _convert_ckpt_and_cache(
         return output_path
 
     # to avoid circular import errors
-    from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
     from ...util.devices import choose_torch_device, torch_dtype
+    from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
 
     model_base_to_model_type = {
         BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",

invokeai/backend/model_management/models/stable_diffusion_onnx.py

@@ -2,15 +2,16 @@ from enum import Enum
 from typing import Literal
 
 from diffusers import OnnxRuntimeModel
+
 from .base import (
-    ModelConfigBase,
     BaseModelType,
+    DiffusersModel,
+    IAIOnnxRuntimeModel,
+    ModelConfigBase,
     ModelType,
     ModelVariantType,
-    DiffusersModel,
     SchedulerPredictionType,
     classproperty,
-    IAIOnnxRuntimeModel,
 )
 
 

invokeai/backend/model_management/models/textual_inversion.py

@@ -1,19 +1,20 @@
 import os
-import torch
 from typing import Optional
-from .base import (
-    ModelBase,
-    ModelConfigBase,
-    BaseModelType,
-    ModelType,
-    SubModelType,
-    classproperty,
-    ModelNotFoundException,
-    InvalidModelException,
-)
+
+import torch
 
 # TODO: naming
 from ..lora import TextualInversionModel as TextualInversionModelRaw
+from .base import (
+    BaseModelType,
+    InvalidModelException,
+    ModelBase,
+    ModelConfigBase,
+    ModelNotFoundException,
+    ModelType,
+    SubModelType,
+    classproperty,
+)
 
 
 class TextualInversionModel(ModelBase):

invokeai/backend/model_management/models/vae.py

@@ -8,19 +8,20 @@ import torch
 from omegaconf import OmegaConf
 
 from invokeai.app.services.config import InvokeAIAppConfig
+
 from .base import (
+    BaseModelType,
+    EmptyConfigLoader,
+    InvalidModelException,
     ModelBase,
     ModelConfigBase,
-    BaseModelType,
-    ModelType,
-    SubModelType,
-    ModelVariantType,
-    EmptyConfigLoader,
-    calc_model_size_by_fs,
-    calc_model_size_by_data,
-    classproperty,
-    InvalidModelException,
     ModelNotFoundException,
+    ModelType,
+    ModelVariantType,
+    SubModelType,
+    calc_model_size_by_data,
+    calc_model_size_by_fs,
+    classproperty,
 )
 
 

invokeai/backend/stable_diffusion/__init__.py

@@ -1,9 +1,6 @@
 """
 Initialization file for the invokeai.backend.stable_diffusion package
 """
-from .diffusers_pipeline import (  # noqa: F401
-    PipelineIntermediateState,
-    StableDiffusionGeneratorPipeline,
-)
+from .diffusers_pipeline import PipelineIntermediateState, StableDiffusionGeneratorPipeline  # noqa: F401
 from .diffusion import InvokeAIDiffuserComponent  # noqa: F401
 from .diffusion.cross_attention_map_saving import AttentionMapSaver  # noqa: F401

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -12,12 +12,8 @@ import torchvision.transforms as T
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.models.controlnet import ControlNetModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
-    StableDiffusionPipeline,
-)
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
 from diffusers.utils.import_utils import is_xformers_available
@@ -27,9 +23,7 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    ConditioningData,
-)
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
 
 from ..util import auto_detect_slice_size, normalize_device
 from .diffusion import AttentionMapSaver, InvokeAIDiffuserComponent

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -11,12 +11,7 @@ import diffusers
 import psutil
 import torch
 from compel.cross_attention_control import Arguments
-from diffusers.models.attention_processor import (
-    Attention,
-    AttentionProcessor,
-    AttnProcessor,
-    SlicedAttnProcessor,
-)
+from diffusers.models.attention_processor import Attention, AttentionProcessor, AttnProcessor, SlicedAttnProcessor
 from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from torch import nn
 

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -1,18 +1,18 @@
 from diffusers import (
     DDIMScheduler,
+    DDPMScheduler,
+    DEISMultistepScheduler,
     DPMSolverMultistepScheduler,
-    KDPM2DiscreteScheduler,
-    KDPM2AncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
+    DPMSolverSDEScheduler,
+    DPMSolverSinglestepScheduler,
     EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
     HeunDiscreteScheduler,
+    KDPM2AncestralDiscreteScheduler,
+    KDPM2DiscreteScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
     UniPCMultistepScheduler,
-    DPMSolverSinglestepScheduler,
-    DEISMultistepScheduler,
-    DDPMScheduler,
-    DPMSolverSDEScheduler,
 )
 
 SCHEDULER_MAP = dict(

invokeai/backend/training/textual_inversion_training.py

@@ -24,13 +24,8 @@ import torch.utils.checkpoint
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
-from accelerate.utils import set_seed, ProjectConfiguration
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
+from accelerate.utils import ProjectConfiguration, set_seed
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
 from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version
 from diffusers.utils.import_utils import is_xformers_available

invokeai/backend/util/__init__.py

@@ -1,6 +1,7 @@
 """
 Initialization file for invokeai.backend.util
 """
+from .attention import auto_detect_slice_size  # noqa: F401
 from .devices import (  # noqa: F401
     CPU_DEVICE,
     CUDA_DEVICE,
@@ -10,11 +11,4 @@ from .devices import (  # noqa: F401
     normalize_device,
     torch_dtype,
 )
-from .util import (  # noqa: F401
-    ask_user,
-    download_with_resume,
-    instantiate_from_config,
-    url_attachment_name,
-    Chdir,
-)
-from .attention import auto_detect_slice_size  # noqa: F401
+from .util import Chdir, ask_user, download_with_resume, instantiate_from_config, url_attachment_name  # noqa: F401

invokeai/backend/util/attention.py

@@ -3,8 +3,8 @@
 Utility routine used for autodetection of optimal slice size
 for attention mechanism.
 """
-import torch
 import psutil
+import torch
 
 
 def auto_detect_slice_size(latents: torch.Tensor) -> str:

invokeai/backend/util/devices.py

@@ -1,12 +1,13 @@
 from __future__ import annotations
 
-from contextlib import nullcontext
-from packaging import version
 import platform
+from contextlib import nullcontext
+from typing import Union
 
 import torch
+from packaging import version
 from torch import autocast
-from typing import Union
+
 from invokeai.app.services.config import InvokeAIAppConfig
 
 CPU_DEVICE = torch.device("cpu")

invokeai/backend/util/hotfixes.py

@@ -772,11 +772,13 @@ diffusers.models.controlnet.ControlNetModel = ControlNetModel
 # NOTE: with this patch, torch.compile crashes on 2.0 torch(already fixed in nightly)
 # https://github.com/huggingface/diffusers/pull/4315
 # https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/lora.py#L96C18-L96C18
-def new_LoRACompatibleConv_forward(self, x):
+def new_LoRACompatibleConv_forward(self, hidden_states, scale: float = 1.0):
     if self.lora_layer is None:
-        return super(diffusers.models.lora.LoRACompatibleConv, self).forward(x)
+        return super(diffusers.models.lora.LoRACompatibleConv, self).forward(hidden_states)
     else:
-        return super(diffusers.models.lora.LoRACompatibleConv, self).forward(x) + self.lora_layer(x)
+        return super(diffusers.models.lora.LoRACompatibleConv, self).forward(hidden_states) + (
+            scale * self.lora_layer(hidden_states)
+        )
 
 
 diffusers.models.lora.LoRACompatibleConv.forward = new_LoRACompatibleConv_forward

invokeai/backend/util/logging.py

@@ -178,7 +178,6 @@ InvokeAI:
 import logging.handlers
 import socket
 import urllib.parse
-
 from abc import abstractmethod
 from pathlib import Path
 

invokeai/backend/util/util.py

@@ -1,11 +1,10 @@
+import base64
 import importlib
+import io
 import math
 import multiprocessing as mp
 import os
 import re
-import io
-import base64
-
 from collections import abc
 from inspect import isfunction
 from pathlib import Path
@@ -19,6 +18,7 @@ from PIL import Image, ImageDraw, ImageFont
 from tqdm import tqdm
 
 import invokeai.backend.util.logging as logger
+
 from .devices import torch_dtype