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Resolving merge conflicts for flake8

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This commit is contained in:
Martin Kristiansen
2023-08-17 18:45:25 -04:00
committed by psychedelicious
parent f6db9da06c
commit 537ae2f901
101 changed files with 393 additions and 408 deletions
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4
invokeai/backend/__init__.py
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@ -1,5 +1,5 @@
"""
Initialization file for invokeai.backend
"""
from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo
from .model_management.models import SilenceWarnings
from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo # noqa: F401
from .model_management.models import SilenceWarnings # noqa: F401

12
invokeai/backend/image_util/__init__.py
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@ -1,14 +1,16 @@
"""
Initialization file for invokeai.backend.image_util methods.
"""
from .patchmatch import PatchMatch
from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata
from .seamless import configure_model_padding
from .txt2mask import Txt2Mask
from .util import InitImageResizer, make_grid
from .patchmatch import PatchMatch # noqa: F401
from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata # noqa: F401
from .seamless import configure_model_padding # noqa: F401
from .txt2mask import Txt2Mask # noqa: F401
from .util import InitImageResizer, make_grid # noqa: F401
def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False):
from PIL import ImageDraw
if not debug_status:
return

6
invokeai/backend/image_util/pngwriter.py
View File

@ -26,7 +26,7 @@ class PngWriter:
dirlist = sorted(os.listdir(self.outdir), reverse=True)
# find the first filename that matches our pattern or return 000000.0.png
existing_name = next(
(f for f in dirlist if re.match("^(\d+)\..*\.png", f)),
(f for f in dirlist if re.match(r"^(\d+)\..*\.png", f)),
"0000000.0.png",
)
basecount = int(existing_name.split(".", 1)[0]) + 1
@ -98,11 +98,11 @@ class PromptFormatter:
# to do: put model name into the t2i object
# switches.append(f'--model{t2i.model_name}')
if opt.seamless or t2i.seamless:
switches.append(f"--seamless")
switches.append("--seamless")
if opt.init_img:
switches.append(f"-I{opt.init_img}")
if opt.fit:
switches.append(f"--fit")
switches.append("--fit")
if opt.strength and opt.init_img is not None:
switches.append(f"-f{opt.strength or t2i.strength}")
if opt.gfpgan_strength:

3
invokeai/backend/install/invokeai_configure.py
View File

@ -52,7 +52,6 @@ from invokeai.frontend.install.widgets import (
SingleSelectColumns,
CenteredButtonPress,
FileBox,
IntTitleSlider,
set_min_terminal_size,
CyclingForm,
MIN_COLS,
@ -308,7 +307,7 @@ class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
first_time = not (config.root_path / "invokeai.yaml").exists()
access_token = HfFolder.get_token()
window_width, window_height = get_terminal_size()
label = """Configure startup settings. You can come back and change these later.
label = """Configure startup settings. You can come back and change these later.
Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.
Use cursor arrows to make a checkbox selection, and space to toggle.
"""

4
invokeai/backend/install/migrate_to_3.py
View File

@ -525,7 +525,7 @@ def do_migrate(src_directory: Path, dest_directory: Path):
if version_3: # write into the dest directory
try:
shutil.copy(dest_directory / "configs" / "models.yaml", config_file)
except:
except Exception:
MigrateTo3.initialize_yaml(config_file)
mgr = ModelManager(config_file) # important to initialize BEFORE moving the models directory
(dest_directory / "models").replace(dest_models)
@ -553,7 +553,7 @@ def main():
parser = argparse.ArgumentParser(
prog="invokeai-migrate3",
description="""
This will copy and convert the models directory and the configs/models.yaml from the InvokeAI 2.3 format
This will copy and convert the models directory and the configs/models.yaml from the InvokeAI 2.3 format
'--from-directory' root to the InvokeAI 3.0 '--to-directory' root. These may be abbreviated '--from' and '--to'.a
The old models directory and config file will be renamed 'models.orig' and 'models.yaml.orig' respectively.

1
invokeai/backend/install/model_install_backend.py
View File

@ -12,7 +12,6 @@ from typing import Optional, List, Dict, Callable, Union, Set
import requests
from diffusers import DiffusionPipeline
from diffusers import logging as dlogging
import onnx
import torch
from huggingface_hub import hf_hub_url, HfFolder, HfApi
from omegaconf import OmegaConf

11
invokeai/backend/model_management/__init__.py
View File

@ -1,10 +1,10 @@
"""
Initialization file for invokeai.backend.model_management
"""
from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
from .model_cache import ModelCache
from .lora import ModelPatcher, ONNXModelPatcher
from .models import (
from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType # noqa: F401
from .model_cache import ModelCache # noqa: F401
from .lora import ModelPatcher, ONNXModelPatcher # noqa: F401
from .models import ( # noqa: F401
BaseModelType,
ModelType,
SubModelType,
@ -12,5 +12,4 @@ from .models import (
ModelNotFoundException,
DuplicateModelException,
)
from .model_merge import ModelMerger, MergeInterpolationMethod
from .lora import ModelPatcher
from .model_merge import ModelMerger, MergeInterpolationMethod # noqa: F401

17
invokeai/backend/model_management/lora.py
View File

@ -5,21 +5,16 @@ from contextlib import contextmanager
from typing import Optional, Dict, Tuple, Any, Union, List
from pathlib import Path
import torch
from safetensors.torch import load_file
from torch.utils.hooks import RemovableHandle
from diffusers.models import UNet2DConditionModel
from transformers import CLIPTextModel
from onnx import numpy_helper
from onnxruntime import OrtValue
import numpy as np
import torch
from compel.embeddings_provider import BaseTextualInversionManager
from diffusers.models import UNet2DConditionModel
from safetensors.torch import load_file
from transformers import CLIPTextModel, CLIPTokenizer
from .models.lora import LoRAModel
"""
loras = [
(lora_model1, 0.7),
@ -52,7 +47,7 @@ class ModelPatcher:
module = module.get_submodule(submodule_name)
module_key += "." + submodule_name
submodule_name = key_parts.pop(0)
except:
except Exception:
submodule_name += "_" + key_parts.pop(0)
module = module.get_submodule(submodule_name)
@ -341,7 +336,7 @@ class ONNXModelPatcher:
def apply_lora(
cls,
model: IAIOnnxRuntimeModel,
loras: List[Tuple[LoraModel, float]],
loras: List[Tuple[LoRAModel, float]],
prefix: str,
):
from .models.base import IAIOnnxRuntimeModel

2
invokeai/backend/model_management/model_cache.py
View File

@ -273,7 +273,7 @@ class ModelCache(object):
self.cache.logger.debug(f"Locking {self.key} in {self.cache.execution_device}")
self.cache._print_cuda_stats()
except:
except Exception:
self.cache_entry.unlock()
raise

8
invokeai/backend/model_management/model_manager.py
View File

@ -419,12 +419,12 @@ class ModelManager(object):
base_model_str, model_type_str, model_name = model_key.split("/", 2)
try:
model_type = ModelType(model_type_str)
except:
except Exception:
raise Exception(f"Unknown model type: {model_type_str}")
try:
base_model = BaseModelType(base_model_str)
except:
except Exception:
raise Exception(f"Unknown base model: {base_model_str}")
return (model_name, base_model, model_type)
@ -855,7 +855,7 @@ class ModelManager(object):
info.pop("config")
result = self.add_model(model_name, base_model, model_type, model_attributes=info, clobber=True)
except:
except Exception:
# something went wrong, so don't leave dangling diffusers model in directory or it will cause a duplicate model error!
rmtree(new_diffusers_path)
raise
@ -1042,7 +1042,7 @@ class ModelManager(object):
# Patch in the SD VAE from core so that it is available for use by the UI
try:
self.heuristic_import({str(self.resolve_model_path("core/convert/sd-vae-ft-mse"))})
except:
except Exception:
pass
installer = ModelInstall(

6
invokeai/backend/model_management/model_probe.py
View File

@ -217,9 +217,9 @@ class ModelProbe(object):
raise "The model {model_name} is potentially infected by malware. Aborting import."
###################################################3
# ##################################################3
# Checkpoint probing
###################################################3
# ##################################################3
class ProbeBase(object):
def get_base_type(self) -> BaseModelType:
pass
@ -431,7 +431,7 @@ class PipelineFolderProbe(FolderProbeBase):
return ModelVariantType.Depth
elif in_channels == 4:
return ModelVariantType.Normal
except:
except Exception:
pass
return ModelVariantType.Normal

4
invokeai/backend/model_management/models/__init__.py
View File

@ -2,7 +2,7 @@ import inspect
from enum import Enum
from pydantic import BaseModel
from typing import Literal, get_origin
from .base import (
from .base import ( # noqa: F401
BaseModelType,
ModelType,
SubModelType,
@ -118,7 +118,7 @@ def get_model_config_enums():
fields = model_config.__annotations__
try:
field = fields["model_format"]
except:
except Exception:
raise Exception("format field not found")
# model_format: None

34
invokeai/backend/model_management/models/base.py
View File

@ -3,27 +3,28 @@ import os
import sys
import typing
import inspect
from enum import Enum
import warnings
from abc import ABCMeta, abstractmethod
from contextlib import suppress
from enum import Enum
from pathlib import Path
from picklescan.scanner import scan_file_path
import torch
import numpy as np
import safetensors.torch
from pathlib import Path
from diffusers import DiffusionPipeline, ConfigMixin, OnnxRuntimeModel
from contextlib import suppress
from pydantic import BaseModel, Field
from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
import onnx
import safetensors.torch
from diffusers import DiffusionPipeline, ConfigMixin
from onnx import numpy_helper
from onnxruntime import (
InferenceSession,
SessionOptions,
get_available_providers,
)
from pydantic import BaseModel, Field
from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
from diffusers import logging as diffusers_logging
from transformers import logging as transformers_logging
class DuplicateModelException(Exception):
@ -171,7 +172,7 @@ class ModelBase(metaclass=ABCMeta):
fields = value.__annotations__
try:
field = fields["model_format"]
except:
except Exception:
raise Exception(f"Invalid config definition - format field not found({cls.__qualname__})")
if isinstance(field, type) and issubclass(field, str) and issubclass(field, Enum):
@ -244,7 +245,7 @@ class DiffusersModel(ModelBase):
try:
config_data = DiffusionPipeline.load_config(self.model_path)
# config_data = json.loads(os.path.join(self.model_path, "model_index.json"))
except:
except Exception:
raise Exception("Invalid diffusers model! (model_index.json not found or invalid)")
config_data.pop("_ignore_files", None)
@ -343,7 +344,7 @@ def calc_model_size_by_fs(model_path: str, subfolder: Optional[str] = None, vari
with open(os.path.join(model_path, file), "r") as f:
index_data = json.loads(f.read())
return int(index_data["metadata"]["total_size"])
except:
except Exception:
pass
# calculate files size if there is no index file
@ -440,7 +441,7 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
if str(path).endswith(".safetensors"):
try:
checkpoint = _fast_safetensors_reader(path)
except:
except Exception:
# TODO: create issue for support "meta"?
checkpoint = safetensors.torch.load_file(path, device="cpu")
else:
@ -452,11 +453,6 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
return checkpoint
import warnings
from diffusers import logging as diffusers_logging
from transformers import logging as transformers_logging
class SilenceWarnings(object):
def __init__(self):
self.transformers_verbosity = transformers_logging.get_verbosity()
@ -639,7 +635,7 @@ class IAIOnnxRuntimeModel:
raise Exception("You should call create_session before running model")
inputs = {k: np.array(v) for k, v in kwargs.items()}
output_names = self.session.get_outputs()
# output_names = self.session.get_outputs()
# for k in inputs:
# self.io_binding.bind_cpu_input(k, inputs[k])
# for name in output_names:

6
invokeai/backend/model_management/models/controlnet.py
View File

@ -43,7 +43,7 @@ class ControlNetModel(ModelBase):
try:
config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
# config = json.loads(os.path.join(self.model_path, "config.json"))
except:
except Exception:
raise Exception("Invalid controlnet model! (config.json not found or invalid)")
model_class_name = config.get("_class_name", None)
@ -53,7 +53,7 @@ class ControlNetModel(ModelBase):
try:
self.model_class = self._hf_definition_to_type(["diffusers", model_class_name])
self.model_size = calc_model_size_by_fs(self.model_path)
except:
except Exception:
raise Exception("Invalid ControlNet model!")
def get_size(self, child_type: Optional[SubModelType] = None):
@ -78,7 +78,7 @@ class ControlNetModel(ModelBase):
variant=variant,
)
break
except:
except Exception:
pass
if not model:
raise ModelNotFoundException()

2
invokeai/backend/model_management/models/stable_diffusion.py
View File

@ -330,5 +330,5 @@ def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
config_path = config_path.relative_to(app_config.root_path)
return str(config_path)
except:
except Exception:
return None

11
invokeai/backend/model_management/models/stable_diffusion_onnx.py
View File

@ -1,25 +1,16 @@
import os
import json
from enum import Enum
from pydantic import Field
from pathlib import Path
from typing import Literal, Optional, Union
from typing import Literal
from .base import (
ModelBase,
ModelConfigBase,
BaseModelType,
ModelType,
SubModelType,
ModelVariantType,
DiffusersModel,
SchedulerPredictionType,
SilenceWarnings,
read_checkpoint_meta,
classproperty,
OnnxRuntimeModel,
IAIOnnxRuntimeModel,
)
from invokeai.app.services.config import InvokeAIAppConfig
class StableDiffusionOnnxModelFormat(str, Enum):

4
invokeai/backend/model_management/models/vae.py
View File

@ -44,14 +44,14 @@ class VaeModel(ModelBase):
try:
config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
# config = json.loads(os.path.join(self.model_path, "config.json"))
except:
except Exception:
raise Exception("Invalid vae model! (config.json not found or invalid)")
try:
vae_class_name = config.get("_class_name", "AutoencoderKL")
self.vae_class = self._hf_definition_to_type(["diffusers", vae_class_name])
self.model_size = calc_model_size_by_fs(self.model_path)
except:
except Exception:
raise Exception("Invalid vae model! (Unkown vae type)")
def get_size(self, child_type: Optional[SubModelType] = None):

12
invokeai/backend/stable_diffusion/__init__.py
View File

@ -1,11 +1,15 @@
"""
Initialization file for the invokeai.backend.stable_diffusion package
"""
from .diffusers_pipeline import (
from .diffusers_pipeline import ( # noqa: F401
ConditioningData,
PipelineIntermediateState,
StableDiffusionGeneratorPipeline,
)
from .diffusion import InvokeAIDiffuserComponent
from .diffusion.cross_attention_map_saving import AttentionMapSaver
from .diffusion.shared_invokeai_diffusion import PostprocessingSettings, BasicConditioningInfo, SDXLConditioningInfo
from .diffusion import InvokeAIDiffuserComponent # noqa: F401
from .diffusion.cross_attention_map_saving import AttentionMapSaver # noqa: F401
from .diffusion.shared_invokeai_diffusion import ( # noqa: F401
PostprocessingSettings,
BasicConditioningInfo,
SDXLConditioningInfo,
)

4
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -2,10 +2,8 @@ from __future__ import annotations
import dataclasses
import inspect
import math
import secrets
from dataclasses import dataclass, field
from typing import Any, Callable, Generic, List, Optional, Type, Union
from typing import Any, Callable, List, Optional, Union
import PIL.Image
import einops

6
invokeai/backend/stable_diffusion/diffusion/__init__.py
View File

@ -1,9 +1,9 @@
"""
Initialization file for invokeai.models.diffusion
"""
from .cross_attention_control import InvokeAICrossAttentionMixin
from .cross_attention_map_saving import AttentionMapSaver
from .shared_invokeai_diffusion import (
from .cross_attention_control import InvokeAICrossAttentionMixin # noqa: F401
from .cross_attention_map_saving import AttentionMapSaver # noqa: F401
from .shared_invokeai_diffusion import ( # noqa: F401
InvokeAIDiffuserComponent,
PostprocessingSettings,
BasicConditioningInfo,

14
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
View File

@ -4,6 +4,7 @@
import enum
import math
from dataclasses import dataclass, field
from typing import Callable, Optional
import diffusers
@ -12,6 +13,11 @@ import torch
from compel.cross_attention_control import Arguments
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from diffusers.models.attention_processor import AttentionProcessor
from diffusers.models.attention_processor import (
Attention,
AttnProcessor,
SlicedAttnProcessor,
)
from torch import nn
import invokeai.backend.util.logging as logger
@ -522,14 +528,6 @@ class AttnProcessor:
return hidden_states
"""
from dataclasses import dataclass, field
import torch
from diffusers.models.attention_processor import (
Attention,
AttnProcessor,
SlicedAttnProcessor,
)
@dataclass

2
invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py
View File

@ -5,8 +5,6 @@ import torch
from torchvision.transforms.functional import InterpolationMode
from torchvision.transforms.functional import resize as tv_resize
from .cross_attention_control import CrossAttentionType, get_cross_attention_modules
class AttentionMapSaver:
def __init__(self, token_ids: range, latents_shape: torch.Size):

9
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -3,15 +3,12 @@ from __future__ import annotations
from contextlib import contextmanager
from dataclasses import dataclass
import math
from typing import Any, Callable, Dict, Optional, Union, List
from typing import Any, Callable, Optional, Union
import numpy as np
import torch
from diffusers import UNet2DConditionModel
from diffusers.models.attention_processor import AttentionProcessor
from typing_extensions import TypeAlias
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
from .cross_attention_control import (
@ -579,7 +576,7 @@ class InvokeAIDiffuserComponent:
latents.to(device="cpu")
if (
h_symmetry_time_pct != None
h_symmetry_time_pct is not None
and self.last_percent_through < h_symmetry_time_pct
and percent_through >= h_symmetry_time_pct
):
@ -595,7 +592,7 @@ class InvokeAIDiffuserComponent:
)
if (
v_symmetry_time_pct != None
v_symmetry_time_pct is not None
and self.last_percent_through < v_symmetry_time_pct
and percent_through >= v_symmetry_time_pct
):

4
invokeai/backend/stable_diffusion/image_degradation/__init__.py
View File

@ -1,6 +1,6 @@
from ldm.modules.image_degradation.bsrgan import (
from ldm.modules.image_degradation.bsrgan import ( # noqa: F401
degradation_bsrgan_variant as degradation_fn_bsr,
)
from ldm.modules.image_degradation.bsrgan_light import (
from ldm.modules.image_degradation.bsrgan_light import ( # noqa: F401
degradation_bsrgan_variant as degradation_fn_bsr_light,
)

12
invokeai/backend/stable_diffusion/image_degradation/bsrgan.py
View File

@ -573,14 +573,15 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
jpeg_prob, scale2_prob = 0.9, 0.25
# isp_prob = 0.25 # uncomment with `if i== 6` block below
# sf_ori = sf # uncomment with `if i== 6` block below
h1, w1 = image.shape[:2]
image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
# hq = image.copy() # uncomment with `if i== 6` block below
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
@ -777,7 +778,7 @@ if __name__ == "__main__":
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
# print(img_hq.shape)
lq_nearest = cv2.resize(
util.single2uint(img_lq),
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
@ -788,5 +789,6 @@ if __name__ == "__main__":
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0,
)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
# img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest], axis=1)
util.imsave(img_concat, str(i) + ".png")

7
invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py
View File

@ -577,14 +577,15 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
jpeg_prob, scale2_prob = 0.9, 0.25
# isp_prob = 0.25 # uncomment with `if i== 6` block below
# sf_ori = sf # uncomment with `if i== 6` block below
h1, w1 = image.shape[:2]
image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
# hq = image.copy() # uncomment with `if i== 6` block below
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:

6
invokeai/backend/stable_diffusion/image_degradation/utils_image.py
View File

@ -8,8 +8,6 @@ import numpy as np
import torch
from torchvision.utils import make_grid
# import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
import invokeai.backend.util.logging as logger
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
@ -50,6 +48,8 @@ def get_timestamp():
def imshow(x, title=None, cbar=False, figsize=None):
import matplotlib.pyplot as plt
plt.figure(figsize=figsize)
plt.imshow(np.squeeze(x), interpolation="nearest", cmap="gray")
if title:
@ -60,6 +60,8 @@ def imshow(x, title=None, cbar=False, figsize=None):
def surf(Z, cmap="rainbow", figsize=None):
import matplotlib.pyplot as plt
plt.figure(figsize=figsize)
ax3 = plt.axes(projection="3d")

2
invokeai/backend/stable_diffusion/schedulers/__init__.py
View File

@ -1 +1 @@
from .schedulers import SCHEDULER_MAP
from .schedulers import SCHEDULER_MAP # noqa: F401

2
invokeai/backend/training/__init__.py
View File

@ -1,4 +1,4 @@
"""
Initialization file for invokeai.backend.training
"""
from .textual_inversion_training import do_textual_inversion_training, parse_args
from .textual_inversion_training import do_textual_inversion_training, parse_args # noqa: F401

6
invokeai/backend/util/__init__.py
View File

@ -1,7 +1,7 @@
"""
Initialization file for invokeai.backend.util
"""
from .devices import (
from .devices import ( # noqa: F401
CPU_DEVICE,
CUDA_DEVICE,
MPS_DEVICE,
@ -10,5 +10,5 @@ from .devices import (
normalize_device,
torch_dtype,
)
from .log import write_log
from .util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name, Chdir
from .log import write_log # noqa: F401
from .util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name, Chdir # noqa: F401

7
invokeai/backend/util/hotfixes.py
View File

@ -25,10 +25,15 @@ from diffusers.models.unet_2d_condition import UNet2DConditionModel
import diffusers
from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
from invokeai.backend.util.logging import InvokeAILogger
# TODO: create PR to diffusers
# Modified ControlNetModel with encoder_attention_mask argument added
logger = InvokeAILogger.getLogger(__name__)
class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
"""
A ControlNet model.
@ -111,7 +116,7 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
"DownBlock2D",
),
only_cross_attention: Union[bool, Tuple[bool]] = False,
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
layers_per_block: int = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,

4
invokeai/backend/util/log.py
View File

@ -27,8 +27,8 @@ def write_log_message(results, output_cntr):
log_lines = [f"{path}: {prompt}\n" for path, prompt in results]
if len(log_lines) > 1:
subcntr = 1
for l in log_lines:
print(f"[{output_cntr}.{subcntr}] {l}", end="")
for ll in log_lines:
print(f"[{output_cntr}.{subcntr}] {ll}", end="")
subcntr += 1
else:
print(f"[{output_cntr}] {log_lines[0]}", end="")

6
invokeai/backend/util/logging.py
View File

@ -182,13 +182,13 @@ import urllib.parse
from abc import abstractmethod
from pathlib import Path
from invokeai.app.services.config import InvokeAIAppConfig, get_invokeai_config
from invokeai.app.services.config import InvokeAIAppConfig
try:
import syslog
SYSLOG_AVAILABLE = True
except:
except ImportError:
SYSLOG_AVAILABLE = False
@ -417,7 +417,7 @@ class InvokeAILogger(object):
syslog_args["socktype"] = _SOCK_MAP[arg_value[0]]
else:
syslog_args["address"] = arg_name
except:
except Exception:
raise ValueError(f"{args} is not a value argument list for syslog logging")
return logging.handlers.SysLogHandler(**syslog_args)

2
invokeai/backend/util/mps_fixes.py
View File

@ -191,7 +191,7 @@ class ChunkedSlicedAttnProcessor:
assert value.shape[0] == 1
assert hidden_states.shape[0] == 1
dtype = query.dtype
# dtype = query.dtype
if attn.upcast_attention:
query = query.float()
key = key.float()

27
invokeai/backend/util/util.py
View File

@ -84,7 +84,7 @@ def count_params(model, verbose=False):
def instantiate_from_config(config, **kwargs):
if not "target" in config:
if "target" not in config:
if config == "__is_first_stage__":
return None
elif config == "__is_unconditional__":
@ -234,16 +234,17 @@ def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10
.repeat_interleave(d[1], 1)
)
dot = lambda grad, shift: (
torch.stack(
(
grid[: shape[0], : shape[1], 0] + shift[0],
grid[: shape[0], : shape[1], 1] + shift[1],
),
dim=-1,
)
* grad[: shape[0], : shape[1]]
).sum(dim=-1)
def dot(grad, shift):
return (
torch.stack(
(
grid[: shape[0], : shape[1], 0] + shift[0],
grid[: shape[0], : shape[1], 1] + shift[1],
),
dim=-1,
)
* grad[: shape[0], : shape[1]]
).sum(dim=-1)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0]).to(device)
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0]).to(device)
@ -287,7 +288,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
if dest.is_dir():
try:
file_name = re.search('filename="(.+)"', resp.headers.get("Content-Disposition")).group(1)
except:
except AttributeError:
file_name = os.path.basename(url)
dest = dest / file_name
else:
@ -342,7 +343,7 @@ def url_attachment_name(url: str) -> dict:
resp = requests.get(url, stream=True)
match = re.search('filename="(.+)"', resp.headers.get("Content-Disposition"))
return match.group(1)
except:
except Exception:
return None