Merge branch 'main' into feat/select-vram-in-config

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Lincoln Stein 2023-08-07 15:51:59 -04:00 committed by GitHub
commit 0fd13d3604
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18 changed files with 511 additions and 333 deletions

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@ -55,7 +55,7 @@ logger = InvokeAILogger.getLogger()
class ApiDependencies:
"""Contains and initializes all dependencies for the API"""
invoker: Optional[Invoker] = None
invoker: Invoker
@staticmethod
def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
@ -68,8 +68,9 @@ class ApiDependencies:
output_folder = config.output_path
# TODO: build a file/path manager?
db_location = config.db_path
db_location.parent.mkdir(parents=True, exist_ok=True)
db_path = config.db_path
db_path.parent.mkdir(parents=True, exist_ok=True)
db_location = str(db_path)
graph_execution_manager = SqliteItemStorage[GraphExecutionState](
filename=db_location, table_name="graph_executions"

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@ -3,6 +3,7 @@
from typing import Literal, Optional
import numpy
import cv2
from PIL import Image, ImageFilter, ImageOps, ImageChops
from pydantic import Field
from pathlib import Path
@ -650,3 +651,143 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
width=image_dto.width,
height=image_dto.height,
)
class ImageHueAdjustmentInvocation(BaseInvocation):
"""Adjusts the Hue of an image."""
# fmt: off
type: Literal["img_hue_adjust"] = "img_hue_adjust"
# Inputs
image: ImageField = Field(default=None, description="The image to adjust")
hue: int = Field(default=0, description="The degrees by which to rotate the hue, 0-360")
# fmt: on
def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.services.images.get_pil_image(self.image.image_name)
# Convert image to HSV color space
hsv_image = numpy.array(pil_image.convert("HSV"))
# Convert hue from 0..360 to 0..256
hue = int(256 * ((self.hue % 360) / 360))
# Increment each hue and wrap around at 255
hsv_image[:, :, 0] = (hsv_image[:, :, 0] + hue) % 256
# Convert back to PIL format and to original color mode
pil_image = Image.fromarray(hsv_image, mode="HSV").convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
)
return ImageOutput(
image=ImageField(
image_name=image_dto.image_name,
),
width=image_dto.width,
height=image_dto.height,
)
class ImageLuminosityAdjustmentInvocation(BaseInvocation):
"""Adjusts the Luminosity (Value) of an image."""
# fmt: off
type: Literal["img_luminosity_adjust"] = "img_luminosity_adjust"
# Inputs
image: ImageField = Field(default=None, description="The image to adjust")
luminosity: float = Field(default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)")
# fmt: on
def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.services.images.get_pil_image(self.image.image_name)
# Convert PIL image to OpenCV format (numpy array), note color channel
# ordering is changed from RGB to BGR
image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
# Convert image to HSV color space
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Adjust the luminosity (value)
hsv_image[:, :, 2] = numpy.clip(hsv_image[:, :, 2] * self.luminosity, 0, 255)
# Convert image back to BGR color space
image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
# Convert back to PIL format and to original color mode
pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
)
return ImageOutput(
image=ImageField(
image_name=image_dto.image_name,
),
width=image_dto.width,
height=image_dto.height,
)
class ImageSaturationAdjustmentInvocation(BaseInvocation):
"""Adjusts the Saturation of an image."""
# fmt: off
type: Literal["img_saturation_adjust"] = "img_saturation_adjust"
# Inputs
image: ImageField = Field(default=None, description="The image to adjust")
saturation: float = Field(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation")
# fmt: on
def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.services.images.get_pil_image(self.image.image_name)
# Convert PIL image to OpenCV format (numpy array), note color channel
# ordering is changed from RGB to BGR
image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
# Convert image to HSV color space
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Adjust the saturation
hsv_image[:, :, 1] = numpy.clip(hsv_image[:, :, 1] * self.saturation, 0, 255)
# Convert image back to BGR color space
image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
# Convert back to PIL format and to original color mode
pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
is_intermediate=self.is_intermediate,
session_id=context.graph_execution_state_id,
)
return ImageOutput(
image=ImageField(
image_name=image_dto.image_name,
),
width=image_dto.width,
height=image_dto.height,
)

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@ -3,9 +3,10 @@
from __future__ import annotations
from abc import ABC, abstractmethod
from logging import Logger
from pathlib import Path
from pydantic import Field
from typing import Optional, Union, Callable, List, Tuple, TYPE_CHECKING
from typing import Literal, Optional, Union, Callable, List, Tuple, TYPE_CHECKING
from types import ModuleType
from invokeai.backend.model_management import (
@ -193,7 +194,7 @@ class ModelManagerServiceBase(ABC):
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main, ModelType.Vae],
model_type: Literal[ModelType.Main, ModelType.Vae],
) -> AddModelResult:
"""
Convert a checkpoint file into a diffusers folder, deleting the cached
@ -292,7 +293,7 @@ class ModelManagerService(ModelManagerServiceBase):
def __init__(
self,
config: InvokeAIAppConfig,
logger: ModuleType,
logger: Logger,
):
"""
Initialize with the path to the models.yaml config file.
@ -396,7 +397,7 @@ class ModelManagerService(ModelManagerServiceBase):
model_type,
)
def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
"""
Given a model name returns a dict-like (OmegaConf) object describing it.
"""
@ -416,7 +417,7 @@ class ModelManagerService(ModelManagerServiceBase):
"""
return self.mgr.list_models(base_model, model_type)
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
"""
Return information about the model using the same format as list_models()
"""
@ -429,7 +430,7 @@ class ModelManagerService(ModelManagerServiceBase):
model_type: ModelType,
model_attributes: dict,
clobber: bool = False,
) -> None:
) -> AddModelResult:
"""
Update the named model with a dictionary of attributes. Will fail with an
assertion error if the name already exists. Pass clobber=True to overwrite.
@ -478,7 +479,7 @@ class ModelManagerService(ModelManagerServiceBase):
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main, ModelType.Vae],
model_type: Literal[ModelType.Main, ModelType.Vae],
convert_dest_directory: Optional[Path] = Field(
default=None, description="Optional directory location for merged model"
),
@ -573,9 +574,9 @@ class ModelManagerService(ModelManagerServiceBase):
default=None, description="Base model shared by all models to be merged"
),
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
alpha: Optional[float] = 0.5,
alpha: float = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
force: bool = False,
merge_dest_directory: Optional[Path] = Field(
default=None, description="Optional directory location for merged model"
),
@ -633,8 +634,8 @@ class ModelManagerService(ModelManagerServiceBase):
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
new_name: Optional[str] = None,
new_base: Optional[BaseModelType] = None,
):
"""
Rename the indicated model. Can provide a new name and/or a new base.

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@ -18,5 +18,5 @@ SEED_MAX = np.iinfo(np.uint32).max
def get_random_seed():
rng = np.random.default_rng(seed=0)
rng = np.random.default_rng(seed=None)
return int(rng.integers(0, SEED_MAX))

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@ -101,9 +101,9 @@ class ModelInstall(object):
def __init__(
self,
config: InvokeAIAppConfig,
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
model_manager: ModelManager = None,
access_token: str = None,
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
model_manager: Optional[ModelManager] = None,
access_token: Optional[str] = None,
):
self.config = config
self.mgr = model_manager or ModelManager(config.model_conf_path)

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@ -234,7 +234,7 @@ import textwrap
import yaml
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, List, Tuple, Union, Dict, Set, Callable, types
from typing import Literal, Optional, List, Tuple, Union, Dict, Set, Callable, types
from shutil import rmtree, move
import torch
@ -518,7 +518,7 @@ class ModelManager(object):
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
) -> dict:
) -> Union[dict, None]:
"""
Given a model name returns the OmegaConf (dict-like) object describing it.
"""
@ -540,13 +540,15 @@ class ModelManager(object):
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
) -> dict:
) -> Union[dict, None]:
"""
Returns a dict describing one installed model, using
the combined format of the list_models() method.
"""
models = self.list_models(base_model, model_type, model_name)
return models[0] if models else None
if len(models) > 1:
return models[0]
return None
def list_models(
self,
@ -560,7 +562,7 @@ class ModelManager(object):
model_keys = (
[self.create_key(model_name, base_model, model_type)]
if model_name
if model_name and base_model and model_type
else sorted(self.models, key=str.casefold)
)
models = []
@ -596,7 +598,7 @@ class ModelManager(object):
Print a table of models and their descriptions. This needs to be redone
"""
# TODO: redo
for model_type, model_dict in self.list_models().items():
for model_dict in self.list_models():
for model_name, model_info in model_dict.items():
line = f'{model_info["name"]:25s} {model_info["type"]:10s} {model_info["description"]}'
print(line)
@ -699,8 +701,8 @@ class ModelManager(object):
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
new_name: Optional[str] = None,
new_base: Optional[BaseModelType] = None,
):
"""
Rename or rebase a model.
@ -753,7 +755,7 @@ class ModelManager(object):
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main, ModelType.Vae],
model_type: Literal[ModelType.Main, ModelType.Vae],
dest_directory: Optional[Path] = None,
) -> AddModelResult:
"""
@ -767,6 +769,10 @@ class ModelManager(object):
This will raise a ValueError unless the model is a checkpoint.
"""
info = self.model_info(model_name, base_model, model_type)
if info is None:
raise FileNotFoundError(f"model not found: {model_name}")
if info["model_format"] != "checkpoint":
raise ValueError(f"not a checkpoint format model: {model_name}")
@ -836,7 +842,7 @@ class ModelManager(object):
return search_folder, found_models
def commit(self, conf_file: Path = None) -> None:
def commit(self, conf_file: Optional[Path] = None) -> None:
"""
Write current configuration out to the indicated file.
"""
@ -983,7 +989,7 @@ class ModelManager(object):
# LS: hacky
# Patch in the SD VAE from core so that it is available for use by the UI
try:
self.heuristic_import({self.resolve_model_path("core/convert/sd-vae-ft-mse")})
self.heuristic_import({str(self.resolve_model_path("core/convert/sd-vae-ft-mse"))})
except:
pass
@ -1011,7 +1017,7 @@ class ModelManager(object):
def heuristic_import(
self,
items_to_import: Set[str],
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
) -> Dict[str, AddModelResult]:
"""Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.

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@ -33,7 +33,7 @@ class ModelMerger(object):
self,
model_paths: List[Path],
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
interp: Optional[MergeInterpolationMethod] = None,
force: bool = False,
**kwargs,
) -> DiffusionPipeline:
@ -73,7 +73,7 @@ class ModelMerger(object):
base_model: Union[BaseModelType, str],
merged_model_name: str,
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
interp: Optional[MergeInterpolationMethod] = None,
force: bool = False,
merge_dest_directory: Optional[Path] = None,
**kwargs,
@ -122,7 +122,7 @@ class ModelMerger(object):
dump_path.mkdir(parents=True, exist_ok=True)
dump_path = dump_path / merged_model_name
merged_pipe.save_pretrained(dump_path, safe_serialization=1)
merged_pipe.save_pretrained(dump_path, safe_serialization=True)
attributes = dict(
path=str(dump_path),
description=f"Merge of models {', '.join(model_names)}",

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@ -1,6 +1,8 @@
from __future__ import annotations
from contextlib import nullcontext
from packaging import version
import platform
import torch
from torch import autocast
@ -30,7 +32,7 @@ def choose_precision(device: torch.device) -> str:
device_name = torch.cuda.get_device_name(device)
if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):
return "float16"
elif device.type == "mps":
elif device.type == "mps" and version.parse(platform.mac_ver()[0]) < version.parse("14.0.0"):
return "float16"
return "float32"

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@ -1,4 +1,4 @@
import{A as m,f$ as Je,z as y,a4 as Ka,g0 as Xa,af as va,aj as d,g1 as b,g2 as t,g3 as Ya,g4 as h,g5 as ua,g6 as Ja,g7 as Qa,aI as Za,g8 as et,ad as rt,g9 as at}from"./index-18f2f740.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./MantineProvider-b20a2267.js";var za=String.raw,Ca=za`
import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-de589048.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-11348abc.js";var za=String.raw,Ca=za`
:root,
:host {
--chakra-vh: 100vh;

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@ -12,7 +12,7 @@
margin: 0;
}
</style>
<script type="module" crossorigin src="./assets/index-18f2f740.js"></script>
<script type="module" crossorigin src="./assets/index-de589048.js"></script>
</head>
<body dir="ltr">

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@ -124,7 +124,8 @@
"deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
"deleteImagePermanent": "Deleted images cannot be restored.",
"images": "Images",
"assets": "Assets"
"assets": "Assets",
"autoAssignBoardOnClick": "Auto-Assign Board on Click"
},
"hotkeys": {
"keyboardShortcuts": "Keyboard Shortcuts",

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@ -1 +1 @@
__version__ = "3.0.1post3"
__version__ = "3.0.2a1"