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16
invokeai/app/api_app.py
View File

@ -4,7 +4,6 @@ from inspect import signature
import uvicorn
from invokeai.backend.util.logging import InvokeAILogger
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
@ -15,15 +14,19 @@ from fastapi_events.middleware import EventHandlerASGIMiddleware
from pathlib import Path
from pydantic.schema import schema
#This should come early so that modules can log their initialization properly
from .services.config import InvokeAIAppConfig
from ..backend.util.logging import InvokeAILogger
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
logger = InvokeAILogger.getLogger(config=app_config)
import invokeai.frontend.web as web_dir
from .api.dependencies import ApiDependencies
from .api.routers import sessions, models, images
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation
from .services.config import InvokeAIAppConfig
logger = InvokeAILogger.getLogger()
# Create the app
# TODO: create this all in a method so configuration/etc. can be passed in?
@ -41,11 +44,6 @@ app.add_middleware(
socket_io = SocketIO(app)
# initialize config
# this is a module global
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
# Add startup event to load dependencies
@app.on_event("startup")
async def startup_event():

20
invokeai/app/cli_app.py
View File

@ -13,14 +13,20 @@ from typing import (
from pydantic import BaseModel, ValidationError
from pydantic.fields import Field
# This should come early so that the logger can pick up its configuration options
from .services.config import InvokeAIAppConfig
from invokeai.backend.util.logging import InvokeAILogger
config = InvokeAIAppConfig.get_config()
config.parse_args()
logger = InvokeAILogger().getLogger(config=config)
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService
from invokeai.app.services.metadata import CoreMetadataService
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.urls import LocalUrlService
import invokeai.backend.util.logging as logger
from .services.default_graphs import create_system_graphs
from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
@ -38,7 +44,7 @@ from .services.invocation_services import InvocationServices
from .services.invoker import Invoker
from .services.processor import DefaultInvocationProcessor
from .services.sqlite import SqliteItemStorage
from .services.config import InvokeAIAppConfig
class CliCommand(BaseModel):
command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type") # type: ignore
@ -47,7 +53,6 @@ class CliCommand(BaseModel):
class InvalidArgs(Exception):
pass
def add_invocation_args(command_parser):
# Add linking capability
command_parser.add_argument(
@ -191,14 +196,7 @@ def invoke_all(context: CliContext):
raise SessionError()
logger = logger.InvokeAILogger.getLogger()
def invoke_cli():
# this gets the basic configuration
config = InvokeAIAppConfig.get_config()
config.parse_args()
# get the optional list of invocations to execute on the command line
parser = config.get_parser()

53
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -1,11 +1,12 @@
# InvokeAI nodes for ControlNet image preprocessors
# initial implementation by Gregg Helt, 2023
# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
from builtins import float
import numpy as np
from typing import Literal, Optional, Union, List
from PIL import Image, ImageFilter, ImageOps
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, validator
from ..models.image import ImageField, ImageCategory, ResourceOrigin
from .baseinvocation import (
@ -14,6 +15,7 @@ from .baseinvocation import (
InvocationContext,
InvocationConfig,
)
from controlnet_aux import (
CannyDetector,
HEDdetector,
@ -96,15 +98,32 @@ CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
class ControlField(BaseModel):
image: ImageField = Field(default=None, description="The control image")
control_model: Optional[str] = Field(default=None, description="The ControlNet model to use")
control_weight: Optional[float] = Field(default=1, description="The weight given to the ControlNet")
# control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
begin_step_percent: float = Field(default=0, ge=0, le=1,
description="When the ControlNet is first applied (% of total steps)")
description="When the ControlNet is first applied (% of total steps)")
end_step_percent: float = Field(default=1, ge=0, le=1,
description="When the ControlNet is last applied (% of total steps)")
@validator("control_weight")
def abs_le_one(cls, v):
"""validate that all abs(values) are <=1"""
if isinstance(v, list):
for i in v:
if abs(i) > 1:
raise ValueError('all abs(control_weight) must be <= 1')
else:
if abs(v) > 1:
raise ValueError('abs(control_weight) must be <= 1')
return v
class Config:
schema_extra = {
"required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"]
"required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"],
"ui": {
"type_hints": {
"control_weight": "float",
# "control_weight": "number",
}
}
}
@ -112,7 +131,7 @@ class ControlOutput(BaseInvocationOutput):
"""node output for ControlNet info"""
# fmt: off
type: Literal["control_output"] = "control_output"
control: ControlField = Field(default=None, description="The output control image")
control: ControlField = Field(default=None, description="The control info")
# fmt: on
@ -123,15 +142,28 @@ class ControlNetInvocation(BaseInvocation):
# Inputs
image: ImageField = Field(default=None, description="The control image")
control_model: CONTROLNET_NAME_VALUES = Field(default="lllyasviel/sd-controlnet-canny",
description="The ControlNet model to use")
control_weight: float = Field(default=1.0, ge=0, le=1, description="The weight given to the ControlNet")
description="control model used")
control_weight: Union[float, List[float]] = Field(default=1.0, description="The weight given to the ControlNet")
# TODO: add support in backend core for begin_step_percent, end_step_percent, guess_mode
begin_step_percent: float = Field(default=0, ge=0, le=1,
description="When the ControlNet is first applied (% of total steps)")
description="When the ControlNet is first applied (% of total steps)")
end_step_percent: float = Field(default=1, ge=0, le=1,
description="When the ControlNet is last applied (% of total steps)")
description="When the ControlNet is last applied (% of total steps)")
# fmt: on
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["latents"],
"type_hints": {
"model": "model",
"control": "control",
# "cfg_scale": "float",
"cfg_scale": "number",
"control_weight": "float",
}
},
}
def invoke(self, context: InvocationContext) -> ControlOutput:
@ -161,7 +193,6 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
return image
def invoke(self, context: InvocationContext) -> ImageOutput:
raw_image = context.services.images.get_pil_image(
self.image.image_origin, self.image.image_name
)

37
invokeai/app/invocations/latent.py
View File

@ -174,22 +174,36 @@ class TextToLatentsInvocation(BaseInvocation):
negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
noise: Optional[LatentsField] = Field(description="The noise to use")
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
model: str = Field(default="", description="The model to use (currently ignored)")
control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
control: Union[ControlField, List[ControlField]] = Field(default=None, description="The control to use")
# seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on
@validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
for i in v:
if i < 1:
raise ValueError('cfg_scale must be greater than 1')
else:
if v < 1:
raise ValueError('cfg_scale must be greater than 1')
return v
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["latents", "image"],
"tags": ["latents"],
"type_hints": {
"model": "model",
"control": "control",
# "cfg_scale": "float",
"cfg_scale": "number"
}
},
}
@ -244,10 +258,10 @@ class TextToLatentsInvocation(BaseInvocation):
[c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
conditioning_data = ConditioningData(
uc,
c,
self.cfg_scale,
extra_conditioning_info,
unconditioned_embeddings=uc,
text_embeddings=c,
guidance_scale=self.cfg_scale,
extra=extra_conditioning_info,
postprocessing_settings=PostprocessingSettings(
threshold=0.0,#threshold,
warmup=0.2,#warmup,
@ -348,7 +362,8 @@ class TextToLatentsInvocation(BaseInvocation):
control_data = self.prep_control_data(model=model, context=context, control_input=self.control,
latents_shape=noise.shape,
do_classifier_free_guidance=(self.cfg_scale >= 1.0))
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,)
# TODO: Verify the noise is the right size
result_latents, result_attention_map_saver = model.latents_from_embeddings(
@ -385,6 +400,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
"type_hints": {
"model": "model",
"control": "control",
"cfg_scale": "number",
}
},
}
@ -403,10 +419,11 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
model = self.get_model(context.services.model_manager)
conditioning_data = self.get_conditioning_data(context, model)
print("type of control input: ", type(self.control))
control_data = self.prep_control_data(model=model, context=context, control_input=self.control,
latents_shape=noise.shape,
do_classifier_free_guidance=(self.cfg_scale >= 1.0))
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
)
# TODO: Verify the noise is the right size

237
invokeai/app/invocations/param_easing.py Normal file
View File

@ -0,0 +1,237 @@
import io
from typing import Literal, Optional, Any
# from PIL.Image import Image
import PIL.Image
from matplotlib.ticker import MaxNLocator
from matplotlib.figure import Figure
from pydantic import BaseModel, Field
import numpy as np
import matplotlib.pyplot as plt
from easing_functions import (
LinearInOut,
QuadEaseInOut, QuadEaseIn, QuadEaseOut,
CubicEaseInOut, CubicEaseIn, CubicEaseOut,
QuarticEaseInOut, QuarticEaseIn, QuarticEaseOut,
QuinticEaseInOut, QuinticEaseIn, QuinticEaseOut,
SineEaseInOut, SineEaseIn, SineEaseOut,
CircularEaseIn, CircularEaseInOut, CircularEaseOut,
ExponentialEaseInOut, ExponentialEaseIn, ExponentialEaseOut,
ElasticEaseIn, ElasticEaseInOut, ElasticEaseOut,
BackEaseIn, BackEaseInOut, BackEaseOut,
BounceEaseIn, BounceEaseInOut, BounceEaseOut)
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InvocationContext,
InvocationConfig,
)
from ...backend.util.logging import InvokeAILogger
from .collections import FloatCollectionOutput
class FloatLinearRangeInvocation(BaseInvocation):
"""Creates a range"""
type: Literal["float_range"] = "float_range"
# Inputs
start: float = Field(default=5, description="The first value of the range")
stop: float = Field(default=10, description="The last value of the range")
steps: int = Field(default=30, description="number of values to interpolate over (including start and stop)")
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
param_list = list(np.linspace(self.start, self.stop, self.steps))
return FloatCollectionOutput(
collection=param_list
)
EASING_FUNCTIONS_MAP = {
"Linear": LinearInOut,
"QuadIn": QuadEaseIn,
"QuadOut": QuadEaseOut,
"QuadInOut": QuadEaseInOut,
"CubicIn": CubicEaseIn,
"CubicOut": CubicEaseOut,
"CubicInOut": CubicEaseInOut,
"QuarticIn": QuarticEaseIn,
"QuarticOut": QuarticEaseOut,
"QuarticInOut": QuarticEaseInOut,
"QuinticIn": QuinticEaseIn,
"QuinticOut": QuinticEaseOut,
"QuinticInOut": QuinticEaseInOut,
"SineIn": SineEaseIn,
"SineOut": SineEaseOut,
"SineInOut": SineEaseInOut,
"CircularIn": CircularEaseIn,
"CircularOut": CircularEaseOut,
"CircularInOut": CircularEaseInOut,
"ExponentialIn": ExponentialEaseIn,
"ExponentialOut": ExponentialEaseOut,
"ExponentialInOut": ExponentialEaseInOut,
"ElasticIn": ElasticEaseIn,
"ElasticOut": ElasticEaseOut,
"ElasticInOut": ElasticEaseInOut,
"BackIn": BackEaseIn,
"BackOut": BackEaseOut,
"BackInOut": BackEaseInOut,
"BounceIn": BounceEaseIn,
"BounceOut": BounceEaseOut,
"BounceInOut": BounceEaseInOut,
}
EASING_FUNCTION_KEYS: Any = Literal[
tuple(list(EASING_FUNCTIONS_MAP.keys()))
]
# actually I think for now could just use CollectionOutput (which is list[Any]
class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps"""
type: Literal["step_param_easing"] = "step_param_easing"
# Inputs
# fmt: off
easing: EASING_FUNCTION_KEYS = Field(default="Linear", description="The easing function to use")
num_steps: int = Field(default=20, description="number of denoising steps")
start_value: float = Field(default=0.0, description="easing starting value")
end_value: float = Field(default=1.0, description="easing ending value")
start_step_percent: float = Field(default=0.0, description="fraction of steps at which to start easing")
end_step_percent: float = Field(default=1.0, description="fraction of steps after which to end easing")
# if None, then start_value is used prior to easing start
pre_start_value: Optional[float] = Field(default=None, description="value before easing start")
# if None, then end value is used prior to easing end
post_end_value: Optional[float] = Field(default=None, description="value after easing end")
mirror: bool = Field(default=False, description="include mirror of easing function")
# FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
# alt_mirror: bool = Field(default=False, description="alternative mirroring by dual easing")
show_easing_plot: bool = Field(default=False, description="show easing plot")
# fmt: on
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
log_diagnostics = False
# convert from start_step_percent to nearest step <= (steps * start_step_percent)
# start_step = int(np.floor(self.num_steps * self.start_step_percent))
start_step = int(np.round(self.num_steps * self.start_step_percent))
# convert from end_step_percent to nearest step >= (steps * end_step_percent)
# end_step = int(np.ceil((self.num_steps - 1) * self.end_step_percent))
end_step = int(np.round((self.num_steps - 1) * self.end_step_percent))
# end_step = int(np.ceil(self.num_steps * self.end_step_percent))
num_easing_steps = end_step - start_step + 1
# num_presteps = max(start_step - 1, 0)
num_presteps = start_step
num_poststeps = self.num_steps - (num_presteps + num_easing_steps)
prelist = list(num_presteps * [self.pre_start_value])
postlist = list(num_poststeps * [self.post_end_value])
if log_diagnostics:
logger = InvokeAILogger.getLogger(name="StepParamEasing")
logger.debug("start_step: " + str(start_step))
logger.debug("end_step: " + str(end_step))
logger.debug("num_easing_steps: " + str(num_easing_steps))
logger.debug("num_presteps: " + str(num_presteps))
logger.debug("num_poststeps: " + str(num_poststeps))
logger.debug("prelist size: " + str(len(prelist)))
logger.debug("postlist size: " + str(len(postlist)))
logger.debug("prelist: " + str(prelist))
logger.debug("postlist: " + str(postlist))
easing_class = EASING_FUNCTIONS_MAP[self.easing]
if log_diagnostics:
logger.debug("easing class: " + str(easing_class))
easing_list = list()
if self.mirror: # "expected" mirroring
# if number of steps is even, squeeze duration down to (number_of_steps)/2
# and create reverse copy of list to append
# if number of steps is odd, squeeze duration down to ceil(number_of_steps/2)
# and create reverse copy of list[1:end-1]
# but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
base_easing_duration = int(np.ceil(num_easing_steps/2.0))
if log_diagnostics: logger.debug("base easing duration: " + str(base_easing_duration))
even_num_steps = (num_easing_steps % 2 == 0) # even number of steps
easing_function = easing_class(start=self.start_value,
end=self.end_value,
duration=base_easing_duration - 1)
base_easing_vals = list()
for step_index in range(base_easing_duration):
easing_val = easing_function.ease(step_index)
base_easing_vals.append(easing_val)
if log_diagnostics:
logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
if even_num_steps:
mirror_easing_vals = list(reversed(base_easing_vals))
else:
mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
if log_diagnostics:
logger.debug("base easing vals: " + str(base_easing_vals))
logger.debug("mirror easing vals: " + str(mirror_easing_vals))
easing_list = base_easing_vals + mirror_easing_vals
# FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
# elif self.alt_mirror: # function mirroring (unintuitive behavior (at least to me))
# # half_ease_duration = round(num_easing_steps - 1 / 2)
# half_ease_duration = round((num_easing_steps - 1) / 2)
# easing_function = easing_class(start=self.start_value,
# end=self.end_value,
# duration=half_ease_duration,
# )
#
# mirror_function = easing_class(start=self.end_value,
# end=self.start_value,
# duration=half_ease_duration,
# )
# for step_index in range(num_easing_steps):
# if step_index <= half_ease_duration:
# step_val = easing_function.ease(step_index)
# else:
# step_val = mirror_function.ease(step_index - half_ease_duration)
# easing_list.append(step_val)
# if log_diagnostics: logger.debug(step_index, step_val)
#
else: # no mirroring (default)
easing_function = easing_class(start=self.start_value,
end=self.end_value,
duration=num_easing_steps - 1)
for step_index in range(num_easing_steps):
step_val = easing_function.ease(step_index)
easing_list.append(step_val)
if log_diagnostics:
logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
if log_diagnostics:
logger.debug("prelist size: " + str(len(prelist)))
logger.debug("easing_list size: " + str(len(easing_list)))
logger.debug("postlist size: " + str(len(postlist)))
param_list = prelist + easing_list + postlist
if self.show_easing_plot:
plt.figure()
plt.xlabel("Step")
plt.ylabel("Param Value")
plt.title("Per-Step Values Based On Easing: " + self.easing)
plt.bar(range(len(param_list)), param_list)
# plt.plot(param_list)
ax = plt.gca()
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
im = PIL.Image.open(buf)
im.show()
buf.close()
# output array of size steps, each entry list[i] is param value for step i
return FloatCollectionOutput(
collection=param_list
)

6
invokeai/app/models/metadata.py
View File

@ -1,4 +1,4 @@
from typing import Optional
from typing import Optional, Union, List
from pydantic import BaseModel, Extra, Field, StrictFloat, StrictInt, StrictStr
@ -47,7 +47,9 @@ class ImageMetadata(BaseModel):
default=None, description="The seed used for noise generation."
)
"""The seed used for noise generation"""
cfg_scale: Optional[StrictFloat] = Field(
# cfg_scale: Optional[StrictFloat] = Field(
# cfg_scale: Union[float, list[float]] = Field(
cfg_scale: Union[StrictFloat, List[StrictFloat]] = Field(
default=None, description="The classifier-free guidance scale."
)
"""The classifier-free guidance scale"""

61
invokeai/app/services/graph.py
View File

@ -65,7 +65,6 @@ from typing import Optional, Union, List, get_args
def is_union_subtype(t1, t2):
t1_args = get_args(t1)
t2_args = get_args(t2)
if not t1_args:
# t1 is a single type
return t1 in t2_args
@ -86,7 +85,6 @@ def is_list_or_contains_list(t):
for arg in t_args:
if get_origin(arg) is list:
return True
return False
@ -393,7 +391,7 @@ class Graph(BaseModel):
from_node = self.get_node(edge.source.node_id)
to_node = self.get_node(edge.destination.node_id)
except NodeNotFoundError:
raise InvalidEdgeError("One or both nodes don't exist")
raise InvalidEdgeError("One or both nodes don't exist: {edge.source.node_id} -> {edge.destination.node_id}")
# Validate that an edge to this node+field doesn't already exist
input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
@ -404,41 +402,41 @@ class Graph(BaseModel):
g = self.nx_graph_flat()
g.add_edge(edge.source.node_id, edge.destination.node_id)
if not nx.is_directed_acyclic_graph(g):
raise InvalidEdgeError(f'Edge creates a cycle in the graph')
raise InvalidEdgeError(f'Edge creates a cycle in the graph: {edge.source.node_id} -> {edge.destination.node_id}')
# Validate that the field types are compatible
if not are_connections_compatible(
from_node, edge.source.field, to_node, edge.destination.field
):
raise InvalidEdgeError(f'Fields are incompatible')
raise InvalidEdgeError(f'Fields are incompatible: cannot connect {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
# Validate if iterator output type matches iterator input type (if this edge results in both being set)
if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
if not self._is_iterator_connection_valid(
edge.destination.node_id, new_input=edge.source
):
raise InvalidEdgeError(f'Iterator input type does not match iterator output type')
raise InvalidEdgeError(f'Iterator input type does not match iterator output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
# Validate if iterator input type matches output type (if this edge results in both being set)
if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
if not self._is_iterator_connection_valid(
edge.source.node_id, new_output=edge.destination
):
raise InvalidEdgeError(f'Iterator output type does not match iterator input type')
raise InvalidEdgeError(f'Iterator output type does not match iterator input type:, {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
# Validate if collector input type matches output type (if this edge results in both being set)
if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
if not self._is_collector_connection_valid(
edge.destination.node_id, new_input=edge.source
):
raise InvalidEdgeError(f'Collector output type does not match collector input type')
raise InvalidEdgeError(f'Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
# Validate if collector output type matches input type (if this edge results in both being set)
if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
if not self._is_collector_connection_valid(
edge.source.node_id, new_output=edge.destination
):
raise InvalidEdgeError(f'Collector input type does not match collector output type')
raise InvalidEdgeError(f'Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
def has_node(self, node_path: str) -> bool:
@ -859,11 +857,9 @@ class GraphExecutionState(BaseModel):
if next_node is None:
prepared_id = self._prepare()
# TODO: prepare multiple nodes at once?
# while prepared_id is not None and not isinstance(self.graph.nodes[prepared_id], IterateInvocation):
# prepared_id = self._prepare()
if prepared_id is not None:
# Prepare as many nodes as we can
while prepared_id is not None:
prepared_id = self._prepare()
next_node = self._get_next_node()
# Get values from edges
@ -1010,14 +1006,30 @@ class GraphExecutionState(BaseModel):
# Get flattened source graph
g = self.graph.nx_graph_flat()
# Find next unprepared node where all source nodes are executed
# Find next node that:
# - was not already prepared
# - is not an iterate node whose inputs have not been executed
# - does not have an unexecuted iterate ancestor
sorted_nodes = nx.topological_sort(g)
next_node_id = next(
(
n
for n in sorted_nodes
# exclude nodes that have already been prepared
if n not in self.source_prepared_mapping
and all((e[0] in self.executed for e in g.in_edges(n)))
# exclude iterate nodes whose inputs have not been executed
and not (
isinstance(self.graph.get_node(n), IterateInvocation) # `n` is an iterate node...
and not all((e[0] in self.executed for e in g.in_edges(n))) # ...that has unexecuted inputs
)
# exclude nodes who have unexecuted iterate ancestors
and not any(
(
isinstance(self.graph.get_node(a), IterateInvocation) # `a` is an iterate ancestor of `n`...
and a not in self.executed # ...that is not executed
for a in nx.ancestors(g, n) # for all ancestors `a` of node `n`
)
)
),
None,
)
@ -1114,9 +1126,22 @@ class GraphExecutionState(BaseModel):
)
def _get_next_node(self) -> Optional[BaseInvocation]:
"""Gets the deepest node that is ready to be executed"""
g = self.execution_graph.nx_graph()
sorted_nodes = nx.topological_sort(g)
next_node = next((n for n in sorted_nodes if n not in self.executed), None)
# Depth-first search with pre-order traversal is a depth-first topological sort
sorted_nodes = nx.dfs_preorder_nodes(g)
next_node = next(
(
n
for n in sorted_nodes
if n not in self.executed # the node must not already be executed...
and all((e[0] in self.executed for e in g.in_edges(n))) # ...and all its inputs must be executed
),
None,
)
if next_node is None:
return None

3
invokeai/app/services/invoker.py
View File

@ -22,7 +22,8 @@ class Invoker:
def invoke(
self, graph_execution_state: GraphExecutionState, invoke_all: bool = False
) -> str | None:
"""Determines the next node to invoke and returns the id of the invoked node, or None if there are no nodes to execute"""
"""Determines the next node to invoke and enqueues it, preparing if needed.
Returns the id of the queued node, or `None` if there are no nodes left to enqueue."""
# Get the next invocation
invocation = graph_execution_state.next()

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

@ -40,6 +40,7 @@ import invokeai.configs as configs
from invokeai.app.services.config import (
InvokeAIAppConfig,
)
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
from invokeai.frontend.install.widgets import (
CenteredButtonPress,
@ -80,6 +81,7 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file
# or renaming it and then running invokeai-configure again.
"""
logger=None
# --------------------------------------------
def postscript(errors: None):
@ -824,6 +826,7 @@ def main():
if opt.full_precision:
invoke_args.extend(['--precision','float32'])
config.parse_args(invoke_args)
logger = InvokeAILogger().getLogger(config=config)
errors = set()

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

@ -784,7 +784,7 @@ class ModelManager(object):
self.logger.info(f"Probing {thing} for import")
if thing.startswith(("http:", "https:", "ftp:")):
if str(thing).startswith(("http:", "https:", "ftp:")):
self.logger.info(f"{thing} appears to be a URL")
model_path = self._resolve_path(
thing, "models/ldm/stable-diffusion-v1"

16
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -218,7 +218,7 @@ class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
class ControlNetData:
model: ControlNetModel = Field(default=None)
image_tensor: torch.Tensor= Field(default=None)
weight: float = Field(default=1.0)
weight: Union[float, List[float]]= Field(default=1.0)
begin_step_percent: float = Field(default=0.0)
end_step_percent: float = Field(default=1.0)
@ -226,7 +226,7 @@ class ControlNetData:
class ConditioningData:
unconditioned_embeddings: torch.Tensor
text_embeddings: torch.Tensor
guidance_scale: float
guidance_scale: Union[float, List[float]]
"""
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
@ -662,7 +662,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
down_block_res_samples, mid_block_res_sample = None, None
if control_data is not None:
if conditioning_data.guidance_scale > 1.0:
# FIXME: make sure guidance_scale < 1.0 is handled correctly if doing per-step guidance setting
# if conditioning_data.guidance_scale > 1.0:
if conditioning_data.guidance_scale is not None:
# expand the latents input to control model if doing classifier free guidance
# (which I think for now is always true, there is conditional elsewhere that stops execution if
# classifier_free_guidance is <= 1.0 ?)
@ -679,13 +681,19 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# only apply controlnet if current step is within the controlnet's begin/end step range
if step_index >= first_control_step and step_index <= last_control_step:
# print("running controlnet", i, "for step", step_index)
if isinstance(control_datum.weight, list):
# if controlnet has multiple weights, use the weight for the current step
controlnet_weight = control_datum.weight[step_index]
else:
# if controlnet has a single weight, use it for all steps
controlnet_weight = control_datum.weight
down_samples, mid_sample = control_datum.model(
sample=latent_control_input,
timestep=timestep,
encoder_hidden_states=torch.cat([conditioning_data.unconditioned_embeddings,
conditioning_data.text_embeddings]),
controlnet_cond=control_datum.image_tensor,
conditioning_scale=control_datum.weight,
conditioning_scale=controlnet_weight,
# cross_attention_kwargs,
guess_mode=False,
return_dict=False,

13
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -1,7 +1,7 @@
from contextlib import contextmanager
from dataclasses import dataclass
from math import ceil
from typing import Any, Callable, Dict, Optional, Union
from typing import Any, Callable, Dict, Optional, Union, List
import numpy as np
import torch
@ -180,7 +180,8 @@ class InvokeAIDiffuserComponent:
sigma: torch.Tensor,
unconditioning: Union[torch.Tensor, dict],
conditioning: Union[torch.Tensor, dict],
unconditional_guidance_scale: float,
# unconditional_guidance_scale: float,
unconditional_guidance_scale: Union[float, List[float]],
step_index: Optional[int] = None,
total_step_count: Optional[int] = None,
**kwargs,
@ -195,6 +196,11 @@ class InvokeAIDiffuserComponent:
:return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
"""
if isinstance(unconditional_guidance_scale, list):
guidance_scale = unconditional_guidance_scale[step_index]
else:
guidance_scale = unconditional_guidance_scale
cross_attention_control_types_to_do = []
context: Context = self.cross_attention_control_context
if self.cross_attention_control_context is not None:
@ -243,7 +249,8 @@ class InvokeAIDiffuserComponent:
)
combined_next_x = self._combine(
unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
# unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
unconditioned_next_x, conditioned_next_x, guidance_scale
)
return combined_next_x

174
invokeai/backend/util/logging.py
View File

@ -1,6 +1,7 @@
# Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
"""invokeai.util.logging
"""
invokeai.util.logging
Logging class for InvokeAI that produces console messages
@ -11,6 +12,7 @@ from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.getLogger(name='InvokeAI') // Initialization
(or)
logger = InvokeAILogger.getLogger(__name__) // To use the filename
logger.configure()
logger.critical('this is critical') // Critical Message
logger.error('this is an error') // Error Message
@ -28,6 +30,149 @@ Console messages:
Alternate Method (in this case the logger name will be set to InvokeAI):
import invokeai.backend.util.logging as IAILogger
IAILogger.debug('this is a debugging message')
## Configuration
The default configuration will print to stderr on the console. To add
additional logging handlers, call getLogger with an initialized InvokeAIAppConfig
object:
config = InvokeAIAppConfig.get_config()
config.parse_args()
logger = InvokeAILogger.getLogger(config=config)
### Three command-line options control logging:
`--log_handlers <handler1> <handler2> ...`
This option activates one or more log handlers. Options are "console", "file", "syslog" and "http". To specify more than one, separate them by spaces:
```
invokeai-web --log_handlers console syslog=/dev/log file=C:\\Users\\fred\\invokeai.log
```
The format of these options is described below.
### `--log_format {plain|color|legacy|syslog}`
This controls the format of log messages written to the console. Only the "console" log handler is currently affected by this setting.
* "plain" provides formatted messages like this:
```bash
[2023-05-24 23:18:2[2023-05-24 23:18:50,352]::[InvokeAI]::DEBUG --> this is a debug message
[2023-05-24 23:18:50,352]::[InvokeAI]::INFO --> this is an informational messages
[2023-05-24 23:18:50,352]::[InvokeAI]::WARNING --> this is a warning
[2023-05-24 23:18:50,352]::[InvokeAI]::ERROR --> this is an error
[2023-05-24 23:18:50,352]::[InvokeAI]::CRITICAL --> this is a critical error
```
* "color" produces similar output, but the text will be color coded to indicate the severity of the message.
* "legacy" produces output similar to InvokeAI versions 2.3 and earlier:
```
### this is a critical error
*** this is an error
** this is a warning
>> this is an informational messages
| this is a debug message
```
* "syslog" produces messages suitable for syslog entries:
```bash
InvokeAI [2691178] <CRITICAL> this is a critical error
InvokeAI [2691178] <ERROR> this is an error
InvokeAI [2691178] <WARNING> this is a warning
InvokeAI [2691178] <INFO> this is an informational messages
InvokeAI [2691178] <DEBUG> this is a debug message
```
(note that the date, time and hostname will be added by the syslog system)
### `--log_level {debug|info|warning|error|critical}`
Providing this command-line option will cause only messages at the specified level or above to be emitted.
## Console logging
When "console" is provided to `--log_handlers`, messages will be written to the command line window in which InvokeAI was launched. By default, the color formatter will be used unless overridden by `--log_format`.
## File logging
When "file" is provided to `--log_handlers`, entries will be written to the file indicated in the path argument. By default, the "plain" format will be used:
```bash
invokeai-web --log_handlers file=/var/log/invokeai.log
```
## Syslog logging
When "syslog" is requested, entries will be sent to the syslog system. There are a variety of ways to control where the log message is sent:
* Send to the local machine using the `/dev/log` socket:
```
invokeai-web --log_handlers syslog=/dev/log
```
* Send to the local machine using a UDP message:
```
invokeai-web --log_handlers syslog=localhost
```
* Send to the local machine using a UDP message on a nonstandard port:
```
invokeai-web --log_handlers syslog=localhost:512
```
* Send to a remote machine named "loghost" on the local LAN using facility LOG_USER and UDP packets:
```
invokeai-web --log_handlers syslog=loghost,facility=LOG_USER,socktype=SOCK_DGRAM
```
This can be abbreviated `syslog=loghost`, as LOG_USER and SOCK_DGRAM are defaults.
* Send to a remote machine named "loghost" using the facility LOCAL0 and using a TCP socket:
```
invokeai-web --log_handlers syslog=loghost,facility=LOG_LOCAL0,socktype=SOCK_STREAM
```
If no arguments are specified (just a bare "syslog"), then the logging system will look for a UNIX socket named `/dev/log`, and if not found try to send a UDP message to `localhost`. The Macintosh OS used to support logging to a socket named `/var/run/syslog`, but this feature has since been disabled.
## Web logging
If you have access to a web server that is configured to log messages when a particular URL is requested, you can log using the "http" method:
```
invokeai-web --log_handlers http=http://my.server/path/to/logger,method=POST
```
The optional [,method=] part can be used to specify whether the URL accepts GET (default) or POST messages.
Currently password authentication and SSL are not supported.
## Using the configuration file
You can set and forget logging options by adding a "Logging" section to `invokeai.yaml`:
```
InvokeAI:
[... other settings...]
Logging:
log_handlers:
- console
- syslog=/dev/log
log_level: info
log_format: color
```
"""
import logging.handlers
@ -180,14 +325,17 @@ class InvokeAILogger(object):
loggers = dict()
@classmethod
def getLogger(cls, name: str = 'InvokeAI') -> logging.Logger:
config = get_invokeai_config()
if name not in cls.loggers:
def getLogger(cls,
name: str = 'InvokeAI',
config: InvokeAIAppConfig=InvokeAIAppConfig.get_config())->logging.Logger:
if name in cls.loggers:
logger = cls.loggers[name]
logger.handlers.clear()
else:
logger = logging.getLogger(name)
logger.setLevel(config.log_level.upper()) # yes, strings work here
for ch in cls.getLoggers(config):
logger.addHandler(ch)
logger.setLevel(config.log_level.upper()) # yes, strings work here
for ch in cls.getLoggers(config):
logger.addHandler(ch)
cls.loggers[name] = logger
return cls.loggers[name]
@ -199,9 +347,11 @@ class InvokeAILogger(object):
handler_name,*args = handler.split('=',2)
args = args[0] if len(args) > 0 else None
# console is the only handler that gets a custom formatter
# console and file get the fancy formatter.
# syslog gets a simple one
# http gets no custom formatter
formatter = LOG_FORMATTERS[config.log_format]
if handler_name=='console':
formatter = LOG_FORMATTERS[config.log_format]
ch = logging.StreamHandler()
ch.setFormatter(formatter())
handlers.append(ch)
@ -212,7 +362,9 @@ class InvokeAILogger(object):
handlers.append(ch)
elif handler_name=='file':
handlers.append(cls._parse_file_args(args))
ch = cls._parse_file_args(args)
ch.setFormatter(formatter())
handlers.append(ch)
elif handler_name=='http':
handlers.append(cls._parse_http_args(args))

3
invokeai/frontend/install/model_install.py
View File

@ -28,7 +28,7 @@ import torch
from npyscreen import widget
from omegaconf import OmegaConf
import invokeai.backend.util.logging as logger
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.backend.install.model_install_backend import (
Dataset_path,
@ -939,6 +939,7 @@ def main():
if opt.full_precision:
invoke_args.extend(['--precision','float32'])
config.parse_args(invoke_args)
logger = InvokeAILogger().getLogger(config=config)
if not (config.root_dir / config.conf_path.parent).exists():
logger.info(

10
invokeai/frontend/web/src/app/components/App.tsx
View File

@ -22,6 +22,7 @@ import { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
import GlobalHotkeys from './GlobalHotkeys';
import Toaster from './Toaster';
import DeleteImageModal from 'features/gallery/components/DeleteImageModal';
import { requestCanvasRescale } from 'features/canvas/store/thunks/requestCanvasScale';
const DEFAULT_CONFIG = {};
@ -66,10 +67,17 @@ const App = ({
setIsReady(true);
}
if (isApplicationReady) {
// TODO: This is a jank fix for canvas not filling the screen on first load
setTimeout(() => {
dispatch(requestCanvasRescale());
}, 200);
}
return () => {
setIsReady && setIsReady(false);
};
}, [isApplicationReady, setIsReady]);
}, [dispatch, isApplicationReady, setIsReady]);
return (
<>

4
invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx
View File

@ -40,11 +40,11 @@ const ImageDndContext = (props: ImageDndContextProps) => {
);
const mouseSensor = useSensor(MouseSensor, {
activationConstraint: { delay: 250, tolerance: 5 },
activationConstraint: { delay: 150, tolerance: 5 },
});
const touchSensor = useSensor(TouchSensor, {
activationConstraint: { delay: 250, tolerance: 5 },
activationConstraint: { delay: 150, tolerance: 5 },
});
// TODO: Use KeyboardSensor - needs composition of multiple collisionDetection algos
// Alternatively, fix `rectIntersection` collection detection to work with the drag overlay

6
invokeai/frontend/web/src/app/types/invokeai.ts
View File

@ -1,3 +1,7 @@
import {
CONTROLNET_MODELS,
CONTROLNET_PROCESSORS,
} from 'features/controlNet/store/constants';
import { InvokeTabName } from 'features/ui/store/tabMap';
import { O } from 'ts-toolbelt';
@ -117,6 +121,8 @@ export type AppConfig = {
canRestoreDeletedImagesFromBin: boolean;
sd: {
defaultModel?: string;
disabledControlNetModels: (keyof typeof CONTROLNET_MODELS)[];
disabledControlNetProcessors: (keyof typeof CONTROLNET_PROCESSORS)[];
iterations: {
initial: number;
min: number;

90
invokeai/frontend/web/src/common/components/IAICustomSelect.tsx
View File

@ -2,7 +2,6 @@ import { CheckIcon, ChevronUpIcon } from '@chakra-ui/icons';
import {
Box,
Flex,
FlexProps,
FormControl,
FormControlProps,
FormLabel,
@ -16,42 +15,64 @@ import {
} from '@chakra-ui/react';
import { autoUpdate, offset, shift, useFloating } from '@floating-ui/react-dom';
import { useSelect } from 'downshift';
import { isString } from 'lodash-es';
import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
import { memo, useMemo } from 'react';
import { memo, useLayoutEffect, useMemo } from 'react';
import { getInputOutlineStyles } from 'theme/util/getInputOutlineStyles';
export type ItemTooltips = { [key: string]: string };
export type IAICustomSelectOption = {
value: string;
label: string;
tooltip?: string;
};
type IAICustomSelectProps = {
label?: string;
items: string[];
itemTooltips?: ItemTooltips;
selectedItem: string;
setSelectedItem: (v: string | null | undefined) => void;
value: string;
data: IAICustomSelectOption[] | string[];
onChange: (v: string) => void;
withCheckIcon?: boolean;
formControlProps?: FormControlProps;
buttonProps?: FlexProps;
tooltip?: string;
tooltipProps?: Omit<TooltipProps, 'children'>;
ellipsisPosition?: 'start' | 'end';
isDisabled?: boolean;
};
const IAICustomSelect = (props: IAICustomSelectProps) => {
const {
label,
items,
itemTooltips,
setSelectedItem,
selectedItem,
withCheckIcon,
formControlProps,
tooltip,
buttonProps,
tooltipProps,
ellipsisPosition = 'end',
data,
value,
onChange,
isDisabled = false,
} = props;
const values = useMemo(() => {
return data.map<IAICustomSelectOption>((v) => {
if (isString(v)) {
return { value: v, label: v };
}
return v;
});
}, [data]);
const stringValues = useMemo(() => {
return values.map((v) => v.value);
}, [values]);
const valueData = useMemo(() => {
return values.find((v) => v.value === value);
}, [values, value]);
const {
isOpen,
getToggleButtonProps,
@ -60,17 +81,24 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
highlightedIndex,
getItemProps,
} = useSelect({
items,
selectedItem,
onSelectedItemChange: ({ selectedItem: newSelectedItem }) =>
setSelectedItem(newSelectedItem),
items: stringValues,
selectedItem: value,
onSelectedItemChange: ({ selectedItem: newSelectedItem }) => {
newSelectedItem && onChange(newSelectedItem);
},
});
const { refs, floatingStyles } = useFloating<HTMLButtonElement>({
whileElementsMounted: autoUpdate,
const { refs, floatingStyles, update } = useFloating<HTMLButtonElement>({
// whileElementsMounted: autoUpdate,
middleware: [offset(4), shift({ crossAxis: true, padding: 8 })],
});
useLayoutEffect(() => {
if (isOpen && refs.reference.current && refs.floating.current) {
return autoUpdate(refs.reference.current, refs.floating.current, update);
}
}, [isOpen, update, refs.floating, refs.reference]);
const labelTextDirection = useMemo(() => {
if (ellipsisPosition === 'start') {
return document.dir === 'rtl' ? 'ltr' : 'rtl';
@ -93,8 +121,7 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
)}
<Tooltip label={tooltip} {...tooltipProps}>
<Flex
{...getToggleButtonProps({ ref: refs.setReference })}
{...buttonProps}
{...getToggleButtonProps({ ref: refs.reference })}
sx={{
alignItems: 'center',
userSelect: 'none',
@ -105,6 +132,8 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
px: 2,
gap: 2,
justifyContent: 'space-between',
pointerEvents: isDisabled ? 'none' : undefined,
opacity: isDisabled ? 0.5 : undefined,
...getInputOutlineStyles(),
}}
>
@ -119,7 +148,7 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
direction: labelTextDirection,
}}
>
{selectedItem}
{valueData?.label}
</Text>
<ChevronUpIcon
sx={{
@ -135,7 +164,7 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
{isOpen && (
<List
as={Flex}
ref={refs.setFloating}
ref={refs.floating}
sx={{
...floatingStyles,
top: 0,
@ -155,8 +184,8 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
}}
>
<OverlayScrollbarsComponent>
{items.map((item, index) => {
const isSelected = selectedItem === item;
{values.map((v, index) => {
const isSelected = value === v.value;
const isHighlighted = highlightedIndex === index;
const fontWeight = isSelected ? 700 : 500;
const bg = isHighlighted
@ -166,9 +195,9 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
: undefined;
return (
<Tooltip
isDisabled={!itemTooltips}
key={`${item}${index}`}
label={itemTooltips?.[item]}
isDisabled={!v.tooltip}
key={`${v.value}${index}`}
label={v.tooltip}
hasArrow
placement="right"
>
@ -182,8 +211,7 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
transitionProperty: 'common',
transitionDuration: '0.15s',
}}
key={`${item}${index}`}
{...getItemProps({ item, index })}
{...getItemProps({ item: v.value, index })}
>
{withCheckIcon ? (
<Grid gridTemplateColumns="1.25rem auto">
@ -198,7 +226,7 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
fontWeight,
}}
>
{item}
{v.label}
</Text>
</GridItem>
</Grid>
@ -210,7 +238,7 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
fontWeight,
}}
>
{item}
{v.label}
</Text>
)}
</ListItem>

64
invokeai/frontend/web/src/common/components/IAISlider.tsx
View File

@ -1,4 +1,5 @@
import {
ChakraProps,
FormControl,
FormControlProps,
FormLabel,
@ -39,6 +40,11 @@ import { BiReset } from 'react-icons/bi';
import IAIIconButton, { IAIIconButtonProps } from './IAIIconButton';
import { roundDownToMultiple } from 'common/util/roundDownToMultiple';
const SLIDER_MARK_STYLES: ChakraProps['sx'] = {
mt: 1.5,
fontSize: '2xs',
};
export type IAIFullSliderProps = {
label?: string;
value: number;
@ -57,6 +63,7 @@ export type IAIFullSliderProps = {
hideTooltip?: boolean;
isCompact?: boolean;
isDisabled?: boolean;
sliderMarks?: number[];
sliderFormControlProps?: FormControlProps;
sliderFormLabelProps?: FormLabelProps;
sliderMarkProps?: Omit<SliderMarkProps, 'value'>;
@ -88,6 +95,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
hideTooltip = false,
isCompact = false,
isDisabled = false,
sliderMarks,
handleReset,
sliderFormControlProps,
sliderFormLabelProps,
@ -198,14 +206,14 @@ const IAISlider = (props: IAIFullSliderProps) => {
isDisabled={isDisabled}
{...rest}
>
{withSliderMarks && (
{withSliderMarks && !sliderMarks && (
<>
<SliderMark
value={min}
sx={{
insetInlineStart: '0 !important',
insetInlineEnd: 'unset !important',
mt: 1.5,
...SLIDER_MARK_STYLES,
}}
{...sliderMarkProps}
>
@ -216,7 +224,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
sx={{
insetInlineStart: 'unset !important',
insetInlineEnd: '0 !important',
mt: 1.5,
...SLIDER_MARK_STYLES,
}}
{...sliderMarkProps}
>
@ -224,6 +232,56 @@ const IAISlider = (props: IAIFullSliderProps) => {
</SliderMark>
</>
)}
{withSliderMarks && sliderMarks && (
<>
{sliderMarks.map((m, i) => {
if (i === 0) {
return (
<SliderMark
key={m}
value={m}
sx={{
insetInlineStart: '0 !important',
insetInlineEnd: 'unset !important',
...SLIDER_MARK_STYLES,
}}
{...sliderMarkProps}
>
{m}
</SliderMark>
);
} else if (i === sliderMarks.length - 1) {
return (
<SliderMark
key={m}
value={m}
sx={{
insetInlineStart: 'unset !important',
insetInlineEnd: '0 !important',
...SLIDER_MARK_STYLES,
}}
{...sliderMarkProps}
>
{m}
</SliderMark>
);
} else {
return (
<SliderMark
key={m}
value={m}
sx={{
...SLIDER_MARK_STYLES,
}}
{...sliderMarkProps}
>
{m}
</SliderMark>
);
}
})}
</>
)}
<SliderTrack {...sliderTrackProps}>
<SliderFilledTrack />

2
invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasSettingsButtonPopover.tsx
View File

@ -16,7 +16,6 @@ import {
setShouldShowIntermediates,
setShouldSnapToGrid,
} from 'features/canvas/store/canvasSlice';
import EmptyTempFolderButtonModal from 'features/system/components/ClearTempFolderButtonModal';
import { isEqual } from 'lodash-es';
import { ChangeEvent } from 'react';
@ -159,7 +158,6 @@ const IAICanvasSettingsButtonPopover = () => {
onChange={(e) => dispatch(setShouldAntialias(e.target.checked))}
/>
<ClearCanvasHistoryButtonModal />
<EmptyTempFolderButtonModal />
</Flex>
</IAIPopover>
);

10
invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts
View File

@ -30,7 +30,10 @@ import {
} from './canvasTypes';
import { ImageDTO } from 'services/api';
import { sessionCanceled } from 'services/thunks/session';
import { setShouldUseCanvasBetaLayout } from 'features/ui/store/uiSlice';
import {
setActiveTab,
setShouldUseCanvasBetaLayout,
} from 'features/ui/store/uiSlice';
import { imageUrlsReceived } from 'services/thunks/image';
export const initialLayerState: CanvasLayerState = {
@ -857,6 +860,11 @@ export const canvasSlice = createSlice({
builder.addCase(setShouldUseCanvasBetaLayout, (state, action) => {
state.doesCanvasNeedScaling = true;
});
builder.addCase(setActiveTab, (state, action) => {
state.doesCanvasNeedScaling = true;
});
builder.addCase(imageUrlsReceived.fulfilled, (state, action) => {
const { image_name, image_origin, image_url, thumbnail_url } =
action.payload;

6
invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
View File

@ -143,7 +143,7 @@ const ControlNet = (props: ControlNetProps) => {
flexDir: 'column',
gap: 2,
w: 'full',
h: 24,
h: isExpanded ? 28 : 24,
paddingInlineStart: 1,
paddingInlineEnd: isExpanded ? 1 : 0,
pb: 2,
@ -153,13 +153,13 @@ const ControlNet = (props: ControlNetProps) => {
<ParamControlNetWeight
controlNetId={controlNetId}
weight={weight}
mini
mini={!isExpanded}
/>
<ParamControlNetBeginEnd
controlNetId={controlNetId}
beginStepPct={beginStepPct}
endStepPct={endStepPct}
mini
mini={!isExpanded}
/>
</Flex>
{!isExpanded && (

4
invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx
View File

@ -1,5 +1,6 @@
import { useAppDispatch } from 'app/store/storeHooks';
import IAISwitch from 'common/components/IAISwitch';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
import { controlNetAutoConfigToggled } from 'features/controlNet/store/controlNetSlice';
import { memo, useCallback } from 'react';
@ -11,7 +12,7 @@ type Props = {
const ParamControlNetShouldAutoConfig = (props: Props) => {
const { controlNetId, shouldAutoConfig } = props;
const dispatch = useAppDispatch();
const isReady = useIsReadyToInvoke();
const handleShouldAutoConfigChanged = useCallback(() => {
dispatch(controlNetAutoConfigToggled({ controlNetId }));
}, [controlNetId, dispatch]);
@ -22,6 +23,7 @@ const ParamControlNetShouldAutoConfig = (props: Props) => {
aria-label="Auto configure processor"
isChecked={shouldAutoConfig}
onChange={handleShouldAutoConfigChanged}
isDisabled={!isReady}
/>
);
};

37
invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx
View File

@ -1,4 +1,5 @@
import {
ChakraProps,
FormControl,
FormLabel,
HStack,
@ -10,14 +11,19 @@ import {
Tooltip,
} from '@chakra-ui/react';
import { useAppDispatch } from 'app/store/storeHooks';
import IAIIconButton from 'common/components/IAIIconButton';
import {
controlNetBeginStepPctChanged,
controlNetEndStepPctChanged,
} from 'features/controlNet/store/controlNetSlice';
import { memo, useCallback } from 'react';
import { useTranslation } from 'react-i18next';
import { BiReset } from 'react-icons/bi';
const SLIDER_MARK_STYLES: ChakraProps['sx'] = {
mt: 1.5,
fontSize: '2xs',
fontWeight: '500',
color: 'base.400',
};
type Props = {
controlNetId: string;
@ -29,7 +35,7 @@ type Props = {
const formatPct = (v: number) => `${Math.round(v * 100)}%`;
const ParamControlNetBeginEnd = (props: Props) => {
const { controlNetId, beginStepPct, endStepPct, mini = false } = props;
const { controlNetId, beginStepPct, mini = false, endStepPct } = props;
const dispatch = useAppDispatch();
const { t } = useTranslation();
@ -75,12 +81,9 @@ const ParamControlNetBeginEnd = (props: Props) => {
<RangeSliderMark
value={0}
sx={{
fontSize: 'xs',
fontWeight: '500',
color: 'base.200',
insetInlineStart: '0 !important',
insetInlineEnd: 'unset !important',
mt: 1.5,
...SLIDER_MARK_STYLES,
}}
>
0%
@ -88,10 +91,7 @@ const ParamControlNetBeginEnd = (props: Props) => {
<RangeSliderMark
value={0.5}
sx={{
fontSize: 'xs',
fontWeight: '500',
color: 'base.200',
mt: 1.5,
...SLIDER_MARK_STYLES,
}}
>
50%
@ -99,12 +99,9 @@ const ParamControlNetBeginEnd = (props: Props) => {
<RangeSliderMark
value={1}
sx={{
fontSize: 'xs',
fontWeight: '500',
color: 'base.200',
insetInlineStart: 'unset !important',
insetInlineEnd: '0 !important',
mt: 1.5,
...SLIDER_MARK_STYLES,
}}
>
100%
@ -112,16 +109,6 @@ const ParamControlNetBeginEnd = (props: Props) => {
</>
)}
</RangeSlider>
{!mini && (
<IAIIconButton
size="sm"
aria-label={t('accessibility.reset')}
tooltip={t('accessibility.reset')}
icon={<BiReset />}
onClick={handleStepPctReset}
/>
)}
</HStack>
</FormControl>
);

70
invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx
View File

@ -1,41 +1,85 @@
import { useAppDispatch } from 'app/store/storeHooks';
import IAICustomSelect from 'common/components/IAICustomSelect';
import { createSelector } from '@reduxjs/toolkit';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAICustomSelect, {
IAICustomSelectOption,
} from 'common/components/IAICustomSelect';
import IAISelect from 'common/components/IAISelect';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
import {
CONTROLNET_MODELS,
ControlNetModel,
ControlNetModelName,
} from 'features/controlNet/store/constants';
import { controlNetModelChanged } from 'features/controlNet/store/controlNetSlice';
import { memo, useCallback } from 'react';
import { configSelector } from 'features/system/store/configSelectors';
import { map } from 'lodash-es';
import { ChangeEvent, memo, useCallback } from 'react';
type ParamControlNetModelProps = {
controlNetId: string;
model: ControlNetModel;
model: ControlNetModelName;
};
const selector = createSelector(configSelector, (config) => {
return map(CONTROLNET_MODELS, (m) => ({
key: m.label,
value: m.type,
})).filter((d) => !config.sd.disabledControlNetModels.includes(d.value));
});
// const DATA: IAICustomSelectOption[] = map(CONTROLNET_MODELS, (m) => ({
// value: m.type,
// label: m.label,
// tooltip: m.type,
// }));
const ParamControlNetModel = (props: ParamControlNetModelProps) => {
const { controlNetId, model } = props;
const controlNetModels = useAppSelector(selector);
const dispatch = useAppDispatch();
const isReady = useIsReadyToInvoke();
const handleModelChanged = useCallback(
(val: string | null | undefined) => {
(e: ChangeEvent<HTMLSelectElement>) => {
// TODO: do not cast
const model = val as ControlNetModel;
const model = e.target.value as ControlNetModelName;
dispatch(controlNetModelChanged({ controlNetId, model }));
},
[controlNetId, dispatch]
);
// const handleModelChanged = useCallback(
// (val: string | null | undefined) => {
// // TODO: do not cast
// const model = val as ControlNetModelName;
// dispatch(controlNetModelChanged({ controlNetId, model }));
// },
// [controlNetId, dispatch]
// );
return (
<IAICustomSelect
<IAISelect
tooltip={model}
tooltipProps={{ placement: 'top', hasArrow: true }}
items={CONTROLNET_MODELS}
selectedItem={model}
setSelectedItem={handleModelChanged}
ellipsisPosition="start"
withCheckIcon
validValues={controlNetModels}
value={model}
onChange={handleModelChanged}
isDisabled={!isReady}
// ellipsisPosition="start"
// withCheckIcon
/>
);
// return (
// <IAICustomSelect
// tooltip={model}
// tooltipProps={{ placement: 'top', hasArrow: true }}
// data={DATA}
// value={model}
// onChange={handleModelChanged}
// isDisabled={!isReady}
// ellipsisPosition="start"
// withCheckIcon
// />
// );
};
export default memo(ParamControlNetModel);

94
invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx
View File

@ -1,47 +1,115 @@
import IAICustomSelect from 'common/components/IAICustomSelect';
import { memo, useCallback } from 'react';
import IAICustomSelect, {
IAICustomSelectOption,
} from 'common/components/IAICustomSelect';
import { ChangeEvent, memo, useCallback } from 'react';
import {
ControlNetProcessorNode,
ControlNetProcessorType,
} from '../../store/types';
import { controlNetProcessorTypeChanged } from '../../store/controlNetSlice';
import { useAppDispatch } from 'app/store/storeHooks';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { CONTROLNET_PROCESSORS } from '../../store/constants';
import { map } from 'lodash-es';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
import IAISelect from 'common/components/IAISelect';
import { createSelector } from '@reduxjs/toolkit';
import { configSelector } from 'features/system/store/configSelectors';
type ParamControlNetProcessorSelectProps = {
controlNetId: string;
processorNode: ControlNetProcessorNode;
};
const CONTROLNET_PROCESSOR_TYPES = Object.keys(
CONTROLNET_PROCESSORS
) as ControlNetProcessorType[];
const CONTROLNET_PROCESSOR_TYPES = map(CONTROLNET_PROCESSORS, (p) => ({
value: p.type,
key: p.label,
})).sort((a, b) =>
// sort 'none' to the top
a.value === 'none' ? -1 : b.value === 'none' ? 1 : a.key.localeCompare(b.key)
);
const selector = createSelector(configSelector, (config) => {
return map(CONTROLNET_PROCESSORS, (p) => ({
value: p.type,
key: p.label,
}))
.sort((a, b) =>
// sort 'none' to the top
a.value === 'none'
? -1
: b.value === 'none'
? 1
: a.key.localeCompare(b.key)
)
.filter((d) => !config.sd.disabledControlNetProcessors.includes(d.value));
});
// const CONTROLNET_PROCESSOR_TYPES: IAICustomSelectOption[] = map(
// CONTROLNET_PROCESSORS,
// (p) => ({
// value: p.type,
// label: p.label,
// tooltip: p.description,
// })
// ).sort((a, b) =>
// // sort 'none' to the top
// a.value === 'none'
// ? -1
// : b.value === 'none'
// ? 1
// : a.label.localeCompare(b.label)
// );
const ParamControlNetProcessorSelect = (
props: ParamControlNetProcessorSelectProps
) => {
const { controlNetId, processorNode } = props;
const dispatch = useAppDispatch();
const isReady = useIsReadyToInvoke();
const controlNetProcessors = useAppSelector(selector);
const handleProcessorTypeChanged = useCallback(
(v: string | null | undefined) => {
(e: ChangeEvent<HTMLSelectElement>) => {
dispatch(
controlNetProcessorTypeChanged({
controlNetId,
processorType: v as ControlNetProcessorType,
processorType: e.target.value as ControlNetProcessorType,
})
);
},
[controlNetId, dispatch]
);
// const handleProcessorTypeChanged = useCallback(
// (v: string | null | undefined) => {
// dispatch(
// controlNetProcessorTypeChanged({
// controlNetId,
// processorType: v as ControlNetProcessorType,
// })
// );
// },
// [controlNetId, dispatch]
// );
return (
<IAICustomSelect
<IAISelect
label="Processor"
items={CONTROLNET_PROCESSOR_TYPES}
selectedItem={processorNode.type ?? 'canny_image_processor'}
setSelectedItem={handleProcessorTypeChanged}
withCheckIcon
value={processorNode.type ?? 'canny_image_processor'}
validValues={controlNetProcessors}
onChange={handleProcessorTypeChanged}
isDisabled={!isReady}
/>
);
// return (
// <IAICustomSelect
// label="Processor"
// value={processorNode.type ?? 'canny_image_processor'}
// data={CONTROLNET_PROCESSOR_TYPES}
// onChange={handleProcessorTypeChanged}
// withCheckIcon
// isDisabled={!isReady}
// />
// );
};
export default memo(ParamControlNetProcessorSelect);

29
invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx
View File

@ -20,36 +20,17 @@ const ParamControlNetWeight = (props: ParamControlNetWeightProps) => {
[controlNetId, dispatch]
);
const handleWeightReset = () => {
dispatch(controlNetWeightChanged({ controlNetId, weight: 1 }));
};
if (mini) {
return (
<IAISlider
label={'Weight'}
sliderFormLabelProps={{ pb: 1 }}
value={weight}
onChange={handleWeightChanged}
min={0}
max={1}
step={0.01}
/>
);
}
return (
<IAISlider
label="Weight"
label={'Weight'}
sliderFormLabelProps={{ pb: 2 }}
value={weight}
onChange={handleWeightChanged}
withInput
withReset
handleReset={handleWeightReset}
withSliderMarks
min={0}
min={-1}
max={1}
step={0.01}
withSliderMarks={!mini}
sliderMarks={[-1, 0, 1]}
/>
);
};

6
invokeai/frontend/web/src/features/controlNet/components/processors/CannyProcessor.tsx
View File

@ -4,6 +4,7 @@ import { RequiredCannyImageProcessorInvocation } from 'features/controlNet/store
import { memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.canny_image_processor.default;
@ -15,6 +16,7 @@ type CannyProcessorProps = {
const CannyProcessor = (props: CannyProcessorProps) => {
const { controlNetId, processorNode } = props;
const { low_threshold, high_threshold } = processorNode;
const isReady = useIsReadyToInvoke();
const processorChanged = useProcessorNodeChanged();
const handleLowThresholdChanged = useCallback(
@ -46,6 +48,7 @@ const CannyProcessor = (props: CannyProcessorProps) => {
return (
<ProcessorWrapper>
<IAISlider
isDisabled={!isReady}
label="Low Threshold"
value={low_threshold}
onChange={handleLowThresholdChanged}
@ -54,8 +57,10 @@ const CannyProcessor = (props: CannyProcessorProps) => {
min={0}
max={255}
withInput
withSliderMarks
/>
<IAISlider
isDisabled={!isReady}
label="High Threshold"
value={high_threshold}
onChange={handleHighThresholdChanged}
@ -64,6 +69,7 @@ const CannyProcessor = (props: CannyProcessorProps) => {
min={0}
max={255}
withInput
withSliderMarks
/>
</ProcessorWrapper>
);

12
invokeai/frontend/web/src/features/controlNet/components/processors/ContentShuffleProcessor.tsx
View File

@ -4,6 +4,7 @@ import { RequiredContentShuffleImageProcessorInvocation } from 'features/control
import { memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.content_shuffle_image_processor.default;
@ -16,6 +17,7 @@ const ContentShuffleProcessor = (props: Props) => {
const { controlNetId, processorNode } = props;
const { image_resolution, detect_resolution, w, h, f } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleDetectResolutionChanged = useCallback(
(v: number) => {
@ -93,6 +95,8 @@ const ContentShuffleProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Image Resolution"
@ -103,6 +107,8 @@ const ContentShuffleProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="W"
@ -113,6 +119,8 @@ const ContentShuffleProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="H"
@ -123,6 +131,8 @@ const ContentShuffleProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="F"
@ -133,6 +143,8 @@ const ContentShuffleProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

8
invokeai/frontend/web/src/features/controlNet/components/processors/HedProcessor.tsx
View File

@ -5,6 +5,7 @@ import { RequiredHedImageProcessorInvocation } from 'features/controlNet/store/t
import { ChangeEvent, memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.hed_image_processor.default;
@ -18,7 +19,7 @@ const HedPreprocessor = (props: HedProcessorProps) => {
controlNetId,
processorNode: { detect_resolution, image_resolution, scribble },
} = props;
const isReady = useIsReadyToInvoke();
const processorChanged = useProcessorNodeChanged();
const handleDetectResolutionChanged = useCallback(
@ -65,6 +66,8 @@ const HedPreprocessor = (props: HedProcessorProps) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Image Resolution"
@ -75,11 +78,14 @@ const HedPreprocessor = (props: HedProcessorProps) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISwitch
label="Scribble"
isChecked={scribble}
onChange={handleScribbleChanged}
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

6
invokeai/frontend/web/src/features/controlNet/components/processors/LineartAnimeProcessor.tsx
View File

@ -4,6 +4,7 @@ import { RequiredLineartAnimeImageProcessorInvocation } from 'features/controlNe
import { memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.lineart_anime_image_processor.default;
@ -16,6 +17,7 @@ const LineartAnimeProcessor = (props: Props) => {
const { controlNetId, processorNode } = props;
const { image_resolution, detect_resolution } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleDetectResolutionChanged = useCallback(
(v: number) => {
@ -54,6 +56,8 @@ const LineartAnimeProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Image Resolution"
@ -64,6 +68,8 @@ const LineartAnimeProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

7
invokeai/frontend/web/src/features/controlNet/components/processors/LineartProcessor.tsx
View File

@ -5,6 +5,7 @@ import { RequiredLineartImageProcessorInvocation } from 'features/controlNet/sto
import { ChangeEvent, memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.lineart_image_processor.default;
@ -17,6 +18,7 @@ const LineartProcessor = (props: LineartProcessorProps) => {
const { controlNetId, processorNode } = props;
const { image_resolution, detect_resolution, coarse } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleDetectResolutionChanged = useCallback(
(v: number) => {
@ -62,6 +64,8 @@ const LineartProcessor = (props: LineartProcessorProps) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Image Resolution"
@ -72,11 +76,14 @@ const LineartProcessor = (props: LineartProcessorProps) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISwitch
label="Coarse"
isChecked={coarse}
onChange={handleCoarseChanged}
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

6
invokeai/frontend/web/src/features/controlNet/components/processors/MediapipeFaceProcessor.tsx
View File

@ -4,6 +4,7 @@ import { RequiredMediapipeFaceProcessorInvocation } from 'features/controlNet/st
import { memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.mediapipe_face_processor.default;
@ -16,6 +17,7 @@ const MediapipeFaceProcessor = (props: Props) => {
const { controlNetId, processorNode } = props;
const { max_faces, min_confidence } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleMaxFacesChanged = useCallback(
(v: number) => {
@ -50,6 +52,8 @@ const MediapipeFaceProcessor = (props: Props) => {
min={1}
max={20}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Min Confidence"
@ -61,6 +65,8 @@ const MediapipeFaceProcessor = (props: Props) => {
max={1}
step={0.01}
withInput
withSliderMarks
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

6
invokeai/frontend/web/src/features/controlNet/components/processors/MidasDepthProcessor.tsx
View File

@ -4,6 +4,7 @@ import { RequiredMidasDepthImageProcessorInvocation } from 'features/controlNet/
import { memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.midas_depth_image_processor.default;
@ -16,6 +17,7 @@ const MidasDepthProcessor = (props: Props) => {
const { controlNetId, processorNode } = props;
const { a_mult, bg_th } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleAMultChanged = useCallback(
(v: number) => {
@ -51,6 +53,8 @@ const MidasDepthProcessor = (props: Props) => {
max={20}
step={0.01}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="bg_th"
@ -62,6 +66,8 @@ const MidasDepthProcessor = (props: Props) => {
max={20}
step={0.01}
withInput
withSliderMarks
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

10
invokeai/frontend/web/src/features/controlNet/components/processors/MlsdImageProcessor.tsx
View File

@ -4,6 +4,7 @@ import { RequiredMlsdImageProcessorInvocation } from 'features/controlNet/store/
import { memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.mlsd_image_processor.default;
@ -16,6 +17,7 @@ const MlsdImageProcessor = (props: Props) => {
const { controlNetId, processorNode } = props;
const { image_resolution, detect_resolution, thr_d, thr_v } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleDetectResolutionChanged = useCallback(
(v: number) => {
@ -76,6 +78,8 @@ const MlsdImageProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Image Resolution"
@ -86,6 +90,8 @@ const MlsdImageProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="W"
@ -97,6 +103,8 @@ const MlsdImageProcessor = (props: Props) => {
max={1}
step={0.01}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="H"
@ -108,6 +116,8 @@ const MlsdImageProcessor = (props: Props) => {
max={1}
step={0.01}
withInput
withSliderMarks
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

6
invokeai/frontend/web/src/features/controlNet/components/processors/NormalBaeProcessor.tsx
View File

@ -4,6 +4,7 @@ import { RequiredNormalbaeImageProcessorInvocation } from 'features/controlNet/s
import { memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.normalbae_image_processor.default;
@ -16,6 +17,7 @@ const NormalBaeProcessor = (props: Props) => {
const { controlNetId, processorNode } = props;
const { image_resolution, detect_resolution } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleDetectResolutionChanged = useCallback(
(v: number) => {
@ -54,6 +56,8 @@ const NormalBaeProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Image Resolution"
@ -64,6 +68,8 @@ const NormalBaeProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

7
invokeai/frontend/web/src/features/controlNet/components/processors/OpenposeProcessor.tsx
View File

@ -5,6 +5,7 @@ import { RequiredOpenposeImageProcessorInvocation } from 'features/controlNet/st
import { ChangeEvent, memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.openpose_image_processor.default;
@ -17,6 +18,7 @@ const OpenposeProcessor = (props: Props) => {
const { controlNetId, processorNode } = props;
const { image_resolution, detect_resolution, hand_and_face } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleDetectResolutionChanged = useCallback(
(v: number) => {
@ -62,6 +64,8 @@ const OpenposeProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Image Resolution"
@ -72,11 +76,14 @@ const OpenposeProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISwitch
label="Hand and Face"
isChecked={hand_and_face}
onChange={handleHandAndFaceChanged}
isDisabled={!isReady}
/>
</ProcessorWrapper>
);

13
invokeai/frontend/web/src/features/controlNet/components/processors/PidiProcessor.tsx
View File

@ -5,6 +5,7 @@ import { RequiredPidiImageProcessorInvocation } from 'features/controlNet/store/
import { ChangeEvent, memo, useCallback } from 'react';
import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
import ProcessorWrapper from './common/ProcessorWrapper';
import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
const DEFAULTS = CONTROLNET_PROCESSORS.pidi_image_processor.default;
@ -17,6 +18,7 @@ const PidiProcessor = (props: Props) => {
const { controlNetId, processorNode } = props;
const { image_resolution, detect_resolution, scribble, safe } = processorNode;
const processorChanged = useProcessorNodeChanged();
const isReady = useIsReadyToInvoke();
const handleDetectResolutionChanged = useCallback(
(v: number) => {
@ -69,6 +71,8 @@ const PidiProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISlider
label="Image Resolution"
@ -79,13 +83,20 @@ const PidiProcessor = (props: Props) => {
min={0}
max={4096}
withInput
withSliderMarks
isDisabled={!isReady}
/>
<IAISwitch
label="Scribble"
isChecked={scribble}
onChange={handleScribbleChanged}
/>
<IAISwitch label="Safe" isChecked={safe} onChange={handleSafeChanged} />
<IAISwitch
label="Safe"
isChecked={safe}
onChange={handleSafeChanged}
isDisabled={!isReady}
/>
</ProcessorWrapper>
);
};

127
invokeai/frontend/web/src/features/controlNet/store/constants.ts
View File

@ -5,12 +5,12 @@ import {
} from './types';
type ControlNetProcessorsDict = Record<
ControlNetProcessorType,
string,
{
type: ControlNetProcessorType;
type: ControlNetProcessorType | 'none';
label: string;
description: string;
default: RequiredControlNetProcessorNode;
default: RequiredControlNetProcessorNode | { type: 'none' };
}
>;
@ -26,7 +26,7 @@ type ControlNetProcessorsDict = Record<
export const CONTROLNET_PROCESSORS = {
none: {
type: 'none',
label: 'None',
label: 'none',
description: '',
default: {
type: 'none',
@ -116,7 +116,7 @@ export const CONTROLNET_PROCESSORS = {
},
mlsd_image_processor: {
type: 'mlsd_image_processor',
label: 'MLSD',
label: 'M-LSD',
description: '',
default: {
id: 'mlsd_image_processor',
@ -129,7 +129,7 @@ export const CONTROLNET_PROCESSORS = {
},
normalbae_image_processor: {
type: 'normalbae_image_processor',
label: 'NormalBae',
label: 'Normal BAE',
description: '',
default: {
id: 'normalbae_image_processor',
@ -174,39 +174,84 @@ export const CONTROLNET_PROCESSORS = {
},
};
export const CONTROLNET_MODELS = [
'lllyasviel/control_v11p_sd15_canny',
'lllyasviel/control_v11p_sd15_inpaint',
'lllyasviel/control_v11p_sd15_mlsd',
'lllyasviel/control_v11f1p_sd15_depth',
'lllyasviel/control_v11p_sd15_normalbae',
'lllyasviel/control_v11p_sd15_seg',
'lllyasviel/control_v11p_sd15_lineart',
'lllyasviel/control_v11p_sd15s2_lineart_anime',
'lllyasviel/control_v11p_sd15_scribble',
'lllyasviel/control_v11p_sd15_softedge',
'lllyasviel/control_v11e_sd15_shuffle',
'lllyasviel/control_v11p_sd15_openpose',
'lllyasviel/control_v11f1e_sd15_tile',
'lllyasviel/control_v11e_sd15_ip2p',
'CrucibleAI/ControlNetMediaPipeFace',
];
export type ControlNetModel = (typeof CONTROLNET_MODELS)[number];
export const CONTROLNET_MODEL_MAP: Record<
ControlNetModel,
ControlNetProcessorType
> = {
'lllyasviel/control_v11p_sd15_canny': 'canny_image_processor',
'lllyasviel/control_v11p_sd15_mlsd': 'mlsd_image_processor',
'lllyasviel/control_v11f1p_sd15_depth': 'midas_depth_image_processor',
'lllyasviel/control_v11p_sd15_normalbae': 'normalbae_image_processor',
'lllyasviel/control_v11p_sd15_lineart': 'lineart_image_processor',
'lllyasviel/control_v11p_sd15s2_lineart_anime':
'lineart_anime_image_processor',
'lllyasviel/control_v11p_sd15_softedge': 'hed_image_processor',
'lllyasviel/control_v11e_sd15_shuffle': 'content_shuffle_image_processor',
'lllyasviel/control_v11p_sd15_openpose': 'openpose_image_processor',
'CrucibleAI/ControlNetMediaPipeFace': 'mediapipe_face_processor',
type ControlNetModel = {
type: string;
label: string;
description?: string;
defaultProcessor?: ControlNetProcessorType;
};
export const CONTROLNET_MODELS = {
'lllyasviel/control_v11p_sd15_canny': {
type: 'lllyasviel/control_v11p_sd15_canny',
label: 'Canny',
defaultProcessor: 'canny_image_processor',
},
'lllyasviel/control_v11p_sd15_inpaint': {
type: 'lllyasviel/control_v11p_sd15_inpaint',
label: 'Inpaint',
},
'lllyasviel/control_v11p_sd15_mlsd': {
type: 'lllyasviel/control_v11p_sd15_mlsd',
label: 'M-LSD',
defaultProcessor: 'mlsd_image_processor',
},
'lllyasviel/control_v11f1p_sd15_depth': {
type: 'lllyasviel/control_v11f1p_sd15_depth',
label: 'Depth',
defaultProcessor: 'midas_depth_image_processor',
},
'lllyasviel/control_v11p_sd15_normalbae': {
type: 'lllyasviel/control_v11p_sd15_normalbae',
label: 'Normal Map (BAE)',
defaultProcessor: 'normalbae_image_processor',
},
'lllyasviel/control_v11p_sd15_seg': {
type: 'lllyasviel/control_v11p_sd15_seg',
label: 'Segmentation',
},
'lllyasviel/control_v11p_sd15_lineart': {
type: 'lllyasviel/control_v11p_sd15_lineart',
label: 'Lineart',
defaultProcessor: 'lineart_image_processor',
},
'lllyasviel/control_v11p_sd15s2_lineart_anime': {
type: 'lllyasviel/control_v11p_sd15s2_lineart_anime',
label: 'Lineart Anime',
defaultProcessor: 'lineart_anime_image_processor',
},
'lllyasviel/control_v11p_sd15_scribble': {
type: 'lllyasviel/control_v11p_sd15_scribble',
label: 'Scribble',
},
'lllyasviel/control_v11p_sd15_softedge': {
type: 'lllyasviel/control_v11p_sd15_softedge',
label: 'Soft Edge',
defaultProcessor: 'hed_image_processor',
},
'lllyasviel/control_v11e_sd15_shuffle': {
type: 'lllyasviel/control_v11e_sd15_shuffle',
label: 'Content Shuffle',
defaultProcessor: 'content_shuffle_image_processor',
},
'lllyasviel/control_v11p_sd15_openpose': {
type: 'lllyasviel/control_v11p_sd15_openpose',
label: 'Openpose',
defaultProcessor: 'openpose_image_processor',
},
'lllyasviel/control_v11f1e_sd15_tile': {
type: 'lllyasviel/control_v11f1e_sd15_tile',
label: 'Tile (experimental)',
},
'lllyasviel/control_v11e_sd15_ip2p': {
type: 'lllyasviel/control_v11e_sd15_ip2p',
label: 'Pix2Pix (experimental)',
},
'CrucibleAI/ControlNetMediaPipeFace': {
type: 'CrucibleAI/ControlNetMediaPipeFace',
label: 'Mediapipe Face',
defaultProcessor: 'mediapipe_face_processor',
},
};
export type ControlNetModelName = keyof typeof CONTROLNET_MODELS;

17
invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
View File

@ -9,9 +9,8 @@ import {
} from './types';
import {
CONTROLNET_MODELS,
CONTROLNET_MODEL_MAP,
CONTROLNET_PROCESSORS,
ControlNetModel,
ControlNetModelName,
} from './constants';
import { controlNetImageProcessed } from './actions';
import { imageDeleted, imageUrlsReceived } from 'services/thunks/image';
@ -21,7 +20,7 @@ import { appSocketInvocationError } from 'services/events/actions';
export const initialControlNet: Omit<ControlNetConfig, 'controlNetId'> = {
isEnabled: true,
model: CONTROLNET_MODELS[0],
model: CONTROLNET_MODELS['lllyasviel/control_v11p_sd15_canny'].type,
weight: 1,
beginStepPct: 0,
endStepPct: 1,
@ -36,7 +35,7 @@ export const initialControlNet: Omit<ControlNetConfig, 'controlNetId'> = {
export type ControlNetConfig = {
controlNetId: string;
isEnabled: boolean;
model: ControlNetModel;
model: ControlNetModelName;
weight: number;
beginStepPct: number;
endStepPct: number;
@ -138,14 +137,17 @@ export const controlNetSlice = createSlice({
},
controlNetModelChanged: (
state,
action: PayloadAction<{ controlNetId: string; model: ControlNetModel }>
action: PayloadAction<{
controlNetId: string;
model: ControlNetModelName;
}>
) => {
const { controlNetId, model } = action.payload;
state.controlNets[controlNetId].model = model;
state.controlNets[controlNetId].processedControlImage = null;
if (state.controlNets[controlNetId].shouldAutoConfig) {
const processorType = CONTROLNET_MODEL_MAP[model];
const processorType = CONTROLNET_MODELS[model].defaultProcessor;
if (processorType) {
state.controlNets[controlNetId].processorType = processorType;
state.controlNets[controlNetId].processorNode = CONTROLNET_PROCESSORS[
@ -225,7 +227,8 @@ export const controlNetSlice = createSlice({
if (newShouldAutoConfig) {
// manage the processor for the user
const processorType =
CONTROLNET_MODEL_MAP[state.controlNets[controlNetId].model];
CONTROLNET_MODELS[state.controlNets[controlNetId].model]
.defaultProcessor;
if (processorType) {
state.controlNets[controlNetId].processorType = processorType;
state.controlNets[controlNetId].processorNode = CONTROLNET_PROCESSORS[

2
invokeai/frontend/web/src/features/nodes/types/constants.ts
View File

@ -18,6 +18,8 @@ export const FIELD_TYPE_MAP: Record<string, FieldType> = {
ColorField: 'color',
ControlField: 'control',
control: 'control',
cfg_scale: 'float',
control_weight: 'float',
};
const COLOR_TOKEN_VALUE = 500;

12
invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts
View File

@ -1,5 +1,5 @@
import { RootState } from 'app/store/store';
import { forEach, size } from 'lodash-es';
import { filter, forEach, size } from 'lodash-es';
import { CollectInvocation, ControlNetInvocation } from 'services/api';
import { NonNullableGraph } from '../types/types';
@ -12,8 +12,16 @@ export const addControlNetToLinearGraph = (
): void => {
const { isEnabled: isControlNetEnabled, controlNets } = state.controlNet;
const validControlNets = filter(
controlNets,
(c) =>
c.isEnabled &&
(Boolean(c.processedControlImage) ||
(c.processorType === 'none' && Boolean(c.controlImage)))
);
// Add ControlNet
if (isControlNetEnabled) {
if (isControlNetEnabled && validControlNets.length > 0) {
if (size(controlNets) > 1) {
const controlNetIterateNode: CollectInvocation = {
id: CONTROL_NET_COLLECT,

46
invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx
View File

@ -3,10 +3,11 @@ import { Scheduler } from 'app/constants';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import IAICustomSelect from 'common/components/IAICustomSelect';
import IAISelect from 'common/components/IAISelect';
import { generationSelector } from 'features/parameters/store/generationSelectors';
import { setScheduler } from 'features/parameters/store/generationSlice';
import { uiSelector } from 'features/ui/store/uiSelectors';
import { memo, useCallback } from 'react';
import { ChangeEvent, memo, useCallback } from 'react';
import { useTranslation } from 'react-i18next';
const selector = createSelector(
@ -14,9 +15,11 @@ const selector = createSelector(
(ui, generation) => {
// TODO: DPMSolverSinglestepScheduler is fixed in https://github.com/huggingface/diffusers/pull/3413
// but we need to wait for the next release before removing this special handling.
const allSchedulers = ui.schedulers.filter((scheduler) => {
return !['dpmpp_2s'].includes(scheduler);
});
const allSchedulers = ui.schedulers
.filter((scheduler) => {
return !['dpmpp_2s'].includes(scheduler);
})
.sort((a, b) => a.localeCompare(b));
return {
scheduler: generation.scheduler,
@ -33,24 +36,39 @@ const ParamScheduler = () => {
const { t } = useTranslation();
const handleChange = useCallback(
(v: string | null | undefined) => {
if (!v) {
return;
}
dispatch(setScheduler(v as Scheduler));
(e: ChangeEvent<HTMLSelectElement>) => {
dispatch(setScheduler(e.target.value as Scheduler));
},
[dispatch]
);
// const handleChange = useCallback(
// (v: string | null | undefined) => {
// if (!v) {
// return;
// }
// dispatch(setScheduler(v as Scheduler));
// },
// [dispatch]
// );
return (
<IAICustomSelect
<IAISelect
label={t('parameters.scheduler')}
selectedItem={scheduler}
setSelectedItem={handleChange}
items={allSchedulers}
withCheckIcon
value={scheduler}
validValues={allSchedulers}
onChange={handleChange}
/>
);
// return (
// <IAICustomSelect
// label={t('parameters.scheduler')}
// value={scheduler}
// data={allSchedulers}
// onChange={handleChange}
// withCheckIcon
// />
// );
};
export default memo(ParamScheduler);

41
invokeai/frontend/web/src/features/system/components/ClearTempFolderButtonModal.tsx
View File

@ -1,41 +0,0 @@
// import { emptyTempFolder } from 'app/socketio/actions';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAIAlertDialog from 'common/components/IAIAlertDialog';
import IAIButton from 'common/components/IAIButton';
import { isStagingSelector } from 'features/canvas/store/canvasSelectors';
import {
clearCanvasHistory,
resetCanvas,
} from 'features/canvas/store/canvasSlice';
import { useTranslation } from 'react-i18next';
import { FaTrash } from 'react-icons/fa';
const EmptyTempFolderButtonModal = () => {
const isStaging = useAppSelector(isStagingSelector);
const dispatch = useAppDispatch();
const { t } = useTranslation();
const acceptCallback = () => {
dispatch(emptyTempFolder());
dispatch(resetCanvas());
dispatch(clearCanvasHistory());
};
return (
<IAIAlertDialog
title={t('unifiedCanvas.emptyTempImageFolder')}
acceptCallback={acceptCallback}
acceptButtonText={t('unifiedCanvas.emptyFolder')}
triggerComponent={
<IAIButton leftIcon={<FaTrash />} size="sm" isDisabled={isStaging}>
{t('unifiedCanvas.emptyTempImageFolder')}
</IAIButton>
}
>
<p>{t('unifiedCanvas.emptyTempImagesFolderMessage')}</p>
<br />
<p>{t('unifiedCanvas.emptyTempImagesFolderConfirm')}</p>
</IAIAlertDialog>
);
};
export default EmptyTempFolderButtonModal;

77
invokeai/frontend/web/src/features/system/components/ModelSelect.tsx
View File

@ -1,37 +1,39 @@
import { createSelector } from '@reduxjs/toolkit';
import { memo, useCallback } from 'react';
import { ChangeEvent, memo, useCallback } from 'react';
import { isEqual } from 'lodash-es';
import { useTranslation } from 'react-i18next';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import {
selectModelsAll,
selectModelsById,
selectModelsIds,
} from '../store/modelSlice';
import { selectModelsAll, selectModelsById } from '../store/modelSlice';
import { RootState } from 'app/store/store';
import { modelSelected } from 'features/parameters/store/generationSlice';
import { generationSelector } from 'features/parameters/store/generationSelectors';
import IAICustomSelect, {
ItemTooltips,
IAICustomSelectOption,
} from 'common/components/IAICustomSelect';
import IAISelect from 'common/components/IAISelect';
const selector = createSelector(
[(state: RootState) => state, generationSelector],
(state, generation) => {
const selectedModel = selectModelsById(state, generation.model);
const allModelNames = selectModelsIds(state).map((id) => String(id));
const allModelTooltips = selectModelsAll(state).reduce(
(allModelTooltips, model) => {
allModelTooltips[model.name] = model.description ?? '';
return allModelTooltips;
},
{} as ItemTooltips
);
const modelData = selectModelsAll(state)
.map((m) => ({
value: m.name,
key: m.name,
}))
.sort((a, b) => a.key.localeCompare(b.key));
// const modelData = selectModelsAll(state)
// .map<IAICustomSelectOption>((m) => ({
// value: m.name,
// label: m.name,
// tooltip: m.description,
// }))
// .sort((a, b) => a.label.localeCompare(b.label));
return {
allModelNames,
allModelTooltips,
selectedModel,
modelData,
};
},
{
@ -44,30 +46,45 @@ const selector = createSelector(
const ModelSelect = () => {
const dispatch = useAppDispatch();
const { t } = useTranslation();
const { allModelNames, allModelTooltips, selectedModel } =
useAppSelector(selector);
const { selectedModel, modelData } = useAppSelector(selector);
const handleChangeModel = useCallback(
(v: string | null | undefined) => {
if (!v) {
return;
}
dispatch(modelSelected(v));
(e: ChangeEvent<HTMLSelectElement>) => {
dispatch(modelSelected(e.target.value));
},
[dispatch]
);
// const handleChangeModel = useCallback(
// (v: string | null | undefined) => {
// if (!v) {
// return;
// }
// dispatch(modelSelected(v));
// },
// [dispatch]
// );
return (
<IAICustomSelect
<IAISelect
label={t('modelManager.model')}
tooltip={selectedModel?.description}
items={allModelNames}
itemTooltips={allModelTooltips}
selectedItem={selectedModel?.name ?? ''}
setSelectedItem={handleChangeModel}
withCheckIcon={true}
validValues={modelData}
value={selectedModel?.name ?? ''}
onChange={handleChangeModel}
tooltipProps={{ placement: 'top', hasArrow: true }}
/>
);
// return (
// <IAICustomSelect
// label={t('modelManager.model')}
// tooltip={selectedModel?.description}
// data={modelData}
// value={selectedModel?.name ?? ''}
// onChange={handleChangeModel}
// withCheckIcon={true}
// tooltipProps={{ placement: 'top', hasArrow: true }}
// />
// );
};
export default memo(ModelSelect);

2
invokeai/frontend/web/src/features/system/store/configSlice.ts
View File

@ -10,6 +10,8 @@ export const initialConfigState: AppConfig = {
disabledSDFeatures: [],
canRestoreDeletedImagesFromBin: true,
sd: {
disabledControlNetModels: [],
disabledControlNetProcessors: [],
iterations: {
initial: 1,
min: 1,

3
invokeai/frontend/web/src/features/system/store/systemSelectors.ts
View File

@ -47,3 +47,6 @@ export const languageSelector = createSelector(
(system) => system.language,
defaultSelectorOptions
);
export const isProcessingSelector = (state: RootState) =>
state.system.isProcessing;

20
invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx
View File

@ -14,7 +14,7 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { setIsLightboxOpen } from 'features/lightbox/store/lightboxSlice';
import { InvokeTabName } from 'features/ui/store/tabMap';
import { setActiveTab, togglePanels } from 'features/ui/store/uiSlice';
import { memo, ReactNode, useCallback, useMemo } from 'react';
import { memo, MouseEvent, ReactNode, useCallback, useMemo } from 'react';
import { useHotkeys } from 'react-hotkeys-hook';
import { MdDeviceHub, MdGridOn } from 'react-icons/md';
import { GoTextSize } from 'react-icons/go';
@ -47,22 +47,22 @@ export interface InvokeTabInfo {
const tabs: InvokeTabInfo[] = [
{
id: 'txt2img',
icon: <Icon as={GoTextSize} sx={{ boxSize: 6 }} />,
icon: <Icon as={GoTextSize} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
content: <TextToImageTab />,
},
{
id: 'img2img',
icon: <Icon as={FaImage} sx={{ boxSize: 6 }} />,
icon: <Icon as={FaImage} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
content: <ImageTab />,
},
{
id: 'unifiedCanvas',
icon: <Icon as={MdGridOn} sx={{ boxSize: 6 }} />,
icon: <Icon as={MdGridOn} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
content: <UnifiedCanvasTab />,
},
{
id: 'nodes',
icon: <Icon as={MdDeviceHub} sx={{ boxSize: 6 }} />,
icon: <Icon as={MdDeviceHub} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
content: <NodesTab />,
},
];
@ -119,6 +119,12 @@ const InvokeTabs = () => {
}
}, [dispatch, activeTabName]);
const handleClickTab = useCallback((e: MouseEvent<HTMLElement>) => {
if (e.target instanceof HTMLElement) {
e.target.blur();
}
}, []);
const tabs = useMemo(
() =>
enabledTabs.map((tab) => (
@ -128,7 +134,7 @@ const InvokeTabs = () => {
label={String(t(`common.${tab.id}` as ResourceKey))}
placement="end"
>
<Tab>
<Tab onClick={handleClickTab}>
<VisuallyHidden>
{String(t(`common.${tab.id}` as ResourceKey))}
</VisuallyHidden>
@ -136,7 +142,7 @@ const InvokeTabs = () => {
</Tab>
</Tooltip>
)),
[t, enabledTabs]
[enabledTabs, t, handleClickTab]
);
const tabPanels = useMemo(

2
invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasBeta/UnifiedCanvasToolSettings/UnifiedCanvasSettings.tsx
View File

@ -12,7 +12,6 @@ import {
setShouldShowCanvasDebugInfo,
setShouldShowIntermediates,
} from 'features/canvas/store/canvasSlice';
import EmptyTempFolderButtonModal from 'features/system/components/ClearTempFolderButtonModal';
import { FaWrench } from 'react-icons/fa';
@ -105,7 +104,6 @@ const UnifiedCanvasSettings = () => {
onChange={(e) => dispatch(setShouldAntialias(e.target.checked))}
/>
<ClearCanvasHistoryButtonModal />
<EmptyTempFolderButtonModal />
</Flex>
</IAIPopover>
);

2
invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasContent.tsx
View File

@ -55,8 +55,6 @@ const UnifiedCanvasContent = () => {
});
useLayoutEffect(() => {
dispatch(requestCanvasRescale());
const resizeCallback = () => {
dispatch(requestCanvasRescale());
};

14
invokeai/frontend/web/src/services/api/models/HedImageProcessorInvocation.ts
View File

@ -7,30 +7,26 @@ import type { ImageField } from './ImageField';
/**
* Applies HED edge detection to image
*/
export type HedImageProcessorInvocation = {
export type HedImageprocessorInvocation = {
/**
* The id of this node. Must be unique among all nodes.
*/
id: string;
/**
* Whether or not this node is an intermediate node.
*/
is_intermediate?: boolean;
type?: 'hed_image_processor';
/**
* The image to process
* image to process
*/
image?: ImageField;
/**
* The pixel resolution for detection
* pixel resolution for edge detection
*/
detect_resolution?: number;
/**
* The pixel resolution for the output image
* pixel resolution for output image
*/
image_resolution?: number;
/**
* Whether to use scribble mode
* whether to use scribble mode
*/
scribble?: boolean;
};

33
invokeai/frontend/web/src/services/api/models/HedImageprocessorInvocation.ts Normal file
View File

@ -0,0 +1,33 @@
/* istanbul ignore file */
/* tslint:disable */
/* eslint-disable */
import type { ImageField } from './ImageField';
/**
* Applies HED edge detection to image
*/
export type HedImageprocessorInvocation = {
/**
* The id of this node. Must be unique among all nodes.
*/
id: string;
type?: 'hed_image_processor';
/**
* image to process
*/
image?: ImageField;
/**
* pixel resolution for edge detection
*/
detect_resolution?: number;
/**
* pixel resolution for output image
*/
image_resolution?: number;
/**
* whether to use scribble mode
*/
scribble?: boolean;
};

2
invokeai/frontend/web/src/theme/components/slider.ts
View File

@ -30,7 +30,7 @@ const invokeAIMark = defineStyle((_props) => {
return {
fontSize: 'xs',
fontWeight: '500',
color: 'base.200',
color: 'base.400',
mt: 2,
insetInlineStart: 'unset',
};

2
pyproject.toml
View File

@ -44,6 +44,7 @@ dependencies = [
"datasets",
"diffusers[torch]~=0.17.0",
"dnspython==2.2.1",
"easing-functions",
"einops",
"eventlet",
"facexlib",
@ -56,6 +57,7 @@ dependencies = [
"flaskwebgui==1.0.3",
"gfpgan==1.3.8",
"huggingface-hub>=0.11.1",
"matplotlib", # needed for plotting of Penner easing functions
"mediapipe", # needed for "mediapipeface" controlnet model
"npyscreen",
"numpy<1.24",

75
tests/nodes/test_graph_execution_state.py
View File

@ -121,3 +121,78 @@ def test_graph_state_collects(mock_services):
assert isinstance(n6[0], CollectInvocation)
assert sorted(g.results[n6[0].id].collection) == sorted(test_prompts)
def test_graph_state_prepares_eagerly(mock_services):
"""Tests that all prepareable nodes are prepared"""
graph = Graph()
test_prompts = ["Banana sushi", "Cat sushi"]
graph.add_node(PromptCollectionTestInvocation(id="prompt_collection", collection=list(test_prompts)))
graph.add_node(IterateInvocation(id="iterate"))
graph.add_node(PromptTestInvocation(id="prompt_iterated"))
graph.add_edge(create_edge("prompt_collection", "collection", "iterate", "collection"))
graph.add_edge(create_edge("iterate", "item", "prompt_iterated", "prompt"))
# separated, fully-preparable chain of nodes
graph.add_node(PromptTestInvocation(id="prompt_chain_1", prompt="Dinosaur sushi"))
graph.add_node(PromptTestInvocation(id="prompt_chain_2"))
graph.add_node(PromptTestInvocation(id="prompt_chain_3"))
graph.add_edge(create_edge("prompt_chain_1", "prompt", "prompt_chain_2", "prompt"))
graph.add_edge(create_edge("prompt_chain_2", "prompt", "prompt_chain_3", "prompt"))
g = GraphExecutionState(graph=graph)
g.next()
assert "prompt_collection" in g.source_prepared_mapping
assert "prompt_chain_1" in g.source_prepared_mapping
assert "prompt_chain_2" in g.source_prepared_mapping
assert "prompt_chain_3" in g.source_prepared_mapping
assert "iterate" not in g.source_prepared_mapping
assert "prompt_iterated" not in g.source_prepared_mapping
def test_graph_executes_depth_first(mock_services):
"""Tests that the graph executes depth-first, executing a branch as far as possible before moving to the next branch"""
graph = Graph()
test_prompts = ["Banana sushi", "Cat sushi"]
graph.add_node(PromptCollectionTestInvocation(id="prompt_collection", collection=list(test_prompts)))
graph.add_node(IterateInvocation(id="iterate"))
graph.add_node(PromptTestInvocation(id="prompt_iterated"))
graph.add_node(PromptTestInvocation(id="prompt_successor"))
graph.add_edge(create_edge("prompt_collection", "collection", "iterate", "collection"))
graph.add_edge(create_edge("iterate", "item", "prompt_iterated", "prompt"))
graph.add_edge(create_edge("prompt_iterated", "prompt", "prompt_successor", "prompt"))
g = GraphExecutionState(graph=graph)
n1 = invoke_next(g, mock_services)
n2 = invoke_next(g, mock_services)
n3 = invoke_next(g, mock_services)
n4 = invoke_next(g, mock_services)
# Because ordering is not guaranteed, we cannot compare results directly.
# Instead, we must count the number of results.
def get_completed_count(g, id):
ids = [i for i in g.source_prepared_mapping[id]]
completed_ids = [i for i in g.executed if i in ids]
return len(completed_ids)
# Check at each step that the number of executed nodes matches the expectation for depth-first execution
assert get_completed_count(g, "prompt_iterated") == 1
assert get_completed_count(g, "prompt_successor") == 0
n5 = invoke_next(g, mock_services)
assert get_completed_count(g, "prompt_iterated") == 1
assert get_completed_count(g, "prompt_successor") == 1
n6 = invoke_next(g, mock_services)
assert get_completed_count(g, "prompt_iterated") == 2
assert get_completed_count(g, "prompt_successor") == 1
n7 = invoke_next(g, mock_services)
assert get_completed_count(g, "prompt_iterated") == 2
assert get_completed_count(g, "prompt_successor") == 2