diff --git a/invokeai/app/api_app.py b/invokeai/app/api_app.py
index df3337cf81..fa46762d56 100644
--- a/invokeai/app/api_app.py
+++ b/invokeai/app/api_app.py
@@ -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():
diff --git a/invokeai/app/cli_app.py b/invokeai/app/cli_app.py
index d9dec16b32..42f0f8cde3 100644
--- a/invokeai/app/cli_app.py
+++ b/invokeai/app/cli_app.py
@@ -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(
@@ -191,15 +196,8 @@ 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()
     parser.add_argument('commands',nargs='*')
diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py
index 15aecde851..b32afe4941 100644
--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -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
         )
diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index 56f5ffbd81..679cc23dcd 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -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
 
diff --git a/invokeai/app/invocations/param_easing.py b/invokeai/app/invocations/param_easing.py
new file mode 100644
index 0000000000..1ff6261b88
--- /dev/null
+++ b/invokeai/app/invocations/param_easing.py
@@ -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
+        )
diff --git a/invokeai/app/models/metadata.py b/invokeai/app/models/metadata.py
index ac87405423..8d90ca0bc8 100644
--- a/invokeai/app/models/metadata.py
+++ b/invokeai/app/models/metadata.py
@@ -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"""
diff --git a/invokeai/app/services/graph.py b/invokeai/app/services/graph.py
index 60e196faa1..e3cd3d47ce 100644
--- a/invokeai/app/services/graph.py
+++ b/invokeai/app/services/graph.py
@@ -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
 
diff --git a/invokeai/app/services/invoker.py b/invokeai/app/services/invoker.py
index a7c9ae444d..f12ba79c15 100644
--- a/invokeai/app/services/invoker.py
+++ b/invokeai/app/services/invoker.py
@@ -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()
diff --git a/invokeai/backend/install/invokeai_configure.py b/invokeai/backend/install/invokeai_configure.py
index aaa744ba09..603760c0c1 100755
--- a/invokeai/backend/install/invokeai_configure.py
+++ b/invokeai/backend/install/invokeai_configure.py
@@ -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()
 
diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py
index 4fcc4dee8d..c41b7684e2 100644
--- a/invokeai/backend/model_management/model_manager.py
+++ b/invokeai/backend/model_management/model_manager.py
@@ -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"
diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index 3c2b119d22..ffc0f30692 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -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,
diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
index 79043b13f5..eec8097857 100644
--- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
+++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
@@ -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
@@ -497,7 +504,7 @@ class InvokeAIDiffuserComponent:
             logger.debug(
                 f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}"
             )
-            logger.debug(   
+            logger.debug(
                 f"{outside / latents.numel() * 100:.2f}% values outside threshold"
             )
 
diff --git a/invokeai/backend/util/logging.py b/invokeai/backend/util/logging.py
index 970cfa9658..e664061bc3 100644
--- a/invokeai/backend/util/logging.py
+++ b/invokeai/backend/util/logging.py
@@ -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))
diff --git a/invokeai/frontend/install/model_install.py b/invokeai/frontend/install/model_install.py
index d956299bdf..265c456e3a 100644
--- a/invokeai/frontend/install/model_install.py
+++ b/invokeai/frontend/install/model_install.py
@@ -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(
diff --git a/invokeai/frontend/web/src/app/components/App.tsx b/invokeai/frontend/web/src/app/components/App.tsx
index bb2f140716..ddc6dace27 100644
--- a/invokeai/frontend/web/src/app/components/App.tsx
+++ b/invokeai/frontend/web/src/app/components/App.tsx
@@ -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 (
     <>
diff --git a/invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx b/invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx
index c6349be9a7..104073c023 100644
--- a/invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx
+++ b/invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx
@@ -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
diff --git a/invokeai/frontend/web/src/app/types/invokeai.ts b/invokeai/frontend/web/src/app/types/invokeai.ts
index 4931c498bf..4532783ae6 100644
--- a/invokeai/frontend/web/src/app/types/invokeai.ts
+++ b/invokeai/frontend/web/src/app/types/invokeai.ts
@@ -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;
diff --git a/invokeai/frontend/web/src/common/components/IAICustomSelect.tsx b/invokeai/frontend/web/src/common/components/IAICustomSelect.tsx
index 9accceb846..1d9ae763b1 100644
--- a/invokeai/frontend/web/src/common/components/IAICustomSelect.tsx
+++ b/invokeai/frontend/web/src/common/components/IAICustomSelect.tsx
@@ -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>
diff --git a/invokeai/frontend/web/src/common/components/IAISlider.tsx b/invokeai/frontend/web/src/common/components/IAISlider.tsx
index 2777e35967..a435b50bbf 100644
--- a/invokeai/frontend/web/src/common/components/IAISlider.tsx
+++ b/invokeai/frontend/web/src/common/components/IAISlider.tsx
@@ -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 />
diff --git a/invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasSettingsButtonPopover.tsx b/invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasSettingsButtonPopover.tsx
index 638332809c..ae03df8409 100644
--- a/invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasSettingsButtonPopover.tsx
+++ b/invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasSettingsButtonPopover.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts b/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts
index 4742de0483..dc86783642 100644
--- a/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts
+++ b/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts
@@ -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;
diff --git a/invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx b/invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
index 5001a9ba24..927daa9dc0 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
@@ -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 && (
diff --git a/invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx b/invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx
index c60d0ee95b..1b3553e3be 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx
@@ -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}
     />
   );
 };
diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx
index bb2f151193..7d0c53fe40 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx
index 187d296a4f..cbdd727b42 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx
@@ -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);
diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx
index 019b5ef849..b48fa50188 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx
@@ -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);
diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx
index 007ef355c3..c2b77125d0 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx
@@ -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]}
     />
   );
 };
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/CannyProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/CannyProcessor.tsx
index 6887d1abb0..1b62056c7a 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/CannyProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/CannyProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/ContentShuffleProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/ContentShuffleProcessor.tsx
index 7ce6ab2297..70f97fc6ad 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/ContentShuffleProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/ContentShuffleProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/HedProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/HedProcessor.tsx
index a1aced5a8f..c51a44d1c3 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/HedProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/HedProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/LineartAnimeProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/LineartAnimeProcessor.tsx
index 17dc9b43df..bc64e3c843 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/LineartAnimeProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/LineartAnimeProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/LineartProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/LineartProcessor.tsx
index 99765ff62f..11245bf4a7 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/LineartProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/LineartProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/MediapipeFaceProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/MediapipeFaceProcessor.tsx
index 6e1a3959f2..27aa22ca40 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/MediapipeFaceProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/MediapipeFaceProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/MidasDepthProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/MidasDepthProcessor.tsx
index a552c90f3a..ffecb68061 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/MidasDepthProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/MidasDepthProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/MlsdImageProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/MlsdImageProcessor.tsx
index d753d3b266..728c4b44fa 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/MlsdImageProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/MlsdImageProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/NormalBaeProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/NormalBaeProcessor.tsx
index ea3270adb3..d5dce8d492 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/NormalBaeProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/NormalBaeProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/OpenposeProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/OpenposeProcessor.tsx
index 57b45fffa4..e97b933b6b 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/OpenposeProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/OpenposeProcessor.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/controlNet/components/processors/PidiProcessor.tsx b/invokeai/frontend/web/src/features/controlNet/components/processors/PidiProcessor.tsx
index 7fb5b92b9c..8251447195 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/processors/PidiProcessor.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/processors/PidiProcessor.tsx
@@ -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>
   );
 };
diff --git a/invokeai/frontend/web/src/features/controlNet/store/constants.ts b/invokeai/frontend/web/src/features/controlNet/store/constants.ts
index c8689badf5..7df0fda8e6 100644
--- a/invokeai/frontend/web/src/features/controlNet/store/constants.ts
+++ b/invokeai/frontend/web/src/features/controlNet/store/constants.ts
@@ -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;
diff --git a/invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts b/invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
index 2558a38ab2..d71ff4da68 100644
--- a/invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
+++ b/invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
@@ -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[
diff --git a/invokeai/frontend/web/src/features/nodes/types/constants.ts b/invokeai/frontend/web/src/features/nodes/types/constants.ts
index a9ae209178..e69625778b 100644
--- a/invokeai/frontend/web/src/features/nodes/types/constants.ts
+++ b/invokeai/frontend/web/src/features/nodes/types/constants.ts
@@ -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;
diff --git a/invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts b/invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts
index 6ae3f21bdf..9aab13784f 100644
--- a/invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts
@@ -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,
diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx
index f4413c4cf6..b12345ae8d 100644
--- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx
+++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx
@@ -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);
diff --git a/invokeai/frontend/web/src/features/system/components/ClearTempFolderButtonModal.tsx b/invokeai/frontend/web/src/features/system/components/ClearTempFolderButtonModal.tsx
deleted file mode 100644
index a220c93b3f..0000000000
--- a/invokeai/frontend/web/src/features/system/components/ClearTempFolderButtonModal.tsx
+++ /dev/null
@@ -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;
diff --git a/invokeai/frontend/web/src/features/system/components/ModelSelect.tsx b/invokeai/frontend/web/src/features/system/components/ModelSelect.tsx
index be4be8ceaa..48be6d263a 100644
--- a/invokeai/frontend/web/src/features/system/components/ModelSelect.tsx
+++ b/invokeai/frontend/web/src/features/system/components/ModelSelect.tsx
@@ -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);
diff --git a/invokeai/frontend/web/src/features/system/store/configSlice.ts b/invokeai/frontend/web/src/features/system/store/configSlice.ts
index 5f4dd68959..02440894a2 100644
--- a/invokeai/frontend/web/src/features/system/store/configSlice.ts
+++ b/invokeai/frontend/web/src/features/system/store/configSlice.ts
@@ -10,6 +10,8 @@ export const initialConfigState: AppConfig = {
   disabledSDFeatures: [],
   canRestoreDeletedImagesFromBin: true,
   sd: {
+    disabledControlNetModels: [],
+    disabledControlNetProcessors: [],
     iterations: {
       initial: 1,
       min: 1,
diff --git a/invokeai/frontend/web/src/features/system/store/systemSelectors.ts b/invokeai/frontend/web/src/features/system/store/systemSelectors.ts
index d9fd836ece..e280210069 100644
--- a/invokeai/frontend/web/src/features/system/store/systemSelectors.ts
+++ b/invokeai/frontend/web/src/features/system/store/systemSelectors.ts
@@ -47,3 +47,6 @@ export const languageSelector = createSelector(
   (system) => system.language,
   defaultSelectorOptions
 );
+
+export const isProcessingSelector = (state: RootState) =>
+  state.system.isProcessing;
diff --git a/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx b/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx
index c164b87515..b566967b7c 100644
--- a/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx
@@ -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(
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasBeta/UnifiedCanvasToolSettings/UnifiedCanvasSettings.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasBeta/UnifiedCanvasToolSettings/UnifiedCanvasSettings.tsx
index a173211258..a179a95c3f 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasBeta/UnifiedCanvasToolSettings/UnifiedCanvasSettings.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasBeta/UnifiedCanvasToolSettings/UnifiedCanvasSettings.tsx
@@ -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>
   );
diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasContent.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasContent.tsx
index 1fadd0ada5..898f7db839 100644
--- a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasContent.tsx
+++ b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasContent.tsx
@@ -55,8 +55,6 @@ const UnifiedCanvasContent = () => {
   });
 
   useLayoutEffect(() => {
-    dispatch(requestCanvasRescale());
-
     const resizeCallback = () => {
       dispatch(requestCanvasRescale());
     };
diff --git a/invokeai/frontend/web/src/services/api/models/HedImageProcessorInvocation.ts b/invokeai/frontend/web/src/services/api/models/HedImageProcessorInvocation.ts
index 1132012c5a..6dea43dc32 100644
--- a/invokeai/frontend/web/src/services/api/models/HedImageProcessorInvocation.ts
+++ b/invokeai/frontend/web/src/services/api/models/HedImageProcessorInvocation.ts
@@ -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;
 };
diff --git a/invokeai/frontend/web/src/services/api/models/HedImageprocessorInvocation.ts b/invokeai/frontend/web/src/services/api/models/HedImageprocessorInvocation.ts
new file mode 100644
index 0000000000..6dea43dc32
--- /dev/null
+++ b/invokeai/frontend/web/src/services/api/models/HedImageprocessorInvocation.ts
@@ -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;
+};
+
diff --git a/invokeai/frontend/web/src/theme/components/slider.ts b/invokeai/frontend/web/src/theme/components/slider.ts
index ef3d84196e..e706aab7b8 100644
--- a/invokeai/frontend/web/src/theme/components/slider.ts
+++ b/invokeai/frontend/web/src/theme/components/slider.ts
@@ -30,7 +30,7 @@ const invokeAIMark = defineStyle((_props) => {
   return {
     fontSize: 'xs',
     fontWeight: '500',
-    color: 'base.200',
+    color: 'base.400',
     mt: 2,
     insetInlineStart: 'unset',
   };
diff --git a/pyproject.toml b/pyproject.toml
index 3af9b005dd..c88e2bdcd2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -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",
diff --git a/tests/nodes/test_graph_execution_state.py b/tests/nodes/test_graph_execution_state.py
index 9f433aa330..5363cc480b 100644
--- a/tests/nodes/test_graph_execution_state.py
+++ b/tests/nodes/test_graph_execution_state.py
@@ -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