Merge branch 'main' into lstein/new-model-manager

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")
+                                                  description="control model used")
-    control_weight: float = Field(default=1.0, ge=0, le=1, description="The weight given to the ControlNet")
+    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
         )

invokeai/app/invocations/latent.py

@@ -3,8 +3,6 @@
 from contextlib import ExitStack
 from typing import List, Literal, Optional, Union
 
-import einops
-
 from pydantic import BaseModel, Field, validator
 import torch
 from diffusers import ControlNetModel
@@ -173,23 +171,36 @@ 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" )
-    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'")
-
     unet: UNetField = Field(default=None, description="UNet submodel")
     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"
                 }
             },
         }
@@ -210,10 +221,10 @@ class TextToLatentsInvocation(BaseInvocation):
         uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
 
         conditioning_data = ConditioningData(
-            uc,
+            unconditioned_embeddings=uc,
-            c,
+            text_embeddings=c,
-            self.cfg_scale,
+            guidance_scale=self.cfg_scale,
-            extra_conditioning_info,
+            extra=extra_conditioning_info,
             postprocessing_settings=PostprocessingSettings(
                 threshold=0.0,#threshold,
                 warmup=0.2,#warmup,
@@ -351,10 +362,10 @@ class TextToLatentsInvocation(BaseInvocation):
 
             loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
 
-            print("type of control input: ", type(self.control))
+            control_data = self.prep_control_data(model=model, context=context, control_input=self.control,
-            control_data = self.prep_control_data(model=pipeline, context=context, control_input=self.control,
+                                              latents_shape=noise.shape,
-                                                  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,)
 
             with ModelPatcher.apply_lora_unet(pipeline.unet, loras):
                 # TODO: Verify the noise is the right size
@@ -364,7 +375,7 @@ class TextToLatentsInvocation(BaseInvocation):
                     num_inference_steps=self.steps,
                     conditioning_data=conditioning_data,
                     control_data=control_data, # list[ControlNetData]
-                    callback=step_callback
+                    callback=step_callback,
                 )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
@@ -391,6 +402,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
                 "type_hints": {
                     "model": "model",
                     "control": "control",
+                    "cfg_scale": "number",
                 }
             },
         }
@@ -421,6 +433,12 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
             pipeline = self.create_pipeline(unet, scheduler)
             conditioning_data = self.get_conditioning_data(context, scheduler)
+            
+            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=True,
+                                              )
 
             # TODO: Verify the noise is the right size
             initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
@@ -442,6 +460,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
                     noise=noise,
                     num_inference_steps=self.steps,
                     conditioning_data=conditioning_data,
+                    control_data=control_data,  # list[ControlNetData]
                     callback=step_callback
                 )
 

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
+        )

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"""

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:

invokeai/backend/generator/txt2img.py

@@ -6,7 +6,7 @@ import torch
 
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 from diffusers.models.controlnet import ControlNetModel, ControlNetOutput
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
+from diffusers.pipelines.controlnet import MultiControlNetModel
 
 from ..stable_diffusion import (
     ConditioningData,

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -23,7 +23,7 @@ from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
     StableDiffusionPipeline,
 )
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
+from diffusers.pipelines.controlnet import MultiControlNetModel
 
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import (
     StableDiffusionImg2ImgPipeline,
@@ -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,

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"
             )
 

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;

invokeai/frontend/web/src/common/components/IAICustomSelect.tsx

@@ -18,7 +18,7 @@ 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 };
@@ -39,6 +39,7 @@ type IAICustomSelectProps = {
   tooltip?: string;
   tooltipProps?: Omit<TooltipProps, 'children'>;
   ellipsisPosition?: 'start' | 'end';
+  isDisabled?: boolean;
 };
 
 const IAICustomSelect = (props: IAICustomSelectProps) => {
@@ -52,6 +53,7 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
     data,
     value,
     onChange,
+    isDisabled = false,
   } = props;
 
   const values = useMemo(() => {
@@ -86,11 +88,17 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
     },
   });
 
-  const { refs, floatingStyles } = useFloating<HTMLButtonElement>({
+  const { refs, floatingStyles, update } = useFloating<HTMLButtonElement>({
-    whileElementsMounted: autoUpdate,
+    // 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';
@@ -124,6 +132,8 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
             px: 2,
             gap: 2,
             justifyContent: 'space-between',
+            pointerEvents: isDisabled ? 'none' : undefined,
+            opacity: isDisabled ? 0.5 : undefined,
             ...getInputOutlineStyles(),
           }}
         >

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 />

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 && (

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}
     />
   );
 };

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',
+                  ...SLIDER_MARK_STYLES,
-                  fontWeight: '500',
-                  color: 'base.200',
-                  mt: 1.5,
                 }}
               >
                 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>
   );

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

@@ -1,50 +1,85 @@
-import { useAppDispatch } from 'app/store/storeHooks';
+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,
   ControlNetModelName,
 } from 'features/controlNet/store/constants';
 import { controlNetModelChanged } from 'features/controlNet/store/controlNetSlice';
+import { configSelector } from 'features/system/store/configSelectors';
 import { map } from 'lodash-es';
-import { memo, useCallback } from 'react';
+import { ChangeEvent, memo, useCallback } from 'react';
 
 type ParamControlNetModelProps = {
   controlNetId: string;
   model: ControlNetModelName;
 };
 
-const DATA: IAICustomSelectOption[] = map(CONTROLNET_MODELS, (m) => ({
+const selector = createSelector(configSelector, (config) => {
-  value: m.type,
+  return map(CONTROLNET_MODELS, (m) => ({
-  label: m.label,
+    key: m.label,
-  tooltip: m.type,
+    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 ControlNetModelName;
+      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 }}
-      data={DATA}
+      validValues={controlNetModels}
       value={model}
       onChange={handleModelChanged}
-      ellipsisPosition="start"
+      isDisabled={!isReady}
-      withCheckIcon
+      // 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);

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

@@ -1,62 +1,115 @@
 import IAICustomSelect, {
   IAICustomSelectOption,
 } from 'common/components/IAICustomSelect';
-import { memo, useCallback } from 'react';
+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: IAICustomSelectOption[] = map(
+const CONTROLNET_PROCESSOR_TYPES = map(CONTROLNET_PROCESSORS, (p) => ({
-  CONTROLNET_PROCESSORS,
+  value: p.type,
-  (p) => ({
+  key: p.label,
-    value: p.type,
+})).sort((a, b) =>
-    label: p.label,
-    tooltip: p.description,
-  })
-).sort((a, b) =>
   // sort 'none' to the top
-  a.value === 'none'
+  a.value === 'none' ? -1 : b.value === 'none' ? 1 : a.key.localeCompare(b.key)
-    ? -1
-    : b.value === 'none'
-    ? 1
-    : a.label.localeCompare(b.label)
 );
 
+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"
       value={processorNode.type ?? 'canny_image_processor'}
-      data={CONTROLNET_PROCESSOR_TYPES}
+      validValues={controlNetProcessors}
       onChange={handleProcessorTypeChanged}
-      withCheckIcon
+      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);

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
+      min={-1}
-      withReset
-      handleReset={handleWeightReset}
-      withSliderMarks
-      min={0}
       max={1}
       step={0.01}
+      withSliderMarks={!mini}
+      sliderMarks={[-1, 0, 1]}
     />
   );
 };

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>
   );

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>
   );

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>
   );

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>
   );

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>
   );

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>
   );

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>
   );

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>
   );

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>
   );

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>
   );

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>
   );
 };

invokeai/frontend/web/src/features/controlNet/store/constants.ts

@@ -23,7 +23,7 @@ type ControlNetProcessorsDict = Record<
  *
  * TODO: Generate from the OpenAPI schema
  */
-export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
+export const CONTROLNET_PROCESSORS = {
   none: {
     type: 'none',
     label: 'none',
@@ -129,7 +129,7 @@ export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
   },
   normalbae_image_processor: {
     type: 'normalbae_image_processor',
-    label: 'NormalBae',
+    label: 'Normal BAE',
     description: '',
     default: {
       id: 'normalbae_image_processor',
@@ -181,7 +181,7 @@ type ControlNetModel = {
   defaultProcessor?: ControlNetProcessorType;
 };
 
-export const CONTROLNET_MODELS: Record<string, ControlNetModel> = {
+export const CONTROLNET_MODELS = {
   'lllyasviel/control_v11p_sd15_canny': {
     type: 'lllyasviel/control_v11p_sd15_canny',
     label: 'Canny',
@@ -208,7 +208,7 @@ export const CONTROLNET_MODELS: Record<string, ControlNetModel> = {
   },
   'lllyasviel/control_v11p_sd15_seg': {
     type: 'lllyasviel/control_v11p_sd15_seg',
-    label: 'Segment Anything',
+    label: 'Segmentation',
   },
   'lllyasviel/control_v11p_sd15_lineart': {
     type: 'lllyasviel/control_v11p_sd15_lineart',

invokeai/frontend/web/src/features/nodes/types/constants.ts

@@ -21,6 +21,8 @@ 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;

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,

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(
@@ -35,24 +36,39 @@ const ParamScheduler = () => {
   const { t } = useTranslation();
 
   const handleChange = useCallback(
-    (v: string | null | undefined) => {
+    (e: ChangeEvent<HTMLSelectElement>) => {
-      if (!v) {
+      dispatch(setScheduler(e.target.value as Scheduler));
-        return;
-      }
-      dispatch(setScheduler(v 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')}
       value={scheduler}
-      data={allSchedulers}
+      validValues={allSchedulers}
       onChange={handleChange}
-      withCheckIcon
     />
   );
+
+  // return (
+  //   <IAICustomSelect
+  //     label={t('parameters.scheduler')}
+  //     value={scheduler}
+  //     data={allSchedulers}
+  //     onChange={handleChange}
+  //     withCheckIcon
+  //   />
+  // );
 };
 
 export default memo(ParamScheduler);

invokeai/frontend/web/src/features/system/components/ModelSelect.tsx

@@ -1,5 +1,5 @@
 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';
 
@@ -11,6 +11,7 @@ import { generationSelector } from 'features/parameters/store/generationSelector
 import IAICustomSelect, {
   IAICustomSelectOption,
 } from 'common/components/IAICustomSelect';
+import IAISelect from 'common/components/IAISelect';
 
 const selector = createSelector(
   [(state: RootState) => state, generationSelector],
@@ -18,12 +19,18 @@ const selector = createSelector(
     const selectedModel = selectModelsById(state, generation.model);
 
     const modelData = selectModelsAll(state)
-      .map<IAICustomSelectOption>((m) => ({
+      .map((m) => ({
         value: m.name,
-        label: m.name,
+        key: m.name,
-        tooltip: m.description,
       }))
-      .sort((a, b) => a.label.localeCompare(b.label));
+      .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 {
       selectedModel,
       modelData,
@@ -41,26 +48,43 @@ const ModelSelect = () => {
   const { t } = useTranslation();
   const { selectedModel, modelData } = useAppSelector(selector);
   const handleChangeModel = useCallback(
-    (v: string | null | undefined) => {
+    (e: ChangeEvent<HTMLSelectElement>) => {
-      if (!v) {
+      dispatch(modelSelected(e.target.value));
-        return;
-      }
-      dispatch(modelSelected(v));
     },
     [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}
-      data={modelData}
+      validValues={modelData}
       value={selectedModel?.name ?? ''}
       onChange={handleChangeModel}
-      withCheckIcon={true}
       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);

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,

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;

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;
 };

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;
+};
+

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',
   };

pyproject.toml

@@ -42,8 +42,9 @@ dependencies = [
   "controlnet-aux>=0.0.4",
   "timm==0.6.13",           # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
   "datasets",
-  "diffusers[torch]~=0.16.1",
+  "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",