merge with main

docs/features/LOGGING.md

@@ -0,0 +1,171 @@
+---
+title: Controlling Logging
+---
+
+# :material-image-off: Controlling Logging
+
+## Controlling How InvokeAI Logs Status Messages
+
+InvokeAI logs status messages using a configurable logging system. You
+can log to the terminal window, to a designated file on the local
+machine, to the syslog facility on a Linux or Mac, or to a properly
+configured web server. You can configure several logs at the same
+time, and control the level of message logged and the logging format
+(to a limited extent).
+
+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:
+
+```bash
+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:
+
+```bash
+### 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
+```

docs/features/index.md

@@ -57,6 +57,9 @@ Personalize models by adding your own style or subjects.
 ## * [The NSFW Checker](NSFW.md)
 Prevent InvokeAI from displaying unwanted racy images.
 
+## * [Controlling Logging](LOGGING.md)
+Control how InvokeAI logs status messages.
+
 ## * [Miscellaneous](OTHER.md)
 Run InvokeAI on Google Colab, generate images with repeating patterns,
 batch process a file of prompts, increase the "creativity" of image

invokeai/app/api_app.py

@@ -39,7 +39,8 @@ socket_io = SocketIO(app)
 
 # initialize config
 # this is a module global
-app_config = InvokeAIAppConfig()
+app_config = InvokeAIAppConfig.get_config()
+app_config.parse_args()
 
 # Add startup event to load dependencies
 @app.on_event("startup")

invokeai/app/cli_app.py

@@ -39,7 +39,7 @@ from .services.model_manager_service import ModelManagerService
 from .services.processor import DefaultInvocationProcessor
 from .services.restoration_services import RestorationServices
 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
@@ -198,7 +198,8 @@ logger = logger.InvokeAILogger.getLogger()
 
 def invoke_cli():
     # this gets the basic configuration
-    config = get_invokeai_config()
+    config = InvokeAIAppConfig.get_config()
+    config.parse_args()
 
     # get the optional list of invocations to execute on the command line
     parser = config.get_parser()

invokeai/app/invocations/compel.py

@@ -4,12 +4,10 @@ from contextlib import ExitStack
 import re
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-
 from .model import ClipField
 
-from ...backend.util.devices import choose_torch_device, torch_dtype
+from ...backend.util.devices import torch_dtype
 from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
-from ...backend.stable_diffusion.textual_inversion_manager import TextualInversionManager
 from ...backend.model_management import SDModelType
 from ...backend.model_management.lora import ModelPatcher
 
@@ -18,7 +16,7 @@ from compel.prompt_parser import (
     Blend,
     CrossAttentionControlSubstitute,
     FlattenedPrompt,
-    Fragment,
+    Fragment, Conjunction,
 )
 
 
@@ -81,7 +79,7 @@ class CompelInvocation(BaseInvocation):
                             context.services.model_manager.get_model(model_name=name, model_type=SDModelType.TextualInversion)
                         )
                     )
-                except Exception as e:
+                except Exception:
                     #print(e)
                     #import traceback
                     #print(traceback.format_exc())
@@ -98,7 +96,6 @@ class CompelInvocation(BaseInvocation):
                     truncate_long_prompts=True, # TODO:
                 )
                 
-
                 conjunction = Compel.parse_prompt_string(self.prompt)
                 prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]
 
@@ -106,16 +103,15 @@ class CompelInvocation(BaseInvocation):
                     log_tokenization_for_prompt_object(prompt, tokenizer)
 
                 c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
-
+                
                 # TODO: long prompt support
                 #if not self.truncate_long_prompts:
                 #    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
-
                 ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
-                    tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt),
+                    tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                     cross_attention_control_args=options.get("cross_attention_control", None),
                 )
-            
+                
             conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
 
             # TODO: hacky but works ;D maybe rename latents somehow?
@@ -129,14 +125,22 @@ class CompelInvocation(BaseInvocation):
 
 
 def get_max_token_count(
-    tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
+    tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
 ) -> int:
     if type(prompt) is Blend:
         blend: Blend = prompt
         return max(
             [
-                get_max_token_count(tokenizer, c, truncate_if_too_long)
-                for c in blend.prompts
+                get_max_token_count(tokenizer, p, truncate_if_too_long)
+                for p in blend.prompts
+            ]
+        )
+    elif type(prompt) is Conjunction:
+        conjunction: Conjunction = prompt
+        return sum(
+            [
+                get_max_token_count(tokenizer, p, truncate_if_too_long)
+                for p in conjunction.prompts
             ]
         )
     else:
@@ -171,6 +175,22 @@ def get_tokens_for_prompt_object(
     return tokens
 
 
+def log_tokenization_for_conjunction(
+    c: Conjunction, tokenizer, display_label_prefix=None
+):
+    display_label_prefix = display_label_prefix or ""
+    for i, p in enumerate(c.prompts):
+        if len(c.prompts)>1:
+            this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
+        else:
+            this_display_label_prefix = display_label_prefix
+        log_tokenization_for_prompt_object(
+            p,
+            tokenizer,
+            display_label_prefix=this_display_label_prefix
+        )
+
+
 def log_tokenization_for_prompt_object(
     p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
 ):

invokeai/app/invocations/controlnet_image_processors.py

@@ -94,13 +94,13 @@ CONTROLNET_DEFAULT_MODELS = [
 CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
 
 class ControlField(BaseModel):
-    image: ImageField = Field(default=None, description="processed image")
-    control_model: Optional[str] = Field(default=None, description="control model used")
-    control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
+    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")
     begin_step_percent: float = Field(default=0, ge=0, le=1,
-                                                description="% of total steps at which controlnet is first applied")
+                                                description="When the ControlNet is first applied (% of total steps)")
     end_step_percent: float = Field(default=1, ge=0, le=1,
-                                    description="% of total steps at which controlnet is last applied")
+                                    description="When the ControlNet is last applied (% of total steps)")
 
     class Config:
         schema_extra = {
@@ -112,7 +112,7 @@ class ControlOutput(BaseInvocationOutput):
     """node output for ControlNet info"""
     # fmt: off
     type: Literal["control_output"] = "control_output"
-    control: ControlField = Field(default=None, description="The control info dict")
+    control: ControlField = Field(default=None, description="The output control image")
     # fmt: on
 
 
@@ -121,15 +121,15 @@ class ControlNetInvocation(BaseInvocation):
     # fmt: off
     type: Literal["controlnet"] = "controlnet"
     # Inputs
-    image: ImageField = Field(default=None, description="image to process")
+    image: ImageField = Field(default=None, description="The control image")
     control_model: CONTROLNET_NAME_VALUES = Field(default="lllyasviel/sd-controlnet-canny",
-                                                  description="control model used")
-    control_weight: float = Field(default=1.0, ge=0, le=1, description="weight given to controlnet")
+                                                  description="The ControlNet model to use")
+    control_weight: float = Field(default=1.0, ge=0, le=1, 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="% of total steps at which controlnet is first applied")
+                                        description="When the ControlNet is first applied (% of total steps)")
     end_step_percent: float = Field(default=1, ge=0, le=1,
-                                      description="% of total steps at which controlnet is last applied")
+                                      description="When the ControlNet is last applied (% of total steps)")
     # fmt: on
 
 
@@ -152,7 +152,7 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
     # fmt: off
     type: Literal["image_processor"] = "image_processor"
     # Inputs
-    image: ImageField = Field(default=None, description="image to process")
+    image: ImageField = Field(default=None, description="The image to process")
     # fmt: on
 
 
@@ -204,8 +204,8 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfi
     # fmt: off
     type: Literal["canny_image_processor"] = "canny_image_processor"
     # Input
-    low_threshold: float = Field(default=100, ge=0, description="low threshold of Canny pixel gradient")
-    high_threshold: float = Field(default=200, ge=0, description="high threshold of Canny pixel gradient")
+    low_threshold: int = Field(default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)")
+    high_threshold: int = Field(default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)")
     # fmt: on
 
     def run_processor(self, image):
@@ -214,16 +214,16 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfi
         return processed_image
 
 
-class HedImageprocessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
+class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies HED edge detection to image"""
     # fmt: off
     type: Literal["hed_image_processor"] = "hed_image_processor"
     # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="pixel resolution for edge detection")
-    image_resolution: int = Field(default=512, ge=0, description="pixel resolution for output image")
+    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
+    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
     # safe not supported in controlnet_aux v0.0.3
     # safe: bool = Field(default=False, description="whether to use safe mode")
-    scribble: bool = Field(default=False, description="whether to use scribble mode")
+    scribble: bool = Field(default=False, description="Whether to use scribble mode")
     # fmt: on
 
     def run_processor(self, image):
@@ -243,9 +243,9 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCon
     # fmt: off
     type: Literal["lineart_image_processor"] = "lineart_image_processor"
     # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="pixel resolution for edge detection")
-    image_resolution: int = Field(default=512, ge=0, description="pixel resolution for output image")
-    coarse: bool = Field(default=False, description="whether to use coarse mode")
+    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
+    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
+    coarse: bool = Field(default=False, description="Whether to use coarse mode")
     # fmt: on
 
     def run_processor(self, image):
@@ -262,8 +262,8 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocati
     # fmt: off
     type: Literal["lineart_anime_image_processor"] = "lineart_anime_image_processor"
     # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="pixel resolution for edge detection")
-    image_resolution: int = Field(default=512, ge=0, description="pixel resolution for output image")
+    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
+    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
     # fmt: on
 
     def run_processor(self, image):
@@ -280,9 +280,9 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
     # fmt: off
     type: Literal["openpose_image_processor"] = "openpose_image_processor"
     # Inputs
-    hand_and_face: bool = Field(default=False, description="whether to use hands and face mode")
-    detect_resolution: int = Field(default=512, ge=0, description="pixel resolution for edge detection")
-    image_resolution: int = Field(default=512, ge=0, description="pixel resolution for output image")
+    hand_and_face: bool = Field(default=False, description="Whether to use hands and face mode")
+    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
+    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
     # fmt: on
 
     def run_processor(self, image):
@@ -300,8 +300,8 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocation
     # fmt: off
     type: Literal["midas_depth_image_processor"] = "midas_depth_image_processor"
     # Inputs
-    a_mult: float = Field(default=2.0, ge=0, description="Midas parameter a = amult * PI")
-    bg_th: float = Field(default=0.1, ge=0, description="Midas parameter bg_th")
+    a_mult: float = Field(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
+    bg_th: float = Field(default=0.1, ge=0, description="Midas parameter `bg_th`")
     # depth_and_normal not supported in controlnet_aux v0.0.3
     # depth_and_normal: bool = Field(default=False, description="whether to use depth and normal mode")
     # fmt: on
@@ -322,8 +322,8 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationC
     # fmt: off
     type: Literal["normalbae_image_processor"] = "normalbae_image_processor"
     # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="pixel resolution for edge detection")
-    image_resolution: int = Field(default=512, ge=0, description="pixel resolution for output image")
+    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
+    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
     # fmt: on
 
     def run_processor(self, image):
@@ -339,10 +339,10 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig
     # fmt: off
     type: Literal["mlsd_image_processor"] = "mlsd_image_processor"
     # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="pixel resolution for edge detection")
-    image_resolution: int = Field(default=512, ge=0, description="pixel resolution for output image")
-    thr_v: float = Field(default=0.1, ge=0, description="MLSD parameter thr_v")
-    thr_d: float = Field(default=0.1, ge=0, description="MLSD parameter thr_d")
+    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
+    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
+    thr_v: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_v`")
+    thr_d: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_d`")
     # fmt: on
 
     def run_processor(self, image):
@@ -360,10 +360,10 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig
     # fmt: off
     type: Literal["pidi_image_processor"] = "pidi_image_processor"
     # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="pixel resolution for edge detection")
-    image_resolution: int = Field(default=512, ge=0, description="pixel resolution for output image")
-    safe: bool = Field(default=False, description="whether to use safe mode")
-    scribble: bool = Field(default=False, description="whether to use scribble mode")
+    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
+    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
+    safe: bool = Field(default=False, description="Whether to use safe mode")
+    scribble: bool = Field(default=False, description="Whether to use scribble mode")
     # fmt: on
 
     def run_processor(self, image):
@@ -381,11 +381,11 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvoca
     # fmt: off
     type: Literal["content_shuffle_image_processor"] = "content_shuffle_image_processor"
     # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="pixel resolution for edge detection")
-    image_resolution: int = Field(default=512, ge=0, description="pixel resolution for output image")
-    h: Union[int | None] = Field(default=512, ge=0, description="content shuffle h parameter")
-    w: Union[int | None] = Field(default=512, ge=0, description="content shuffle w parameter")
-    f: Union[int | None] = Field(default=256, ge=0, description="cont")
+    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
+    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
+    h: Union[int, None] = Field(default=512, ge=0, description="Content shuffle `h` parameter")
+    w: Union[int, None] = Field(default=512, ge=0, description="Content shuffle `w` parameter")
+    f: Union[int, None] = Field(default=256, ge=0, description="Content shuffle `f` parameter")
     # fmt: on
 
     def run_processor(self, image):
@@ -418,8 +418,8 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
     # fmt: off
     type: Literal["mediapipe_face_processor"] = "mediapipe_face_processor"
     # Inputs
-    max_faces: int = Field(default=1, ge=1, description="maximum number of faces to detect")
-    min_confidence: float = Field(default=0.5, ge=0, le=1, description="minimum confidence for face detection")
+    max_faces: int = Field(default=1, ge=1, description="Maximum number of faces to detect")
+    min_confidence: float = Field(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
     # fmt: on
 
     def run_processor(self, image):

invokeai/app/invocations/latent.py

@@ -4,11 +4,12 @@ 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
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.schedulers import SchedulerMixin as Scheduler
-from pydantic import BaseModel, Field, validator
 
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.app.util.step_callback import stable_diffusion_step_callback

invokeai/app/services/config.py

@@ -51,18 +51,32 @@ in INVOKEAI_ROOT. You can replace supersede this by providing any
 OmegaConf dictionary object initialization time:
 
  omegaconf = OmegaConf.load('/tmp/init.yaml')
- conf = InvokeAIAppConfig(conf=omegaconf)
+ conf = InvokeAIAppConfig()
+ conf.parse_args(conf=omegaconf)
 
-By default, InvokeAIAppConfig will parse the contents of `sys.argv` at
-initialization time. You may pass a list of strings in the optional
+InvokeAIAppConfig.parse_args() will parse the contents of `sys.argv`
+at initialization time. You may pass a list of strings in the optional
 `argv` argument to use instead of the system argv:
 
- conf = InvokeAIAppConfig(arg=['--xformers_enabled'])
+ conf.parse_args(argv=['--xformers_enabled'])
 
-It is also possible to set a value at initialization time. This value
-has highest priority.
+It is also possible to set a value at initialization time. However, if
+you call parse_args() it may be overwritten.
 
  conf = InvokeAIAppConfig(xformers_enabled=True)
+ conf.parse_args(argv=['--no-xformers'])
+ conf.xformers_enabled
+ # False
+
+
+To avoid this, use `get_config()` to retrieve the application-wide
+configuration object. This will retain any properties set at object
+creation time:
+
+ conf = InvokeAIAppConfig.get_config(xformers_enabled=True)
+ conf.parse_args(argv=['--no-xformers'])
+ conf.xformers_enabled
+ # True
 
 Any setting can be overwritten by setting an environment variable of
 form: "INVOKEAI_<setting>", as in:
@@ -76,18 +90,23 @@ Order of precedence (from highest):
    4) config file options
    5) pydantic defaults
 
-Typical usage:
+Typical usage at the top level file:
 
  from invokeai.app.services.config import InvokeAIAppConfig
- from invokeai.invocations.generate import TextToImageInvocation
 
  # get global configuration and print its nsfw_checker value
- conf = InvokeAIAppConfig()
+ conf = InvokeAIAppConfig.get_config()
+ conf.parse_args()
+ print(conf.nsfw_checker)
+
+Typical usage in a backend module:
+
+ from invokeai.app.services.config import InvokeAIAppConfig
+
+ # get global configuration and print its nsfw_checker value
+ conf = InvokeAIAppConfig.get_config()
  print(conf.nsfw_checker)
 
- # get the text2image invocation and print its step value
- text2image = TextToImageInvocation()
- print(text2image.steps)
 
 Computed properties:
 
@@ -103,10 +122,11 @@ a Path object:
  lora_path          - path to the LoRA directory
 
 In most cases, you will want to create a single InvokeAIAppConfig
-object for the entire application. The get_invokeai_config() function
+object for the entire application. The InvokeAIAppConfig.get_config() function
 does this:
 
-  config = get_invokeai_config()
+  config = InvokeAIAppConfig.get_config()
+  config.parse_args()   # read values from the command line/config file
   print(config.root)
 
 # Subclassing
@@ -140,24 +160,22 @@ two configs are kept in separate sections of the config file:
         legacy_conf_dir: configs/stable-diffusion
         outdir: outputs
      ...
+
 '''
+from __future__ import annotations
 import argparse
 import pydoc
-import typing
 import os
 import sys
 from argparse import ArgumentParser
 from omegaconf import OmegaConf, DictConfig
 from pathlib import Path
 from pydantic import BaseSettings, Field, parse_obj_as
-from typing import Any, ClassVar, Dict, List, Literal, Type, Union, get_origin, get_type_hints, get_args
+from typing import ClassVar, Dict, List, Literal, Type, Union, get_origin, get_type_hints, get_args
 
 INIT_FILE = Path('invokeai.yaml')
 LEGACY_INIT_FILE = Path('invokeai.init')
 
-# This global stores a singleton InvokeAIAppConfig configuration object
-global_config = None
-
 class InvokeAISettings(BaseSettings):
     '''
     Runtime configuration settings in which default values are
@@ -168,7 +186,7 @@ class InvokeAISettings(BaseSettings):
 
     def parse_args(self, argv: list=sys.argv[1:]):
         parser = self.get_parser()
-        opt, _ = parser.parse_known_args(argv)
+        opt = parser.parse_args(argv)
         for name in self.__fields__:
             if name not in self._excluded():
                 setattr(self, name, getattr(opt,name))
@@ -330,6 +348,9 @@ the command-line client (recommended for experts only), or
 can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
 setting environment variables INVOKEAI_<setting>.
      '''
+    singleton_config: ClassVar[InvokeAIAppConfig] = None
+    singleton_init: ClassVar[Dict] = None
+    
     #fmt: off
     type: Literal["InvokeAI"] = "InvokeAI"
     host                : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
@@ -367,35 +388,51 @@ setting environment variables INVOKEAI_<setting>.
 
     model               : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
     embeddings          : bool = Field(default=True, description='Load contents of embeddings directory', category='Models')
+
+    log_handlers        : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
+    # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
+    log_format          : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
+    log_level           : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="debug", description="Emit logging messages at this level or  higher", category="Logging")
     #fmt: on
 
-    def __init__(self, conf: DictConfig = None, argv: List[str]=None, **kwargs):
+    def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):
         '''
-        Initialize InvokeAIAppconfig.
+        Update settings with contents of init file, environment, and 
+        command-line settings.
         :param conf: alternate Omegaconf dictionary object
         :param argv: aternate sys.argv list
-        :param **kwargs: attributes to initialize with
+        :param clobber: ovewrite any initialization parameters passed during initialization
         '''
-        super().__init__(**kwargs)
-
         # Set the runtime root directory. We parse command-line switches here
         # in order to pick up the --root_dir option.
-        self.parse_args(argv)
+        super().parse_args(argv)
         if conf is None:
             try:
                 conf = OmegaConf.load(self.root_dir / INIT_FILE)
             except:
                 pass
         InvokeAISettings.initconf = conf
-
+        
         # parse args again in order to pick up settings in configuration file
-        self.parse_args(argv)
+        super().parse_args(argv)
 
-        # restore initialization values
-        hints = get_type_hints(self)
-        for k in kwargs:
-            setattr(self,k,parse_obj_as(hints[k],kwargs[k]))
+        if self.singleton_init and not clobber:
+            hints = get_type_hints(self.__class__)
+            for k in self.singleton_init:
+                setattr(self,k,parse_obj_as(hints[k],self.singleton_init[k]))
 
+    @classmethod
+    def get_config(cls,**kwargs)->InvokeAIAppConfig:
+        '''
+        This returns a singleton InvokeAIAppConfig configuration object.
+        '''
+        if cls.singleton_config is None \
+           or type(cls.singleton_config)!=cls \
+           or (kwargs and cls.singleton_init != kwargs):
+            cls.singleton_config = cls(**kwargs)
+            cls.singleton_init = kwargs
+        return cls.singleton_config
+        
     @property
     def root_path(self)->Path:
         '''
@@ -520,11 +557,8 @@ class PagingArgumentParser(argparse.ArgumentParser):
         text = self.format_help()
         pydoc.pager(text)
 
-def get_invokeai_config(cls:Type[InvokeAISettings]=InvokeAIAppConfig,**kwargs)->InvokeAIAppConfig:
+def get_invokeai_config(**kwargs)->InvokeAIAppConfig:
     '''
-    This returns a singleton InvokeAIAppConfig configuration object.
+    Legacy function which returns InvokeAIAppConfig.get_config()
     '''
-    global global_config
-    if global_config is None or type(global_config)!=cls:
-        global_config = cls(**kwargs)
-    return global_config
+    return InvokeAIAppConfig.get_config(**kwargs)

invokeai/app/services/sqlite.py

@@ -26,7 +26,6 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
         self._table_name = table_name
         self._id_field = id_field  # TODO: validate that T has this field
         self._lock = Lock()
-
         self._conn = sqlite3.connect(
             self._filename, check_same_thread=False
         )  # TODO: figure out a better threading solution

invokeai/backend/config/invokeai_configure.py

@@ -35,15 +35,19 @@ from transformers import (
     CLIPTextModel,
     CLIPTokenizer,
 )
-
 import invokeai.configs as configs
 
+from invokeai.app.services.config import (
+    get_invokeai_config,
+    InvokeAIAppConfig,
+)
 from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
 from invokeai.frontend.install.widgets import (
     CenteredButtonPress,
     IntTitleSlider,
     set_min_terminal_size,
 )
+
 from invokeai.backend.config.legacy_arg_parsing import legacy_parser
 from invokeai.backend.config.model_install_backend import (
     default_dataset,
@@ -51,10 +55,8 @@ from invokeai.backend.config.model_install_backend import (
     hf_download_with_resume,
     recommended_datasets,
 )
-from invokeai.app.services.config import (
-    get_invokeai_config,
-    InvokeAIAppConfig,
-)
+
+from invokeai.app.services.config import InvokeAIAppConfig
 
 warnings.filterwarnings("ignore")
 
@@ -62,7 +64,8 @@ transformers.logging.set_verbosity_error()
 
 
 # --------------------------globals-----------------------
-config = get_invokeai_config()
+
+config = InvokeAIAppConfig.get_config()
 
 Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
@@ -634,7 +637,7 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
 
 
 def default_startup_options(init_file: Path) -> Namespace:
-    opts = InvokeAIAppConfig(argv=[])
+    opts = InvokeAIAppConfig.get_config()
     outdir = Path(opts.outdir)
     if not outdir.is_absolute():
         opts.outdir = str(config.root / opts.outdir)
@@ -699,7 +702,7 @@ def write_opts(opts: Namespace, init_file: Path):
     """
 
     # this will load current settings
-    config = InvokeAIAppConfig()
+    config = InvokeAIAppConfig.get_config()
     for key,value in opts.__dict__.items():
         if hasattr(config,key):
             setattr(config,key,value)
@@ -731,7 +734,7 @@ def write_default_options(program_opts: Namespace, initfile: Path):
 # yaml format.
 def migrate_init_file(legacy_format:Path):
     old = legacy_parser.parse_args([f'@{str(legacy_format)}'])
-    new = InvokeAIAppConfig(conf={})
+    new = InvokeAIAppConfig.get_config()
 
     fields = list(get_type_hints(InvokeAIAppConfig).keys())
     for attr in fields:
@@ -820,8 +823,9 @@ def main():
         if old_init_file.exists() and not new_init_file.exists():
             print('** Migrating invokeai.init to invokeai.yaml')
             migrate_init_file(old_init_file)
-            config = get_invokeai_config()  # reread defaults
 
+            # Load new init file into config
+            config.parse_args(argv=[],conf=OmegaConf.load(new_init_file))
 
         if not config.model_conf_path.exists():
             initialize_rootdir(config.root, opt.yes_to_all)

invokeai/backend/config/model_install_backend.py

@@ -19,7 +19,7 @@ from tqdm import tqdm
 
 import invokeai.configs as configs
 
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ..model_management import ModelManager
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
@@ -27,7 +27,8 @@ from ..stable_diffusion import StableDiffusionGeneratorPipeline
 warnings.filterwarnings("ignore")
 
 # --------------------------globals-----------------------
-config = get_invokeai_config()
+config = InvokeAIAppConfig.get_config()
+
 Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
 

invokeai/backend/image_util/patchmatch.py

@@ -6,7 +6,8 @@ be suppressed or deferred
 """
 import numpy as np
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
+config = InvokeAIAppConfig.get_config()
 
 class PatchMatch:
     """
@@ -21,7 +22,6 @@ class PatchMatch:
 
     @classmethod
     def _load_patch_match(self):
-        config = get_invokeai_config()
         if self.tried_load:
             return
         if config.try_patchmatch:

invokeai/backend/image_util/txt2mask.py

@@ -33,10 +33,11 @@ from PIL import Image, ImageOps
 from transformers import AutoProcessor, CLIPSegForImageSegmentation
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
 CLIPSEG_SIZE = 352
+config = InvokeAIAppConfig.get_config()
 
 class SegmentedGrayscale(object):
     def __init__(self, image: Image, heatmap: torch.Tensor):
@@ -83,7 +84,6 @@ class Txt2Mask(object):
 
     def __init__(self, device="cpu", refined=False):
         logger.info("Initializing clipseg model for text to mask inference")
-        config = get_invokeai_config()
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -26,7 +26,7 @@ import torch
 from safetensors.torch import load_file
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 from .model_manager import ModelManager, SDLegacyType
 from .model_cache import ModelCache
@@ -857,7 +857,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
 
 def convert_ldm_clip_checkpoint(checkpoint):
     text_model = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14", cache_dir=get_invokeai_config().cache_dir
+        "openai/clip-vit-large-patch14", cache_dir=InvokeAIAppConfig.get_config().cache_dir
     )
 
     keys = list(checkpoint.keys())
@@ -912,7 +912,7 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 
 
 def convert_paint_by_example_checkpoint(checkpoint):
-    cache_dir = get_invokeai_config().cache_dir
+    cache_dir = InvokeAIAppConfig.get_config().cache_dir
     config = CLIPVisionConfig.from_pretrained(
         "openai/clip-vit-large-patch14", cache_dir=cache_dir
     )
@@ -984,7 +984,7 @@ def convert_paint_by_example_checkpoint(checkpoint):
 
 
 def convert_open_clip_checkpoint(checkpoint):
-    cache_dir = get_invokeai_config().cache_dir
+    cache_dir = InvokeAIAppConfig.get_config().cache_dir
     text_model = CLIPTextModel.from_pretrained(
         "stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir
     )
@@ -1120,7 +1120,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     :param vae: A diffusers VAE to load into the pipeline.
     :param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
     """
-    config = get_invokeai_config()
+    config = InvokeAIAppConfig.get_config()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         verbosity = dlogging.get_verbosity()

invokeai/backend/model_management/model_manager.py

@@ -149,7 +149,7 @@ from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.util import download_with_resume
 
 from ..util import CUDA_DEVICE
@@ -224,7 +224,7 @@ class ModelManager(object):
             raise ValueError('config argument must be an OmegaConf object, a Path or a string')
 
         # check config version number and update on disk/RAM if necessary
-        self.globals = get_invokeai_config()
+        self.globals = InvokeAIAppConfig.get_config()
         self._update_config_file_version()
         self.logger = logger
         self.cache = ModelCache(
@@ -1149,13 +1149,17 @@ class ModelManager(object):
             """\
             # This file describes the alternative machine learning models
             # available to InvokeAI script.
-            """
+            #
+            # To add a new model, follow the examples below. Each
+            # model requires a model config file, a weights file,
+            # and the width and height of the images it
+            # was trained on.
+        """
         )
 
-
     @classmethod
     def _delete_model_from_cache(cls,repo_id):
-        cache_info = scan_cache_dir(get_invokeai_config().cache_dir)
+        cache_info = scan_cache_dir(InvokeAIAppConfig.get_config().cache_dir)
 
         # I'm sure there is a way to do this with comprehensions
         # but the code quickly became incomprehensible!
@@ -1172,7 +1176,7 @@ class ModelManager(object):
 
     @staticmethod
     def _abs_path(path: str | Path) -> Path:
-        globals = get_invokeai_config()
+        globals = InvokeAIAppConfig.get_config()
         if path is None or Path(path).is_absolute():
             return path
         return Path(globals.root_dir, path).resolve()

invokeai/backend/prompting/conditioning.py

@@ -22,10 +22,12 @@ from compel.prompt_parser import (
 
 import invokeai.backend.util.logging as logger
 
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ..stable_diffusion import InvokeAIDiffuserComponent
 from ..util import torch_dtype
 
+config = InvokeAIAppConfig.get_config()
+
 def get_uc_and_c_and_ec(prompt_string,
                         model: InvokeAIDiffuserComponent,
                         log_tokens=False, skip_normalize_legacy_blend=False):
@@ -38,9 +40,7 @@ def get_uc_and_c_and_ec(prompt_string,
                     textual_inversion_manager=model.textual_inversion_manager,
                     dtype_for_device_getter=torch_dtype,
                     truncate_long_prompts=False,
-                    )
-    
-    config = get_invokeai_config()
+                   )
 
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
@@ -283,6 +283,8 @@ def split_weighted_subprompts(text, skip_normalize=False) -> list:
         (match.group("prompt").replace("\\:", ":"), float(match.group("weight") or 1))
         for match in re.finditer(prompt_parser, text)
     ]
+    if len(parsed_prompts) == 0:
+        return []
     if skip_normalize:
         return parsed_prompts
     weight_sum = sum(map(lambda x: x[1], parsed_prompts))

invokeai/backend/restoration/codeformer.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 pretrained_model_url = (
     "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
@@ -18,7 +18,7 @@ class CodeFormerRestoration:
         self, codeformer_dir="models/codeformer", codeformer_model_path="codeformer.pth"
     ) -> None:
 
-        self.globals = get_invokeai_config()
+        self.globals = InvokeAIAppConfig.get_config()
         codeformer_dir = self.globals.root_dir / codeformer_dir
         self.model_path = codeformer_dir / codeformer_model_path
         self.codeformer_model_exists = self.model_path.exists()

invokeai/backend/restoration/gfpgan.py

@@ -7,11 +7,11 @@ import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 class GFPGAN:
     def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
-        self.globals = get_invokeai_config()
+        self.globals = InvokeAIAppConfig.get_config()
         if not os.path.isabs(gfpgan_model_path):
             gfpgan_model_path = self.globals.root_dir / gfpgan_model_path
         self.model_path = gfpgan_model_path

invokeai/backend/restoration/realesrgan.py

@@ -6,8 +6,8 @@ from PIL import Image
 from PIL.Image import Image as ImageType
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
-config = get_invokeai_config()
+from invokeai.app.services.config import InvokeAIAppConfig
+config = InvokeAIAppConfig.get_config()
 
 class ESRGAN:
     def __init__(self, bg_tile_size=400) -> None:

invokeai/backend/safety_checker.py

@@ -15,9 +15,11 @@ from transformers import AutoFeatureExtractor
 
 import invokeai.assets.web as web_assets
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from .util import CPU_DEVICE
 
+config = InvokeAIAppConfig.get_config()
+
 class SafetyChecker(object):
     CAUTION_IMG = "caution.png"
     
@@ -26,7 +28,6 @@ class SafetyChecker(object):
         caution = Image.open(path)
         self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
         self.device = device
-        config = get_invokeai_config()
 
         try:
             safety_model_id = "CompVis/stable-diffusion-safety-checker"

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -17,15 +17,16 @@ from huggingface_hub import (
     hf_hub_url,
 )
 
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.backend.util.logging import InvokeAILogger
+from invokeai.app.services.config import InvokeAIAppConfig
+logger = InvokeAILogger.getLogger()
 
 class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):
         """
         Initialize the Concepts object. May optionally pass a root directory.
         """
-        self.config = get_invokeai_config()
+        self.config = InvokeAIAppConfig.get_config()
         self.root = root or self.config.root
         self.hf_api = HfApi()
         self.local_concepts = dict()

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -40,7 +40,7 @@ from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from typing_extensions import ParamSpec
 
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ..util import CPU_DEVICE, normalize_device
 from .diffusion import (
     AttentionMapSaver,
@@ -364,7 +364,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
-        config = get_invokeai_config()
+        config = InvokeAIAppConfig.get_config()
         if (
             torch.cuda.is_available()
             and is_xformers_available()

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -10,7 +10,7 @@ from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 from .cross_attention_control import (
     Arguments,
@@ -72,7 +72,7 @@ class InvokeAIDiffuserComponent:
         :param model: the unet model to pass through to cross attention control
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
         """
-        config = get_invokeai_config()
+        config = InvokeAIAppConfig.get_config()
         self.conditioning = None
         self.model = model
         self.is_running_diffusers = is_running_diffusers
@@ -112,23 +112,25 @@ class InvokeAIDiffuserComponent:
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 
-    def override_cross_attention(
-        self, conditioning: ExtraConditioningInfo, step_count: int
-    ) -> Dict[str, AttentionProcessor]:
-        """
-        setup cross attention .swap control. for diffusers this replaces the attention processor, so
-        the previous attention processor is returned so that the caller can restore it later.
-        """
-        self.conditioning = conditioning
-        self.cross_attention_control_context = Context(
-            arguments=self.conditioning.cross_attention_control_args,
-            step_count=step_count,
-        )
-        return override_cross_attention(
-            self.model,
-            self.cross_attention_control_context,
-            is_running_diffusers=self.is_running_diffusers,
-        )
+    # apparently unused code
+    # TODO: delete
+    # def override_cross_attention(
+    #     self, conditioning: ExtraConditioningInfo, step_count: int
+    # ) -> Dict[str, AttentionProcessor]:
+    #     """
+    #     setup cross attention .swap control. for diffusers this replaces the attention processor, so
+    #     the previous attention processor is returned so that the caller can restore it later.
+    #     """
+    #     self.conditioning = conditioning
+    #     self.cross_attention_control_context = Context(
+    #         arguments=self.conditioning.cross_attention_control_args,
+    #         step_count=step_count,
+    #     )
+    #     return override_cross_attention(
+    #         self.model,
+    #         self.cross_attention_control_context,
+    #         is_running_diffusers=self.is_running_diffusers,
+    #     )
 
     def restore_default_cross_attention(
         self, restore_attention_processor: Optional["AttentionProcessor"] = None

invokeai/backend/training/textual_inversion_training.py

@@ -88,7 +88,7 @@ def save_progress(
 
 
 def parse_args():
-    config = InvokeAIAppConfig(argv=[])
+    config = InvokeAIAppConfig.get_config()
     parser = PagingArgumentParser(
         description="Textual inversion training"
     )

invokeai/backend/util/__init__.py

@@ -17,3 +17,5 @@ from .util import (
     instantiate_from_config,
     url_attachment_name,
 )
+
+

invokeai/backend/util/devices.py

@@ -4,15 +4,15 @@ from contextlib import nullcontext
 
 import torch
 from torch import autocast
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
+config = InvokeAIAppConfig.get_config()
 
 def choose_torch_device() -> torch.device:
     """Convenience routine for guessing which GPU device to run model on"""
-    config = get_invokeai_config()    
     if config.always_use_cpu:
         return CPU_DEVICE
     if torch.cuda.is_available():
@@ -32,7 +32,6 @@ def choose_precision(device: torch.device) -> str:
 
 
 def torch_dtype(device: torch.device) -> torch.dtype:
-    config = get_invokeai_config()
     if config.full_precision:
         return torch.float32
     if choose_precision(device) == "float16":

invokeai/backend/util/logging.py

@@ -31,7 +31,20 @@ IAILogger.debug('this is a debugging message')
 """
 
 import logging
+import logging.handlers
+import socket
+import urllib.parse
 
+from abc import abstractmethod
+from pathlib import Path
+
+from invokeai.app.services.config import InvokeAIAppConfig, get_invokeai_config
+
+try:
+    import syslog
+    SYSLOG_AVAILABLE = True
+except:
+    SYSLOG_AVAILABLE = False
 
 # module level functions
 def debug(msg, *args, **kwargs):
@@ -62,11 +75,77 @@ def getLogger(name: str = None) -> logging.Logger:
     return InvokeAILogger.getLogger(name)
 
 
-class InvokeAILogFormatter(logging.Formatter):
+_FACILITY_MAP = dict(
+    LOG_KERN = syslog.LOG_KERN,
+    LOG_USER = syslog.LOG_USER,
+    LOG_MAIL = syslog.LOG_MAIL,
+    LOG_DAEMON = syslog.LOG_DAEMON,
+    LOG_AUTH = syslog.LOG_AUTH,
+    LOG_LPR = syslog.LOG_LPR,
+    LOG_NEWS = syslog.LOG_NEWS,
+    LOG_UUCP = syslog.LOG_UUCP,
+    LOG_CRON = syslog.LOG_CRON,
+    LOG_SYSLOG = syslog.LOG_SYSLOG,
+    LOG_LOCAL0 = syslog.LOG_LOCAL0,
+    LOG_LOCAL1 = syslog.LOG_LOCAL1,
+    LOG_LOCAL2 = syslog.LOG_LOCAL2,
+    LOG_LOCAL3 = syslog.LOG_LOCAL3,
+    LOG_LOCAL4 = syslog.LOG_LOCAL4,
+    LOG_LOCAL5 = syslog.LOG_LOCAL5,
+    LOG_LOCAL6 = syslog.LOG_LOCAL6,
+    LOG_LOCAL7 = syslog.LOG_LOCAL7,
+) if SYSLOG_AVAILABLE else dict()
+
+_SOCK_MAP = dict(
+    SOCK_STREAM = socket.SOCK_STREAM,
+    SOCK_DGRAM = socket.SOCK_DGRAM,
+)
+
+class InvokeAIFormatter(logging.Formatter):
+    '''
+    Base class for logging formatter
+
+    '''
+    def format(self, record):
+        formatter = logging.Formatter(self.log_fmt(record.levelno))
+        return formatter.format(record)
+
+    @abstractmethod
+    def log_fmt(self, levelno: int)->str:
+        pass
+    
+class InvokeAISyslogFormatter(InvokeAIFormatter):
+    '''
+    Formatting for syslog
+    '''
+    def log_fmt(self, levelno: int)->str:
+        return '%(name)s [%(process)d] <%(levelname)s> %(message)s'
+
+class InvokeAILegacyLogFormatter(InvokeAIFormatter):
+    '''
+    Formatting for the InvokeAI Logger (legacy version)
+    '''
+    FORMATS = {
+        logging.DEBUG: "   | %(message)s",
+        logging.INFO: ">> %(message)s",
+        logging.WARNING: "** %(message)s",
+        logging.ERROR: "*** %(message)s",
+        logging.CRITICAL: "### %(message)s",
+    }
+    def log_fmt(self,levelno:int)->str:
+        return self.FORMATS.get(levelno)
+
+class InvokeAIPlainLogFormatter(InvokeAIFormatter):
+    '''
+    Custom Formatting for the InvokeAI Logger (plain version)
+    '''
+    def log_fmt(self, levelno: int)->str:
+        return "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
+
+class InvokeAIColorLogFormatter(InvokeAIFormatter):
     '''
     Custom Formatting for the InvokeAI Logger
     '''
-
     # Color Codes
     grey = "\x1b[38;20m"
     yellow = "\x1b[33;20m"
@@ -88,23 +167,109 @@ class InvokeAILogFormatter(logging.Formatter):
         logging.CRITICAL: bold_red + log_format + reset
     }
 
-    def format(self, record):
-        log_fmt = self.FORMATS.get(record.levelno)
-        formatter = logging.Formatter(log_fmt, datefmt="%d-%m-%Y %H:%M:%S")
-        return formatter.format(record)
+    def log_fmt(self, levelno: int)->str:
+        return self.FORMATS.get(levelno)
 
+LOG_FORMATTERS = {
+    'plain': InvokeAIPlainLogFormatter,
+    'color': InvokeAIColorLogFormatter,
+    'syslog': InvokeAISyslogFormatter,
+    'legacy': InvokeAILegacyLogFormatter,
+}
 
 class InvokeAILogger(object):
     loggers = dict()
 
     @classmethod
     def getLogger(cls, name: str = 'InvokeAI') -> logging.Logger:
+        config = get_invokeai_config()
+        
         if name not in cls.loggers:
             logger = logging.getLogger(name)
-            logger.setLevel(logging.DEBUG)
-            ch = logging.StreamHandler()
-            fmt = InvokeAILogFormatter()
-            ch.setFormatter(fmt)
-            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]
+
+    @classmethod
+    def getLoggers(cls, config: InvokeAIAppConfig) -> list[logging.Handler]:
+        handler_strs = config.log_handlers
+        print(f'handler_strs={handler_strs}')
+        handlers = list()
+        for handler in handler_strs:
+            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
+            if handler_name=='console':
+                formatter = LOG_FORMATTERS[config.log_format]
+                ch = logging.StreamHandler()
+                ch.setFormatter(formatter())
+                handlers.append(ch)
+                
+            elif handler_name=='syslog':
+                ch = cls._parse_syslog_args(args)
+                ch.setFormatter(InvokeAISyslogFormatter())
+                handlers.append(ch)
+                
+            elif handler_name=='file':
+                handlers.append(cls._parse_file_args(args))
+                
+            elif handler_name=='http':
+                handlers.append(cls._parse_http_args(args))
+        return handlers
+
+    @staticmethod
+    def _parse_syslog_args(
+            args: str=None
+    )-> logging.Handler:
+        if not SYSLOG_AVAILABLE:
+            raise ValueError("syslog is not available on this system")
+        if not args:
+            args='/dev/log' if Path('/dev/log').exists() else 'address:localhost:514'
+        syslog_args = dict()
+        try:
+            for a in args.split(','):
+                arg_name,*arg_value = a.split(':',2)
+                if arg_name=='address':
+                    host,*port = arg_value
+                    port = 514 if len(port)==0 else int(port[0])
+                    syslog_args['address'] = (host,port)
+                elif arg_name=='facility':
+                    syslog_args['facility'] = _FACILITY_MAP[arg_value[0]]
+                elif arg_name=='socktype':
+                    syslog_args['socktype'] = _SOCK_MAP[arg_value[0]]
+                else:
+                    syslog_args['address'] = arg_name
+        except:
+            raise ValueError(f"{args} is not a value argument list for syslog logging")
+        return logging.handlers.SysLogHandler(**syslog_args)
+            
+    @staticmethod
+    def _parse_file_args(args: str=None)-> logging.Handler:
+        if not args:
+            raise ValueError("please provide filename for file logging using format 'file=/path/to/logfile.txt'")
+        return logging.FileHandler(args)
+
+    @staticmethod
+    def _parse_http_args(args: str=None)-> logging.Handler:
+        if not args:
+            raise ValueError("please provide destination for http logging using format 'http=url'")
+        arg_list = args.split(',')
+        url = urllib.parse.urlparse(arg_list.pop(0))
+        if url.scheme != 'http':
+            raise ValueError(f"the http logging module can only log to HTTP URLs, but {url.scheme} was specified")
+        host = url.hostname
+        path = url.path
+        port = url.port or 80
+        
+        syslog_args = dict()
+        for a in arg_list:
+            arg_name, *arg_value = a.split(':',2)
+            if arg_name=='method':
+                arg_value = arg_value[0] if len(arg_value)>0 else 'GET'
+                syslog_args[arg_name] = arg_value
+            else:  # TODO: Provide support for SSL context and credentials
+                pass
+        return logging.handlers.HTTPHandler(f'{host}:{port}',path,**syslog_args)

invokeai/frontend/install/model_install.py

@@ -40,13 +40,13 @@ from .widgets import (
     TextBox,
     set_min_terminal_size,
 )
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 
 # minimum size for the UI
 MIN_COLS = 120
 MIN_LINES = 45
 
-config = get_invokeai_config()
+config = InvokeAIAppConfig.get_config()
 
 class addModelsForm(npyscreen.FormMultiPage):
     # for responsive resizing - disabled

invokeai/frontend/merge/merge_diffusers.py

@@ -20,12 +20,12 @@ from npyscreen import widget
 from omegaconf import OmegaConf
 
 import invokeai.backend.util.logging as logger
-from invokeai.services.config import get_invokeai_config
+from invokeai.services.config import InvokeAIAppConfig
 from ...backend.model_management import ModelManager
 from ...frontend.install.widgets import FloatTitleSlider
 
 DEST_MERGED_MODEL_DIR = "merged_models"
-config = get_invokeai_config()
+config = InvokeAIAppConfig.get_config()
 
 def merge_diffusion_models(
     model_ids_or_paths: List[Union[str, Path]],

invokeai/frontend/training/textual_inversion.py

@@ -22,7 +22,7 @@ from omegaconf import OmegaConf
 
 import invokeai.backend.util.logging as logger
 
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config import InvokeAIAppConfig
 from ...backend.training import (
     do_textual_inversion_training,
     parse_args
@@ -423,7 +423,7 @@ def do_front_end(args: Namespace):
         save_args(args)
 
         try:
-            do_textual_inversion_training(get_invokeai_config(),**args)
+            do_textual_inversion_training(InvokeAIAppConfig.get_config(),**args)
             copy_to_embeddings_folder(args)
         except Exception as e:
             logger.error("An exception occurred during training. The exception was:")
@@ -436,7 +436,7 @@ def main():
     global config
     
     args = parse_args()
-    config = get_invokeai_config(argv=[])
+    config = InvokeAIAppConfig.get_config()
 
     # change root if needed
     if args.root_dir:

invokeai/frontend/web/docs/API_CLIENT.md

@@ -26,10 +26,10 @@ We need to start the nodes web server, which serves the OpenAPI schema to the ge
 
 ```bash
 # from the repo root
-python scripts/invoke-new.py --web
+python scripts/invokeai-web.py
 ```
 
-2. Generate the API client.
+2. Generate the API client. 
 
 ```bash
 # from invokeai/frontend/web/

invokeai/frontend/web/docs/README.md

@@ -12,7 +12,14 @@ Code in `invokeai/frontend/web/` if you want to have a look.
 
 ## Stack
 
-State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a custom redux middleware to help).
+State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). We lean heavily on RTK:
+- `createAsyncThunk` for HTTP requests
+- `createEntityAdapter` for fetching images and models
+- `createListenerMiddleware` for workflows
+
+The API client and associated types are generated from the OpenAPI schema. See API_CLIENT.md.
+
+Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a simple socket.io redux middleware to help).
 
 [Chakra-UI](https://github.com/chakra-ui/chakra-ui) for components and styling.
 
@@ -37,9 +44,15 @@ From `invokeai/frontend/web/` run `yarn install` to get everything set up.
 Start everything in dev mode:
 
 1. Start the dev server: `yarn dev`
-2. Start the InvokeAI Nodes backend: `python scripts/invokeai-new.py --web # run from the repo root`
+2. Start the InvokeAI Nodes backend: `python scripts/invokeai-web.py # run from the repo root`
 3. Point your browser to the dev server address e.g. <http://localhost:5173/>
 
+#### VSCode Remote Dev
+
+We've noticed an intermittent issue with the VSCode Remote Dev port forwarding. If you use this feature of VSCode, you may intermittently click the Invoke button and then get nothing until the request times out. Suggest disabling the IDE's port forwarding feature and doing it manually via SSH:
+
+`ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host`
+
 ### Production builds
 
 For a number of technical and logistical reasons, we need to commit UI build artefacts to the repo.

invokeai/frontend/web/package.json

@@ -60,6 +60,8 @@
     "@chakra-ui/styled-system": "^2.9.0",
     "@chakra-ui/theme-tools": "^2.0.16",
     "@dagrejs/graphlib": "^2.1.12",
+    "@dnd-kit/core": "^6.0.8",
+    "@dnd-kit/modifiers": "^6.0.1",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
     "@floating-ui/react-dom": "^2.0.0",
@@ -87,7 +89,7 @@
     "react-dropzone": "^14.2.3",
     "react-hotkeys-hook": "4.4.0",
     "react-i18next": "^12.2.2",
-    "react-icons": "^4.7.1",
+    "react-icons": "^4.9.0",
     "react-konva": "^18.2.7",
     "react-redux": "^8.0.5",
     "react-resizable-panels": "^0.0.42",

invokeai/frontend/web/src/app/components/App.tsx

@@ -21,6 +21,7 @@ import { ReactNode, memo, useCallback, useEffect, useState } from 'react';
 import { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
 import GlobalHotkeys from './GlobalHotkeys';
 import Toaster from './Toaster';
+import DeleteImageModal from 'features/gallery/components/DeleteImageModal';
 
 const DEFAULT_CONFIG = {};
 
@@ -76,18 +77,21 @@ const App = ({
         {isLightboxEnabled && <Lightbox />}
         <ImageUploader>
           <Grid
-            gap={4}
-            p={4}
-            gridAutoRows="min-content auto"
-            w={APP_WIDTH}
-            h={APP_HEIGHT}
+            sx={{
+              gap: 4,
+              p: 4,
+              gridAutoRows: 'min-content auto',
+              w: 'full',
+              h: 'full',
+            }}
           >
             {headerComponent || <SiteHeader />}
             <Flex
-              gap={4}
-              w={{ base: '100vw', xl: 'full' }}
-              h="full"
-              flexDir={{ base: 'column', xl: 'row' }}
+              sx={{
+                gap: 4,
+                w: 'full',
+                h: 'full',
+              }}
             >
               <InvokeTabs />
             </Flex>
@@ -130,6 +134,7 @@ const App = ({
           <FloatingGalleryButton />
         </Portal>
       </Grid>
+      <DeleteImageModal />
       <Toaster />
       <GlobalHotkeys />
     </>

invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx

@@ -0,0 +1,71 @@
+import {
+  DndContext,
+  DragEndEvent,
+  DragOverlay,
+  DragStartEvent,
+  KeyboardSensor,
+  MouseSensor,
+  TouchSensor,
+  pointerWithin,
+  useSensor,
+  useSensors,
+} from '@dnd-kit/core';
+import { PropsWithChildren, memo, useCallback, useState } from 'react';
+import OverlayDragImage from './OverlayDragImage';
+import { ImageDTO } from 'services/api';
+import { isImageDTO } from 'services/types/guards';
+import { snapCenterToCursor } from '@dnd-kit/modifiers';
+
+type ImageDndContextProps = PropsWithChildren;
+
+const ImageDndContext = (props: ImageDndContextProps) => {
+  const [draggedImage, setDraggedImage] = useState<ImageDTO | null>(null);
+
+  const handleDragStart = useCallback((event: DragStartEvent) => {
+    const dragData = event.active.data.current;
+    if (dragData && 'image' in dragData && isImageDTO(dragData.image)) {
+      setDraggedImage(dragData.image);
+    }
+  }, []);
+
+  const handleDragEnd = useCallback(
+    (event: DragEndEvent) => {
+      const handleDrop = event.over?.data.current?.handleDrop;
+      if (handleDrop && typeof handleDrop === 'function' && draggedImage) {
+        handleDrop(draggedImage);
+      }
+      setDraggedImage(null);
+    },
+    [draggedImage]
+  );
+
+  const mouseSensor = useSensor(MouseSensor, {
+    activationConstraint: { distance: 15 },
+  });
+
+  const touchSensor = useSensor(TouchSensor, {
+    activationConstraint: { distance: 15 },
+  });
+  // TODO: Use KeyboardSensor - needs composition of multiple collisionDetection algos
+  // Alternatively, fix `rectIntersection` collection detection to work with the drag overlay
+  // (currently the drag element collision rect is not correctly calculated)
+  // const keyboardSensor = useSensor(KeyboardSensor);
+
+  const sensors = useSensors(mouseSensor, touchSensor);
+
+  return (
+    <DndContext
+      onDragStart={handleDragStart}
+      onDragEnd={handleDragEnd}
+      sensors={sensors}
+      collisionDetection={pointerWithin}
+    >
+      {props.children}
+      <DragOverlay dropAnimation={null} modifiers={[snapCenterToCursor]}>
+        {draggedImage && <OverlayDragImage image={draggedImage} />}
+      </DragOverlay>
+    </DndContext>
+  );
+};
+
+export default memo(ImageDndContext);

invokeai/frontend/web/src/app/components/ImageDnd/OverlayDragImage.tsx

@@ -0,0 +1,36 @@
+import { Box, Image } from '@chakra-ui/react';
+import { memo } from 'react';
+import { ImageDTO } from 'services/api';
+
+type OverlayDragImageProps = {
+  image: ImageDTO;
+};
+
+const OverlayDragImage = (props: OverlayDragImageProps) => {
+  return (
+    <Box
+      style={{
+        width: '100%',
+        height: '100%',
+        display: 'flex',
+        alignItems: 'center',
+        justifyContent: 'center',
+        userSelect: 'none',
+        cursor: 'grabbing',
+        opacity: 0.5,
+      }}
+    >
+      <Image
+        sx={{
+          maxW: 36,
+          maxH: 36,
+          borderRadius: 'base',
+          shadow: 'dark-lg',
+        }}
+        src={props.image.thumbnail_url}
+      />
+    </Box>
+  );
+};
+
+export default memo(OverlayDragImage);

invokeai/frontend/web/src/app/components/InvokeAIUI.tsx

@@ -16,6 +16,11 @@ import { PartialAppConfig } from 'app/types/invokeai';
 import '../../i18n';
 import { socketMiddleware } from 'services/events/middleware';
 import { Middleware } from '@reduxjs/toolkit';
+import ImageDndContext from './ImageDnd/ImageDndContext';
+import {
+  DeleteImageContext,
+  DeleteImageContextProvider,
+} from 'app/contexts/DeleteImageContext';
 
 const App = lazy(() => import('./App'));
 const ThemeLocaleProvider = lazy(() => import('./ThemeLocaleProvider'));
@@ -69,11 +74,15 @@ const InvokeAIUI = ({
       <Provider store={store}>
         <React.Suspense fallback={<Loading />}>
           <ThemeLocaleProvider>
-            <App
-              config={config}
-              headerComponent={headerComponent}
-              setIsReady={setIsReady}
-            />
+            <ImageDndContext>
+              <DeleteImageContextProvider>
+                <App
+                  config={config}
+                  headerComponent={headerComponent}
+                  setIsReady={setIsReady}
+                />
+              </DeleteImageContextProvider>
+            </ImageDndContext>
           </ThemeLocaleProvider>
         </React.Suspense>
       </Provider>

invokeai/frontend/web/src/app/contexts/DeleteImageContext.tsx

@@ -0,0 +1,203 @@
+import { useDisclosure } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { requestedImageDeletion } from 'features/gallery/store/actions';
+import { systemSelector } from 'features/system/store/systemSelectors';
+import {
+  PropsWithChildren,
+  createContext,
+  useCallback,
+  useEffect,
+  useState,
+} from 'react';
+import { ImageDTO } from 'services/api';
+import { RootState } from 'app/store/store';
+import { canvasSelector } from 'features/canvas/store/canvasSelectors';
+import { controlNetSelector } from 'features/controlNet/store/controlNetSlice';
+import { nodesSelecter } from 'features/nodes/store/nodesSlice';
+import { generationSelector } from 'features/parameters/store/generationSelectors';
+import { some } from 'lodash-es';
+
+export type ImageUsage = {
+  isInitialImage: boolean;
+  isCanvasImage: boolean;
+  isNodesImage: boolean;
+  isControlNetImage: boolean;
+};
+
+export const selectImageUsage = createSelector(
+  [
+    generationSelector,
+    canvasSelector,
+    nodesSelecter,
+    controlNetSelector,
+    (state: RootState, image_name?: string) => image_name,
+  ],
+  (generation, canvas, nodes, controlNet, image_name) => {
+    const isInitialImage = generation.initialImage?.image_name === image_name;
+
+    const isCanvasImage = canvas.layerState.objects.some(
+      (obj) => obj.kind === 'image' && obj.image.image_name === image_name
+    );
+
+    const isNodesImage = nodes.nodes.some((node) => {
+      return some(
+        node.data.inputs,
+        (input) =>
+          input.type === 'image' && input.value?.image_name === image_name
+      );
+    });
+
+    const isControlNetImage = some(
+      controlNet.controlNets,
+      (c) =>
+        c.controlImage?.image_name === image_name ||
+        c.processedControlImage?.image_name === image_name
+    );
+
+    const imageUsage: ImageUsage = {
+      isInitialImage,
+      isCanvasImage,
+      isNodesImage,
+      isControlNetImage,
+    };
+
+    return imageUsage;
+  },
+  defaultSelectorOptions
+);
+
+type DeleteImageContextValue = {
+  /**
+   * Whether the delete image dialog is open.
+   */
+  isOpen: boolean;
+  /**
+   * Closes the delete image dialog.
+   */
+  onClose: () => void;
+  /**
+   * Opens the delete image dialog and handles all deletion-related checks.
+   */
+  onDelete: (image?: ImageDTO) => void;
+  /**
+   * The image pending deletion
+   */
+  image?: ImageDTO;
+  /**
+   * The features in which this image is used
+   */
+  imageUsage?: ImageUsage;
+  /**
+   * Immediately deletes an image.
+   *
+   * You probably don't want to use this - use `onDelete` instead.
+   */
+  onImmediatelyDelete: () => void;
+};
+
+export const DeleteImageContext = createContext<DeleteImageContextValue>({
+  isOpen: false,
+  onClose: () => undefined,
+  onImmediatelyDelete: () => undefined,
+  onDelete: () => undefined,
+});
+
+const selector = createSelector(
+  [systemSelector],
+  (system) => {
+    const { isProcessing, isConnected, shouldConfirmOnDelete } = system;
+
+    return {
+      canDeleteImage: isConnected && !isProcessing,
+      shouldConfirmOnDelete,
+    };
+  },
+  defaultSelectorOptions
+);
+
+type Props = PropsWithChildren;
+
+export const DeleteImageContextProvider = (props: Props) => {
+  const { canDeleteImage, shouldConfirmOnDelete } = useAppSelector(selector);
+  const [imageToDelete, setImageToDelete] = useState<ImageDTO>();
+  const dispatch = useAppDispatch();
+  const { isOpen, onOpen, onClose } = useDisclosure();
+
+  // Check where the image to be deleted is used (eg init image, controlnet, etc.)
+  const imageUsage = useAppSelector((state) =>
+    selectImageUsage(state, imageToDelete?.image_name)
+  );
+
+  // Clean up after deleting or dismissing the modal
+  const closeAndClearImageToDelete = useCallback(() => {
+    setImageToDelete(undefined);
+    onClose();
+  }, [onClose]);
+
+  // Dispatch the actual deletion action, to be handled by listener middleware
+  const handleActualDeletion = useCallback(
+    (image: ImageDTO) => {
+      dispatch(requestedImageDeletion({ image, imageUsage }));
+      closeAndClearImageToDelete();
+    },
+    [closeAndClearImageToDelete, dispatch, imageUsage]
+  );
+
+  // This is intended to be called by the delete button in the dialog
+  const onImmediatelyDelete = useCallback(() => {
+    if (canDeleteImage && imageToDelete) {
+      handleActualDeletion(imageToDelete);
+    }
+    closeAndClearImageToDelete();
+  }, [
+    canDeleteImage,
+    imageToDelete,
+    closeAndClearImageToDelete,
+    handleActualDeletion,
+  ]);
+
+  const handleGatedDeletion = useCallback(
+    (image: ImageDTO) => {
+      if (shouldConfirmOnDelete || some(imageUsage)) {
+        // If we should confirm on delete, or if the image is in use, open the dialog
+        onOpen();
+      } else {
+        handleActualDeletion(image);
+      }
+    },
+    [imageUsage, shouldConfirmOnDelete, onOpen, handleActualDeletion]
+  );
+
+  // Consumers of the context call this to delete an image
+  const onDelete = useCallback((image?: ImageDTO) => {
+    if (!image) {
+      return;
+    }
+    // Set the image to delete, then let the effect call the actual deletion
+    setImageToDelete(image);
+  }, []);
+
+  useEffect(() => {
+    // We need to use an effect here to trigger the image usage selector, else we get a stale value
+    if (imageToDelete) {
+      handleGatedDeletion(imageToDelete);
+    }
+  }, [handleGatedDeletion, imageToDelete]);
+
+  return (
+    <DeleteImageContext.Provider
+      value={{
+        isOpen,
+        image: imageToDelete,
+        onClose: closeAndClearImageToDelete,
+        onDelete,
+        onImmediatelyDelete,
+        imageUsage,
+      }}
+    >
+      {props.children}
+    </DeleteImageContext.Provider>
+  );
+};

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts

@@ -1,4 +1,5 @@
 import { canvasPersistDenylist } from 'features/canvas/store/canvasPersistDenylist';
+import { controlNetDenylist } from 'features/controlNet/store/controlNetDenylist';
 import { galleryPersistDenylist } from 'features/gallery/store/galleryPersistDenylist';
 import { lightboxPersistDenylist } from 'features/lightbox/store/lightboxPersistDenylist';
 import { nodesPersistDenylist } from 'features/nodes/store/nodesPersistDenylist';
@@ -23,6 +24,7 @@ const serializationDenylist: {
   system: systemPersistDenylist,
   // config: configPersistDenyList,
   ui: uiPersistDenylist,
+  controlNet: controlNetDenylist,
   // hotkeys: hotkeysPersistDenylist,
 };
 

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/unserialize.ts

@@ -1,4 +1,5 @@
 import { initialCanvasState } from 'features/canvas/store/canvasSlice';
+import { initialControlNetState } from 'features/controlNet/store/controlNetSlice';
 import { initialGalleryState } from 'features/gallery/store/gallerySlice';
 import { initialImagesState } from 'features/gallery/store/imagesSlice';
 import { initialLightboxState } from 'features/lightbox/store/lightboxSlice';
@@ -28,6 +29,7 @@ const initialStates: {
   ui: initialUIState,
   hotkeys: initialHotkeysState,
   images: initialImagesState,
+  controlNet: initialControlNetState,
 };
 
 export const unserialize: UnserializeFunction = (data, key) => {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -70,6 +70,9 @@ import {
 import { addStagingAreaImageSavedListener } from './listeners/stagingAreaImageSaved';
 import { addCommitStagingAreaImageListener } from './listeners/addCommitStagingAreaImageListener';
 import { addImageCategoriesChangedListener } from './listeners/imageCategoriesChanged';
+import { addControlNetImageProcessedListener } from './listeners/controlNetImageProcessed';
+import { addControlNetAutoProcessListener } from './listeners/controlNetAutoProcess';
+import { addUpdateImageUrlsOnConnectListener } from './listeners/updateImageUrlsOnConnect';
 
 export const listenerMiddleware = createListenerMiddleware();
 
@@ -173,3 +176,10 @@ addReceivedPageOfImagesRejectedListener();
 
 // Gallery
 addImageCategoriesChangedListener();
+
+// ControlNet
+addControlNetImageProcessedListener();
+addControlNetAutoProcessListener();
+
+// Update image URLs on connect
+addUpdateImageUrlsOnConnectListener();

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasCopiedToClipboard.ts

@@ -28,6 +28,13 @@ export const addCanvasCopiedToClipboardListener = () => {
       }
 
       copyBlobToClipboard(blob);
+
+      dispatch(
+        addToast({
+          title: 'Canvas Copied to Clipboard',
+          status: 'success',
+        })
+      );
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasDownloadedAsImage.ts

@@ -27,7 +27,8 @@ export const addCanvasDownloadedAsImageListener = () => {
         return;
       }
 
-      downloadBlob(blob, 'mergedCanvas.png');
+      downloadBlob(blob, 'canvas.png');
+      dispatch(addToast({ title: 'Canvas Downloaded', status: 'success' }));
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMerged.ts

@@ -1,22 +1,20 @@
 import { canvasMerged } from 'features/canvas/store/actions';
 import { startAppListening } from '..';
 import { log } from 'app/logging/useLogger';
-import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
 import { addToast } from 'features/system/store/systemSlice';
 import { imageUploaded } from 'services/thunks/image';
-import { v4 as uuidv4 } from 'uuid';
 import { setMergedCanvas } from 'features/canvas/store/canvasSlice';
 import { getCanvasBaseLayer } from 'features/canvas/util/konvaInstanceProvider';
+import { getFullBaseLayerBlob } from 'features/canvas/util/getFullBaseLayerBlob';
 
 const moduleLog = log.child({ namespace: 'canvasCopiedToClipboardListener' });
+export const MERGED_CANVAS_FILENAME = 'mergedCanvas.png';
 
 export const addCanvasMergedListener = () => {
   startAppListening({
     actionCreator: canvasMerged,
     effect: async (action, { dispatch, getState, take }) => {
-      const state = getState();
-
-      const blob = await getBaseLayerBlob(state, true);
+      const blob = await getFullBaseLayerBlob();
 
       if (!blob) {
         moduleLog.error('Problem getting base layer blob');
@@ -48,12 +46,12 @@ export const addCanvasMergedListener = () => {
         relativeTo: canvasBaseLayer.getParent(),
       });
 
-      const filename = `mergedCanvas_${uuidv4()}.png`;
-
-      dispatch(
+      const imageUploadedRequest = dispatch(
         imageUploaded({
           formData: {
-            file: new File([blob], filename, { type: 'image/png' }),
+            file: new File([blob], MERGED_CANVAS_FILENAME, {
+              type: 'image/png',
+            }),
           },
           imageCategory: 'general',
           isIntermediate: true,
@@ -61,9 +59,11 @@ export const addCanvasMergedListener = () => {
       );
 
       const [{ payload }] = await take(
-        (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
-          imageUploaded.fulfilled.match(action) &&
-          action.meta.arg.formData.file.name === filename
+        (
+          uploadedImageAction
+        ): uploadedImageAction is ReturnType<typeof imageUploaded.fulfilled> =>
+          imageUploaded.fulfilled.match(uploadedImageAction) &&
+          uploadedImageAction.meta.requestId === imageUploadedRequest.requestId
       );
 
       const mergedCanvasImage = payload;

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery.ts

@@ -4,9 +4,10 @@ import { log } from 'app/logging/useLogger';
 import { imageUploaded } from 'services/thunks/image';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
 import { addToast } from 'features/system/store/systemSlice';
-import { v4 as uuidv4 } from 'uuid';
 import { imageUpserted } from 'features/gallery/store/imagesSlice';
 
+export const SAVED_CANVAS_FILENAME = 'savedCanvas.png';
+
 const moduleLog = log.child({ namespace: 'canvasSavedToGalleryListener' });
 
 export const addCanvasSavedToGalleryListener = () => {
@@ -15,7 +16,7 @@ export const addCanvasSavedToGalleryListener = () => {
     effect: async (action, { dispatch, getState, take }) => {
       const state = getState();
 
-      const blob = await getBaseLayerBlob(state, true);
+      const blob = await getBaseLayerBlob(state);
 
       if (!blob) {
         moduleLog.error('Problem getting base layer blob');
@@ -29,12 +30,12 @@ export const addCanvasSavedToGalleryListener = () => {
         return;
       }
 
-      const filename = `mergedCanvas_${uuidv4()}.png`;
-
-      dispatch(
+      const imageUploadedRequest = dispatch(
         imageUploaded({
           formData: {
-            file: new File([blob], filename, { type: 'image/png' }),
+            file: new File([blob], SAVED_CANVAS_FILENAME, {
+              type: 'image/png',
+            }),
           },
           imageCategory: 'general',
           isIntermediate: false,
@@ -42,9 +43,11 @@ export const addCanvasSavedToGalleryListener = () => {
       );
 
       const [{ payload: uploadedImageDTO }] = await take(
-        (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
-          imageUploaded.fulfilled.match(action) &&
-          action.meta.arg.formData.file.name === filename
+        (
+          uploadedImageAction
+        ): uploadedImageAction is ReturnType<typeof imageUploaded.fulfilled> =>
+          imageUploaded.fulfilled.match(uploadedImageAction) &&
+          uploadedImageAction.meta.requestId === imageUploadedRequest.requestId
       );
 
       dispatch(imageUpserted(uploadedImageDTO));

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess.ts

@@ -0,0 +1,59 @@
+import { AnyAction } from '@reduxjs/toolkit';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { controlNetImageProcessed } from 'features/controlNet/store/actions';
+import {
+  controlNetImageChanged,
+  controlNetProcessorParamsChanged,
+  controlNetProcessorTypeChanged,
+} from 'features/controlNet/store/controlNetSlice';
+import { RootState } from 'app/store/store';
+
+const moduleLog = log.child({ namespace: 'controlNet' });
+
+const predicate = (action: AnyAction, state: RootState) => {
+  const isActionMatched =
+    controlNetProcessorParamsChanged.match(action) ||
+    controlNetImageChanged.match(action) ||
+    controlNetProcessorTypeChanged.match(action);
+
+  if (!isActionMatched) {
+    return false;
+  }
+
+  const { controlImage, processorType } =
+    state.controlNet.controlNets[action.payload.controlNetId];
+
+  const isProcessorSelected = processorType !== 'none';
+
+  const isBusy = state.system.isProcessing;
+
+  const hasControlImage = Boolean(controlImage);
+
+  return isProcessorSelected && !isBusy && hasControlImage;
+};
+
+/**
+ * Listener that automatically processes a ControlNet image when its processor parameters are changed.
+ *
+ * The network request is debounced by 1 second.
+ */
+export const addControlNetAutoProcessListener = () => {
+  startAppListening({
+    predicate,
+    effect: async (
+      action,
+      { dispatch, getState, cancelActiveListeners, delay }
+    ) => {
+      const { controlNetId } = action.payload;
+
+      // Cancel any in-progress instances of this listener
+      cancelActiveListeners();
+
+      // Delay before starting actual work
+      await delay(300);
+
+      dispatch(controlNetImageProcessed({ controlNetId }));
+    },
+  });
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts

@@ -0,0 +1,93 @@
+import { startAppListening } from '..';
+import { imageMetadataReceived } from 'services/thunks/image';
+import { log } from 'app/logging/useLogger';
+import { controlNetImageProcessed } from 'features/controlNet/store/actions';
+import { Graph } from 'services/api';
+import { sessionCreated } from 'services/thunks/session';
+import { sessionReadyToInvoke } from 'features/system/store/actions';
+import { socketInvocationComplete } from 'services/events/actions';
+import { isImageOutput } from 'services/types/guards';
+import { controlNetProcessedImageChanged } from 'features/controlNet/store/controlNetSlice';
+import { pick } from 'lodash-es';
+
+const moduleLog = log.child({ namespace: 'controlNet' });
+
+export const addControlNetImageProcessedListener = () => {
+  startAppListening({
+    actionCreator: controlNetImageProcessed,
+    effect: async (
+      action,
+      { dispatch, getState, take, unsubscribe, subscribe }
+    ) => {
+      const { controlNetId } = action.payload;
+      const controlNet = getState().controlNet.controlNets[controlNetId];
+
+      if (!controlNet.controlImage) {
+        moduleLog.error('Unable to process ControlNet image');
+        return;
+      }
+
+      // ControlNet one-off procressing graph is just the processor node, no edges.
+      // Also we need to grab the image.
+      const graph: Graph = {
+        nodes: {
+          [controlNet.processorNode.id]: {
+            ...controlNet.processorNode,
+            is_intermediate: true,
+            image: pick(controlNet.controlImage, [
+              'image_name',
+              'image_origin',
+            ]),
+          },
+        },
+      };
+
+      // Create a session to run the graph & wait til it's ready to invoke
+      const sessionCreatedAction = dispatch(sessionCreated({ graph }));
+      const [sessionCreatedFulfilledAction] = await take(
+        (action): action is ReturnType<typeof sessionCreated.fulfilled> =>
+          sessionCreated.fulfilled.match(action) &&
+          action.meta.requestId === sessionCreatedAction.requestId
+      );
+
+      const sessionId = sessionCreatedFulfilledAction.payload.id;
+
+      // Invoke the session & wait til it's complete
+      dispatch(sessionReadyToInvoke());
+      const [invocationCompleteAction] = await take(
+        (action): action is ReturnType<typeof socketInvocationComplete> =>
+          socketInvocationComplete.match(action) &&
+          action.payload.data.graph_execution_state_id === sessionId
+      );
+
+      // We still have to check the output type
+      if (isImageOutput(invocationCompleteAction.payload.data.result)) {
+        const { image_name } =
+          invocationCompleteAction.payload.data.result.image;
+
+        // Wait for the ImageDTO to be received
+        const [imageMetadataReceivedAction] = await take(
+          (
+            action
+          ): action is ReturnType<typeof imageMetadataReceived.fulfilled> =>
+            imageMetadataReceived.fulfilled.match(action) &&
+            action.payload.image_name === image_name
+        );
+        const processedControlImage = imageMetadataReceivedAction.payload;
+
+        moduleLog.debug(
+          { data: { arg: action.payload, processedControlImage } },
+          'ControlNet image processed'
+        );
+
+        // Update the processed image in the store
+        dispatch(
+          controlNetProcessedImageChanged({
+            controlNetId,
+            processedControlImage,
+          })
+        );
+      }
+    },
+  });
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts

@@ -6,10 +6,13 @@ import { clamp } from 'lodash-es';
 import { imageSelected } from 'features/gallery/store/gallerySlice';
 import {
   imageRemoved,
-  imagesAdapter,
   selectImagesEntities,
   selectImagesIds,
 } from 'features/gallery/store/imagesSlice';
+import { resetCanvas } from 'features/canvas/store/canvasSlice';
+import { controlNetReset } from 'features/controlNet/store/controlNetSlice';
+import { clearInitialImage } from 'features/parameters/store/generationSlice';
+import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
 
 const moduleLog = log.child({ namespace: 'addRequestedImageDeletionListener' });
 
@@ -20,11 +23,7 @@ export const addRequestedImageDeletionListener = () => {
   startAppListening({
     actionCreator: requestedImageDeletion,
     effect: (action, { dispatch, getState }) => {
-      const image = action.payload;
-      if (!image) {
-        moduleLog.warn('No image provided');
-        return;
-      }
+      const { image, imageUsage } = action.payload;
 
       const { image_name, image_origin } = image;
 
@@ -58,8 +57,28 @@ export const addRequestedImageDeletionListener = () => {
         }
       }
 
+      // We need to reset the features where the image is in use - none of these work if their image(s) don't exist
+
+      if (imageUsage.isCanvasImage) {
+        dispatch(resetCanvas());
+      }
+
+      if (imageUsage.isControlNetImage) {
+        dispatch(controlNetReset());
+      }
+
+      if (imageUsage.isInitialImage) {
+        dispatch(clearInitialImage());
+      }
+
+      if (imageUsage.isNodesImage) {
+        dispatch(nodeEditorReset());
+      }
+
+      // Preemptively remove from gallery
       dispatch(imageRemoved(image_name));
 
+      // Delete from server
       dispatch(
         imageDeleted({ imageName: image_name, imageOrigin: image_origin })
       );
@@ -74,9 +93,7 @@ export const addImageDeletedPendingListener = () => {
   startAppListening({
     actionCreator: imageDeleted.pending,
     effect: (action, { dispatch, getState }) => {
-      const { imageName, imageOrigin } = action.meta.arg;
-      // Preemptively remove the image from the gallery
-      imagesAdapter.removeOne(getState().images, imageName);
+      //
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageMetadataReceived.ts

@@ -1,6 +1,6 @@
 import { log } from 'app/logging/useLogger';
 import { startAppListening } from '..';
-import { imageMetadataReceived } from 'services/thunks/image';
+import { imageMetadataReceived, imageUpdated } from 'services/thunks/image';
 import { imageUpserted } from 'features/gallery/store/imagesSlice';
 
 const moduleLog = log.child({ namespace: 'image' });
@@ -10,10 +10,29 @@ export const addImageMetadataReceivedFulfilledListener = () => {
     actionCreator: imageMetadataReceived.fulfilled,
     effect: (action, { getState, dispatch }) => {
       const image = action.payload;
-      if (image.is_intermediate) {
+
+      const state = getState();
+
+      if (
+        image.session_id === state.canvas.layerState.stagingArea.sessionId &&
+        state.canvas.shouldAutoSave
+      ) {
+        dispatch(
+          imageUpdated({
+            imageName: image.image_name,
+            imageOrigin: image.image_origin,
+            requestBody: { is_intermediate: false },
+          })
+        );
+      } else if (image.is_intermediate) {
         // No further actions needed for intermediate images
+        moduleLog.trace(
+          { data: { image } },
+          'Image metadata received (intermediate), skipping'
+        );
         return;
       }
+
       moduleLog.debug({ data: { image } }, 'Image metadata received');
       dispatch(imageUpserted(image));
     },

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts

@@ -3,6 +3,8 @@ import { imageUploaded } from 'services/thunks/image';
 import { addToast } from 'features/system/store/systemSlice';
 import { log } from 'app/logging/useLogger';
 import { imageUpserted } from 'features/gallery/store/imagesSlice';
+import { SAVED_CANVAS_FILENAME } from './canvasSavedToGallery';
+import { MERGED_CANVAS_FILENAME } from './canvasMerged';
 
 const moduleLog = log.child({ namespace: 'image' });
 
@@ -19,9 +21,22 @@ export const addImageUploadedFulfilledListener = () => {
         return;
       }
 
-      const state = getState();
+      const originalFileName = action.meta.arg.formData.file.name;
 
       dispatch(imageUpserted(image));
+
+      if (originalFileName === SAVED_CANVAS_FILENAME) {
+        dispatch(
+          addToast({ title: 'Canvas Saved to Gallery', status: 'success' })
+        );
+        return;
+      }
+
+      if (originalFileName === MERGED_CANVAS_FILENAME) {
+        dispatch(addToast({ title: 'Canvas Merged', status: 'success' }));
+        return;
+      }
+
       dispatch(addToast({ title: 'Image Uploaded', status: 'success' }));
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUrlsReceived.ts

@@ -1,7 +1,7 @@
 import { log } from 'app/logging/useLogger';
 import { startAppListening } from '..';
 import { imageUrlsReceived } from 'services/thunks/image';
-import { imagesAdapter } from 'features/gallery/store/imagesSlice';
+import { imageUpdatedOne } from 'features/gallery/store/imagesSlice';
 
 const moduleLog = log.child({ namespace: 'image' });
 
@@ -14,13 +14,12 @@ export const addImageUrlsReceivedFulfilledListener = () => {
 
       const { image_name, image_url, thumbnail_url } = image;
 
-      imagesAdapter.updateOne(getState().images, {
-        id: image_name,
-        changes: {
-          image_url,
-          thumbnail_url,
-        },
-      });
+      dispatch(
+        imageUpdatedOne({
+          id: image_name,
+          changes: { image_url, thumbnail_url },
+        })
+      );
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/initialImageSelected.ts

@@ -2,12 +2,10 @@ import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { t } from 'i18next';
 import { addToast } from 'features/system/store/systemSlice';
 import { startAppListening } from '..';
-import {
-  initialImageSelected,
-  isImageDTO,
-} from 'features/parameters/store/actions';
+import { initialImageSelected } from 'features/parameters/store/actions';
 import { makeToast } from 'app/components/Toaster';
 import { selectImagesById } from 'features/gallery/store/imagesSlice';
+import { isImageDTO } from 'services/types/guards';
 
 export const addInitialImageSelectedListener = () => {
   startAppListening({

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/updateImageUrlsOnConnect.ts

@@ -0,0 +1,93 @@
+import { socketConnected } from 'services/events/actions';
+import { startAppListening } from '..';
+import { createSelector } from '@reduxjs/toolkit';
+import { generationSelector } from 'features/parameters/store/generationSelectors';
+import { canvasSelector } from 'features/canvas/store/canvasSelectors';
+import { nodesSelecter } from 'features/nodes/store/nodesSlice';
+import { controlNetSelector } from 'features/controlNet/store/controlNetSlice';
+import { ImageDTO } from 'services/api';
+import { forEach, uniqBy } from 'lodash-es';
+import { imageUrlsReceived } from 'services/thunks/image';
+import { log } from 'app/logging/useLogger';
+import { selectImagesEntities } from 'features/gallery/store/imagesSlice';
+
+const moduleLog = log.child({ namespace: 'images' });
+
+const selectAllUsedImages = createSelector(
+  [
+    generationSelector,
+    canvasSelector,
+    nodesSelecter,
+    controlNetSelector,
+    selectImagesEntities,
+  ],
+  (generation, canvas, nodes, controlNet, imageEntities) => {
+    const allUsedImages: ImageDTO[] = [];
+
+    if (generation.initialImage) {
+      allUsedImages.push(generation.initialImage);
+    }
+
+    canvas.layerState.objects.forEach((obj) => {
+      if (obj.kind === 'image') {
+        allUsedImages.push(obj.image);
+      }
+    });
+
+    nodes.nodes.forEach((node) => {
+      forEach(node.data.inputs, (input) => {
+        if (input.type === 'image' && input.value) {
+          allUsedImages.push(input.value);
+        }
+      });
+    });
+
+    forEach(controlNet.controlNets, (c) => {
+      if (c.controlImage) {
+        allUsedImages.push(c.controlImage);
+      }
+      if (c.processedControlImage) {
+        allUsedImages.push(c.processedControlImage);
+      }
+    });
+
+    forEach(imageEntities, (image) => {
+      if (image) {
+        allUsedImages.push(image);
+      }
+    });
+
+    const uniqueImages = uniqBy(allUsedImages, 'image_name');
+
+    return uniqueImages;
+  }
+);
+
+export const addUpdateImageUrlsOnConnectListener = () => {
+  startAppListening({
+    actionCreator: socketConnected,
+    effect: async (action, { dispatch, getState, take }) => {
+      const state = getState();
+
+      if (!state.config.shouldUpdateImagesOnConnect) {
+        return;
+      }
+
+      const allUsedImages = selectAllUsedImages(state);
+
+      moduleLog.trace(
+        { data: allUsedImages },
+        `Fetching new image URLs for ${allUsedImages.length} images`
+      );
+
+      allUsedImages.forEach(({ image_name, image_origin }) => {
+        dispatch(
+          imageUrlsReceived({
+            imageName: image_name,
+            imageOrigin: image_origin,
+          })
+        );
+      });
+    },
+  });
+};

invokeai/frontend/web/src/app/store/store.ts

@@ -13,6 +13,7 @@ import galleryReducer from 'features/gallery/store/gallerySlice';
 import imagesReducer from 'features/gallery/store/imagesSlice';
 import lightboxReducer from 'features/lightbox/store/lightboxSlice';
 import generationReducer from 'features/parameters/store/generationSlice';
+import controlNetReducer from 'features/controlNet/store/controlNetSlice';
 import postprocessingReducer from 'features/parameters/store/postprocessingSlice';
 import systemReducer from 'features/system/store/systemSlice';
 // import sessionReducer from 'features/system/store/sessionSlice';
@@ -45,6 +46,7 @@ const allReducers = {
   ui: uiReducer,
   hotkeys: hotkeysReducer,
   images: imagesReducer,
+  controlNet: controlNetReducer,
   // session: sessionReducer,
 };
 
@@ -62,6 +64,7 @@ const rememberedKeys: (keyof typeof allReducers)[] = [
   'postprocessing',
   'system',
   'ui',
+  'controlNet',
   // 'hotkeys',
   // 'config',
 ];

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -95,6 +95,7 @@ export type AppFeature =
  * A disable-able Stable Diffusion feature
  */
 export type SDFeature =
+  | 'controlNet'
   | 'noise'
   | 'variation'
   | 'symmetry'
@@ -107,12 +108,9 @@ export type SDFeature =
  */
 export type AppConfig = {
   /**
-   * Whether or not URLs should be transformed to use a different host
-   */
-  shouldTransformUrls: boolean;
-  /**
-   * Whether or not we need to re-fetch images
+   * Whether or not we should update image urls when image loading errors
    */
+  shouldUpdateImagesOnConnect: boolean;
   disabledTabs: InvokeTabName[];
   disabledFeatures: AppFeature[];
   disabledSDFeatures: SDFeature[];

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

@@ -1,17 +0,0 @@
-import { Checkbox, CheckboxProps } from '@chakra-ui/react';
-import { memo, ReactNode } from 'react';
-
-type IAICheckboxProps = CheckboxProps & {
-  label: string | ReactNode;
-};
-
-const IAICheckbox = (props: IAICheckboxProps) => {
-  const { label, ...rest } = props;
-  return (
-    <Checkbox colorScheme="accent" {...rest}>
-      {label}
-    </Checkbox>
-  );
-};
-
-export default memo(IAICheckbox);

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

@@ -49,7 +49,7 @@ const IAICollapse = (props: IAIToggleCollapseProps) => {
           />
         )}
       </Flex>
-      <Collapse in={isOpen} animateOpacity>
+      <Collapse in={isOpen} animateOpacity style={{ overflow: 'unset' }}>
         <Box sx={{ p: 4, borderBottomRadius: 'base', bg: 'base.800' }}>
           {children}
         </Box>

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

@@ -1,4 +1,4 @@
-import { CheckIcon } from '@chakra-ui/icons';
+import { CheckIcon, ChevronUpIcon } from '@chakra-ui/icons';
 import {
   Box,
   Flex,
@@ -10,7 +10,6 @@ import {
   GridItem,
   List,
   ListItem,
-  Select,
   Text,
   Tooltip,
   TooltipProps,
@@ -19,7 +18,8 @@ import { autoUpdate, offset, shift, useFloating } from '@floating-ui/react-dom';
 import { useSelect } from 'downshift';
 import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
 
-import { memo } from 'react';
+import { memo, useMemo } from 'react';
+import { getInputOutlineStyles } from 'theme/util/getInputOutlineStyles';
 
 export type ItemTooltips = { [key: string]: string };
 
@@ -34,6 +34,7 @@ type IAICustomSelectProps = {
   buttonProps?: FlexProps;
   tooltip?: string;
   tooltipProps?: Omit<TooltipProps, 'children'>;
+  ellipsisPosition?: 'start' | 'end';
 };
 
 const IAICustomSelect = (props: IAICustomSelectProps) => {
@@ -48,6 +49,7 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
     tooltip,
     buttonProps,
     tooltipProps,
+    ellipsisPosition = 'end',
   } = props;
 
   const {
@@ -69,6 +71,14 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
     middleware: [offset(4), shift({ crossAxis: true, padding: 8 })],
   });
 
+  const labelTextDirection = useMemo(() => {
+    if (ellipsisPosition === 'start') {
+      return document.dir === 'rtl' ? 'ltr' : 'rtl';
+    }
+
+    return document.dir;
+  }, [ellipsisPosition]);
+
   return (
     <FormControl sx={{ w: 'full' }} {...formControlProps}>
       {label && (
@@ -82,20 +92,44 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
         </FormLabel>
       )}
       <Tooltip label={tooltip} {...tooltipProps}>
-        <Select
+        <Flex
           {...getToggleButtonProps({ ref: refs.setReference })}
           {...buttonProps}
-          as={Flex}
           sx={{
             alignItems: 'center',
             userSelect: 'none',
             cursor: 'pointer',
+            overflow: 'hidden',
+            width: 'full',
+            py: 1,
+            px: 2,
+            gap: 2,
+            justifyContent: 'space-between',
+            ...getInputOutlineStyles(),
           }}
         >
-          <Text sx={{ fontSize: 'sm', fontWeight: 500, color: 'base.100' }}>
+          <Text
+            sx={{
+              fontSize: 'sm',
+              fontWeight: 500,
+              color: 'base.100',
+              whiteSpace: 'nowrap',
+              overflow: 'hidden',
+              textOverflow: 'ellipsis',
+              direction: labelTextDirection,
+            }}
+          >
             {selectedItem}
           </Text>
-        </Select>
+          <ChevronUpIcon
+            sx={{
+              color: 'base.300',
+              transform: isOpen ? 'rotate(0deg)' : 'rotate(180deg)',
+              transitionProperty: 'common',
+              transitionDuration: 'normal',
+            }}
+          />
+        </Flex>
       </Tooltip>
       <Box {...getMenuProps()}>
         {isOpen && (
@@ -104,11 +138,10 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
             ref={refs.setFloating}
             sx={{
               ...floatingStyles,
-              width: 'max-content',
               top: 0,
-              left: 0,
+              insetInlineStart: 0,
               flexDirection: 'column',
-              zIndex: 1,
+              zIndex: 2,
               bg: 'base.800',
               borderRadius: 'base',
               border: '1px',
@@ -118,61 +151,72 @@ const IAICustomSelect = (props: IAICustomSelectProps) => {
               px: 0,
               h: 'fit-content',
               maxH: 64,
+              minW: 48,
             }}
           >
             <OverlayScrollbarsComponent>
-              {items.map((item, index) => (
-                <Tooltip
-                  isDisabled={!itemTooltips}
-                  key={`${item}${index}`}
-                  label={itemTooltips?.[item]}
-                  hasArrow
-                  placement="right"
-                >
-                  <ListItem
-                    sx={{
-                      bg: highlightedIndex === index ? 'base.700' : undefined,
-                      py: 1,
-                      paddingInlineStart: 3,
-                      paddingInlineEnd: 6,
-                      cursor: 'pointer',
-                      transitionProperty: 'common',
-                      transitionDuration: '0.15s',
-                    }}
+              {items.map((item, index) => {
+                const isSelected = selectedItem === item;
+                const isHighlighted = highlightedIndex === index;
+                const fontWeight = isSelected ? 700 : 500;
+                const bg = isHighlighted
+                  ? 'base.700'
+                  : isSelected
+                  ? 'base.750'
+                  : undefined;
+                return (
+                  <Tooltip
+                    isDisabled={!itemTooltips}
                     key={`${item}${index}`}
-                    {...getItemProps({ item, index })}
+                    label={itemTooltips?.[item]}
+                    hasArrow
+                    placement="right"
                   >
-                    {withCheckIcon ? (
-                      <Grid gridTemplateColumns="1.25rem auto">
-                        <GridItem>
-                          {selectedItem === item && <CheckIcon boxSize={2} />}
-                        </GridItem>
-                        <GridItem>
-                          <Text
-                            sx={{
-                              fontSize: 'sm',
-                              color: 'base.100',
-                              fontWeight: 500,
-                            }}
-                          >
-                            {item}
-                          </Text>
-                        </GridItem>
-                      </Grid>
-                    ) : (
-                      <Text
-                        sx={{
-                          fontSize: 'sm',
-                          color: 'base.100',
-                          fontWeight: 500,
-                        }}
-                      >
-                        {item}
-                      </Text>
-                    )}
-                  </ListItem>
-                </Tooltip>
-              ))}
+                    <ListItem
+                      sx={{
+                        bg,
+                        py: 1,
+                        paddingInlineStart: 3,
+                        paddingInlineEnd: 6,
+                        cursor: 'pointer',
+                        transitionProperty: 'common',
+                        transitionDuration: '0.15s',
+                      }}
+                      key={`${item}${index}`}
+                      {...getItemProps({ item, index })}
+                    >
+                      {withCheckIcon ? (
+                        <Grid gridTemplateColumns="1.25rem auto">
+                          <GridItem>
+                            {isSelected && <CheckIcon boxSize={2} />}
+                          </GridItem>
+                          <GridItem>
+                            <Text
+                              sx={{
+                                fontSize: 'sm',
+                                color: 'base.100',
+                                fontWeight,
+                              }}
+                            >
+                              {item}
+                            </Text>
+                          </GridItem>
+                        </Grid>
+                      ) : (
+                        <Text
+                          sx={{
+                            fontSize: 'sm',
+                            color: 'base.50',
+                            fontWeight,
+                          }}
+                        >
+                          {item}
+                        </Text>
+                      )}
+                    </ListItem>
+                  </Tooltip>
+                );
+              })}
             </OverlayScrollbarsComponent>
           </List>
         )}

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

@@ -0,0 +1,167 @@
+import { Box, Flex, Icon, IconButtonProps, Image } from '@chakra-ui/react';
+import { useDraggable, useDroppable } from '@dnd-kit/core';
+import { useCombinedRefs } from '@dnd-kit/utilities';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { IAIImageFallback } from 'common/components/IAIImageFallback';
+import ImageMetadataOverlay from 'common/components/ImageMetadataOverlay';
+import { AnimatePresence } from 'framer-motion';
+import { ReactElement, SyntheticEvent } from 'react';
+import { memo, useRef } from 'react';
+import { FaImage, FaTimes } from 'react-icons/fa';
+import { ImageDTO } from 'services/api';
+import { v4 as uuidv4 } from 'uuid';
+import IAIDropOverlay from './IAIDropOverlay';
+
+type IAIDndImageProps = {
+  image: ImageDTO | null | undefined;
+  onDrop: (droppedImage: ImageDTO) => void;
+  onReset?: () => void;
+  onError?: (event: SyntheticEvent<HTMLImageElement>) => void;
+  onLoad?: (event: SyntheticEvent<HTMLImageElement>) => void;
+  resetIconSize?: IconButtonProps['size'];
+  withResetIcon?: boolean;
+  withMetadataOverlay?: boolean;
+  isDragDisabled?: boolean;
+  isDropDisabled?: boolean;
+  fallback?: ReactElement;
+  payloadImage?: ImageDTO | null | undefined;
+  minSize?: number;
+};
+
+const IAIDndImage = (props: IAIDndImageProps) => {
+  const {
+    image,
+    onDrop,
+    onReset,
+    onError,
+    resetIconSize = 'md',
+    withResetIcon = false,
+    withMetadataOverlay = false,
+    isDropDisabled = false,
+    isDragDisabled = false,
+    fallback = <IAIImageFallback />,
+    payloadImage,
+    minSize = 24,
+  } = props;
+  const dndId = useRef(uuidv4());
+  const {
+    isOver,
+    setNodeRef: setDroppableRef,
+    active,
+  } = useDroppable({
+    id: dndId.current,
+    disabled: isDropDisabled,
+    data: {
+      handleDrop: onDrop,
+    },
+  });
+
+  const {
+    attributes,
+    listeners,
+    setNodeRef: setDraggableRef,
+  } = useDraggable({
+    id: dndId.current,
+    data: {
+      image: payloadImage ? payloadImage : image,
+    },
+    disabled: isDragDisabled,
+  });
+
+  const setNodeRef = useCombinedRefs(setDroppableRef, setDraggableRef);
+
+  return (
+    <Flex
+      sx={{
+        width: 'full',
+        height: 'full',
+        alignItems: 'center',
+        justifyContent: 'center',
+        position: 'relative',
+        minW: minSize,
+        minH: minSize,
+        userSelect: 'none',
+        cursor: 'grab',
+      }}
+      {...attributes}
+      {...listeners}
+      ref={setNodeRef}
+    >
+      {image && (
+        <Flex
+          sx={{
+            w: 'full',
+            h: 'full',
+            position: 'relative',
+            alignItems: 'center',
+            justifyContent: 'center',
+          }}
+        >
+          <Image
+            src={image.image_url}
+            fallbackStrategy="beforeLoadOrError"
+            fallback={fallback}
+            onError={onError}
+            objectFit="contain"
+            draggable={false}
+            sx={{
+              maxW: 'full',
+              maxH: 'full',
+              borderRadius: 'base',
+            }}
+          />
+          {withMetadataOverlay && <ImageMetadataOverlay image={image} />}
+          {onReset && withResetIcon && (
+            <Box
+              sx={{
+                position: 'absolute',
+                top: 0,
+                right: 0,
+                p: 2,
+              }}
+            >
+              <IAIIconButton
+                size={resetIconSize}
+                tooltip="Reset Image"
+                aria-label="Reset Image"
+                icon={<FaTimes />}
+                onClick={onReset}
+              />
+            </Box>
+          )}
+          <AnimatePresence>
+            {active && <IAIDropOverlay isOver={isOver} />}
+          </AnimatePresence>
+        </Flex>
+      )}
+      {!image && (
+        <>
+          <Flex
+            sx={{
+              minH: minSize,
+              bg: 'base.850',
+              w: 'full',
+              h: 'full',
+              alignItems: 'center',
+              justifyContent: 'center',
+              borderRadius: 'base',
+            }}
+          >
+            <Icon
+              as={FaImage}
+              sx={{
+                boxSize: 24,
+                color: 'base.500',
+              }}
+            />
+          </Flex>
+          <AnimatePresence>
+            {active && <IAIDropOverlay isOver={isOver} />}
+          </AnimatePresence>
+        </>
+      )}
+    </Flex>
+  );
+};
+
+export default memo(IAIDndImage);

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

@@ -0,0 +1,91 @@
+import { Flex, Text } from '@chakra-ui/react';
+import { motion } from 'framer-motion';
+import { memo, useRef } from 'react';
+import { v4 as uuidv4 } from 'uuid';
+
+type Props = {
+  isOver: boolean;
+  label?: string;
+};
+
+export const IAIDropOverlay = (props: Props) => {
+  const { isOver, label = 'Drop' } = props;
+  const motionId = useRef(uuidv4());
+  return (
+    <motion.div
+      key={motionId.current}
+      initial={{
+        opacity: 0,
+      }}
+      animate={{
+        opacity: 1,
+        transition: { duration: 0.1 },
+      }}
+      exit={{
+        opacity: 0,
+        transition: { duration: 0.1 },
+      }}
+    >
+      <Flex
+        sx={{
+          position: 'absolute',
+          top: 0,
+          left: 0,
+          w: 'full',
+          h: 'full',
+        }}
+      >
+        <Flex
+          sx={{
+            position: 'absolute',
+            top: 0,
+            left: 0,
+            w: 'full',
+            h: 'full',
+            bg: 'base.900',
+            opacity: 0.7,
+            borderRadius: 'base',
+            alignItems: 'center',
+            justifyContent: 'center',
+            transitionProperty: 'common',
+            transitionDuration: '0.1s',
+          }}
+        />
+
+        <Flex
+          sx={{
+            position: 'absolute',
+            top: 0,
+            left: 0,
+            w: 'full',
+            h: 'full',
+            opacity: 1,
+            borderWidth: 2,
+            borderColor: isOver ? 'base.200' : 'base.500',
+            borderRadius: 'base',
+            borderStyle: 'dashed',
+            transitionProperty: 'common',
+            transitionDuration: '0.1s',
+            alignItems: 'center',
+            justifyContent: 'center',
+          }}
+        >
+          <Text
+            sx={{
+              fontSize: '2xl',
+              fontWeight: 600,
+              transform: isOver ? 'scale(1.1)' : 'scale(1)',
+              color: isOver ? 'base.100' : 'base.500',
+              transitionProperty: 'common',
+              transitionDuration: '0.1s',
+            }}
+          >
+            {label}
+          </Text>
+        </Flex>
+      </Flex>
+    </motion.div>
+  );
+};
+
+export default memo(IAIDropOverlay);

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

@@ -0,0 +1,25 @@
+import {
+  Checkbox,
+  CheckboxProps,
+  FormControl,
+  FormControlProps,
+  FormLabel,
+} from '@chakra-ui/react';
+import { memo, ReactNode } from 'react';
+
+type IAIFullCheckboxProps = CheckboxProps & {
+  label: string | ReactNode;
+  formControlProps?: FormControlProps;
+};
+
+const IAIFullCheckbox = (props: IAIFullCheckboxProps) => {
+  const { label, formControlProps, ...rest } = props;
+  return (
+    <FormControl {...formControlProps}>
+      <FormLabel>{label}</FormLabel>
+      <Checkbox colorScheme="accent" {...rest} />
+    </FormControl>
+  );
+};
+
+export default memo(IAIFullCheckbox);

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

@@ -0,0 +1,27 @@
+import { Flex, FlexProps, Spinner, SpinnerProps } from '@chakra-ui/react';
+
+type Props = FlexProps & {
+  spinnerProps?: SpinnerProps;
+};
+
+export const IAIImageFallback = (props: Props) => {
+  const { spinnerProps, ...rest } = props;
+  const { sx, ...restFlexProps } = rest;
+  return (
+    <Flex
+      sx={{
+        bg: 'base.900',
+        opacity: 0.7,
+        w: 'full',
+        h: 'full',
+        alignItems: 'center',
+        justifyContent: 'center',
+        borderRadius: 'base',
+        ...sx,
+      }}
+      {...restFlexProps}
+    >
+      <Spinner size="xl" {...spinnerProps} />
+    </Flex>
+  );
+};

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

@@ -0,0 +1,19 @@
+import { Checkbox, CheckboxProps, Text } from '@chakra-ui/react';
+import { memo, ReactElement } from 'react';
+
+type IAISimpleCheckboxProps = CheckboxProps & {
+  label: string | ReactElement;
+};
+
+const IAISimpleCheckbox = (props: IAISimpleCheckboxProps) => {
+  const { label, ...rest } = props;
+  return (
+    <Checkbox colorScheme="accent" {...rest}>
+      <Text color="base.200" fontSize="md">
+        {label}
+      </Text>
+    </Checkbox>
+  );
+};
+
+export default memo(IAISimpleCheckbox);

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

@@ -40,7 +40,7 @@ import IAIIconButton, { IAIIconButtonProps } from './IAIIconButton';
 import { roundDownToMultiple } from 'common/util/roundDownToMultiple';
 
 export type IAIFullSliderProps = {
-  label: string;
+  label?: string;
   value: number;
   min?: number;
   max?: number;
@@ -178,9 +178,11 @@ const IAISlider = (props: IAIFullSliderProps) => {
       isDisabled={isDisabled}
       {...sliderFormControlProps}
     >
-      <FormLabel {...sliderFormLabelProps} mb={-1}>
-        {label}
-      </FormLabel>
+      {label && (
+        <FormLabel {...sliderFormLabelProps} mb={-1}>
+          {label}
+        </FormLabel>
+      )}
 
       <HStack w="100%" gap={2} alignItems="center">
         <Slider
@@ -203,6 +205,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
                 sx={{
                   insetInlineStart: '0 !important',
                   insetInlineEnd: 'unset !important',
+                  mt: 1.5,
                 }}
                 {...sliderMarkProps}
               >
@@ -213,6 +216,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
                 sx={{
                   insetInlineStart: 'unset !important',
                   insetInlineEnd: '0 !important',
+                  mt: 1.5,
                 }}
                 {...sliderMarkProps}
               >

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

@@ -5,6 +5,7 @@ import {
   FormLabelProps,
   Switch,
   SwitchProps,
+  Tooltip,
 } from '@chakra-ui/react';
 import { memo } from 'react';
 
@@ -13,6 +14,7 @@ interface Props extends SwitchProps {
   width?: string | number;
   formControlProps?: FormControlProps;
   formLabelProps?: FormLabelProps;
+  tooltip?: string;
 }
 
 /**
@@ -25,22 +27,27 @@ const IAISwitch = (props: Props) => {
     width = 'auto',
     formControlProps,
     formLabelProps,
+    tooltip,
     ...rest
   } = props;
   return (
-    <FormControl
-      isDisabled={isDisabled}
-      width={width}
-      display="flex"
-      gap={4}
-      alignItems="center"
-      {...formControlProps}
-    >
-      <FormLabel my={1} flexGrow={1} {...formLabelProps}>
-        {label}
-      </FormLabel>
-      <Switch {...rest} />
-    </FormControl>
+    <Tooltip label={tooltip} hasArrow placement="top" isDisabled={!tooltip}>
+      <FormControl
+        isDisabled={isDisabled}
+        width={width}
+        display="flex"
+        gap={4}
+        alignItems="center"
+        {...formControlProps}
+      >
+        {label && (
+          <FormLabel my={1} flexGrow={1} {...formLabelProps}>
+            {label}
+          </FormLabel>
+        )}
+        <Switch {...rest} />
+      </FormControl>
+    </Tooltip>
   );
 };
 

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

@@ -1,5 +1,5 @@
 import { Badge, Flex } from '@chakra-ui/react';
-import { isNumber, isString } from 'lodash-es';
+import { isString } from 'lodash-es';
 import { useMemo } from 'react';
 import { ImageDTO } from 'services/api';
 
@@ -8,14 +8,6 @@ type ImageMetadataOverlayProps = {
 };
 
 const ImageMetadataOverlay = ({ image }: ImageMetadataOverlayProps) => {
-  const dimensions = useMemo(() => {
-    if (!isNumber(image.metadata?.width) || isNumber(!image.metadata?.height)) {
-      return;
-    }
-
-    return `${image.metadata?.width} × ${image.metadata?.height}`;
-  }, [image.metadata]);
-
   const model = useMemo(() => {
     if (!isString(image.metadata?.model)) {
       return;
@@ -31,17 +23,15 @@ const ImageMetadataOverlay = ({ image }: ImageMetadataOverlayProps) => {
         flexDirection: 'column',
         position: 'absolute',
         top: 0,
-        right: 0,
+        insetInlineStart: 0,
         p: 2,
-        alignItems: 'flex-end',
+        alignItems: 'flex-start',
         gap: 2,
       }}
     >
-      {dimensions && (
-        <Badge variant="solid" colorScheme="base">
-          {dimensions}
-        </Badge>
-      )}
+      <Badge variant="solid" colorScheme="base">
+        {image.width} × {image.height}
+      </Badge>
       {model && (
         <Badge variant="solid" colorScheme="base">
           {model}

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

@@ -1,42 +0,0 @@
-import { ButtonGroup, Flex, Spacer, Text } from '@chakra-ui/react';
-import IAIIconButton from 'common/components/IAIIconButton';
-
-import { useTranslation } from 'react-i18next';
-import { FaUndo, FaUpload } from 'react-icons/fa';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { useCallback } from 'react';
-import { clearInitialImage } from 'features/parameters/store/generationSlice';
-import useImageUploader from 'common/hooks/useImageUploader';
-
-const InitialImageButtons = () => {
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-  const { openUploader } = useImageUploader();
-
-  const handleResetInitialImage = useCallback(() => {
-    dispatch(clearInitialImage());
-  }, [dispatch]);
-
-  return (
-    <Flex w="full" alignItems="center">
-      <Text size="sm" fontWeight={500} color="base.300">
-        {t('parameters.initialImage')}
-      </Text>
-      <Spacer />
-      <ButtonGroup>
-        <IAIIconButton
-          icon={<FaUndo />}
-          aria-label={t('accessibility.reset')}
-          onClick={handleResetInitialImage}
-        />
-        <IAIIconButton
-          icon={<FaUpload />}
-          onClick={openUploader}
-          aria-label={t('common.upload')}
-        />
-      </ButtonGroup>
-    </Flex>
-  );
-};
-
-export default InitialImageButtons;

invokeai/frontend/web/src/common/hooks/useIsReadyToInvoke.ts

@@ -1,12 +1,12 @@
 import { createSelector } from '@reduxjs/toolkit';
+import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { validateSeedWeights } from 'common/util/seedWeightPairs';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-import { isEqual } from 'lodash-es';
 
-export const readinessSelector = createSelector(
+const readinessSelector = createSelector(
   [generationSelector, systemSelector, activeTabNameSelector],
   (generation, system, activeTabName) => {
     const {
@@ -60,3 +60,8 @@ export const readinessSelector = createSelector(
   },
   defaultSelectorOptions
 );
+
+export const useIsReadyToInvoke = () => {
+  const { isReady } = useAppSelector(readinessSelector);
+  return isReady;
+};

invokeai/frontend/web/src/common/util/getUrl.ts

@@ -1,34 +0,0 @@
-import { RootState } from 'app/store/store';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useCallback } from 'react';
-import { OpenAPI } from 'services/api';
-
-export const getUrlAlt = (url: string, shouldTransformUrls: boolean) => {
-  if (OpenAPI.BASE && shouldTransformUrls) {
-    return [OpenAPI.BASE, url].join('/');
-  }
-
-  return url;
-};
-
-export const useGetUrl = () => {
-  const shouldTransformUrls = useAppSelector(
-    (state: RootState) => state.config.shouldTransformUrls
-  );
-
-  const getUrl = useCallback(
-    (url?: string) => {
-      if (OpenAPI.BASE && shouldTransformUrls) {
-        return [OpenAPI.BASE, url].join('/');
-      }
-
-      return url;
-    },
-    [shouldTransformUrls]
-  );
-
-  return {
-    shouldTransformUrls,
-    getUrl,
-  };
-};

invokeai/frontend/web/src/features/canvas/components/IAICanvasObjectRenderer.tsx

@@ -1,6 +1,5 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
-import { useGetUrl } from 'common/util/getUrl';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { rgbaColorToString } from 'features/canvas/util/colorToString';
 import { isEqual } from 'lodash-es';
@@ -33,7 +32,6 @@ const selector = createSelector(
 
 const IAICanvasObjectRenderer = () => {
   const { objects } = useAppSelector(selector);
-  const { getUrl } = useGetUrl();
 
   if (!objects) return null;
 
@@ -46,7 +44,7 @@ const IAICanvasObjectRenderer = () => {
               key={i}
               x={obj.x}
               y={obj.y}
-              url={getUrl(obj.image.image_url)}
+              url={obj.image.image_url}
             />
           );
         } else if (isCanvasBaseLine(obj)) {

invokeai/frontend/web/src/features/canvas/components/IAICanvasStagingArea.tsx

@@ -1,6 +1,5 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
-import { useGetUrl } from 'common/util/getUrl';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { GroupConfig } from 'konva/lib/Group';
 import { isEqual } from 'lodash-es';
@@ -56,13 +55,12 @@ const IAICanvasStagingArea = (props: Props) => {
     width,
     height,
   } = useAppSelector(selector);
-  const { getUrl } = useGetUrl();
 
   return (
     <Group {...rest}>
       {shouldShowStagingImage && currentStagingAreaImage && (
         <IAICanvasImage
-          url={getUrl(currentStagingAreaImage.image.image_url) ?? ''}
+          url={currentStagingAreaImage.image.image_url}
           x={x}
           y={y}
         />

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasMaskOptions.tsx

@@ -2,7 +2,7 @@ import { ButtonGroup, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
-import IAICheckbox from 'common/components/IAICheckbox';
+import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox';
 import IAIColorPicker from 'common/components/IAIColorPicker';
 import IAIIconButton from 'common/components/IAIIconButton';
 import IAIPopover from 'common/components/IAIPopover';
@@ -117,12 +117,12 @@ const IAICanvasMaskOptions = () => {
       }
     >
       <Flex direction="column" gap={2}>
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={`${t('unifiedCanvas.enableMask')} (H)`}
           isChecked={isMaskEnabled}
           onChange={handleToggleEnableMask}
         />
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.preserveMaskedArea')}
           isChecked={shouldPreserveMaskedArea}
           onChange={(e) =>

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

@@ -1,7 +1,7 @@
 import { Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAICheckbox from 'common/components/IAICheckbox';
+import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox';
 import IAIIconButton from 'common/components/IAIIconButton';
 import IAIPopover from 'common/components/IAIPopover';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
@@ -102,50 +102,50 @@ const IAICanvasSettingsButtonPopover = () => {
       }
     >
       <Flex direction="column" gap={2}>
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.showIntermediates')}
           isChecked={shouldShowIntermediates}
           onChange={(e) =>
             dispatch(setShouldShowIntermediates(e.target.checked))
           }
         />
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.showGrid')}
           isChecked={shouldShowGrid}
           onChange={(e) => dispatch(setShouldShowGrid(e.target.checked))}
         />
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.snapToGrid')}
           isChecked={shouldSnapToGrid}
           onChange={handleChangeShouldSnapToGrid}
         />
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.darkenOutsideSelection')}
           isChecked={shouldDarkenOutsideBoundingBox}
           onChange={(e) =>
             dispatch(setShouldDarkenOutsideBoundingBox(e.target.checked))
           }
         />
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.autoSaveToGallery')}
           isChecked={shouldAutoSave}
           onChange={(e) => dispatch(setShouldAutoSave(e.target.checked))}
         />
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.saveBoxRegionOnly')}
           isChecked={shouldCropToBoundingBoxOnSave}
           onChange={(e) =>
             dispatch(setShouldCropToBoundingBoxOnSave(e.target.checked))
           }
         />
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.limitStrokesToBox')}
           isChecked={shouldRestrictStrokesToBox}
           onChange={(e) =>
             dispatch(setShouldRestrictStrokesToBox(e.target.checked))
           }
         />
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.showCanvasDebugInfo')}
           isChecked={shouldShowCanvasDebugInfo}
           onChange={(e) =>
@@ -153,7 +153,7 @@ const IAICanvasSettingsButtonPopover = () => {
           }
         />
 
-        <IAICheckbox
+        <IAISimpleCheckbox
           label={t('unifiedCanvas.antialiasing')}
           isChecked={shouldAntialias}
           onChange={(e) => dispatch(setShouldAntialias(e.target.checked))}

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasToolbar.tsx

@@ -1,4 +1,4 @@
-import { ButtonGroup, Flex } from '@chakra-ui/react';
+import { Box, ButtonGroup, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIIconButton from 'common/components/IAIIconButton';
@@ -210,16 +210,19 @@ const IAICanvasToolbar = () => {
       sx={{
         alignItems: 'center',
         gap: 2,
+        flexWrap: 'wrap',
       }}
     >
-      <IAISelect
-        tooltip={`${t('unifiedCanvas.layer')} (Q)`}
-        tooltipProps={{ hasArrow: true, placement: 'top' }}
-        value={layer}
-        validValues={LAYER_NAMES_DICT}
-        onChange={handleChangeLayer}
-        isDisabled={isStaging}
-      />
+      <Box w={24}>
+        <IAISelect
+          tooltip={`${t('unifiedCanvas.layer')} (Q)`}
+          tooltipProps={{ hasArrow: true, placement: 'top' }}
+          value={layer}
+          validValues={LAYER_NAMES_DICT}
+          onChange={handleChangeLayer}
+          isDisabled={isStaging}
+        />
+      </Box>
 
       <IAICanvasMaskOptions />
       <IAICanvasToolChooserOptions />

invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts

@@ -30,6 +30,8 @@ import {
 } from './canvasTypes';
 import { ImageDTO } from 'services/api';
 import { sessionCanceled } from 'services/thunks/session';
+import { setShouldUseCanvasBetaLayout } from 'features/ui/store/uiSlice';
+import { imageUrlsReceived } from 'services/thunks/image';
 
 export const initialLayerState: CanvasLayerState = {
   objects: [],
@@ -851,6 +853,30 @@ export const canvasSlice = createSlice({
         state.layerState.stagingArea = initialLayerState.stagingArea;
       }
     });
+
+    builder.addCase(setShouldUseCanvasBetaLayout, (state, action) => {
+      state.doesCanvasNeedScaling = true;
+    });
+    builder.addCase(imageUrlsReceived.fulfilled, (state, action) => {
+      const { image_name, image_origin, image_url, thumbnail_url } =
+        action.payload;
+
+      state.layerState.objects.forEach((object) => {
+        if (object.kind === 'image') {
+          if (object.image.image_name === image_name) {
+            object.image.image_url = image_url;
+            object.image.thumbnail_url = thumbnail_url;
+          }
+        }
+      });
+
+      state.layerState.stagingArea.images.forEach((stagedImage) => {
+        if (stagedImage.image.image_name === image_name) {
+          stagedImage.image.image_url = image_url;
+          stagedImage.image.thumbnail_url = thumbnail_url;
+        }
+      });
+    });
   },
 });
 

invokeai/frontend/web/src/features/canvas/util/getBaseLayerBlob.ts

@@ -2,10 +2,10 @@ import { getCanvasBaseLayer } from './konvaInstanceProvider';
 import { RootState } from 'app/store/store';
 import { konvaNodeToBlob } from './konvaNodeToBlob';
 
-export const getBaseLayerBlob = async (
-  state: RootState,
-  withoutBoundingBox?: boolean
-) => {
+/**
+ * Get the canvas base layer blob, with or without bounding box according to `shouldCropToBoundingBoxOnSave`
+ */
+export const getBaseLayerBlob = async (state: RootState) => {
   const canvasBaseLayer = getCanvasBaseLayer();
 
   if (!canvasBaseLayer) {
@@ -24,15 +24,14 @@ export const getBaseLayerBlob = async (
 
   const absPos = clonedBaseLayer.getAbsolutePosition();
 
-  const boundingBox =
-    shouldCropToBoundingBoxOnSave && !withoutBoundingBox
-      ? {
-          x: boundingBoxCoordinates.x + absPos.x,
-          y: boundingBoxCoordinates.y + absPos.y,
-          width: boundingBoxDimensions.width,
-          height: boundingBoxDimensions.height,
-        }
-      : clonedBaseLayer.getClientRect();
+  const boundingBox = shouldCropToBoundingBoxOnSave
+    ? {
+        x: boundingBoxCoordinates.x + absPos.x,
+        y: boundingBoxCoordinates.y + absPos.y,
+        width: boundingBoxDimensions.width,
+        height: boundingBoxDimensions.height,
+      }
+    : clonedBaseLayer.getClientRect();
 
   return konvaNodeToBlob(clonedBaseLayer, boundingBox);
 };

invokeai/frontend/web/src/features/canvas/util/getFullBaseLayerBlob.ts

@@ -0,0 +1,19 @@
+import { getCanvasBaseLayer } from './konvaInstanceProvider';
+import { konvaNodeToBlob } from './konvaNodeToBlob';
+
+/**
+ * Gets the canvas base layer blob, without bounding box
+ */
+export const getFullBaseLayerBlob = async () => {
+  const canvasBaseLayer = getCanvasBaseLayer();
+
+  if (!canvasBaseLayer) {
+    return;
+  }
+
+  const clonedBaseLayer = canvasBaseLayer.clone();
+
+  clonedBaseLayer.scale({ x: 1, y: 1 });
+
+  return konvaNodeToBlob(clonedBaseLayer, clonedBaseLayer.getClientRect());
+};

invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx

@@ -0,0 +1,258 @@
+import { memo, useCallback } from 'react';
+import {
+  ControlNetConfig,
+  controlNetAdded,
+  controlNetRemoved,
+  controlNetToggled,
+} from '../store/controlNetSlice';
+import { useAppDispatch } from 'app/store/storeHooks';
+import ParamControlNetModel from './parameters/ParamControlNetModel';
+import ParamControlNetWeight from './parameters/ParamControlNetWeight';
+import {
+  Checkbox,
+  Flex,
+  FormControl,
+  FormLabel,
+  HStack,
+  TabList,
+  TabPanels,
+  Tabs,
+  Tab,
+  TabPanel,
+  Box,
+} from '@chakra-ui/react';
+import { FaCopy, FaPlus, FaTrash, FaWrench } from 'react-icons/fa';
+
+import ParamControlNetBeginEnd from './parameters/ParamControlNetBeginEnd';
+import ControlNetImagePreview from './ControlNetImagePreview';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { v4 as uuidv4 } from 'uuid';
+import { useToggle } from 'react-use';
+import ParamControlNetProcessorSelect from './parameters/ParamControlNetProcessorSelect';
+import ControlNetProcessorComponent from './ControlNetProcessorComponent';
+import ControlNetPreprocessButton from './ControlNetPreprocessButton';
+import IAIButton from 'common/components/IAIButton';
+import IAISwitch from 'common/components/IAISwitch';
+import { ChevronDownIcon, ChevronUpIcon } from '@chakra-ui/icons';
+
+type ControlNetProps = {
+  controlNet: ControlNetConfig;
+};
+
+const ControlNet = (props: ControlNetProps) => {
+  const {
+    controlNetId,
+    isEnabled,
+    model,
+    weight,
+    beginStepPct,
+    endStepPct,
+    controlImage,
+    processedControlImage,
+    processorNode,
+    processorType,
+  } = props.controlNet;
+  const dispatch = useAppDispatch();
+  const [shouldShowAdvanced, onToggleAdvanced] = useToggle(false);
+
+  const handleDelete = useCallback(() => {
+    dispatch(controlNetRemoved({ controlNetId }));
+  }, [controlNetId, dispatch]);
+
+  const handleDuplicate = useCallback(() => {
+    dispatch(
+      controlNetAdded({ controlNetId: uuidv4(), controlNet: props.controlNet })
+    );
+  }, [dispatch, props.controlNet]);
+
+  const handleToggleIsEnabled = useCallback(() => {
+    dispatch(controlNetToggled({ controlNetId }));
+  }, [controlNetId, dispatch]);
+
+  return (
+    <Flex
+      sx={{
+        flexDir: 'column',
+        gap: 2,
+        p: 3,
+        bg: 'base.850',
+        borderRadius: 'base',
+      }}
+    >
+      <Flex sx={{ gap: 2 }}>
+        <IAISwitch
+          tooltip="Toggle"
+          aria-label="Toggle"
+          isChecked={isEnabled}
+          onChange={handleToggleIsEnabled}
+        />
+        <Box
+          sx={{
+            w: 'full',
+            minW: 0,
+            opacity: isEnabled ? 1 : 0.5,
+            pointerEvents: isEnabled ? 'auto' : 'none',
+            transitionProperty: 'common',
+            transitionDuration: '0.1s',
+          }}
+        >
+          <ParamControlNetModel controlNetId={controlNetId} model={model} />
+        </Box>
+        <IAIIconButton
+          size="sm"
+          tooltip="Duplicate"
+          aria-label="Duplicate"
+          onClick={handleDuplicate}
+          icon={<FaCopy />}
+        />
+        <IAIIconButton
+          size="sm"
+          tooltip="Delete"
+          aria-label="Delete"
+          colorScheme="error"
+          onClick={handleDelete}
+          icon={<FaTrash />}
+        />
+        <IAIIconButton
+          size="sm"
+          aria-label="Expand"
+          onClick={onToggleAdvanced}
+          variant="link"
+          icon={
+            <ChevronUpIcon
+              sx={{
+                boxSize: 4,
+                color: 'base.300',
+                transform: shouldShowAdvanced
+                  ? 'rotate(0deg)'
+                  : 'rotate(180deg)',
+                transitionProperty: 'common',
+                transitionDuration: 'normal',
+              }}
+            />
+          }
+        />
+      </Flex>
+      {isEnabled && (
+        <>
+          <Flex sx={{ gap: 4 }}>
+            <Flex
+              sx={{
+                flexDir: 'column',
+                gap: 2,
+                w: 'full',
+                h: 24,
+                paddingInlineStart: 1,
+                paddingInlineEnd: shouldShowAdvanced ? 1 : 0,
+                pb: 2,
+                justifyContent: 'space-between',
+              }}
+            >
+              <ParamControlNetWeight
+                controlNetId={controlNetId}
+                weight={weight}
+                mini
+              />
+              <ParamControlNetBeginEnd
+                controlNetId={controlNetId}
+                beginStepPct={beginStepPct}
+                endStepPct={endStepPct}
+                mini
+              />
+            </Flex>
+            {!shouldShowAdvanced && (
+              <Flex
+                sx={{
+                  alignItems: 'center',
+                  justifyContent: 'center',
+                  h: 24,
+                  w: 24,
+                  aspectRatio: '1/1',
+                }}
+              >
+                <ControlNetImagePreview controlNet={props.controlNet} />
+              </Flex>
+            )}
+          </Flex>
+          {shouldShowAdvanced && (
+            <>
+              <Box pt={2}>
+                <ControlNetImagePreview controlNet={props.controlNet} />
+              </Box>
+              <ParamControlNetProcessorSelect
+                controlNetId={controlNetId}
+                processorNode={processorNode}
+              />
+              <ControlNetProcessorComponent
+                controlNetId={controlNetId}
+                processorNode={processorNode}
+              />
+            </>
+          )}
+        </>
+      )}
+    </Flex>
+  );
+
+  return (
+    <Flex sx={{ flexDir: 'column', gap: 3 }}>
+      <ControlNetImagePreview controlNet={props.controlNet} />
+      <ParamControlNetModel controlNetId={controlNetId} model={model} />
+      <Tabs
+        isFitted
+        orientation="horizontal"
+        variant="enclosed"
+        size="sm"
+        colorScheme="accent"
+      >
+        <TabList>
+          <Tab
+            sx={{ 'button&': { _selected: { borderBottomColor: 'base.800' } } }}
+          >
+            Model Config
+          </Tab>
+          <Tab
+            sx={{ 'button&': { _selected: { borderBottomColor: 'base.800' } } }}
+          >
+            Preprocess
+          </Tab>
+        </TabList>
+        <TabPanels sx={{ pt: 2 }}>
+          <TabPanel sx={{ p: 0 }}>
+            <ParamControlNetWeight
+              controlNetId={controlNetId}
+              weight={weight}
+            />
+            <ParamControlNetBeginEnd
+              controlNetId={controlNetId}
+              beginStepPct={beginStepPct}
+              endStepPct={endStepPct}
+            />
+          </TabPanel>
+          <TabPanel sx={{ p: 0 }}>
+            <ParamControlNetProcessorSelect
+              controlNetId={controlNetId}
+              processorNode={processorNode}
+            />
+            <ControlNetProcessorComponent
+              controlNetId={controlNetId}
+              processorNode={processorNode}
+            />
+            <ControlNetPreprocessButton controlNet={props.controlNet} />
+            {/* <IAIButton
+              size="sm"
+              leftIcon={<FaUndo />}
+              onClick={handleReset}
+              isDisabled={Boolean(!processedControlImage)}
+            >
+              Reset Processing
+            </IAIButton> */}
+          </TabPanel>
+        </TabPanels>
+      </Tabs>
+      <IAIButton onClick={handleDelete}>Remove ControlNet</IAIButton>
+    </Flex>
+  );
+};
+
+export default memo(ControlNet);

invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx

@@ -0,0 +1,144 @@
+import { memo, useCallback, useRef, useState } from 'react';
+import { ImageDTO } from 'services/api';
+import {
+  ControlNetConfig,
+  controlNetImageChanged,
+  controlNetSelector,
+} from '../store/controlNetSlice';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { Box } from '@chakra-ui/react';
+import IAIDndImage from 'common/components/IAIDndImage';
+import { createSelector } from '@reduxjs/toolkit';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { AnimatePresence, motion } from 'framer-motion';
+import { IAIImageFallback } from 'common/components/IAIImageFallback';
+import { useHoverDirty } from 'react-use';
+
+const selector = createSelector(
+  controlNetSelector,
+  (controlNet) => {
+    const { isProcessingControlImage } = controlNet;
+    return { isProcessingControlImage };
+  },
+  defaultSelectorOptions
+);
+
+type Props = {
+  controlNet: ControlNetConfig;
+};
+
+const ControlNetImagePreview = (props: Props) => {
+  const { controlNetId, controlImage, processedControlImage, processorType } =
+    props.controlNet;
+  const dispatch = useAppDispatch();
+  const { isProcessingControlImage } = useAppSelector(selector);
+  const containerRef = useRef<HTMLDivElement>(null);
+
+  const isMouseOverImage = useHoverDirty(containerRef);
+
+  const handleDrop = useCallback(
+    (droppedImage: ImageDTO) => {
+      if (controlImage?.image_name === droppedImage.image_name) {
+        return;
+      }
+      dispatch(
+        controlNetImageChanged({ controlNetId, controlImage: droppedImage })
+      );
+    },
+    [controlImage, controlNetId, dispatch]
+  );
+
+  const shouldShowProcessedImageBackdrop =
+    Number(controlImage?.width) > Number(processedControlImage?.width) ||
+    Number(controlImage?.height) > Number(processedControlImage?.height);
+
+  const shouldShowProcessedImage =
+    controlImage &&
+    processedControlImage &&
+    !isMouseOverImage &&
+    !isProcessingControlImage &&
+    processorType !== 'none';
+
+  return (
+    <Box
+      ref={containerRef}
+      sx={{ position: 'relative', w: 'full', h: 'full', aspectRatio: '1/1' }}
+    >
+      <IAIDndImage
+        image={controlImage}
+        onDrop={handleDrop}
+        isDropDisabled={Boolean(
+          processedControlImage && processorType !== 'none'
+        )}
+      />
+      <AnimatePresence>
+        {shouldShowProcessedImage && (
+          <motion.div
+            initial={{
+              opacity: 0,
+            }}
+            animate={{
+              opacity: 1,
+              transition: { duration: 0.1 },
+            }}
+            exit={{
+              opacity: 0,
+              transition: { duration: 0.1 },
+            }}
+          >
+            <Box
+              sx={{
+                position: 'absolute',
+                w: 'full',
+                h: 'full',
+                top: 0,
+                insetInlineStart: 0,
+              }}
+            >
+              {shouldShowProcessedImageBackdrop && (
+                <Box
+                  sx={{
+                    w: 'full',
+                    h: 'full',
+                    bg: 'base.900',
+                    opacity: 0.7,
+                  }}
+                />
+              )}
+              <Box
+                sx={{
+                  position: 'absolute',
+                  top: 0,
+                  insetInlineStart: 0,
+                  w: 'full',
+                  h: 'full',
+                }}
+              >
+                <IAIDndImage
+                  image={processedControlImage}
+                  onDrop={handleDrop}
+                  payloadImage={controlImage}
+                />
+              </Box>
+            </Box>
+          </motion.div>
+        )}
+      </AnimatePresence>
+      {isProcessingControlImage && (
+        <Box
+          sx={{
+            position: 'absolute',
+            top: 0,
+            insetInlineStart: 0,
+            w: 'full',
+            h: 'full',
+          }}
+        >
+          <IAIImageFallback />
+        </Box>
+      )}
+    </Box>
+  );
+};
+
+export default memo(ControlNetImagePreview);

invokeai/frontend/web/src/features/controlNet/components/ControlNetPreprocessButton.tsx

@@ -0,0 +1,36 @@
+import IAIButton from 'common/components/IAIButton';
+import { memo, useCallback } from 'react';
+import { ControlNetConfig } from '../store/controlNetSlice';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { controlNetImageProcessed } from '../store/actions';
+import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke';
+
+type Props = {
+  controlNet: ControlNetConfig;
+};
+
+const ControlNetPreprocessButton = (props: Props) => {
+  const { controlNetId, controlImage } = props.controlNet;
+  const dispatch = useAppDispatch();
+  const isReady = useIsReadyToInvoke();
+
+  const handleProcess = useCallback(() => {
+    dispatch(
+      controlNetImageProcessed({
+        controlNetId,
+      })
+    );
+  }, [controlNetId, dispatch]);
+
+  return (
+    <IAIButton
+      size="sm"
+      onClick={handleProcess}
+      isDisabled={Boolean(!controlImage) || !isReady}
+    >
+      Preprocess
+    </IAIButton>
+  );
+};
+
+export default memo(ControlNetPreprocessButton);

invokeai/frontend/web/src/features/controlNet/components/ControlNetProcessorComponent.tsx

@@ -0,0 +1,131 @@
+import { memo } from 'react';
+import { RequiredControlNetProcessorNode } from '../store/types';
+import CannyProcessor from './processors/CannyProcessor';
+import HedProcessor from './processors/HedProcessor';
+import LineartProcessor from './processors/LineartProcessor';
+import LineartAnimeProcessor from './processors/LineartAnimeProcessor';
+import ContentShuffleProcessor from './processors/ContentShuffleProcessor';
+import MediapipeFaceProcessor from './processors/MediapipeFaceProcessor';
+import MidasDepthProcessor from './processors/MidasDepthProcessor';
+import MlsdImageProcessor from './processors/MlsdImageProcessor';
+import NormalBaeProcessor from './processors/NormalBaeProcessor';
+import OpenposeProcessor from './processors/OpenposeProcessor';
+import PidiProcessor from './processors/PidiProcessor';
+import ZoeDepthProcessor from './processors/ZoeDepthProcessor';
+
+export type ControlNetProcessorProps = {
+  controlNetId: string;
+  processorNode: RequiredControlNetProcessorNode;
+};
+
+const ControlNetProcessorComponent = (props: ControlNetProcessorProps) => {
+  const { controlNetId, processorNode } = props;
+  if (processorNode.type === 'canny_image_processor') {
+    return (
+      <CannyProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'hed_image_processor') {
+    return (
+      <HedProcessor controlNetId={controlNetId} processorNode={processorNode} />
+    );
+  }
+
+  if (processorNode.type === 'lineart_image_processor') {
+    return (
+      <LineartProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'content_shuffle_image_processor') {
+    return (
+      <ContentShuffleProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'lineart_anime_image_processor') {
+    return (
+      <LineartAnimeProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'mediapipe_face_processor') {
+    return (
+      <MediapipeFaceProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'midas_depth_image_processor') {
+    return (
+      <MidasDepthProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'mlsd_image_processor') {
+    return (
+      <MlsdImageProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'normalbae_image_processor') {
+    return (
+      <NormalBaeProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'openpose_image_processor') {
+    return (
+      <OpenposeProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'pidi_image_processor') {
+    return (
+      <PidiProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  if (processorNode.type === 'zoe_depth_image_processor') {
+    return (
+      <ZoeDepthProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+      />
+    );
+  }
+
+  return null;
+};
+
+export default memo(ControlNetProcessorComponent);

invokeai/frontend/web/src/features/controlNet/components/hooks/useProcessorNodeChanged.ts

@@ -0,0 +1,20 @@
+import { useAppDispatch } from 'app/store/storeHooks';
+import { controlNetProcessorParamsChanged } from 'features/controlNet/store/controlNetSlice';
+import { ControlNetProcessorNode } from 'features/controlNet/store/types';
+import { useCallback } from 'react';
+
+export const useProcessorNodeChanged = () => {
+  const dispatch = useAppDispatch();
+  const handleProcessorNodeChanged = useCallback(
+    (controlNetId: string, changes: Partial<ControlNetProcessorNode>) => {
+      dispatch(
+        controlNetProcessorParamsChanged({
+          controlNetId,
+          changes,
+        })
+      );
+    },
+    [dispatch]
+  );
+  return handleProcessorNodeChanged;
+};

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

@@ -0,0 +1,130 @@
+import {
+  FormControl,
+  FormLabel,
+  HStack,
+  RangeSlider,
+  RangeSliderFilledTrack,
+  RangeSliderMark,
+  RangeSliderThumb,
+  RangeSliderTrack,
+  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';
+
+type Props = {
+  controlNetId: string;
+  beginStepPct: number;
+  endStepPct: number;
+  mini?: boolean;
+};
+
+const formatPct = (v: number) => `${Math.round(v * 100)}%`;
+
+const ParamControlNetBeginEnd = (props: Props) => {
+  const { controlNetId, beginStepPct, endStepPct, mini = false } = props;
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const handleStepPctChanged = useCallback(
+    (v: number[]) => {
+      dispatch(
+        controlNetBeginStepPctChanged({ controlNetId, beginStepPct: v[0] })
+      );
+      dispatch(controlNetEndStepPctChanged({ controlNetId, endStepPct: v[1] }));
+    },
+    [controlNetId, dispatch]
+  );
+
+  const handleStepPctReset = useCallback(() => {
+    dispatch(controlNetBeginStepPctChanged({ controlNetId, beginStepPct: 0 }));
+    dispatch(controlNetEndStepPctChanged({ controlNetId, endStepPct: 1 }));
+  }, [controlNetId, dispatch]);
+
+  return (
+    <FormControl>
+      <FormLabel>Begin / End Step Percentage</FormLabel>
+      <HStack w="100%" gap={2} alignItems="center">
+        <RangeSlider
+          aria-label={['Begin Step %', 'End Step %']}
+          value={[beginStepPct, endStepPct]}
+          onChange={handleStepPctChanged}
+          min={0}
+          max={1}
+          step={0.01}
+          minStepsBetweenThumbs={5}
+        >
+          <RangeSliderTrack>
+            <RangeSliderFilledTrack />
+          </RangeSliderTrack>
+          <Tooltip label={formatPct(beginStepPct)} placement="top" hasArrow>
+            <RangeSliderThumb index={0} />
+          </Tooltip>
+          <Tooltip label={formatPct(endStepPct)} placement="top" hasArrow>
+            <RangeSliderThumb index={1} />
+          </Tooltip>
+          {!mini && (
+            <>
+              <RangeSliderMark
+                value={0}
+                sx={{
+                  fontSize: 'xs',
+                  fontWeight: '500',
+                  color: 'base.200',
+                  insetInlineStart: '0 !important',
+                  insetInlineEnd: 'unset !important',
+                  mt: 1.5,
+                }}
+              >
+                0%
+              </RangeSliderMark>
+              <RangeSliderMark
+                value={0.5}
+                sx={{
+                  fontSize: 'xs',
+                  fontWeight: '500',
+                  color: 'base.200',
+                  mt: 1.5,
+                }}
+              >
+                50%
+              </RangeSliderMark>
+              <RangeSliderMark
+                value={1}
+                sx={{
+                  fontSize: 'xs',
+                  fontWeight: '500',
+                  color: 'base.200',
+                  insetInlineStart: 'unset !important',
+                  insetInlineEnd: '0 !important',
+                  mt: 1.5,
+                }}
+              >
+                100%
+              </RangeSliderMark>
+            </>
+          )}
+        </RangeSlider>
+
+        {!mini && (
+          <IAIIconButton
+            size="sm"
+            aria-label={t('accessibility.reset')}
+            tooltip={t('accessibility.reset')}
+            icon={<BiReset />}
+            onClick={handleStepPctReset}
+          />
+        )}
+      </HStack>
+    </FormControl>
+  );
+};
+
+export default memo(ParamControlNetBeginEnd);

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

@@ -0,0 +1,28 @@
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAISwitch from 'common/components/IAISwitch';
+import { controlNetToggled } from 'features/controlNet/store/controlNetSlice';
+import { memo, useCallback } from 'react';
+
+type ParamControlNetIsEnabledProps = {
+  controlNetId: string;
+  isEnabled: boolean;
+};
+
+const ParamControlNetIsEnabled = (props: ParamControlNetIsEnabledProps) => {
+  const { controlNetId, isEnabled } = props;
+  const dispatch = useAppDispatch();
+
+  const handleIsEnabledChanged = useCallback(() => {
+    dispatch(controlNetToggled({ controlNetId }));
+  }, [dispatch, controlNetId]);
+
+  return (
+    <IAISwitch
+      label="Enabled"
+      isChecked={isEnabled}
+      onChange={handleIsEnabledChanged}
+    />
+  );
+};
+
+export default memo(ParamControlNetIsEnabled);

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

@@ -0,0 +1,36 @@
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIFullCheckbox from 'common/components/IAIFullCheckbox';
+import IAISwitch from 'common/components/IAISwitch';
+import {
+  controlNetToggled,
+  isControlNetImagePreprocessedToggled,
+} from 'features/controlNet/store/controlNetSlice';
+import { memo, useCallback } from 'react';
+
+type ParamControlNetIsEnabledProps = {
+  controlNetId: string;
+  isControlImageProcessed: boolean;
+};
+
+const ParamControlNetIsEnabled = (props: ParamControlNetIsEnabledProps) => {
+  const { controlNetId, isControlImageProcessed } = props;
+  const dispatch = useAppDispatch();
+
+  const handleIsControlImageProcessedToggled = useCallback(() => {
+    dispatch(
+      isControlNetImagePreprocessedToggled({
+        controlNetId,
+      })
+    );
+  }, [controlNetId, dispatch]);
+
+  return (
+    <IAISwitch
+      label="Preprocess"
+      isChecked={isControlImageProcessed}
+      onChange={handleIsControlImageProcessedToggled}
+    />
+  );
+};
+
+export default memo(ParamControlNetIsEnabled);

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

@@ -0,0 +1,41 @@
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAICustomSelect from 'common/components/IAICustomSelect';
+import {
+  CONTROLNET_MODELS,
+  ControlNetModel,
+} from 'features/controlNet/store/constants';
+import { controlNetModelChanged } from 'features/controlNet/store/controlNetSlice';
+import { memo, useCallback } from 'react';
+
+type ParamIsControlNetModelProps = {
+  controlNetId: string;
+  model: ControlNetModel;
+};
+
+const ParamIsControlNetModel = (props: ParamIsControlNetModelProps) => {
+  const { controlNetId, model } = props;
+  const dispatch = useAppDispatch();
+
+  const handleModelChanged = useCallback(
+    (val: string | null | undefined) => {
+      // TODO: do not cast
+      const model = val as ControlNetModel;
+      dispatch(controlNetModelChanged({ controlNetId, model }));
+    },
+    [controlNetId, dispatch]
+  );
+
+  return (
+    <IAICustomSelect
+      tooltip={model}
+      tooltipProps={{ placement: 'top', hasArrow: true }}
+      items={CONTROLNET_MODELS}
+      selectedItem={model}
+      setSelectedItem={handleModelChanged}
+      ellipsisPosition="start"
+      withCheckIcon
+    />
+  );
+};
+
+export default memo(ParamIsControlNetModel);

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

@@ -0,0 +1,47 @@
+import IAICustomSelect from 'common/components/IAICustomSelect';
+import { memo, useCallback } from 'react';
+import {
+  ControlNetProcessorNode,
+  ControlNetProcessorType,
+} from '../../store/types';
+import { controlNetProcessorTypeChanged } from '../../store/controlNetSlice';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { CONTROLNET_PROCESSORS } from '../../store/constants';
+
+type ParamControlNetProcessorSelectProps = {
+  controlNetId: string;
+  processorNode: ControlNetProcessorNode;
+};
+
+const CONTROLNET_PROCESSOR_TYPES = Object.keys(
+  CONTROLNET_PROCESSORS
+) as ControlNetProcessorType[];
+
+const ParamControlNetProcessorSelect = (
+  props: ParamControlNetProcessorSelectProps
+) => {
+  const { controlNetId, processorNode } = props;
+  const dispatch = useAppDispatch();
+  const handleProcessorTypeChanged = useCallback(
+    (v: string | null | undefined) => {
+      dispatch(
+        controlNetProcessorTypeChanged({
+          controlNetId,
+          processorType: v as ControlNetProcessorType,
+        })
+      );
+    },
+    [controlNetId, dispatch]
+  );
+  return (
+    <IAICustomSelect
+      label="Processor"
+      items={CONTROLNET_PROCESSOR_TYPES}
+      selectedItem={processorNode.type ?? 'canny_image_processor'}
+      setSelectedItem={handleProcessorTypeChanged}
+      withCheckIcon
+    />
+  );
+};
+
+export default memo(ParamControlNetProcessorSelect);

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

@@ -0,0 +1,57 @@
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAISlider from 'common/components/IAISlider';
+import { controlNetWeightChanged } from 'features/controlNet/store/controlNetSlice';
+import { memo, useCallback } from 'react';
+
+type ParamControlNetWeightProps = {
+  controlNetId: string;
+  weight: number;
+  mini?: boolean;
+};
+
+const ParamControlNetWeight = (props: ParamControlNetWeightProps) => {
+  const { controlNetId, weight, mini = false } = props;
+  const dispatch = useAppDispatch();
+
+  const handleWeightChanged = useCallback(
+    (weight: number) => {
+      dispatch(controlNetWeightChanged({ controlNetId, weight }));
+    },
+    [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"
+      value={weight}
+      onChange={handleWeightChanged}
+      withInput
+      withReset
+      handleReset={handleWeightReset}
+      withSliderMarks
+      min={0}
+      max={1}
+      step={0.01}
+    />
+  );
+};
+
+export default memo(ParamControlNetWeight);

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

@@ -0,0 +1,72 @@
+import IAISlider from 'common/components/IAISlider';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredCannyImageProcessorInvocation } from 'features/controlNet/store/types';
+import { memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.canny_image_processor.default;
+
+type CannyProcessorProps = {
+  controlNetId: string;
+  processorNode: RequiredCannyImageProcessorInvocation;
+};
+
+const CannyProcessor = (props: CannyProcessorProps) => {
+  const { controlNetId, processorNode } = props;
+  const { low_threshold, high_threshold } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleLowThresholdChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { low_threshold: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleLowThresholdReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      low_threshold: DEFAULTS.low_threshold,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleHighThresholdChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { high_threshold: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleHighThresholdReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      high_threshold: DEFAULTS.high_threshold,
+    });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Low Threshold"
+        value={low_threshold}
+        onChange={handleLowThresholdChanged}
+        handleReset={handleLowThresholdReset}
+        withReset
+        min={0}
+        max={255}
+        withInput
+      />
+      <IAISlider
+        label="High Threshold"
+        value={high_threshold}
+        onChange={handleHighThresholdChanged}
+        handleReset={handleHighThresholdReset}
+        withReset
+        min={0}
+        max={255}
+        withInput
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(CannyProcessor);

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

@@ -0,0 +1,141 @@
+import IAISlider from 'common/components/IAISlider';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredContentShuffleImageProcessorInvocation } from 'features/controlNet/store/types';
+import { memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.content_shuffle_image_processor.default;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredContentShuffleImageProcessorInvocation;
+};
+
+const ContentShuffleProcessor = (props: Props) => {
+  const { controlNetId, processorNode } = props;
+  const { image_resolution, detect_resolution, w, h, f } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDetectResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      detect_resolution: DEFAULTS.detect_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      image_resolution: DEFAULTS.image_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleWChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { w: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleWReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      w: DEFAULTS.w,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleHChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { h: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleHReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      h: DEFAULTS.h,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleFChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { f: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleFReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      f: DEFAULTS.f,
+    });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Detect Resolution"
+        value={detect_resolution}
+        onChange={handleDetectResolutionChanged}
+        handleReset={handleDetectResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="Image Resolution"
+        value={image_resolution}
+        onChange={handleImageResolutionChanged}
+        handleReset={handleImageResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="W"
+        value={w}
+        onChange={handleWChanged}
+        handleReset={handleWReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="H"
+        value={h}
+        onChange={handleHChanged}
+        handleReset={handleHReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="F"
+        value={f}
+        onChange={handleFChanged}
+        handleReset={handleFReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(ContentShuffleProcessor);

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

@@ -0,0 +1,88 @@
+import IAISlider from 'common/components/IAISlider';
+import IAISwitch from 'common/components/IAISwitch';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredHedImageProcessorInvocation } from 'features/controlNet/store/types';
+import { ChangeEvent, memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.hed_image_processor.default;
+
+type HedProcessorProps = {
+  controlNetId: string;
+  processorNode: RequiredHedImageProcessorInvocation;
+};
+
+const HedPreprocessor = (props: HedProcessorProps) => {
+  const {
+    controlNetId,
+    processorNode: { detect_resolution, image_resolution, scribble },
+  } = props;
+
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleScribbleChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { scribble: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDetectResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      detect_resolution: DEFAULTS.detect_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleImageResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      image_resolution: DEFAULTS.image_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Detect Resolution"
+        value={detect_resolution}
+        onChange={handleDetectResolutionChanged}
+        handleReset={handleDetectResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="Image Resolution"
+        value={image_resolution}
+        onChange={handleImageResolutionChanged}
+        handleReset={handleImageResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISwitch
+        label="Scribble"
+        isChecked={scribble}
+        onChange={handleScribbleChanged}
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(HedPreprocessor);

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

@@ -0,0 +1,72 @@
+import IAISlider from 'common/components/IAISlider';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredLineartAnimeImageProcessorInvocation } from 'features/controlNet/store/types';
+import { memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.lineart_anime_image_processor.default;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredLineartAnimeImageProcessorInvocation;
+};
+
+const LineartAnimeProcessor = (props: Props) => {
+  const { controlNetId, processorNode } = props;
+  const { image_resolution, detect_resolution } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDetectResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      detect_resolution: DEFAULTS.detect_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleImageResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      image_resolution: DEFAULTS.image_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Detect Resolution"
+        value={detect_resolution}
+        onChange={handleDetectResolutionChanged}
+        handleReset={handleDetectResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="Image Resolution"
+        value={image_resolution}
+        onChange={handleImageResolutionChanged}
+        handleReset={handleImageResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(LineartAnimeProcessor);

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

@@ -0,0 +1,85 @@
+import IAISlider from 'common/components/IAISlider';
+import IAISwitch from 'common/components/IAISwitch';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredLineartImageProcessorInvocation } from 'features/controlNet/store/types';
+import { ChangeEvent, memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.lineart_image_processor.default;
+
+type LineartProcessorProps = {
+  controlNetId: string;
+  processorNode: RequiredLineartImageProcessorInvocation;
+};
+
+const LineartProcessor = (props: LineartProcessorProps) => {
+  const { controlNetId, processorNode } = props;
+  const { image_resolution, detect_resolution, coarse } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDetectResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      detect_resolution: DEFAULTS.detect_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleImageResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      image_resolution: DEFAULTS.image_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleCoarseChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { coarse: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Detect Resolution"
+        value={detect_resolution}
+        onChange={handleDetectResolutionChanged}
+        handleReset={handleDetectResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="Image Resolution"
+        value={image_resolution}
+        onChange={handleImageResolutionChanged}
+        handleReset={handleImageResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISwitch
+        label="Coarse"
+        isChecked={coarse}
+        onChange={handleCoarseChanged}
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(LineartProcessor);

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

@@ -0,0 +1,69 @@
+import IAISlider from 'common/components/IAISlider';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredMediapipeFaceProcessorInvocation } from 'features/controlNet/store/types';
+import { memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.mediapipe_face_processor.default;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredMediapipeFaceProcessorInvocation;
+};
+
+const MediapipeFaceProcessor = (props: Props) => {
+  const { controlNetId, processorNode } = props;
+  const { max_faces, min_confidence } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleMaxFacesChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { max_faces: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleMinConfidenceChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { min_confidence: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleMaxFacesReset = useCallback(() => {
+    processorChanged(controlNetId, { max_faces: DEFAULTS.max_faces });
+  }, [controlNetId, processorChanged]);
+
+  const handleMinConfidenceReset = useCallback(() => {
+    processorChanged(controlNetId, { min_confidence: DEFAULTS.min_confidence });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Max Faces"
+        value={max_faces}
+        onChange={handleMaxFacesChanged}
+        handleReset={handleMaxFacesReset}
+        withReset
+        min={1}
+        max={20}
+        withInput
+      />
+      <IAISlider
+        label="Min Confidence"
+        value={min_confidence}
+        onChange={handleMinConfidenceChanged}
+        handleReset={handleMinConfidenceReset}
+        withReset
+        min={0}
+        max={1}
+        step={0.01}
+        withInput
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(MediapipeFaceProcessor);

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

@@ -0,0 +1,70 @@
+import IAISlider from 'common/components/IAISlider';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredMidasDepthImageProcessorInvocation } from 'features/controlNet/store/types';
+import { memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.midas_depth_image_processor.default;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredMidasDepthImageProcessorInvocation;
+};
+
+const MidasDepthProcessor = (props: Props) => {
+  const { controlNetId, processorNode } = props;
+  const { a_mult, bg_th } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleAMultChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { a_mult: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleBgThChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { bg_th: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleAMultReset = useCallback(() => {
+    processorChanged(controlNetId, { a_mult: DEFAULTS.a_mult });
+  }, [controlNetId, processorChanged]);
+
+  const handleBgThReset = useCallback(() => {
+    processorChanged(controlNetId, { bg_th: DEFAULTS.bg_th });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="a_mult"
+        value={a_mult}
+        onChange={handleAMultChanged}
+        handleReset={handleAMultReset}
+        withReset
+        min={0}
+        max={20}
+        step={0.01}
+        withInput
+      />
+      <IAISlider
+        label="bg_th"
+        value={bg_th}
+        onChange={handleBgThChanged}
+        handleReset={handleBgThReset}
+        withReset
+        min={0}
+        max={20}
+        step={0.01}
+        withInput
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(MidasDepthProcessor);

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

@@ -0,0 +1,116 @@
+import IAISlider from 'common/components/IAISlider';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredMlsdImageProcessorInvocation } from 'features/controlNet/store/types';
+import { memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.mlsd_image_processor.default;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredMlsdImageProcessorInvocation;
+};
+
+const MlsdImageProcessor = (props: Props) => {
+  const { controlNetId, processorNode } = props;
+  const { image_resolution, detect_resolution, thr_d, thr_v } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleThrDChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { thr_d: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleThrVChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { thr_v: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDetectResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      detect_resolution: DEFAULTS.detect_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleImageResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      image_resolution: DEFAULTS.image_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleThrDReset = useCallback(() => {
+    processorChanged(controlNetId, { thr_d: DEFAULTS.thr_d });
+  }, [controlNetId, processorChanged]);
+
+  const handleThrVReset = useCallback(() => {
+    processorChanged(controlNetId, { thr_v: DEFAULTS.thr_v });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Detect Resolution"
+        value={detect_resolution}
+        onChange={handleDetectResolutionChanged}
+        handleReset={handleDetectResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="Image Resolution"
+        value={image_resolution}
+        onChange={handleImageResolutionChanged}
+        handleReset={handleImageResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="W"
+        value={thr_d}
+        onChange={handleThrDChanged}
+        handleReset={handleThrDReset}
+        withReset
+        min={0}
+        max={1}
+        step={0.01}
+        withInput
+      />
+      <IAISlider
+        label="H"
+        value={thr_v}
+        onChange={handleThrVChanged}
+        handleReset={handleThrVReset}
+        withReset
+        min={0}
+        max={1}
+        step={0.01}
+        withInput
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(MlsdImageProcessor);

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

@@ -0,0 +1,72 @@
+import IAISlider from 'common/components/IAISlider';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredNormalbaeImageProcessorInvocation } from 'features/controlNet/store/types';
+import { memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.normalbae_image_processor.default;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredNormalbaeImageProcessorInvocation;
+};
+
+const NormalBaeProcessor = (props: Props) => {
+  const { controlNetId, processorNode } = props;
+  const { image_resolution, detect_resolution } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDetectResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      detect_resolution: DEFAULTS.detect_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleImageResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      image_resolution: DEFAULTS.image_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Detect Resolution"
+        value={detect_resolution}
+        onChange={handleDetectResolutionChanged}
+        handleReset={handleDetectResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="Image Resolution"
+        value={image_resolution}
+        onChange={handleImageResolutionChanged}
+        handleReset={handleImageResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(NormalBaeProcessor);

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

@@ -0,0 +1,85 @@
+import IAISlider from 'common/components/IAISlider';
+import IAISwitch from 'common/components/IAISwitch';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredOpenposeImageProcessorInvocation } from 'features/controlNet/store/types';
+import { ChangeEvent, memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.openpose_image_processor.default;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredOpenposeImageProcessorInvocation;
+};
+
+const OpenposeProcessor = (props: Props) => {
+  const { controlNetId, processorNode } = props;
+  const { image_resolution, detect_resolution, hand_and_face } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDetectResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      detect_resolution: DEFAULTS.detect_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleImageResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      image_resolution: DEFAULTS.image_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleHandAndFaceChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { hand_and_face: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Detect Resolution"
+        value={detect_resolution}
+        onChange={handleDetectResolutionChanged}
+        handleReset={handleDetectResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="Image Resolution"
+        value={image_resolution}
+        onChange={handleImageResolutionChanged}
+        handleReset={handleImageResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISwitch
+        label="Hand and Face"
+        isChecked={hand_and_face}
+        onChange={handleHandAndFaceChanged}
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(OpenposeProcessor);

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

@@ -0,0 +1,93 @@
+import IAISlider from 'common/components/IAISlider';
+import IAISwitch from 'common/components/IAISwitch';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredPidiImageProcessorInvocation } from 'features/controlNet/store/types';
+import { ChangeEvent, memo, useCallback } from 'react';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.pidi_image_processor.default;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredPidiImageProcessorInvocation;
+};
+
+const PidiProcessor = (props: Props) => {
+  const { controlNetId, processorNode } = props;
+  const { image_resolution, detect_resolution, scribble, safe } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDetectResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      detect_resolution: DEFAULTS.detect_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleImageResolutionReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      image_resolution: DEFAULTS.image_resolution,
+    });
+  }, [controlNetId, processorChanged]);
+
+  const handleScribbleChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { scribble: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleSafeChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { safe: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        label="Detect Resolution"
+        value={detect_resolution}
+        onChange={handleDetectResolutionChanged}
+        handleReset={handleDetectResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISlider
+        label="Image Resolution"
+        value={image_resolution}
+        onChange={handleImageResolutionChanged}
+        handleReset={handleImageResolutionReset}
+        withReset
+        min={0}
+        max={4096}
+        withInput
+      />
+      <IAISwitch
+        label="Scribble"
+        isChecked={scribble}
+        onChange={handleScribbleChanged}
+      />
+      <IAISwitch label="Safe" isChecked={safe} onChange={handleSafeChanged} />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(PidiProcessor);

invokeai/frontend/web/src/features/controlNet/components/processors/ZoeDepthProcessor.tsx

@@ -0,0 +1,14 @@
+import { RequiredZoeDepthImageProcessorInvocation } from 'features/controlNet/store/types';
+import { memo } from 'react';
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredZoeDepthImageProcessorInvocation;
+};
+
+const ZoeDepthProcessor = (props: Props) => {
+  // Has no parameters?
+  return null;
+};
+
+export default memo(ZoeDepthProcessor);