Merge branch 'main' into feat/lora_model_patch

.github/workflows/test-invoke-pip-skip.yml

@@ -1,10 +1,16 @@
 name: Test invoke.py pip
+
+# This is a dummy stand-in for the actual tests
+# we don't need to run python tests on non-Python changes
+# But PRs require passing tests to be mergeable
+
 on:
   pull_request:
     paths:
       - '**'
       - '!pyproject.toml'
       - '!invokeai/**'
+      - '!tests/**'
       - 'invokeai/frontend/web/**'
   merge_group:
   workflow_dispatch:
@@ -19,48 +25,26 @@ jobs:
     strategy:
       matrix:
         python-version:
-          # - '3.9'
           - '3.10'
         pytorch:
-          # - linux-cuda-11_6
           - linux-cuda-11_7
           - linux-rocm-5_2
           - linux-cpu
           - macos-default
           - windows-cpu
-          # - windows-cuda-11_6
-          # - windows-cuda-11_7
         include:
-          # - pytorch: linux-cuda-11_6
-          #   os: ubuntu-22.04
-          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
-          #   github-env: $GITHUB_ENV
           - pytorch: linux-cuda-11_7
             os: ubuntu-22.04
-            github-env: $GITHUB_ENV
           - pytorch: linux-rocm-5_2
             os: ubuntu-22.04
-            extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
-            github-env: $GITHUB_ENV
           - pytorch: linux-cpu
             os: ubuntu-22.04
-            extra-index-url: 'https://download.pytorch.org/whl/cpu'
-            github-env: $GITHUB_ENV
           - pytorch: macos-default
             os: macOS-12
-            github-env: $GITHUB_ENV
           - pytorch: windows-cpu
             os: windows-2022
-            github-env: $env:GITHUB_ENV
-          # - pytorch: windows-cuda-11_6
-          #   os: windows-2022
-          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
-          #   github-env: $env:GITHUB_ENV
-          # - pytorch: windows-cuda-11_7
-          #   os: windows-2022
-          #   extra-index-url: 'https://download.pytorch.org/whl/cu117'
-          #   github-env: $env:GITHUB_ENV
     name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
     steps:
-      - run: 'echo "No build required"'
+      - name: skip
+        run: echo "no build required"

.github/workflows/test-invoke-pip.yml

@@ -11,6 +11,7 @@ on:
     paths:
       - 'pyproject.toml'
       - 'invokeai/**'
+      - 'tests/**'
       - '!invokeai/frontend/web/**'
     types:
       - 'ready_for_review'
@@ -32,19 +33,12 @@ jobs:
           # - '3.9'
           - '3.10'
         pytorch:
-          # - linux-cuda-11_6
           - linux-cuda-11_7
           - linux-rocm-5_2
           - linux-cpu
           - macos-default
           - windows-cpu
-          # - windows-cuda-11_6
-          # - windows-cuda-11_7
         include:
-          # - pytorch: linux-cuda-11_6
-          #   os: ubuntu-22.04
-          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
-          #   github-env: $GITHUB_ENV
           - pytorch: linux-cuda-11_7
             os: ubuntu-22.04
             github-env: $GITHUB_ENV
@@ -62,14 +56,6 @@ jobs:
           - pytorch: windows-cpu
             os: windows-2022
             github-env: $env:GITHUB_ENV
-          # - pytorch: windows-cuda-11_6
-          #   os: windows-2022
-          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
-          #   github-env: $env:GITHUB_ENV
-          # - pytorch: windows-cuda-11_7
-          #   os: windows-2022
-          #   extra-index-url: 'https://download.pytorch.org/whl/cu117'
-          #   github-env: $env:GITHUB_ENV
     name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
     env:
@@ -100,40 +86,38 @@ jobs:
         id: run-pytest
         run: pytest
 
-      - name: run invokeai-configure
-        id: run-preload-models
-        env:
-          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
-        run: >
-          invokeai-configure
-          --yes
-          --default_only
-          --full-precision
-        # can't use fp16 weights without a GPU
+      # - name: run invokeai-configure
+      #   env:
+      #     HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
+      #   run: >
+      #     invokeai-configure
+      #     --yes
+      #     --default_only
+      #     --full-precision
+      #   # can't use fp16 weights without a GPU
 
-      - name: run invokeai
-        id: run-invokeai
-        env:
-          # Set offline mode to make sure configure preloaded successfully.
-          HF_HUB_OFFLINE: 1
-          HF_DATASETS_OFFLINE: 1
-          TRANSFORMERS_OFFLINE: 1
-          INVOKEAI_OUTDIR: ${{ github.workspace }}/results
-        run: >
-          invokeai
-          --no-patchmatch
-          --no-nsfw_checker
-          --precision=float32
-          --always_use_cpu
-          --use_memory_db
-          --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
-          --from_file ${{ env.TEST_PROMPTS }}
+      # - name: run invokeai
+      #   id: run-invokeai
+      #   env:
+      #     # Set offline mode to make sure configure preloaded successfully.
+      #     HF_HUB_OFFLINE: 1
+      #     HF_DATASETS_OFFLINE: 1
+      #     TRANSFORMERS_OFFLINE: 1
+      #     INVOKEAI_OUTDIR: ${{ github.workspace }}/results
+      #   run: >
+      #     invokeai
+      #     --no-patchmatch
+      #     --no-nsfw_checker
+      #     --precision=float32
+      #     --always_use_cpu
+      #     --use_memory_db
+      #     --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
+      #     --from_file ${{ env.TEST_PROMPTS }}
 
-      - name: Archive results
-        id: archive-results
-        env:
-          INVOKEAI_OUTDIR: ${{ github.workspace }}/results
-        uses: actions/upload-artifact@v3
-        with:
-          name: results
-          path: ${{ env.INVOKEAI_OUTDIR }}
+      # - name: Archive results
+      #   env:
+      #     INVOKEAI_OUTDIR: ${{ github.workspace }}/results
+      #   uses: actions/upload-artifact@v3
+      #   with:
+      #     name: results
+      #     path: ${{ env.INVOKEAI_OUTDIR }}

docs/installation/060_INSTALL_PATCHMATCH.md

@@ -87,16 +87,16 @@ Prior to installing PyPatchMatch, you need to take the following steps:
     sudo pacman -S --needed base-devel
     ```
 
-2. Install `opencv`:
+2. Install `opencv` and `blas`:
 
     ```sh
-    sudo pacman -S opencv
+    sudo pacman -S opencv blas
     ```
 
     or for CUDA support
 
     ```sh
-    sudo pacman -S opencv-cuda
+    sudo pacman -S opencv-cuda blas
     ```
     
 3. Fix the naming of the `opencv` package configuration file:

installer/install.bat.in

@@ -38,6 +38,7 @@ echo    https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist
 echo.
 echo See %INSTRUCTIONS% for more details.
 echo.
+echo "For the best user experience we suggest enlarging or maximizing this window now."
 pause
 
 @rem ---------------------------- check Python version ---------------

installer/install.sh.in

@@ -26,6 +26,7 @@ done
 if [ -z "$PYTHON" ]; then
     echo "A suitable Python interpreter could not be found"
     echo "Please install Python $MINIMUM_PYTHON_VERSION or higher (maximum $MAXIMUM_PYTHON_VERSION) before running this script. See instructions at $INSTRUCTIONS for help."
+    echo "For the best user experience we suggest enlarging or maximizing this window now."
     read -p "Press any key to exit"
     exit -1
 fi

installer/lib/messages.py

@@ -293,6 +293,8 @@ def introduction() -> None:
                 "3. Create initial configuration files.",
                 "",
                 "[i]At any point you may interrupt this program and resume later.",
+                "",
+                "[b]For the best user experience, please enlarge or maximize this window",
             ),
         )
     )

invokeai/app/invocations/controlnet_image_processors.py

@@ -1,10 +1,11 @@
-# InvokeAI nodes for ControlNet image preprocessors
+# Invocations for ControlNet image preprocessors
 # initial implementation by Gregg Helt, 2023
 # heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
-from builtins import float
+from builtins import float, bool
 
+import cv2
 import numpy as np
-from typing import Literal, Optional, Union, List
+from typing import Literal, Optional, Union, List, Dict
 from PIL import Image, ImageFilter, ImageOps
 from pydantic import BaseModel, Field, validator
 
@@ -29,8 +30,13 @@ from controlnet_aux import (
     ContentShuffleDetector,
     ZoeDetector,
     MediapipeFaceDetector,
+    SamDetector,
+    LeresDetector,
 )
 
+from controlnet_aux.util import HWC3, ade_palette
+
+
 from .image import ImageOutput, PILInvocationConfig
 
 CONTROLNET_DEFAULT_MODELS = [
@@ -94,6 +100,10 @@ CONTROLNET_DEFAULT_MODELS = [
 ]
 
 CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
+CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control", "unbalanced"])]
+# crop and fill options not ready yet
+# CONTROLNET_RESIZE_VALUES = Literal[tuple(["just_resize", "crop_resize", "fill_resize"])]
+
 
 class ControlField(BaseModel):
     image: ImageField = Field(default=None, description="The control image")
@@ -104,6 +114,9 @@ class ControlField(BaseModel):
                                       description="When the ControlNet is first applied (% of total steps)")
     end_step_percent: float = Field(default=1, ge=0, le=1,
                                     description="When the ControlNet is last applied (% of total steps)")
+    control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
+    # resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
     @validator("control_weight")
     def abs_le_one(cls, v):
         """validate that all abs(values) are <=1"""
@@ -144,11 +157,11 @@ class ControlNetInvocation(BaseInvocation):
     control_model: CONTROLNET_NAME_VALUES = Field(default="lllyasviel/sd-controlnet-canny",
                                                   description="control model used")
     control_weight: Union[float, List[float]] = Field(default=1.0, description="The weight given to the ControlNet")
-    # TODO: add support in backend core for begin_step_percent, end_step_percent, guess_mode
     begin_step_percent: float = Field(default=0, ge=0, le=1,
                                       description="When the ControlNet is first applied (% of total steps)")
     end_step_percent: float = Field(default=1, ge=0, le=1,
                                     description="When the ControlNet is last applied (% of total steps)")
+    control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode used")
     # fmt: on
 
     class Config(InvocationConfig):
@@ -166,7 +179,6 @@ class ControlNetInvocation(BaseInvocation):
         }
 
     def invoke(self, context: InvocationContext) -> ControlOutput:
-
         return ControlOutput(
             control=ControlField(
                 image=self.image,
@@ -174,10 +186,11 @@ class ControlNetInvocation(BaseInvocation):
                 control_weight=self.control_weight,
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,
+                control_mode=self.control_mode,
             ),
         )
 
-# TODO: move image processors to separate file (image_analysis.py
+
 class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
     """Base class for invocations that preprocess images for ControlNet"""
 
@@ -449,6 +462,104 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
     # fmt: on
 
     def run_processor(self, image):
+        # MediaPipeFaceDetector throws an error if image has alpha channel
+        #     so convert to RGB if needed
+        if image.mode == 'RGBA':
+            image = image.convert('RGB')
         mediapipe_face_processor = MediapipeFaceDetector()
         processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
         return processed_image
+
+class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
+    """Applies leres processing to image"""
+    # fmt: off
+    type: Literal["leres_image_processor"] = "leres_image_processor"
+    # Inputs
+    thr_a: float = Field(default=0, description="Leres parameter `thr_a`")
+    thr_b: float = Field(default=0, description="Leres parameter `thr_b`")
+    boost: bool = Field(default=False, description="Whether to use boost 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):
+        leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = leres_processor(image,
+                                          thr_a=self.thr_a,
+                                          thr_b=self.thr_b,
+                                          boost=self.boost,
+                                          detect_resolution=self.detect_resolution,
+                                          image_resolution=self.image_resolution)
+        return processed_image
+
+
+class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
+
+    # fmt: off
+    type: Literal["tile_image_processor"] = "tile_image_processor"
+    # Inputs
+    #res: int = Field(default=512, ge=0, le=1024, description="The pixel resolution for each tile")
+    down_sampling_rate: float = Field(default=1.0, ge=1.0, le=8.0, description="Down sampling rate")
+    # fmt: on
+
+    # tile_resample copied from sd-webui-controlnet/scripts/processor.py
+    def tile_resample(self,
+                      np_img: np.ndarray,
+                      res=512,   # never used?
+                      down_sampling_rate=1.0,
+                      ):
+        np_img = HWC3(np_img)
+        if down_sampling_rate < 1.1:
+            return np_img
+        H, W, C = np_img.shape
+        H = int(float(H) / float(down_sampling_rate))
+        W = int(float(W) / float(down_sampling_rate))
+        np_img = cv2.resize(np_img, (W, H), interpolation=cv2.INTER_AREA)
+        return np_img
+
+    def run_processor(self, img):
+        np_img = np.array(img, dtype=np.uint8)
+        processed_np_image = self.tile_resample(np_img,
+                                                #res=self.tile_size,
+                                                down_sampling_rate=self.down_sampling_rate
+                                                )
+        processed_image = Image.fromarray(processed_np_image)
+        return processed_image
+
+
+
+
+class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
+    """Applies segment anything processing to image"""
+    # fmt: off
+    type: Literal["segment_anything_processor"] = "segment_anything_processor"
+    # fmt: on
+
+    def run_processor(self, image):
+        # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
+        segment_anything_processor = SamDetectorReproducibleColors.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
+        np_img = np.array(image, dtype=np.uint8)
+        processed_image = segment_anything_processor(np_img)
+        return processed_image
+
+class SamDetectorReproducibleColors(SamDetector):
+
+    # overriding SamDetector.show_anns() method to use reproducible colors for segmentation image
+    #     base class show_anns() method randomizes colors,
+    #     which seems to also lead to non-reproducible image generation
+    # so using ADE20k color palette instead
+    def show_anns(self, anns: List[Dict]):
+        if len(anns) == 0:
+            return
+        sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
+        h, w = anns[0]['segmentation'].shape
+        final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
+        palette = ade_palette()
+        for i, ann in enumerate(sorted_anns):
+            m = ann['segmentation']
+            img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8)
+            # doing modulo just in case number of annotated regions exceeds number of colors in palette
+            ann_color = palette[i % len(palette)]
+            img[:, :] = ann_color
+            final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
+        return np.array(final_img, dtype=np.uint8)

invokeai/app/invocations/latent.py

@@ -23,7 +23,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
     PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.util.devices import choose_torch_device, torch_dtype
+from ...backend.util.devices import torch_dtype
 from ...backend.model_management.lora import ModelPatcher
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)
@@ -59,31 +59,12 @@ def build_latents_output(latents_name: str, latents: torch.Tensor):
           height=latents.size()[2] * 8,
       )
 
-class NoiseOutput(BaseInvocationOutput):
-    """Invocation noise output"""
-    #fmt: off
-    type:  Literal["noise_output"] = "noise_output"
-
-    # Inputs
-    noise: LatentsField            = Field(default=None, description="The output noise")
-    width:                     int = Field(description="The width of the noise in pixels")
-    height:                    int = Field(description="The height of the noise in pixels")
-    #fmt: on
-
-def build_noise_output(latents_name: str, latents: torch.Tensor):
-      return NoiseOutput(
-          noise=LatentsField(latents_name=latents_name),
-          width=latents.size()[3] * 8,
-          height=latents.size()[2] * 8,
-      )
-
 
 SAMPLER_NAME_VALUES = Literal[
     tuple(list(SCHEDULER_MAP.keys()))
 ]
 
 
-
 def get_scheduler(
     context: InvocationContext,
     scheduler_info: ModelInfo,
@@ -105,62 +86,6 @@ def get_scheduler(
     return scheduler
 
 
-def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_channels:int=4, use_mps_noise:bool=False, downsampling_factor:int = 8):
-    # limit noise to only the diffusion image channels, not the mask channels
-    input_channels = min(latent_channels, 4)
-    use_device = "cpu" if (use_mps_noise or device.type == "mps") else device
-    generator = torch.Generator(device=use_device).manual_seed(seed)
-    x = torch.randn(
-        [
-            1,
-            input_channels,
-            height // downsampling_factor,
-            width //  downsampling_factor,
-        ],
-        dtype=torch_dtype(device),
-        device=use_device,
-        generator=generator,
-    ).to(device)
-    # if self.perlin > 0.0:
-    #     perlin_noise = self.get_perlin_noise(
-    #         width // self.downsampling_factor, height // self.downsampling_factor
-    #     )
-    #     x = (1 - self.perlin) * x + self.perlin * perlin_noise
-    return x
-
-class NoiseInvocation(BaseInvocation):
-    """Generates latent noise."""
-
-    type: Literal["noise"] = "noise"
-
-    # Inputs
-    seed:       int = Field(ge=0, le=SEED_MAX, description="The seed to use", default_factory=get_random_seed)
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
-    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting noise", )
-
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "noise"],
-            },
-        }
-
-    @validator("seed", pre=True)
-    def modulo_seed(cls, v):
-        """Returns the seed modulo SEED_MAX to ensure it is within the valid range."""
-        return v % SEED_MAX
-
-    def invoke(self, context: InvocationContext) -> NoiseOutput:
-        device = torch.device(choose_torch_device())
-        noise = get_noise(self.width, self.height, device, self.seed)
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.save(name, noise)
-        return build_noise_output(latents_name=name, latents=noise)
-
-
 # Text to image
 class TextToLatentsInvocation(BaseInvocation):
     """Generates latents from conditionings."""
@@ -287,19 +212,14 @@ class TextToLatentsInvocation(BaseInvocation):
         control_height_resize = latents_shape[2] * 8
         control_width_resize = latents_shape[3] * 8
         if control_input is None:
-            # print("control input is None")
             control_list = None
         elif isinstance(control_input, list) and len(control_input) == 0:
-            # print("control input is empty list")
             control_list = None
         elif isinstance(control_input, ControlField):
-            # print("control input is ControlField")
             control_list = [control_input]
         elif isinstance(control_input, list) and len(control_input) > 0 and isinstance(control_input[0], ControlField):
-            # print("control input is list[ControlField]")
             control_list = control_input
         else:
-            # print("input control is unrecognized:", type(self.control))
             control_list = None
         if (control_list is None):
             control_data = None
@@ -341,12 +261,15 @@ class TextToLatentsInvocation(BaseInvocation):
                     # num_images_per_prompt=num_images_per_prompt,
                     device=control_model.device,
                     dtype=control_model.dtype,
+                    control_mode=control_info.control_mode,
                 )
                 control_item = ControlNetData(model=control_model,
                                               image_tensor=control_image,
                                               weight=control_info.control_weight,
                                               begin_step_percent=control_info.begin_step_percent,
-                                              end_step_percent=control_info.end_step_percent)
+                                              end_step_percent=control_info.end_step_percent,
+                                              control_mode=control_info.control_mode,
+                                              )
                 control_data.append(control_item)
                 # MultiControlNetModel has been refactored out, just need list[ControlNetData]
         return control_data

invokeai/app/invocations/noise.py

@@ -0,0 +1,134 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
+
+import math
+from typing import Literal
+
+from pydantic import Field, validator
+import torch
+from invokeai.app.invocations.latent import LatentsField
+
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from ...backend.util.devices import choose_torch_device, torch_dtype
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InvocationConfig,
+    InvocationContext,
+)
+
+"""
+Utilities
+"""
+
+
+def get_noise(
+    width: int,
+    height: int,
+    device: torch.device,
+    seed: int = 0,
+    latent_channels: int = 4,
+    downsampling_factor: int = 8,
+    use_cpu: bool = True,
+    perlin: float = 0.0,
+):
+    """Generate noise for a given image size."""
+    noise_device_type = "cpu" if (use_cpu or device.type == "mps") else device.type
+
+    # limit noise to only the diffusion image channels, not the mask channels
+    input_channels = min(latent_channels, 4)
+    generator = torch.Generator(device=noise_device_type).manual_seed(seed)
+
+    noise_tensor = torch.randn(
+        [
+            1,
+            input_channels,
+            height // downsampling_factor,
+            width // downsampling_factor,
+        ],
+        dtype=torch_dtype(device),
+        device=noise_device_type,
+        generator=generator,
+    ).to(device)
+
+    return noise_tensor
+
+
+"""
+Nodes
+"""
+
+
+class NoiseOutput(BaseInvocationOutput):
+    """Invocation noise output"""
+
+    # fmt: off
+    type:  Literal["noise_output"] = "noise_output"
+
+    # Inputs
+    noise: LatentsField            = Field(default=None, description="The output noise")
+    width:                     int = Field(description="The width of the noise in pixels")
+    height:                    int = Field(description="The height of the noise in pixels")
+    # fmt: on
+
+
+def build_noise_output(latents_name: str, latents: torch.Tensor):
+    return NoiseOutput(
+        noise=LatentsField(latents_name=latents_name),
+        width=latents.size()[3] * 8,
+        height=latents.size()[2] * 8,
+    )
+
+
+class NoiseInvocation(BaseInvocation):
+    """Generates latent noise."""
+
+    type: Literal["noise"] = "noise"
+
+    # Inputs
+    seed: int = Field(
+        ge=0,
+        le=SEED_MAX,
+        description="The seed to use",
+        default_factory=get_random_seed,
+    )
+    width: int = Field(
+        default=512,
+        multiple_of=8,
+        gt=0,
+        description="The width of the resulting noise",
+    )
+    height: int = Field(
+        default=512,
+        multiple_of=8,
+        gt=0,
+        description="The height of the resulting noise",
+    )
+    use_cpu: bool = Field(
+        default=True,
+        description="Use CPU for noise generation (for reproducible results across platforms)",
+    )
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "noise"],
+            },
+        }
+
+    @validator("seed", pre=True)
+    def modulo_seed(cls, v):
+        """Returns the seed modulo SEED_MAX to ensure it is within the valid range."""
+        return v % SEED_MAX
+
+    def invoke(self, context: InvocationContext) -> NoiseOutput:
+        noise = get_noise(
+            width=self.width,
+            height=self.height,
+            device=choose_torch_device(),
+            seed=self.seed,
+            use_cpu=self.use_cpu,
+        )
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.save(name, noise)
+        return build_noise_output(latents_name=name, latents=noise)

invokeai/app/invocations/param_easing.py

@@ -133,20 +133,19 @@ class StepParamEasingInvocation(BaseInvocation):
         postlist = list(num_poststeps * [self.post_end_value])
 
         if log_diagnostics:
-            logger = InvokeAILogger.getLogger(name="StepParamEasing")
-            logger.debug("start_step: " + str(start_step))
-            logger.debug("end_step: " + str(end_step))
-            logger.debug("num_easing_steps: " + str(num_easing_steps))
-            logger.debug("num_presteps: " + str(num_presteps))
-            logger.debug("num_poststeps: " + str(num_poststeps))
-            logger.debug("prelist size: " + str(len(prelist)))
-            logger.debug("postlist size: " + str(len(postlist)))
-            logger.debug("prelist: " + str(prelist))
-            logger.debug("postlist: " + str(postlist))
+            context.services.logger.debug("start_step: " + str(start_step))
+            context.services.logger.debug("end_step: " + str(end_step))
+            context.services.logger.debug("num_easing_steps: " + str(num_easing_steps))
+            context.services.logger.debug("num_presteps: " + str(num_presteps))
+            context.services.logger.debug("num_poststeps: " + str(num_poststeps))
+            context.services.logger.debug("prelist size: " + str(len(prelist)))
+            context.services.logger.debug("postlist size: " + str(len(postlist)))
+            context.services.logger.debug("prelist: " + str(prelist))
+            context.services.logger.debug("postlist: " + str(postlist))
 
         easing_class = EASING_FUNCTIONS_MAP[self.easing]
         if log_diagnostics:
-            logger.debug("easing class: " + str(easing_class))
+            context.services.logger.debug("easing class: " + str(easing_class))
         easing_list = list()
         if self.mirror:  # "expected" mirroring
             # if number of steps is even, squeeze duration down to (number_of_steps)/2
@@ -156,7 +155,7 @@ class StepParamEasingInvocation(BaseInvocation):
             # but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
 
             base_easing_duration = int(np.ceil(num_easing_steps/2.0))
-            if log_diagnostics: logger.debug("base easing duration: " + str(base_easing_duration))
+            if log_diagnostics: context.services.logger.debug("base easing duration: " + str(base_easing_duration))
             even_num_steps = (num_easing_steps % 2 == 0)  # even number of steps
             easing_function = easing_class(start=self.start_value,
                                            end=self.end_value,
@@ -166,14 +165,14 @@ class StepParamEasingInvocation(BaseInvocation):
                 easing_val = easing_function.ease(step_index)
                 base_easing_vals.append(easing_val)
                 if log_diagnostics:
-                    logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
+                    context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
             if even_num_steps:
                 mirror_easing_vals = list(reversed(base_easing_vals))
             else:
                 mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
             if log_diagnostics:
-                logger.debug("base easing vals: " + str(base_easing_vals))
-                logger.debug("mirror easing vals: " + str(mirror_easing_vals))
+                context.services.logger.debug("base easing vals: " + str(base_easing_vals))
+                context.services.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
             easing_list = base_easing_vals + mirror_easing_vals
 
         # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
@@ -206,12 +205,12 @@ class StepParamEasingInvocation(BaseInvocation):
                 step_val = easing_function.ease(step_index)
                 easing_list.append(step_val)
                 if log_diagnostics:
-                    logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
+                    context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
 
         if log_diagnostics:
-            logger.debug("prelist size: " + str(len(prelist)))
-            logger.debug("easing_list size: " + str(len(easing_list)))
-            logger.debug("postlist size: " + str(len(postlist)))
+            context.services.logger.debug("prelist size: " + str(len(prelist)))
+            context.services.logger.debug("easing_list size: " + str(len(easing_list)))
+            context.services.logger.debug("postlist size: " + str(len(postlist)))
 
         param_list = prelist + easing_list + postlist
 

invokeai/app/services/config.py

@@ -374,8 +374,10 @@ setting environment variables INVOKEAI_<setting>.
     tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
 
     root                : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths')
-    autoimport_dir      : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
-    autoconvert_dir     : Path = Field(default=None, description='Deprecated configuration option.', category='Paths')
+    autoimport_dir      : Path = Field(default='autoimport/main', description='Path to a directory of models files to be imported on startup.', category='Paths')
+    lora_dir            : Path = Field(default='autoimport/lora', description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
+    embedding_dir       : Path = Field(default='autoimport/embedding', description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='Paths')
+    controlnet_dir      : Path = Field(default='autoimport/controlnet', description='Path to a directory of ControlNet embeddings to be imported on startup.', category='Paths')
     conf_path           : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
     models_dir          : Path = Field(default='models', description='Path to the models directory', category='Paths')
     legacy_conf_dir     : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')

invokeai/app/services/default_graphs.py

@@ -1,4 +1,5 @@
-from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
+from ..invocations.latent import LatentsToImageInvocation, TextToLatentsInvocation
+from ..invocations.noise import NoiseInvocation
 from ..invocations.compel import CompelInvocation
 from ..invocations.params import ParamIntInvocation
 from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph

invokeai/backend/install/invokeai_configure.py

@@ -7,8 +7,6 @@
 # Coauthor: Kevin Turner http://github.com/keturn
 #
 import sys
-print("Loading Python libraries...\n",file=sys.stderr)
-
 import argparse
 import io
 import os
@@ -442,6 +440,26 @@ to allow InvokeAI to download restricted styles & subjects from the "Concept Lib
             scroll_exit=True,
         )
         self.nextrely += 1
+        self.add_widget_intelligent(
+            npyscreen.FixedText,
+            value="Directories containing textual inversion, controlnet and LoRA models (<tab> autocompletes, ctrl-N advances):",
+            editable=False,
+            color="CONTROL",
+        )
+        self.autoimport_dirs = {}
+        for description, config_name, path in autoimport_paths(old_opts):
+            self.autoimport_dirs[config_name] = self.add_widget_intelligent(
+                npyscreen.TitleFilename,
+                name=description+':',
+                value=str(path),
+                select_dir=True,
+                must_exist=False,
+                use_two_lines=False,
+                labelColor="GOOD",
+                begin_entry_at=32,
+                scroll_exit=True
+            )
+        self.nextrely += 1
         self.add_widget_intelligent(
             npyscreen.TitleFixedText,
             name="== LICENSE ==",
@@ -505,10 +523,6 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
             bad_fields.append(
                 f"The output directory does not seem to be valid. Please check that {str(Path(opt.outdir).parent)} is an existing directory."
             )
-        # if not Path(opt.embedding_dir).parent.exists():
-        #     bad_fields.append(
-        #         f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_dir).parent)} is an existing directory."
-        #     )
         if len(bad_fields) > 0:
             message = "The following problems were detected and must be corrected:\n"
             for problem in bad_fields:
@@ -528,12 +542,15 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
                 "max_loaded_models",
                 "xformers_enabled",
                 "always_use_cpu",
-#                "embedding_dir",
-#                "lora_dir",
-#                "controlnet_dir",
         ]:
             setattr(new_opts, attr, getattr(self, attr).value)
 
+        for attr in self.autoimport_dirs:
+            directory = Path(self.autoimport_dirs[attr].value)
+            if directory.is_relative_to(config.root_path):
+                directory = directory.relative_to(config.root_path)
+            setattr(new_opts, attr, directory)
+
         new_opts.hf_token = self.hf_token.value
         new_opts.license_acceptance = self.license_acceptance.value
         new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
@@ -595,22 +612,32 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections:
         else [models[x].path or models[x].repo_id for x in installer.recommended_models()]
         if program_opts.yes_to_all
         else list(),
-        scan_directory=None,
-        autoscan_on_startup=None,
+#        scan_directory=None,
+#        autoscan_on_startup=None,
     )
 
+# -------------------------------------
+def autoimport_paths(config: InvokeAIAppConfig):
+    return [
+        ('Checkpoints & diffusers models', 'autoimport_dir', config.root_path / config.autoimport_dir),
+        ('LoRA/LyCORIS models',            'lora_dir',       config.root_path / config.lora_dir),
+        ('Controlnet models',              'controlnet_dir', config.root_path / config.controlnet_dir),
+        ('Textual Inversion Embeddings',   'embedding_dir',  config.root_path / config.embedding_dir),
+    ]
+    
 # -------------------------------------
 def initialize_rootdir(root: Path, yes_to_all: bool = False):
     logger.info("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **")
     for name in (
             "models",
             "databases",
-            "autoimport",
             "text-inversion-output",
             "text-inversion-training-data",
             "configs"
     ):
         os.makedirs(os.path.join(root, name), exist_ok=True)
+    for model_type in ModelType:
+        Path(root, 'autoimport', model_type.value).mkdir(parents=True, exist_ok=True)
 
     configs_src = Path(configs.__path__[0])
     configs_dest = root / "configs"
@@ -618,9 +645,8 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
         shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
 
     dest = root / 'models'
-    for model_base in [BaseModelType.StableDiffusion1,BaseModelType.StableDiffusion2]:
-        for model_type in [ModelType.Main, ModelType.Vae, ModelType.Lora,
-                           ModelType.ControlNet,ModelType.TextualInversion]:
+    for model_base in BaseModelType:
+        for model_type in ModelType:
             path = dest / model_base.value / model_type.value
             path.mkdir(parents=True, exist_ok=True)
     path = dest / 'core'
@@ -632,8 +658,6 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
                                    }
                                   )
                         )
-#    with open(root / 'invokeai.yaml','w') as f:
-#        f.write('#empty invokeai.yaml initialization file')
         
 # -------------------------------------
 def run_console_ui(
@@ -680,18 +704,6 @@ def write_opts(opts: Namespace, init_file: Path):
 def default_output_dir() -> Path:
     return config.root_path / "outputs"
 
-# # -------------------------------------
-# def default_embedding_dir() -> Path:
-#     return config.root_path / "embeddings"
-
-# # -------------------------------------
-# def default_lora_dir() -> Path:
-#     return config.root_path / "loras"
-
-# # -------------------------------------
-# def default_controlnet_dir() -> Path:
-#     return config.root_path / "controlnets"
-
 # -------------------------------------
 def write_default_options(program_opts: Namespace, initfile: Path):
     opt = default_startup_options(initfile)

invokeai/backend/install/model_install_backend.py

@@ -70,8 +70,8 @@ class ModelInstallList:
 class InstallSelections():
     install_models: List[str]= field(default_factory=list)
     remove_models: List[str]=field(default_factory=list)
-    scan_directory: Path = None
-    autoscan_on_startup: bool=False
+#    scan_directory: Path = None
+#    autoscan_on_startup: bool=False
 
 @dataclass
 class ModelLoadInfo():
@@ -155,8 +155,6 @@ class ModelInstall(object):
     def install(self, selections: InstallSelections):
         job = 1
         jobs = len(selections.remove_models) + len(selections.install_models)
-        if selections.scan_directory:
-            jobs += 1
         
         # remove requested models
         for key in selections.remove_models:
@@ -171,18 +169,8 @@ class ModelInstall(object):
             self.heuristic_install(path)
             job += 1
 
-        # import from the scan directory, if any
-        if path := selections.scan_directory:
-            logger.info(f'Scanning and importing models from directory {path} [{job}/{jobs}]')
-            self.heuristic_install(path)
-
         self.mgr.commit()
 
-        if selections.autoscan_on_startup and Path(selections.scan_directory).is_dir():
-            update_autoimport_dir(selections.scan_directory)
-        else:
-            update_autoimport_dir(None)
-
     def heuristic_install(self,
                           model_path_id_or_url: Union[str,Path],
                           models_installed: Set[Path]=None)->Set[Path]:
@@ -228,7 +216,7 @@ class ModelInstall(object):
     # the model from being probed twice in the event that it has already been probed.
     def _install_path(self, path: Path, info: ModelProbeInfo=None)->Path:
         try:
-            logger.info(f'Probing {path}')
+            # logger.debug(f'Probing {path}')
             info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
             model_name = path.stem if info.format=='checkpoint' else path.name
             if self.mgr.model_exists(model_name, info.base_type, info.model_type):
@@ -237,7 +225,7 @@ class ModelInstall(object):
             self.mgr.add_model(model_name = model_name,
                                base_model = info.base_type,
                                model_type = info.model_type,
-                               model_attributes = attributes
+                               model_attributes = attributes,
                                )
         except Exception as e:
             logger.warning(f'{str(e)} Skipping registration.')
@@ -309,11 +297,11 @@ class ModelInstall(object):
             return location.stem
 
     def _make_attributes(self, path: Path, info: ModelProbeInfo)->dict:
-        # convoluted way to retrieve the description from datasets
-        description = f'{info.base_type.value} {info.model_type.value} model'
+        model_name = path.name if path.is_dir() else path.stem
+        description = f'{info.base_type.value} {info.model_type.value} model {model_name}'
         if key := self.reverse_paths.get(self.current_id):
             if key in self.datasets:
-                description = self.datasets[key]['description']
+                description = self.datasets[key].get('description') or description
 
         rel_path = self.relative_to_root(path)
 
@@ -395,23 +383,6 @@ class ModelInstall(object):
         '''
         return {v.get('path') or v.get('repo_id') : k for k, v in datasets.items()}
 
-def update_autoimport_dir(autodir: Path):
-    '''
-    Update the "autoimport_dir" option in invokeai.yaml
-    '''
-    with open('log.txt','a') as f:
-        print(f'autodir = {autodir}',file=f)
-        
-    invokeai_config_path = config.init_file_path
-    conf = OmegaConf.load(invokeai_config_path)
-    conf.InvokeAI.Paths.autoimport_dir = str(autodir) if autodir else None
-    yaml = OmegaConf.to_yaml(conf)
-    tmpfile = invokeai_config_path.parent / "new_config.tmp"
-    with open(tmpfile, "w", encoding="utf-8") as outfile:
-        outfile.write(yaml)
-    tmpfile.replace(invokeai_config_path)
-
-
 # -------------------------------------
 def yes_or_no(prompt: str, default_yes=True):
     default = "y" if default_yes else "n"

invokeai/backend/model_management/model_manager.py

@@ -168,11 +168,27 @@ structure at initialization time by scanning the models directory. The
 in-memory data structure can be resynchronized by calling
 `manager.scan_models_directory()`.
 
-Files and folders placed inside the `autoimport_dir` (path defined in
-`invokeai.yaml`, defaulting to `ROOTDIR/autoimport` will also be
-scanned for new models at initialization time and added to
-`models.yaml`. Files will not be moved from this location but
-preserved in-place.
+Files and folders placed inside the `autoimport` paths (paths
+defined in `invokeai.yaml`) will also be scanned for new models at
+initialization time and added to `models.yaml`. Files will not be
+moved from this location but preserved in-place. These directories
+are:
+
+  configuration    default              description
+  -------------    -------              -----------
+  autoimport_dir  autoimport/main       main models
+  lora_dir        autoimport/lora       LoRA/LyCORIS models
+  embedding_dir   autoimport/embedding  TI embeddings
+  controlnet_dir  autoimport/controlnet ControlNet models
+
+In actuality, models located in any of these directories are scanned
+to determine their type, so it isn't strictly necessary to organize
+the different types in this way. This entry in `invokeai.yaml` will
+recursively scan all subdirectories within `autoimport`, scan models
+files it finds, and import them if recognized.
+
+  Paths:
+     autoimport_dir: autoimport
 
 A model can be manually added using `add_model()` using the model's
 name, base model, type and a dict of model attributes. See
@@ -208,6 +224,7 @@ checkpoint or safetensors file.
 
 The path points to a file or directory on disk. If a relative path,
 the root is the InvokeAI ROOTDIR.
+
 """
 from __future__ import annotations
 
@@ -566,7 +583,7 @@ class ModelManager(object):
         model_config = model_class.create_config(**model_attributes)
         model_key = self.create_key(model_name, base_model, model_type)
 
-        if clobber or model_key not in self.models:
+        if  model_key in self.models and not clobber:
             raise Exception(f'Attempt to overwrite existing model definition "{model_key}"')
 
         old_model = self.models.pop(model_key, None)
@@ -697,49 +714,74 @@ class ModelManager(object):
 
                             if model_path.is_relative_to(self.app_config.root_path):
                                 model_path = model_path.relative_to(self.app_config.root_path)
+                                try:
                                     model_config: ModelConfigBase = model_class.probe_config(str(model_path))
                                     self.models[model_key] = model_config
                                     new_models_found = True
+                                except NotImplementedError as e:
+                                    self.logger.warning(e)
 
         imported_models = self.autoimport()
 
         if (new_models_found or imported_models) and self.config_path:
             self.commit()
 
-    def autoimport(self):
+    def autoimport(self)->set[Path]:
         '''
         Scan the autoimport directory (if defined) and import new models, delete defunct models.
         '''
         # avoid circular import
         from invokeai.backend.install.model_install_backend import ModelInstall
+        from invokeai.frontend.install.model_install import ask_user_for_prediction_type
+        
         installer = ModelInstall(config = self.app_config,
-                                 model_manager = self)
+                                 model_manager = self,
+                                 prediction_type_helper = ask_user_for_prediction_type,
+                                 )
         
         installed = set()
-        if not self.app_config.autoimport_dir:
-            return installed
-        
-        autodir = self.app_config.root_path / self.app_config.autoimport_dir        
-        if not (autodir and autodir.exists()):
-            return installed
-        
-        known_paths = {(self.app_config.root_path / x['path']).resolve() for x in self.list_models()}
         scanned_dirs = set()
+        
+        config = self.app_config
+        known_paths = {(self.app_config.root_path / x['path']) for x in self.list_models()}
+
+        for autodir in [config.autoimport_dir,
+                        config.lora_dir,
+                        config.embedding_dir,
+                        config.controlnet_dir]:
+            if autodir is None:
+                continue
+
+            self.logger.info(f'Scanning {autodir} for models to import')
+        
+            autodir = self.app_config.root_path / autodir
+            if not autodir.exists():
+                continue
+
+            items_scanned = 0
+            new_models_found = set()
+            
             for root, dirs, files in os.walk(autodir):
+                items_scanned += len(dirs) + len(files)
                 for d in dirs:
                     path = Path(root) / d
-                if path in known_paths:
+                    if path in known_paths or path.parent in scanned_dirs:
+                        scanned_dirs.add(path)
                         continue
                     if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
-                    installed.update(installer.heuristic_install(path))
+                        new_models_found.update(installer.heuristic_install(path))
                         scanned_dirs.add(path)
 
                 for f in files:
                     path = Path(root) / f
                     if path in known_paths or path.parent in scanned_dirs:
                         continue
-                if path.suffix in {'.ckpt','.bin','.pth','.safetensors'}:
-                    installed.update(installer.heuristic_install(path))
+                    if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
+                        new_models_found.update(installer.heuristic_install(path))
+
+            self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models')
+            installed.update(new_models_found)
+
         return installed
 
     def heuristic_import(self,

invokeai/backend/model_management/model_probe.py

@@ -22,7 +22,7 @@ class ModelProbeInfo(object):
     variant_type: ModelVariantType
     prediction_type: SchedulerPredictionType
     upcast_attention: bool
-    format: Literal['diffusers','checkpoint']
+    format: Literal['diffusers','checkpoint', 'lycoris']
     image_size: int
 
 class ProbeBase(object):
@@ -75,22 +75,23 @@ class ModelProbe(object):
         between V2-Base and V2-768 SD models.
         '''
         if model_path:
-            format = 'diffusers' if model_path.is_dir() else 'checkpoint'
+            format_type = 'diffusers' if model_path.is_dir() else 'checkpoint'
         else:
-            format = 'diffusers' if isinstance(model,(ConfigMixin,ModelMixin)) else 'checkpoint'
+            format_type = 'diffusers' if isinstance(model,(ConfigMixin,ModelMixin)) else 'checkpoint'
 
         model_info = None
         try:
             model_type = cls.get_model_type_from_folder(model_path, model) \
-                if format == 'diffusers' \
+                if format_type == 'diffusers' \
                    else cls.get_model_type_from_checkpoint(model_path, model)
-            probe_class = cls.PROBES[format].get(model_type)
+            probe_class = cls.PROBES[format_type].get(model_type)
             if not probe_class:
                 return None
             probe = probe_class(model_path, model, prediction_type_helper)
             base_type = probe.get_base_type()
             variant_type = probe.get_variant_type()
             prediction_type = probe.get_scheduler_prediction_type()
+            format = probe.get_format()
             model_info = ModelProbeInfo(
                 model_type = model_type,
                 base_type = base_type,
@@ -116,10 +117,10 @@ class ModelProbe(object):
         if model_path.name == "learned_embeds.bin":
             return ModelType.TextualInversion
 
-        checkpoint = checkpoint or read_checkpoint_meta(model_path, scan=True)
-        checkpoint = checkpoint.get("state_dict", checkpoint)
+        ckpt = checkpoint if checkpoint else read_checkpoint_meta(model_path, scan=True)
+        ckpt = ckpt.get("state_dict", ckpt)
 
-        for key in checkpoint.keys():
+        for key in ckpt.keys():
             if any(key.startswith(v) for v in {"cond_stage_model.", "first_stage_model.", "model.diffusion_model."}):
                 return ModelType.Main
             elif any(key.startswith(v) for v in {"encoder.conv_in", "decoder.conv_in"}):
@@ -133,7 +134,7 @@ class ModelProbe(object):
 
         else:
             # diffusers-ti
-            if len(checkpoint) < 10 and all(isinstance(v, torch.Tensor) for v in checkpoint.values()):
+            if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
                 return ModelType.TextualInversion
         
         raise ValueError("Unable to determine model type")
@@ -201,6 +202,9 @@ class ProbeBase(object):
     def get_scheduler_prediction_type(self)->SchedulerPredictionType:
         pass
 
+    def get_format(self)->str:
+        pass
+
 class CheckpointProbeBase(ProbeBase):
     def __init__(self,
                  checkpoint_path: Path,
@@ -214,6 +218,9 @@ class CheckpointProbeBase(ProbeBase):
     def get_base_type(self)->BaseModelType:
         pass
 
+    def get_format(self)->str:
+        return 'checkpoint'
+
     def get_variant_type(self)-> ModelVariantType:
         model_type = ModelProbe.get_model_type_from_checkpoint(self.checkpoint_path,self.checkpoint)
         if model_type != ModelType.Main:
@@ -255,7 +262,8 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
                     return SchedulerPredictionType.Epsilon
                 elif checkpoint["global_step"] == 110000:
                     return SchedulerPredictionType.VPrediction
-            if self.checkpoint_path and self.helper:
+            if self.checkpoint_path and self.helper \
+               and not self.checkpoint_path.with_suffix('.yaml').exists():  # if a .yaml config file exists, then this step not needed
                 return self.helper(self.checkpoint_path)
             else:
                 return None
@@ -266,6 +274,9 @@ class VaeCheckpointProbe(CheckpointProbeBase):
         return BaseModelType.StableDiffusion1
 
 class LoRACheckpointProbe(CheckpointProbeBase):
+    def get_format(self)->str:
+        return 'lycoris'
+
     def get_base_type(self)->BaseModelType:
         checkpoint = self.checkpoint
         key1 = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
@@ -285,6 +296,9 @@ class LoRACheckpointProbe(CheckpointProbeBase):
             return None
 
 class TextualInversionCheckpointProbe(CheckpointProbeBase):
+    def get_format(self)->str:
+        return None
+
     def get_base_type(self)->BaseModelType:
         checkpoint = self.checkpoint
         if 'string_to_token' in checkpoint:
@@ -331,6 +345,9 @@ class FolderProbeBase(ProbeBase):
     def get_variant_type(self)->ModelVariantType:
         return ModelVariantType.Normal
 
+    def get_format(self)->str:
+        return 'diffusers'
+    
 class PipelineFolderProbe(FolderProbeBase):
     def get_base_type(self)->BaseModelType:
         if self.model:
@@ -386,6 +403,9 @@ class VaeFolderProbe(FolderProbeBase):
         return BaseModelType.StableDiffusion1
 
 class TextualInversionFolderProbe(FolderProbeBase):
+    def get_format(self)->str:
+        return None
+    
     def get_base_type(self)->BaseModelType:
         path = self.folder_path / 'learned_embeds.bin'
         if not path.exists():

invokeai/backend/model_management/models/base.py

@@ -397,7 +397,7 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
             checkpoint = safetensors.torch.load_file(path, device="cpu")
     else:
         if scan:
-            scan_result = scan_file_path(checkpoint)
+            scan_result = scan_file_path(path)
             if scan_result.infected_files != 0:
                 raise Exception(f"The model file \"{path}\" is potentially infected by malware. Aborting import.")
         checkpoint = torch.load(path, map_location=torch.device("meta"))

invokeai/backend/model_management/models/stable_diffusion.py

@@ -69,7 +69,7 @@ class StableDiffusion1Model(DiffusersModel):
                 in_channels = unet_config['in_channels']
 
             else:
-                raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
+                raise NotImplementedError(f"{path} is not a supported stable diffusion diffusers format")
 
         else:
             raise NotImplementedError(f"Unknown stable diffusion 1.* format: {model_format}")
@@ -259,8 +259,8 @@ def _convert_ckpt_and_cache(
     """
     app_config = InvokeAIAppConfig.get_config()
 
-    weights = app_config.root_dir / model_config.path
-    config_file = app_config.root_dir / model_config.config
+    weights = app_config.root_path / model_config.path
+    config_file = app_config.root_path / model_config.config
     output_path = Path(output_path)
 
     # return cached version if it exists

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -215,10 +215,12 @@ class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
 @dataclass
 class ControlNetData:
     model: ControlNetModel = Field(default=None)
-    image_tensor: torch.Tensor= Field(default=None)
-    weight: Union[float, List[float]]= Field(default=1.0)
+    image_tensor: torch.Tensor = Field(default=None)
+    weight: Union[float, List[float]] = Field(default=1.0)
     begin_step_percent: float = Field(default=0.0)
     end_step_percent: float = Field(default=1.0)
+    control_mode: str = Field(default="balanced")
+
 
 @dataclass(frozen=True)
 class ConditioningData:
@@ -599,48 +601,68 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         # TODO: should this scaling happen here or inside self._unet_forward?
         #     i.e. before or after passing it to InvokeAIDiffuserComponent
-        latent_model_input = self.scheduler.scale_model_input(latents, timestep)
+        unet_latent_input = self.scheduler.scale_model_input(latents, timestep)
 
         # default is no controlnet, so set controlnet processing output to None
         down_block_res_samples, mid_block_res_sample = None, None
 
         if control_data is not None:
-            # FIXME: make sure guidance_scale < 1.0 is handled correctly if doing per-step guidance setting
-            # if conditioning_data.guidance_scale > 1.0:
-            if conditioning_data.guidance_scale is not None:
-                # expand the latents input to control model if doing classifier free guidance
-                #    (which I think for now is always true, there is conditional elsewhere that stops execution if
-                #     classifier_free_guidance is <= 1.0 ?)
-                latent_control_input = torch.cat([latent_model_input] * 2)
-            else:
-                latent_control_input = latent_model_input
             # control_data should be type List[ControlNetData]
             # this loop covers both ControlNet (one ControlNetData in list)
             #      and MultiControlNet (multiple ControlNetData in list)
             for i, control_datum in enumerate(control_data):
-                # print("controlnet", i, "==>", type(control_datum))
+                control_mode = control_datum.control_mode
+                # soft_injection and cfg_injection are the two ControlNet control_mode booleans
+                #     that are combined at higher level to make control_mode enum
+                #  soft_injection determines whether to do per-layer re-weighting adjustment (if True)
+                #     or default weighting (if False)
+                soft_injection = (control_mode == "more_prompt" or control_mode == "more_control")
+                #  cfg_injection = determines whether to apply ControlNet to only the conditional (if True)
+                #      or the default both conditional and unconditional (if False)
+                cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
+
                 first_control_step = math.floor(control_datum.begin_step_percent * total_step_count)
                 last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
                 # only apply controlnet if current step is within the controlnet's begin/end step range
                 if step_index >= first_control_step and step_index <= last_control_step:
-                    # print("running controlnet", i, "for step", step_index)
+
+                    if cfg_injection:
+                        control_latent_input = unet_latent_input
+                    else:
+                        # expand the latents input to control model if doing classifier free guidance
+                        #    (which I think for now is always true, there is conditional elsewhere that stops execution if
+                        #     classifier_free_guidance is <= 1.0 ?)
+                        control_latent_input = torch.cat([unet_latent_input] * 2)
+
+                    if cfg_injection:  # only applying ControlNet to conditional instead of in unconditioned
+                        encoder_hidden_states = torch.cat([conditioning_data.unconditioned_embeddings])
+                    else:
+                        encoder_hidden_states = torch.cat([conditioning_data.unconditioned_embeddings,
+                                                           conditioning_data.text_embeddings])
                     if isinstance(control_datum.weight, list):
                         # if controlnet has multiple weights, use the weight for the current step
                         controlnet_weight = control_datum.weight[step_index]
                     else:
                         # if controlnet has a single weight, use it for all steps
                         controlnet_weight = control_datum.weight
+
+                    # controlnet(s) inference
                     down_samples, mid_sample = control_datum.model(
-                        sample=latent_control_input,
+                        sample=control_latent_input,
                         timestep=timestep,
-                        encoder_hidden_states=torch.cat([conditioning_data.unconditioned_embeddings,
-                                                         conditioning_data.text_embeddings]),
+                        encoder_hidden_states=encoder_hidden_states,
                         controlnet_cond=control_datum.image_tensor,
-                        conditioning_scale=controlnet_weight,
-                        # cross_attention_kwargs,
-                        guess_mode=False,
+                        conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale
+                        guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel
                         return_dict=False,
                     )
+                    if cfg_injection:
+                        # Inferred ControlNet only for the conditional batch.
+                        # To apply the output of ControlNet to both the unconditional and conditional batches,
+                        #   add 0 to the unconditional batch to keep it unchanged.
+                        down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples]
+                        mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample])
+
                     if down_block_res_samples is None and mid_block_res_sample is None:
                         down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
                     else:
@@ -653,11 +675,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         # predict the noise residual
         noise_pred = self.invokeai_diffuser.do_diffusion_step(
-            latent_model_input,
-            t,
-            conditioning_data.unconditioned_embeddings,
-            conditioning_data.text_embeddings,
-            conditioning_data.guidance_scale,
+            x=unet_latent_input,
+            sigma=t,
+            unconditioning=conditioning_data.unconditioned_embeddings,
+            conditioning=conditioning_data.text_embeddings,
+            unconditional_guidance_scale=conditioning_data.guidance_scale,
             step_index=step_index,
             total_step_count=total_step_count,
             down_block_additional_residuals=down_block_res_samples,  # from controlnet(s)
@@ -962,6 +984,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         device="cuda",
         dtype=torch.float16,
         do_classifier_free_guidance=True,
+        control_mode="balanced"
     ):
 
         if not isinstance(image, torch.Tensor):
@@ -992,6 +1015,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             repeat_by = num_images_per_prompt
         image = image.repeat_interleave(repeat_by, dim=0)
         image = image.to(device=device, dtype=dtype)
-        if do_classifier_free_guidance:
+        cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
+        if do_classifier_free_guidance and not cfg_injection:
             image = torch.cat([image] * 2)
         return image

invokeai/frontend/install/model_install.py

@@ -131,7 +131,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             window_width=window_width,
             exclude = self.starter_models
         )
-        self.pipeline_models['autoload_pending'] = True
+        # self.pipeline_models['autoload_pending'] = True
         bottom_of_table = max(bottom_of_table,self.nextrely)
 
         self.nextrely = top_of_table
@@ -316,31 +316,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             **kwargs,
         )
 
-        label = "Directory to scan for models to automatically import (<tab> autocompletes):"
-        self.nextrely += 1
-        widgets.update(
-            autoload_directory = self.add_widget_intelligent(
-                FileBox,
-                max_height=3,
-                name=label,
-                value=str(config.root_path / config.autoimport_dir) if config.autoimport_dir else None,
-                select_dir=True,
-                must_exist=True,
-                use_two_lines=False,
-                labelColor="DANGER",
-                begin_entry_at=len(label)+1,
-                scroll_exit=True,
-            )
-        )
-        widgets.update(
-            autoscan_on_startup = self.add_widget_intelligent(
-                npyscreen.Checkbox,
-                name="Scan and import from this directory each time InvokeAI starts",
-                value=config.autoimport_dir is not None,
-                relx=4,
-                scroll_exit=True,
-            )
-        )
         return widgets
 
     def resize(self):
@@ -501,8 +476,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         
         # rebuild the form, saving and restoring some of the fields that need to be preserved.
         saved_messages = self.monitor.entry_widget.values
-        autoload_dir = str(config.root_path / self.pipeline_models['autoload_directory'].value)
-        autoscan = self.pipeline_models['autoscan_on_startup'].value
+        # autoload_dir = str(config.root_path / self.pipeline_models['autoload_directory'].value)
+        # autoscan = self.pipeline_models['autoscan_on_startup'].value
         
         app.main_form = app.addForm(
             "MAIN", addModelsForm, name="Install Stable Diffusion Models", multipage=self.multipage,
@@ -511,8 +486,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         
         app.main_form.monitor.entry_widget.values = saved_messages
         app.main_form.monitor.entry_widget.buffer([''],scroll_end=True)
-        app.main_form.pipeline_models['autoload_directory'].value = autoload_dir
-        app.main_form.pipeline_models['autoscan_on_startup'].value = autoscan
+        # app.main_form.pipeline_models['autoload_directory'].value = autoload_dir
+        # app.main_form.pipeline_models['autoscan_on_startup'].value = autoscan
         
     def marshall_arguments(self):
         """
@@ -546,17 +521,17 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 selections.install_models.extend(downloads.value.split())
 
         # load directory and whether to scan on startup
-        if self.parentApp.autoload_pending:
-            selections.scan_directory = str(config.root_path / self.pipeline_models['autoload_directory'].value)
-            self.parentApp.autoload_pending = False
-        selections.autoscan_on_startup = self.pipeline_models['autoscan_on_startup'].value
+        # if self.parentApp.autoload_pending:
+        #     selections.scan_directory = str(config.root_path / self.pipeline_models['autoload_directory'].value)
+        #     self.parentApp.autoload_pending = False
+        # selections.autoscan_on_startup = self.pipeline_models['autoscan_on_startup'].value
 
 class AddModelApplication(npyscreen.NPSAppManaged):
     def __init__(self,opt):
         super().__init__()
         self.program_opts = opt
         self.user_cancelled = False
-        self.autoload_pending = True
+        # self.autoload_pending = True
         self.install_selections = InstallSelections()
 
     def onStart(self):
@@ -578,20 +553,20 @@ class StderrToMessage():
 # --------------------------------------------------------
 def ask_user_for_prediction_type(model_path: Path,
                                  tui_conn: Connection=None
-                                 )->Path:
+                                 )->SchedulerPredictionType:
     if tui_conn:
         logger.debug('Waiting for user response...')
         return _ask_user_for_pt_tui(model_path, tui_conn)        
     else:
         return _ask_user_for_pt_cmdline(model_path)
 
-def _ask_user_for_pt_cmdline(model_path):
+def _ask_user_for_pt_cmdline(model_path: Path)->SchedulerPredictionType:
     choices = [SchedulerPredictionType.Epsilon, SchedulerPredictionType.VPrediction, None]
     print(
 f"""
 Please select the type of the V2 checkpoint named {model_path.name}:
-[1] A Stable Diffusion v2.x base model (512 pixels; there should be no 'parameterization:' line in its yaml file)
-[2] A Stable Diffusion v2.x v-predictive model (768 pixels; look for a 'parameterization: "v"' line in its yaml file)
+[1] A model based on Stable Diffusion v2 trained on 512 pixel images (SD-2-base)
+[2] A model based on Stable Diffusion v2 trained on 768 pixel images (SD-2-768)
 [3] Skip this model and come back later.
 """
         )
@@ -608,7 +583,7 @@ Please select the type of the V2 checkpoint named {model_path.name}:
             return
     return choice
         
-def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection)->Path:
+def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection)->SchedulerPredictionType:
     try:
         tui_conn.send_bytes(f'*need v2 config for:{model_path}'.encode('utf-8'))
         # note that we don't do any status checking here
@@ -810,11 +785,13 @@ def main():
             logger.error(
                 "Insufficient vertical space for the interface. Please make your window taller and try again"
             )
-        elif str(e).startswith("addwstr"):
+        input('Press any key to continue...')
+    except Exception as e:
+        if str(e).startswith("addwstr"):
             logger.error(
                 "Insufficient horizontal space for the interface. Please make your window wider and try again."
             )
-    except Exception as e:
+        else:
             print(f'An exception has occurred: {str(e)} Details:')
             print(traceback.format_exc(), file=sys.stderr)
         input('Press any key to continue...')

invokeai/frontend/install/widgets.py

@@ -42,6 +42,18 @@ def set_terminal_size(columns: int, lines: int, launch_command: str=None):
     elif OS in ["Darwin", "Linux"]:
         _set_terminal_size_unix(width,height)
 
+    # check whether it worked....
+    ts = get_terminal_size()
+    pause = False
+    if ts.columns < columns:
+        print('\033[1mThis window is too narrow for the user interface. Please make it wider.\033[0m')
+        pause = True
+    if ts.lines < lines:
+        print('\033[1mThis window is too short for the user interface. Please make it taller.\033[0m')
+        pause = True
+    if pause:
+        input('Press any key to continue..')
+
 def _set_terminal_size_powershell(width: int, height: int):
     script=f'''
 $pshost = get-host

invokeai/frontend/web/config/common.ts

@@ -0,0 +1,14 @@
+import react from '@vitejs/plugin-react-swc';
+import { visualizer } from 'rollup-plugin-visualizer';
+import { PluginOption, UserConfig } from 'vite';
+import eslint from 'vite-plugin-eslint';
+import tsconfigPaths from 'vite-tsconfig-paths';
+import { nodePolyfills } from 'vite-plugin-node-polyfills';
+
+export const commonPlugins: UserConfig['plugins'] = [
+  react(),
+  eslint(),
+  tsconfigPaths(),
+  visualizer() as unknown as PluginOption,
+  nodePolyfills(),
+];

invokeai/frontend/web/config/vite.app.config.ts

@@ -1,17 +1,9 @@
-import react from '@vitejs/plugin-react-swc';
-import { visualizer } from 'rollup-plugin-visualizer';
-import { PluginOption, UserConfig } from 'vite';
-import eslint from 'vite-plugin-eslint';
-import tsconfigPaths from 'vite-tsconfig-paths';
+import { UserConfig } from 'vite';
+import { commonPlugins } from './common';
 
 export const appConfig: UserConfig = {
   base: './',
-  plugins: [
-    react(),
-    eslint(),
-    tsconfigPaths(),
-    visualizer() as unknown as PluginOption,
-  ],
+  plugins: [...commonPlugins],
   build: {
     chunkSizeWarningLimit: 1500,
   },

invokeai/frontend/web/config/vite.package.config.ts

@@ -1,19 +1,13 @@
-import react from '@vitejs/plugin-react-swc';
 import path from 'path';
-import { visualizer } from 'rollup-plugin-visualizer';
-import { PluginOption, UserConfig } from 'vite';
+import { UserConfig } from 'vite';
 import dts from 'vite-plugin-dts';
-import eslint from 'vite-plugin-eslint';
-import tsconfigPaths from 'vite-tsconfig-paths';
 import cssInjectedByJsPlugin from 'vite-plugin-css-injected-by-js';
+import { commonPlugins } from './common';
 
 export const packageConfig: UserConfig = {
   base: './',
   plugins: [
-    react(),
-    eslint(),
-    tsconfigPaths(),
-    visualizer() as unknown as PluginOption,
+    ...commonPlugins,
     dts({
       insertTypesEntry: true,
     }),

invokeai/frontend/web/package.json

@@ -23,7 +23,7 @@
     "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
     "dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"",
     "build": "yarn run lint && vite build",
-    "typegen": "npx openapi-typescript http://localhost:9090/openapi.json --output src/services/schema.d.ts -t",
+    "typegen": "npx openapi-typescript http://localhost:9090/openapi.json --output src/services/api/schema.d.ts -t",
     "preview": "vite preview",
     "lint:madge": "madge --circular src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
@@ -53,36 +53,38 @@
     ]
   },
   "dependencies": {
+    "@apidevtools/swagger-parser": "^10.1.0",
     "@chakra-ui/anatomy": "^2.1.1",
     "@chakra-ui/icons": "^2.0.19",
-    "@chakra-ui/react": "^2.6.0",
-    "@chakra-ui/styled-system": "^2.9.0",
-    "@chakra-ui/theme-tools": "^2.0.16",
-    "@dagrejs/graphlib": "^2.1.12",
+    "@chakra-ui/react": "^2.7.1",
+    "@chakra-ui/styled-system": "^2.9.1",
+    "@chakra-ui/theme-tools": "^2.0.18",
+    "@dagrejs/graphlib": "^2.1.13",
     "@dnd-kit/core": "^6.0.8",
     "@dnd-kit/modifiers": "^6.0.1",
     "@emotion/react": "^11.11.1",
-    "@emotion/styled": "^11.10.6",
-    "@floating-ui/react-dom": "^2.0.0",
-    "@fontsource/inter": "^4.5.15",
-    "@mantine/core": "^6.0.13",
-    "@mantine/hooks": "^6.0.13",
+    "@emotion/styled": "^11.11.0",
+    "@floating-ui/react-dom": "^2.0.1",
+    "@fontsource-variable/inter": "^5.0.3",
+    "@fontsource/inter": "^5.0.3",
+    "@mantine/core": "^6.0.14",
+    "@mantine/hooks": "^6.0.14",
     "@reduxjs/toolkit": "^1.9.5",
     "@roarr/browser-log-writer": "^1.1.5",
     "chakra-ui-contextmenu": "^1.0.5",
     "dateformat": "^5.0.3",
     "downshift": "^7.6.0",
-    "formik": "^2.2.9",
-    "framer-motion": "^10.12.4",
+    "formik": "^2.4.2",
+    "framer-motion": "^10.12.17",
     "fuse.js": "^6.6.2",
-    "i18next": "^22.4.15",
-    "i18next-browser-languagedetector": "^7.0.1",
-    "i18next-http-backend": "^2.2.0",
-    "konva": "^9.0.1",
+    "i18next": "^23.2.3",
+    "i18next-browser-languagedetector": "^7.0.2",
+    "i18next-http-backend": "^2.2.1",
+    "konva": "^9.2.0",
     "lodash-es": "^4.17.21",
     "nanostores": "^0.9.2",
     "openapi-fetch": "^0.4.0",
-    "overlayscrollbars": "^2.1.1",
+    "overlayscrollbars": "^2.2.0",
     "overlayscrollbars-react": "^0.5.0",
     "patch-package": "^7.0.0",
     "query-string": "^8.1.0",
@@ -92,21 +94,21 @@
     "react-dom": "^18.2.0",
     "react-dropzone": "^14.2.3",
     "react-hotkeys-hook": "4.4.0",
-    "react-i18next": "^12.2.2",
-    "react-icons": "^4.9.0",
-    "react-konva": "^18.2.7",
-    "react-redux": "^8.0.5",
-    "react-resizable-panels": "^0.0.42",
+    "react-i18next": "^13.0.1",
+    "react-icons": "^4.10.1",
+    "react-konva": "^18.2.10",
+    "react-redux": "^8.1.1",
+    "react-resizable-panels": "^0.0.52",
     "react-use": "^17.4.0",
-    "react-virtuoso": "^4.3.5",
-    "react-zoom-pan-pinch": "^3.0.7",
-    "reactflow": "^11.7.0",
+    "react-virtuoso": "^4.3.11",
+    "react-zoom-pan-pinch": "^3.0.8",
+    "reactflow": "^11.7.4",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^3.3.1",
     "roarr": "^7.15.0",
     "serialize-error": "^11.0.0",
-    "socket.io-client": "^4.6.0",
-    "use-image": "^1.1.0",
+    "socket.io-client": "^4.7.0",
+    "use-image": "^1.1.1",
     "uuid": "^9.0.0",
     "zod": "^3.21.4"
   },
@@ -117,22 +119,22 @@
     "ts-toolbelt": "^9.6.0"
   },
   "devDependencies": {
-    "@chakra-ui/cli": "^2.4.0",
+    "@chakra-ui/cli": "^2.4.1",
     "@types/dateformat": "^5.0.0",
     "@types/lodash-es": "^4.14.194",
-    "@types/node": "^18.16.2",
-    "@types/react": "^18.2.0",
-    "@types/react-dom": "^18.2.1",
+    "@types/node": "^20.3.1",
+    "@types/react": "^18.2.14",
+    "@types/react-dom": "^18.2.6",
     "@types/react-redux": "^7.1.25",
-    "@types/react-transition-group": "^4.4.5",
-    "@types/uuid": "^9.0.0",
-    "@typescript-eslint/eslint-plugin": "^5.59.1",
-    "@typescript-eslint/parser": "^5.59.1",
-    "@vitejs/plugin-react-swc": "^3.3.0",
+    "@types/react-transition-group": "^4.4.6",
+    "@types/uuid": "^9.0.2",
+    "@typescript-eslint/eslint-plugin": "^5.60.0",
+    "@typescript-eslint/parser": "^5.60.0",
+    "@vitejs/plugin-react-swc": "^3.3.2",
     "axios": "^1.4.0",
     "babel-plugin-transform-imports": "^2.0.0",
-    "concurrently": "^8.0.1",
-    "eslint": "^8.39.0",
+    "concurrently": "^8.2.0",
+    "eslint": "^8.43.0",
     "eslint-config-prettier": "^8.8.0",
     "eslint-plugin-prettier": "^4.2.1",
     "eslint-plugin-react": "^7.32.2",
@@ -140,19 +142,20 @@
     "form-data": "^4.0.0",
     "husky": "^8.0.3",
     "lint-staged": "^13.2.2",
-    "madge": "^6.0.0",
-    "openapi-types": "^12.1.0",
+    "madge": "^6.1.0",
+    "openapi-types": "^12.1.3",
     "openapi-typescript": "^6.2.8",
     "openapi-typescript-codegen": "^0.24.0",
     "postinstall-postinstall": "^2.1.0",
     "prettier": "^2.8.8",
-    "rollup-plugin-visualizer": "^5.9.0",
-    "terser": "^5.17.1",
+    "rollup-plugin-visualizer": "^5.9.2",
+    "terser": "^5.18.1",
     "ts-toolbelt": "^9.6.0",
-    "vite": "^4.3.3",
+    "vite": "^4.3.9",
     "vite-plugin-css-injected-by-js": "^3.1.1",
     "vite-plugin-dts": "^2.3.0",
     "vite-plugin-eslint": "^1.8.1",
+    "vite-plugin-node-polyfills": "^0.9.0",
     "vite-tsconfig-paths": "^4.2.0",
     "yarn": "^1.22.19"
   }

invokeai/frontend/web/patches/@chakra-ui+cli+2.4.0.patch

@@ -1,14 +0,0 @@
-diff --git a/node_modules/@chakra-ui/cli/dist/scripts/read-theme-file.worker.js b/node_modules/@chakra-ui/cli/dist/scripts/read-theme-file.worker.js
-index 937cf0d..7dcc0c0 100644
---- a/node_modules/@chakra-ui/cli/dist/scripts/read-theme-file.worker.js
-+++ b/node_modules/@chakra-ui/cli/dist/scripts/read-theme-file.worker.js
-@@ -50,7 +50,8 @@ async function readTheme(themeFilePath) {
-       project: tsConfig.configFileAbsolutePath,
-       compilerOptions: {
-         module: "CommonJS",
--        esModuleInterop: true
-+        esModuleInterop: true,
-+        jsx: 'react'
-       },
-       transpileOnly: true,
-       swc: true

invokeai/frontend/web/public/locales/en.json

@@ -524,7 +524,8 @@
         "initialImage": "Initial Image",
         "showOptionsPanel": "Show Options Panel",
         "hidePreview": "Hide Preview",
-        "showPreview": "Show Preview"
+        "showPreview": "Show Preview",
+        "controlNetControlMode": "Control Mode"
     },
     "settings": {
         "models": "Models",

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

@@ -48,7 +48,7 @@ const App = ({
   const isApplicationReady = useIsApplicationReady();
 
   const { data: pipelineModels } = useListModelsQuery({
-    model_type: 'pipeline',
+    model_type: 'main',
   });
   const { data: controlnetModels } = useListModelsQuery({
     model_type: 'controlnet',

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

@@ -14,7 +14,7 @@ import { invokeAIThemeColors } from 'theme/colors/invokeAI';
 import { lightThemeColors } from 'theme/colors/lightTheme';
 import { oceanBlueColors } from 'theme/colors/oceanBlue';
 
-import '@fontsource/inter/variable.css';
+import '@fontsource-variable/inter';
 import { MantineProvider } from '@mantine/core';
 import { mantineTheme } from 'mantine-theme/theme';
 import 'overlayscrollbars/overlayscrollbars.css';

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

@@ -15,7 +15,7 @@ import { ImageDTO } from 'services/api/types';
 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 { nodesSelector } from 'features/nodes/store/nodesSlice';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { some } from 'lodash-es';
 
@@ -30,7 +30,7 @@ export const selectImageUsage = createSelector(
   [
     generationSelector,
     canvasSelector,
-    nodesSelecter,
+    nodesSelector,
     controlNetSelector,
     (state: RootState, image_name?: string) => image_name,
   ],

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,6 +1,7 @@
 import { AnyAction } from '@reduxjs/toolkit';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
-import { forEach } from 'lodash-es';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import { Graph } from 'services/api/types';
 
 export const actionSanitizer = <A extends AnyAction>(action: A): A => {
@@ -8,17 +9,6 @@ export const actionSanitizer = <A extends AnyAction>(action: A): A => {
     if (action.payload.nodes) {
       const sanitizedNodes: Graph['nodes'] = {};
 
-      // Sanitize nodes as needed
-      forEach(action.payload.nodes, (node, key) => {
-        // Don't log the whole freaking dataURL
-        if (node.type === 'dataURL_image') {
-          const { dataURL, ...rest } = node;
-          sanitizedNodes[key] = { ...rest, dataURL: '<dataURL>' };
-        } else {
-          sanitizedNodes[key] = { ...node };
-        }
-      });
-
       return {
         ...action,
         payload: { ...action.payload, nodes: sanitizedNodes },
@@ -26,5 +16,19 @@ export const actionSanitizer = <A extends AnyAction>(action: A): A => {
     }
   }
 
+  if (receivedOpenAPISchema.fulfilled.match(action)) {
+    return {
+      ...action,
+      payload: '<OpenAPI schema omitted>',
+    };
+  }
+
+  if (nodeTemplatesBuilt.match(action)) {
+    return {
+      ...action,
+      payload: '<Node templates omitted>',
+    };
+  }
+
   return action;
 };

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

@@ -82,6 +82,7 @@ import {
   addImageRemovedFromBoardFulfilledListener,
   addImageRemovedFromBoardRejectedListener,
 } from './listeners/imageRemovedFromBoard';
+import { addReceivedOpenAPISchemaListener } from './listeners/receivedOpenAPISchema';
 
 export const listenerMiddleware = createListenerMiddleware();
 
@@ -205,3 +206,6 @@ addImageAddedToBoardRejectedListener();
 addImageRemovedFromBoardFulfilledListener();
 addImageRemovedFromBoardRejectedListener();
 addBoardIdSelectedListener();
+
+// Node schemas
+addReceivedOpenAPISchemaListener();

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

@@ -0,0 +1,35 @@
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { parseSchema } from 'features/nodes/util/parseSchema';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
+import { size } from 'lodash-es';
+
+const schemaLog = log.child({ namespace: 'schema' });
+
+export const addReceivedOpenAPISchemaListener = () => {
+  startAppListening({
+    actionCreator: receivedOpenAPISchema.fulfilled,
+    effect: (action, { dispatch, getState }) => {
+      const schemaJSON = action.payload;
+
+      schemaLog.info({ data: { schemaJSON } }, 'Dereferenced OpenAPI schema');
+
+      const nodeTemplates = parseSchema(schemaJSON);
+
+      schemaLog.info(
+        { data: { nodeTemplates } },
+        `Built ${size(nodeTemplates)} node templates`
+      );
+
+      dispatch(nodeTemplatesBuilt(nodeTemplates));
+    },
+  });
+
+  startAppListening({
+    actionCreator: receivedOpenAPISchema.rejected,
+    effect: (action, { dispatch, getState }) => {
+      schemaLog.error('Problem dereferencing OpenAPI Schema');
+    },
+  });
+};

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

@@ -3,7 +3,7 @@ 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 { nodesSelector } from 'features/nodes/store/nodesSlice';
 import { controlNetSelector } from 'features/controlNet/store/controlNetSlice';
 import { forEach, uniqBy } from 'lodash-es';
 import { imageUrlsReceived } from 'services/api/thunks/image';
@@ -16,7 +16,7 @@ const selectAllUsedImages = createSelector(
   [
     generationSelector,
     canvasSelector,
-    nodesSelecter,
+    nodesSelector,
     controlNetSelector,
     selectImagesEntities,
   ],

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

@@ -22,6 +22,7 @@ import boardsReducer from 'features/gallery/store/boardSlice';
 import configReducer from 'features/system/store/configSlice';
 import hotkeysReducer from 'features/ui/store/hotkeysSlice';
 import uiReducer from 'features/ui/store/uiSlice';
+import dynamicPromptsReducer from 'features/dynamicPrompts/store/slice';
 
 import { listenerMiddleware } from './middleware/listenerMiddleware';
 
@@ -48,6 +49,7 @@ const allReducers = {
   controlNet: controlNetReducer,
   boards: boardsReducer,
   // session: sessionReducer,
+  dynamicPrompts: dynamicPromptsReducer,
   [api.reducerPath]: api.reducer,
 };
 
@@ -65,6 +67,7 @@ const rememberedKeys: (keyof typeof allReducers)[] = [
   'system',
   'ui',
   'controlNet',
+  'dynamicPrompts',
   // 'boards',
   // 'hotkeys',
   // 'config',
@@ -100,3 +103,4 @@ export type AppGetState = typeof store.getState;
 export type RootState = ReturnType<typeof store.getState>;
 export type AppThunkDispatch = ThunkDispatch<RootState, any, AnyAction>;
 export type AppDispatch = typeof store.dispatch;
+export const stateSelector = (state: RootState) => state;

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

@@ -171,6 +171,14 @@ export type AppConfig = {
       fineStep: number;
       coarseStep: number;
     };
+    dynamicPrompts: {
+      maxPrompts: {
+        initial: number;
+        min: number;
+        sliderMax: number;
+        inputMax: number;
+      };
+    };
   };
 };
 

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

@@ -27,7 +27,6 @@ const IAIMantineMultiSelect = (props: IAIMultiSelectProps) => {
             borderWidth: '2px',
             borderColor: 'var(--invokeai-colors-base-800)',
             color: 'var(--invokeai-colors-base-100)',
-            padding: 10,
             paddingRight: 24,
             fontWeight: 600,
             '&:hover': { borderColor: 'var(--invokeai-colors-base-700)' },

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

@@ -34,6 +34,10 @@ const IAIMantineSelect = (props: IAISelectProps) => {
             '&:focus': {
               borderColor: 'var(--invokeai-colors-accent-600)',
             },
+            '&:disabled': {
+              backgroundColor: 'var(--invokeai-colors-base-700)',
+              color: 'var(--invokeai-colors-base-400)',
+            },
           },
           dropdown: {
             backgroundColor: 'var(--invokeai-colors-base-800)',
@@ -64,7 +68,7 @@ const IAIMantineSelect = (props: IAISelectProps) => {
             },
           },
           rightSection: {
-            width: 24,
+            width: 32,
           },
         })}
         {...rest}

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

@@ -41,7 +41,15 @@ const IAISwitch = (props: Props) => {
         {...formControlProps}
       >
         {label && (
-          <FormLabel my={1} flexGrow={1} {...formLabelProps}>
+          <FormLabel
+            my={1}
+            flexGrow={1}
+            sx={{
+              cursor: isDisabled ? 'not-allowed' : 'pointer',
+              ...formLabelProps?.sx,
+            }}
+            {...formLabelProps}
+          >
             {label}
           </FormLabel>
         )}

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

@@ -9,10 +9,12 @@ type IAICanvasImageProps = {
 };
 const IAICanvasImage = (props: IAICanvasImageProps) => {
   const { width, height, x, y, imageName } = props.canvasImage;
-  const { currentData: imageDTO } = useGetImageDTOQuery(imageName ?? skipToken);
+  const { currentData: imageDTO, isError } = useGetImageDTOQuery(
+    imageName ?? skipToken
+  );
   const [image] = useImage(imageDTO?.image_url ?? '', 'anonymous');
 
-  if (!imageDTO) {
+  if (isError) {
     return <Rect x={x} y={y} width={width} height={height} fill="red" />;
   }
 

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

@@ -1,26 +1,27 @@
+import { Box, ChakraProps, Flex } from '@chakra-ui/react';
+import { useAppDispatch } from 'app/store/storeHooks';
 import { memo, useCallback } from 'react';
+import { FaCopy, FaTrash } from 'react-icons/fa';
 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 { Flex, Box, ChakraProps } from '@chakra-ui/react';
-import { FaCopy, FaTrash } 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 IAISwitch from 'common/components/IAISwitch';
 import { ChevronUpIcon } from '@chakra-ui/icons';
+import IAIIconButton from 'common/components/IAIIconButton';
+import IAISwitch from 'common/components/IAISwitch';
+import { useToggle } from 'react-use';
+import { v4 as uuidv4 } from 'uuid';
+import ControlNetImagePreview from './ControlNetImagePreview';
+import ControlNetProcessorComponent from './ControlNetProcessorComponent';
 import ParamControlNetShouldAutoConfig from './ParamControlNetShouldAutoConfig';
+import ParamControlNetBeginEnd from './parameters/ParamControlNetBeginEnd';
+import ParamControlNetControlMode from './parameters/ParamControlNetControlMode';
+import ParamControlNetProcessorSelect from './parameters/ParamControlNetProcessorSelect';
 
 const expandedControlImageSx: ChakraProps['sx'] = { maxH: 96 };
 
@@ -36,6 +37,7 @@ const ControlNet = (props: ControlNetProps) => {
     weight,
     beginStepPct,
     endStepPct,
+    controlMode,
     controlImage,
     processedControlImage,
     processorNode,
@@ -137,13 +139,13 @@ const ControlNet = (props: ControlNetProps) => {
       </Flex>
       {isEnabled && (
         <>
+          <Flex sx={{ w: 'full', flexDirection: 'column' }}>
             <Flex sx={{ gap: 4, w: 'full' }}>
               <Flex
                 sx={{
                   flexDir: 'column',
-                gap: 2,
+                  gap: 3,
                   w: 'full',
-                h: isExpanded ? 28 : 24,
                   paddingInlineStart: 1,
                   paddingInlineEnd: isExpanded ? 1 : 0,
                   pb: 2,
@@ -179,6 +181,12 @@ const ControlNet = (props: ControlNetProps) => {
                 </Flex>
               )}
             </Flex>
+            <ParamControlNetControlMode
+              controlNetId={controlNetId}
+              controlMode={controlMode}
+            />
+          </Flex>
+
           {isExpanded && (
             <>
               <Box mt={2}>

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

@@ -0,0 +1,45 @@
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import {
+  ControlModes,
+  controlNetControlModeChanged,
+} from 'features/controlNet/store/controlNetSlice';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+type ParamControlNetControlModeProps = {
+  controlNetId: string;
+  controlMode: string;
+};
+
+const CONTROL_MODE_DATA = [
+  { label: 'Balanced', value: 'balanced' },
+  { label: 'Prompt', value: 'more_prompt' },
+  { label: 'Control', value: 'more_control' },
+  { label: 'Mega Control', value: 'unbalanced' },
+];
+
+export default function ParamControlNetControlMode(
+  props: ParamControlNetControlModeProps
+) {
+  const { controlNetId, controlMode = false } = props;
+  const dispatch = useAppDispatch();
+
+  const { t } = useTranslation();
+
+  const handleControlModeChange = useCallback(
+    (controlMode: ControlModes) => {
+      dispatch(controlNetControlModeChanged({ controlNetId, controlMode }));
+    },
+    [controlNetId, dispatch]
+  );
+
+  return (
+    <IAIMantineSelect
+      label={t('parameters.controlNetControlMode')}
+      data={CONTROL_MODE_DATA}
+      value={String(controlMode)}
+      onChange={handleControlModeChange}
+    />
+  );
+}

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

@@ -1,6 +1,5 @@
 import {
   ControlNetProcessorType,
-  RequiredCannyImageProcessorInvocation,
   RequiredControlNetProcessorNode,
 } from './types';
 
@@ -23,7 +22,7 @@ type ControlNetProcessorsDict = Record<
  *
  * TODO: Generate from the OpenAPI schema
  */
-export const CONTROLNET_PROCESSORS = {
+export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
   none: {
     type: 'none',
     label: 'none',
@@ -174,6 +173,8 @@ export const CONTROLNET_PROCESSORS = {
   },
 };
 
+type ControlNetModelsDict = Record<string, ControlNetModel>;
+
 type ControlNetModel = {
   type: string;
   label: string;
@@ -181,7 +182,7 @@ type ControlNetModel = {
   defaultProcessor?: ControlNetProcessorType;
 };
 
-export const CONTROLNET_MODELS = {
+export const CONTROLNET_MODELS: ControlNetModelsDict = {
   'lllyasviel/control_v11p_sd15_canny': {
     type: 'lllyasviel/control_v11p_sd15_canny',
     label: 'Canny',
@@ -190,6 +191,7 @@ export const CONTROLNET_MODELS = {
   'lllyasviel/control_v11p_sd15_inpaint': {
     type: 'lllyasviel/control_v11p_sd15_inpaint',
     label: 'Inpaint',
+    defaultProcessor: 'none',
   },
   'lllyasviel/control_v11p_sd15_mlsd': {
     type: 'lllyasviel/control_v11p_sd15_mlsd',
@@ -209,6 +211,7 @@ export const CONTROLNET_MODELS = {
   'lllyasviel/control_v11p_sd15_seg': {
     type: 'lllyasviel/control_v11p_sd15_seg',
     label: 'Segmentation',
+    defaultProcessor: 'none',
   },
   'lllyasviel/control_v11p_sd15_lineart': {
     type: 'lllyasviel/control_v11p_sd15_lineart',
@@ -223,6 +226,7 @@ export const CONTROLNET_MODELS = {
   'lllyasviel/control_v11p_sd15_scribble': {
     type: 'lllyasviel/control_v11p_sd15_scribble',
     label: 'Scribble',
+    defaultProcessor: 'none',
   },
   'lllyasviel/control_v11p_sd15_softedge': {
     type: 'lllyasviel/control_v11p_sd15_softedge',
@@ -242,10 +246,12 @@ export const CONTROLNET_MODELS = {
   'lllyasviel/control_v11f1e_sd15_tile': {
     type: 'lllyasviel/control_v11f1e_sd15_tile',
     label: 'Tile (experimental)',
+    defaultProcessor: 'none',
   },
   'lllyasviel/control_v11e_sd15_ip2p': {
     type: 'lllyasviel/control_v11e_sd15_ip2p',
     label: 'Pix2Pix (experimental)',
+    defaultProcessor: 'none',
   },
   'CrucibleAI/ControlNetMediaPipeFace': {
     type: 'CrucibleAI/ControlNetMediaPipeFace',

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

@@ -18,12 +18,19 @@ import { forEach } from 'lodash-es';
 import { isAnySessionRejected } from 'services/api/thunks/session';
 import { appSocketInvocationError } from 'services/events/actions';
 
+export type ControlModes =
+  | 'balanced'
+  | 'more_prompt'
+  | 'more_control'
+  | 'unbalanced';
+
 export const initialControlNet: Omit<ControlNetConfig, 'controlNetId'> = {
   isEnabled: true,
   model: CONTROLNET_MODELS['lllyasviel/control_v11p_sd15_canny'].type,
   weight: 1,
   beginStepPct: 0,
   endStepPct: 1,
+  controlMode: 'balanced',
   controlImage: null,
   processedControlImage: null,
   processorType: 'canny_image_processor',
@@ -39,6 +46,7 @@ export type ControlNetConfig = {
   weight: number;
   beginStepPct: number;
   endStepPct: number;
+  controlMode: ControlModes;
   controlImage: string | null;
   processedControlImage: string | null;
   processorType: ControlNetProcessorType;
@@ -181,6 +189,13 @@ export const controlNetSlice = createSlice({
       const { controlNetId, endStepPct } = action.payload;
       state.controlNets[controlNetId].endStepPct = endStepPct;
     },
+    controlNetControlModeChanged: (
+      state,
+      action: PayloadAction<{ controlNetId: string; controlMode: ControlModes }>
+    ) => {
+      const { controlNetId, controlMode } = action.payload;
+      state.controlNets[controlNetId].controlMode = controlMode;
+    },
     controlNetProcessorParamsChanged: (
       state,
       action: PayloadAction<{
@@ -307,6 +322,7 @@ export const {
   controlNetWeightChanged,
   controlNetBeginStepPctChanged,
   controlNetEndStepPctChanged,
+  controlNetControlModeChanged,
   controlNetProcessorParamsChanged,
   controlNetProcessorTypeChanged,
   controlNetReset,

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx

@@ -0,0 +1,45 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAICollapse from 'common/components/IAICollapse';
+import { useCallback } from 'react';
+import { isEnabledToggled } from '../store/slice';
+import ParamDynamicPromptsMaxPrompts from './ParamDynamicPromptsMaxPrompts';
+import ParamDynamicPromptsCombinatorial from './ParamDynamicPromptsCombinatorial';
+import { Flex } from '@chakra-ui/react';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { isEnabled } = state.dynamicPrompts;
+
+    return { isEnabled };
+  },
+  defaultSelectorOptions
+);
+
+const ParamDynamicPromptsCollapse = () => {
+  const dispatch = useAppDispatch();
+  const { isEnabled } = useAppSelector(selector);
+
+  const handleToggleIsEnabled = useCallback(() => {
+    dispatch(isEnabledToggled());
+  }, [dispatch]);
+
+  return (
+    <IAICollapse
+      isOpen={isEnabled}
+      onToggle={handleToggleIsEnabled}
+      label="Dynamic Prompts"
+      withSwitch
+    >
+      <Flex sx={{ gap: 2, flexDir: 'column' }}>
+        <ParamDynamicPromptsCombinatorial />
+        <ParamDynamicPromptsMaxPrompts />
+      </Flex>
+    </IAICollapse>
+  );
+};
+
+export default ParamDynamicPromptsCollapse;

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCombinatorial.tsx

@@ -0,0 +1,36 @@
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { combinatorialToggled } from '../store/slice';
+import { createSelector } from '@reduxjs/toolkit';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { useCallback } from 'react';
+import { stateSelector } from 'app/store/store';
+import IAISwitch from 'common/components/IAISwitch';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { combinatorial } = state.dynamicPrompts;
+
+    return { combinatorial };
+  },
+  defaultSelectorOptions
+);
+
+const ParamDynamicPromptsCombinatorial = () => {
+  const { combinatorial } = useAppSelector(selector);
+  const dispatch = useAppDispatch();
+
+  const handleChange = useCallback(() => {
+    dispatch(combinatorialToggled());
+  }, [dispatch]);
+
+  return (
+    <IAISwitch
+      label="Combinatorial Generation"
+      isChecked={combinatorial}
+      onChange={handleChange}
+    />
+  );
+};
+
+export default ParamDynamicPromptsCombinatorial;

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsMaxPrompts.tsx

@@ -0,0 +1,55 @@
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISlider from 'common/components/IAISlider';
+import { maxPromptsChanged, maxPromptsReset } from '../store/slice';
+import { createSelector } from '@reduxjs/toolkit';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { useCallback } from 'react';
+import { stateSelector } from 'app/store/store';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { maxPrompts, combinatorial } = state.dynamicPrompts;
+    const { min, sliderMax, inputMax } =
+      state.config.sd.dynamicPrompts.maxPrompts;
+
+    return { maxPrompts, min, sliderMax, inputMax, combinatorial };
+  },
+  defaultSelectorOptions
+);
+
+const ParamDynamicPromptsMaxPrompts = () => {
+  const { maxPrompts, min, sliderMax, inputMax, combinatorial } =
+    useAppSelector(selector);
+  const dispatch = useAppDispatch();
+
+  const handleChange = useCallback(
+    (v: number) => {
+      dispatch(maxPromptsChanged(v));
+    },
+    [dispatch]
+  );
+
+  const handleReset = useCallback(() => {
+    dispatch(maxPromptsReset());
+  }, [dispatch]);
+
+  return (
+    <IAISlider
+      label="Max Prompts"
+      isDisabled={!combinatorial}
+      min={min}
+      max={sliderMax}
+      value={maxPrompts}
+      onChange={handleChange}
+      sliderNumberInputProps={{ max: inputMax }}
+      withSliderMarks
+      withInput
+      inputReadOnly
+      withReset
+      handleReset={handleReset}
+    />
+  );
+};
+
+export default ParamDynamicPromptsMaxPrompts;

invokeai/frontend/web/src/features/dynamicPrompts/store/selectors.ts

@@ -0,0 +1 @@
+//

invokeai/frontend/web/src/features/dynamicPrompts/store/slice.ts

@@ -0,0 +1,50 @@
+import { PayloadAction, createSlice } from '@reduxjs/toolkit';
+import { RootState } from 'app/store/store';
+
+export interface DynamicPromptsState {
+  isEnabled: boolean;
+  maxPrompts: number;
+  combinatorial: boolean;
+}
+
+export const initialDynamicPromptsState: DynamicPromptsState = {
+  isEnabled: false,
+  maxPrompts: 100,
+  combinatorial: true,
+};
+
+const initialState: DynamicPromptsState = initialDynamicPromptsState;
+
+export const dynamicPromptsSlice = createSlice({
+  name: 'dynamicPrompts',
+  initialState,
+  reducers: {
+    maxPromptsChanged: (state, action: PayloadAction<number>) => {
+      state.maxPrompts = action.payload;
+    },
+    maxPromptsReset: (state) => {
+      state.maxPrompts = initialDynamicPromptsState.maxPrompts;
+    },
+    combinatorialToggled: (state) => {
+      state.combinatorial = !state.combinatorial;
+    },
+    isEnabledToggled: (state) => {
+      state.isEnabled = !state.isEnabled;
+    },
+  },
+  extraReducers: (builder) => {
+    //
+  },
+});
+
+export const {
+  isEnabledToggled,
+  maxPromptsChanged,
+  maxPromptsReset,
+  combinatorialToggled,
+} = dynamicPromptsSlice.actions;
+
+export default dynamicPromptsSlice.reducer;
+
+export const dynamicPromptsSelector = (state: RootState) =>
+  state.dynamicPrompts;

invokeai/frontend/web/src/features/nodes/components/AddNodeMenu.tsx

@@ -1,28 +1,41 @@
 import 'reactflow/dist/style.css';
-import { memo, useCallback } from 'react';
-import {
-  Tooltip,
-  Menu,
-  MenuButton,
-  MenuList,
-  MenuItem,
-} from '@chakra-ui/react';
-import { FaEllipsisV } from 'react-icons/fa';
+import { useCallback, forwardRef } from 'react';
+import { Flex, Text } from '@chakra-ui/react';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { nodeAdded } from '../store/nodesSlice';
+import { nodeAdded, nodesSelector } from '../store/nodesSlice';
 import { map } from 'lodash-es';
-import { RootState } from 'app/store/store';
 import { useBuildInvocation } from '../hooks/useBuildInvocation';
 import { AnyInvocationType } from 'services/events/types';
-import IAIIconButton from 'common/components/IAIIconButton';
 import { useAppToaster } from 'app/components/Toaster';
+import { createSelector } from '@reduxjs/toolkit';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAIMantineMultiSelect from 'common/components/IAIMantineMultiSelect';
+
+type NodeTemplate = {
+  label: string;
+  value: string;
+  description: string;
+};
+
+const selector = createSelector(
+  nodesSelector,
+  (nodes) => {
+    const data: NodeTemplate[] = map(nodes.invocationTemplates, (template) => {
+      return {
+        label: template.title,
+        value: template.type,
+        description: template.description,
+      };
+    });
+
+    return { data };
+  },
+  defaultSelectorOptions
+);
 
 const AddNodeMenu = () => {
   const dispatch = useAppDispatch();
-
-  const invocationTemplates = useAppSelector(
-    (state: RootState) => state.nodes.invocationTemplates
-  );
+  const { data } = useAppSelector(selector);
 
   const buildInvocation = useBuildInvocation();
 
@@ -46,23 +59,52 @@ const AddNodeMenu = () => {
   );
 
   return (
-    <Menu isLazy>
-      <MenuButton
-        as={IAIIconButton}
-        aria-label="Add Node"
-        icon={<FaEllipsisV />}
+    <Flex sx={{ gap: 2, alignItems: 'center' }}>
+      <IAIMantineMultiSelect
+        selectOnBlur={false}
+        placeholder="Add Node"
+        value={[]}
+        data={data}
+        maxDropdownHeight={400}
+        nothingFound="No matching nodes"
+        itemComponent={SelectItem}
+        filter={(value, selected, item: NodeTemplate) =>
+          item.label.toLowerCase().includes(value.toLowerCase().trim()) ||
+          item.value.toLowerCase().includes(value.toLowerCase().trim()) ||
+          item.description.toLowerCase().includes(value.toLowerCase().trim())
+        }
+        onChange={(v) => {
+          v[0] && addNode(v[0] as AnyInvocationType);
+        }}
+        sx={{
+          width: '18rem',
+        }}
       />
-      <MenuList overflowY="scroll" height={400}>
-        {map(invocationTemplates, ({ title, description, type }, key) => {
-          return (
-            <Tooltip key={key} label={description} placement="end" hasArrow>
-              <MenuItem onClick={() => addNode(type)}>{title}</MenuItem>
-            </Tooltip>
-          );
-        })}
-      </MenuList>
-    </Menu>
+    </Flex>
   );
 };
 
-export default memo(AddNodeMenu);
+interface ItemProps extends React.ComponentPropsWithoutRef<'div'> {
+  value: string;
+  label: string;
+  description: string;
+}
+
+const SelectItem = forwardRef<HTMLDivElement, ItemProps>(
+  ({ label, description, ...others }: ItemProps, ref) => {
+    return (
+      <div ref={ref} {...others}>
+        <div>
+          <Text>{label}</Text>
+          <Text size="xs" color="base.600">
+            {description}
+          </Text>
+        </div>
+      </div>
+    );
+  }
+);
+
+SelectItem.displayName = 'SelectItem';
+
+export default AddNodeMenu;

invokeai/frontend/web/src/features/nodes/components/fields/ModelInputFieldComponent.tsx

@@ -23,7 +23,7 @@ const ModelInputFieldComponent = (
   const { t } = useTranslation();
 
   const { data: pipelineModels } = useListModelsQuery({
-    model_type: 'pipeline',
+    model_type: 'main',
   });
 
   const data = useMemo(() => {

invokeai/frontend/web/src/features/nodes/components/panels/TopLeftPanel.tsx

@@ -1,10 +1,10 @@
 import { memo } from 'react';
 import { Panel } from 'reactflow';
-import NodeSearch from '../search/NodeSearch';
+import AddNodeMenu from '../AddNodeMenu';
 
 const TopLeftPanel = () => (
   <Panel position="top-left">
-    <NodeSearch />
+    <AddNodeMenu />
   </Panel>
 );
 

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -14,9 +14,6 @@ import {
 import { ImageField } from 'services/api/types';
 import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import { InvocationTemplate, InvocationValue } from '../types/types';
-import { parseSchema } from '../util/parseSchema';
-import { log } from 'app/logging/useLogger';
-import { size } from 'lodash-es';
 import { RgbaColor } from 'react-colorful';
 import { RootState } from 'app/store/store';
 
@@ -78,25 +75,17 @@ const nodesSlice = createSlice({
     shouldShowGraphOverlayChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldShowGraphOverlay = action.payload;
     },
-    parsedOpenAPISchema: (state, action: PayloadAction<OpenAPIV3.Document>) => {
-      try {
-        const parsedSchema = parseSchema(action.payload);
-
-        // TODO: Achtung! Side effect in a reducer!
-        log.info(
-          { namespace: 'schema', nodes: parsedSchema },
-          `Parsed ${size(parsedSchema)} nodes`
-        );
-        state.invocationTemplates = parsedSchema;
-      } catch (err) {
-        console.error(err);
-      }
+    nodeTemplatesBuilt: (
+      state,
+      action: PayloadAction<Record<string, InvocationTemplate>>
+    ) => {
+      state.invocationTemplates = action.payload;
     },
     nodeEditorReset: () => {
       return { ...initialNodesState };
     },
   },
-  extraReducers(builder) {
+  extraReducers: (builder) => {
     builder.addCase(receivedOpenAPISchema.fulfilled, (state, action) => {
       state.schema = action.payload;
     });
@@ -112,10 +101,10 @@ export const {
   connectionStarted,
   connectionEnded,
   shouldShowGraphOverlayChanged,
-  parsedOpenAPISchema,
+  nodeTemplatesBuilt,
   nodeEditorReset,
 } = nodesSlice.actions;
 
 export default nodesSlice.reducer;
 
-export const nodesSelecter = (state: RootState) => state.nodes;
+export const nodesSelector = (state: RootState) => state.nodes;

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

@@ -34,12 +34,10 @@ export type InvocationTemplate = {
    * Array of invocation inputs
    */
   inputs: Record<string, InputFieldTemplate>;
-  // inputs: InputField[];
   /**
    * Array of the invocation outputs
    */
   outputs: Record<string, OutputFieldTemplate>;
-  // outputs: OutputField[];
 };
 
 export type FieldUIConfig = {
@@ -335,7 +333,7 @@ export type TypeHints = {
 };
 
 export type InvocationSchemaExtra = {
-  output: OpenAPIV3.ReferenceObject; // the output of the invocation
+  output: OpenAPIV3.SchemaObject; // the output of the invocation
   ui?: {
     tags?: string[];
     type_hints?: TypeHints;

invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts

@@ -1,5 +1,5 @@
 import { RootState } from 'app/store/store';
-import { filter, forEach, size } from 'lodash-es';
+import { filter } from 'lodash-es';
 import { CollectInvocation, ControlNetInvocation } from 'services/api/types';
 import { NonNullableGraph } from '../types/types';
 import { CONTROL_NET_COLLECT } from './graphBuilders/constants';
@@ -19,9 +19,9 @@ export const addControlNetToLinearGraph = (
         (c.processorType === 'none' && Boolean(c.controlImage)))
   );
 
-  // Add ControlNet
-  if (isControlNetEnabled && validControlNets.length > 0) {
-    if (size(controlNets) > 1) {
+  if (isControlNetEnabled && Boolean(validControlNets.length)) {
+    if (validControlNets.length > 1) {
+      // We have multiple controlnets, add ControlNet collector
       const controlNetIterateNode: CollectInvocation = {
         id: CONTROL_NET_COLLECT,
         type: 'collect',
@@ -36,29 +36,25 @@ export const addControlNetToLinearGraph = (
       });
     }
 
-    forEach(controlNets, (controlNet) => {
+    validControlNets.forEach((controlNet) => {
       const {
         controlNetId,
-        isEnabled,
         controlImage,
         processedControlImage,
         beginStepPct,
         endStepPct,
+        controlMode,
         model,
         processorType,
         weight,
       } = controlNet;
 
-      if (!isEnabled) {
-        // Skip disabled ControlNets
-        return;
-      }
-
       const controlNetNode: ControlNetInvocation = {
         id: `control_net_${controlNetId}`,
         type: 'controlnet',
         begin_step_percent: beginStepPct,
         end_step_percent: endStepPct,
+        control_mode: controlMode,
         control_model: model as ControlNetInvocation['control_model'],
         control_weight: weight,
       };
@@ -80,7 +76,8 @@ export const addControlNetToLinearGraph = (
 
       graph.nodes[controlNetNode.id] = controlNetNode;
 
-      if (size(controlNets) > 1) {
+      if (validControlNets.length > 1) {
+        // if we have multiple controlnets, link to the collector
         graph.edges.push({
           source: { node_id: controlNetNode.id, field: 'control' },
           destination: {
@@ -89,6 +86,7 @@ export const addControlNetToLinearGraph = (
           },
         });
       } else {
+        // otherwise, link directly to the base node
         graph.edges.push({
           source: { node_id: controlNetNode.id, field: 'control' },
           destination: {

invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts

@@ -349,21 +349,11 @@ export const getFieldType = (
 
   if (typeHints && name in typeHints) {
     rawFieldType = typeHints[name];
-  } else if (!schemaObject.type) {
-    // if schemaObject has no type, then it should have one of allOf, anyOf, oneOf
-    if (schemaObject.allOf) {
-      rawFieldType = refObjectToFieldType(
-        schemaObject.allOf![0] as OpenAPIV3.ReferenceObject
-      );
-    } else if (schemaObject.anyOf) {
-      rawFieldType = refObjectToFieldType(
-        schemaObject.anyOf![0] as OpenAPIV3.ReferenceObject
-      );
-    } else if (schemaObject.oneOf) {
-      rawFieldType = refObjectToFieldType(
-        schemaObject.oneOf![0] as OpenAPIV3.ReferenceObject
-      );
-    }
+  } else if (!schemaObject.type && schemaObject.allOf) {
+    // if schemaObject has no type, then it should have one of allOf
+    rawFieldType =
+      (schemaObject.allOf[0] as OpenAPIV3.SchemaObject).title ??
+      'Missing Field Type';
   } else if (schemaObject.enum) {
     rawFieldType = 'enum';
   } else if (schemaObject.type) {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addDynamicPromptsToGraph.ts

@@ -0,0 +1,153 @@
+import { RootState } from 'app/store/store';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import {
+  DynamicPromptInvocation,
+  IterateInvocation,
+  NoiseInvocation,
+  RandomIntInvocation,
+  RangeOfSizeInvocation,
+} from 'services/api/types';
+import {
+  DYNAMIC_PROMPT,
+  ITERATE,
+  NOISE,
+  POSITIVE_CONDITIONING,
+  RANDOM_INT,
+  RANGE_OF_SIZE,
+} from './constants';
+import { unset } from 'lodash-es';
+
+export const addDynamicPromptsToGraph = (
+  graph: NonNullableGraph,
+  state: RootState
+): void => {
+  const { positivePrompt, iterations, seed, shouldRandomizeSeed } =
+    state.generation;
+
+  const {
+    combinatorial,
+    isEnabled: isDynamicPromptsEnabled,
+    maxPrompts,
+  } = state.dynamicPrompts;
+
+  if (isDynamicPromptsEnabled) {
+    // iteration is handled via dynamic prompts
+    unset(graph.nodes[POSITIVE_CONDITIONING], 'prompt');
+
+    const dynamicPromptNode: DynamicPromptInvocation = {
+      id: DYNAMIC_PROMPT,
+      type: 'dynamic_prompt',
+      max_prompts: combinatorial ? maxPrompts : iterations,
+      combinatorial,
+      prompt: positivePrompt,
+    };
+
+    const iterateNode: IterateInvocation = {
+      id: ITERATE,
+      type: 'iterate',
+    };
+
+    graph.nodes[DYNAMIC_PROMPT] = dynamicPromptNode;
+    graph.nodes[ITERATE] = iterateNode;
+
+    // connect dynamic prompts to compel nodes
+    graph.edges.push(
+      {
+        source: {
+          node_id: DYNAMIC_PROMPT,
+          field: 'prompt_collection',
+        },
+        destination: {
+          node_id: ITERATE,
+          field: 'collection',
+        },
+      },
+      {
+        source: {
+          node_id: ITERATE,
+          field: 'item',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'prompt',
+        },
+      }
+    );
+
+    if (shouldRandomizeSeed) {
+      // Random int node to generate the starting seed
+      const randomIntNode: RandomIntInvocation = {
+        id: RANDOM_INT,
+        type: 'rand_int',
+      };
+
+      graph.nodes[RANDOM_INT] = randomIntNode;
+
+      // Connect random int to the start of the range of size so the range starts on the random first seed
+      graph.edges.push({
+        source: { node_id: RANDOM_INT, field: 'a' },
+        destination: { node_id: NOISE, field: 'seed' },
+      });
+    } else {
+      // User specified seed, so set the start of the range of size to the seed
+      (graph.nodes[NOISE] as NoiseInvocation).seed = seed;
+    }
+  } else {
+    const rangeOfSizeNode: RangeOfSizeInvocation = {
+      id: RANGE_OF_SIZE,
+      type: 'range_of_size',
+      size: iterations,
+      step: 1,
+    };
+
+    const iterateNode: IterateInvocation = {
+      id: ITERATE,
+      type: 'iterate',
+    };
+
+    graph.nodes[ITERATE] = iterateNode;
+    graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
+
+    graph.edges.push({
+      source: {
+        node_id: RANGE_OF_SIZE,
+        field: 'collection',
+      },
+      destination: {
+        node_id: ITERATE,
+        field: 'collection',
+      },
+    });
+
+    graph.edges.push({
+      source: {
+        node_id: ITERATE,
+        field: 'item',
+      },
+      destination: {
+        node_id: NOISE,
+        field: 'seed',
+      },
+    });
+
+    // handle seed
+    if (shouldRandomizeSeed) {
+      // Random int node to generate the starting seed
+      const randomIntNode: RandomIntInvocation = {
+        id: RANDOM_INT,
+        type: 'rand_int',
+      };
+
+      graph.nodes[RANDOM_INT] = randomIntNode;
+
+      // Connect random int to the start of the range of size so the range starts on the random first seed
+      graph.edges.push({
+        source: { node_id: RANDOM_INT, field: 'a' },
+        destination: { node_id: RANGE_OF_SIZE, field: 'start' },
+      });
+    } else {
+      // User specified seed, so set the start of the range of size to the seed
+      rangeOfSizeNode.start = seed;
+    }
+  }
+};

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts

@@ -2,6 +2,7 @@ import { RootState } from 'app/store/store';
 import {
   ImageDTO,
   ImageResizeInvocation,
+  ImageToLatentsInvocation,
   RandomIntInvocation,
   RangeOfSizeInvocation,
 } from 'services/api/types';
@@ -10,7 +11,7 @@ import { log } from 'app/logging/useLogger';
 import {
   ITERATE,
   LATENTS_TO_IMAGE,
-  MODEL_LOADER,
+  PIPELINE_MODEL_LOADER,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -24,6 +25,7 @@ import {
 import { set } from 'lodash-es';
 import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
 import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
+import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 
 const moduleLog = log.child({ namespace: 'nodes' });
 
@@ -75,31 +77,19 @@ export const buildCanvasImageToImageGraph = (
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
-      [RANGE_OF_SIZE]: {
-        type: 'range_of_size',
-        id: RANGE_OF_SIZE,
-        // seed - must be connected manually
-        // start: 0,
-        size: iterations,
-        step: 1,
-      },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
       },
-      [MODEL_LOADER]: {
+      [PIPELINE_MODEL_LOADER]: {
         type: 'pipeline_model_loader',
-        id: MODEL_LOADER,
+        id: PIPELINE_MODEL_LOADER,
         model,
       },
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
       },
-      [ITERATE]: {
-        type: 'iterate',
-        id: ITERATE,
-      },
       [LATENTS_TO_LATENTS]: {
         type: 'l2l',
         id: LATENTS_TO_LATENTS,
@@ -120,7 +110,7 @@ export const buildCanvasImageToImageGraph = (
     edges: [
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -130,7 +120,7 @@ export const buildCanvasImageToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -140,7 +130,7 @@ export const buildCanvasImageToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'vae',
         },
         destination: {
@@ -148,26 +138,6 @@ export const buildCanvasImageToImageGraph = (
           field: 'vae',
         },
       },
-      {
-        source: {
-          node_id: RANGE_OF_SIZE,
-          field: 'collection',
-        },
-        destination: {
-          node_id: ITERATE,
-          field: 'collection',
-        },
-      },
-      {
-        source: {
-          node_id: ITERATE,
-          field: 'item',
-        },
-        destination: {
-          node_id: NOISE,
-          field: 'seed',
-        },
-      },
       {
         source: {
           node_id: LATENTS_TO_LATENTS,
@@ -200,7 +170,7 @@ export const buildCanvasImageToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'vae',
         },
         destination: {
@@ -210,7 +180,7 @@ export const buildCanvasImageToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -241,26 +211,6 @@ export const buildCanvasImageToImageGraph = (
     ],
   };
 
-  // handle seed
-  if (shouldRandomizeSeed) {
-    // Random int node to generate the starting seed
-    const randomIntNode: RandomIntInvocation = {
-      id: RANDOM_INT,
-      type: 'rand_int',
-    };
-
-    graph.nodes[RANDOM_INT] = randomIntNode;
-
-    // Connect random int to the start of the range of size so the range starts on the random first seed
-    graph.edges.push({
-      source: { node_id: RANDOM_INT, field: 'a' },
-      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
-    });
-  } else {
-    // User specified seed, so set the start of the range of size to the seed
-    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
-  }
-
   // handle `fit`
   if (initialImage.width !== width || initialImage.height !== height) {
     // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
@@ -306,9 +256,9 @@ export const buildCanvasImageToImageGraph = (
     });
   } else {
     // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
-    set(graph.nodes[IMAGE_TO_LATENTS], 'image', {
+    (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image = {
       image_name: initialImage.image_name,
-    });
+    };
 
     // Pass the image's dimensions to the `NOISE` node
     graph.edges.push({
@@ -327,7 +277,10 @@ export const buildCanvasImageToImageGraph = (
     });
   }
 
-  // add controlnet
+  // add dynamic prompts, mutating `graph`
+  addDynamicPromptsToGraph(graph, state);
+
+  // add controlnet, mutating `graph`
   addControlNetToLinearGraph(graph, LATENTS_TO_LATENTS, state);
 
   return graph;

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts

@@ -9,7 +9,7 @@ import { NonNullableGraph } from 'features/nodes/types/types';
 import { log } from 'app/logging/useLogger';
 import {
   ITERATE,
-  MODEL_LOADER,
+  PIPELINE_MODEL_LOADER,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
   RANDOM_INT,
@@ -101,9 +101,9 @@ export const buildCanvasInpaintGraph = (
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
-      [MODEL_LOADER]: {
+      [PIPELINE_MODEL_LOADER]: {
         type: 'pipeline_model_loader',
-        id: MODEL_LOADER,
+        id: PIPELINE_MODEL_LOADER,
         model,
       },
       [RANGE_OF_SIZE]: {
@@ -142,7 +142,7 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -152,7 +152,7 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -162,7 +162,7 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -172,7 +172,7 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'vae',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts

@@ -4,7 +4,7 @@ import { RandomIntInvocation, RangeOfSizeInvocation } from 'services/api/types';
 import {
   ITERATE,
   LATENTS_TO_IMAGE,
-  MODEL_LOADER,
+  PIPELINE_MODEL_LOADER,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -15,6 +15,7 @@ import {
 } from './constants';
 import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
 import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
+import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 
 /**
  * Builds the Canvas tab's Text to Image graph.
@@ -62,13 +63,6 @@ export const buildCanvasTextToImageGraph = (
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
-      [RANGE_OF_SIZE]: {
-        type: 'range_of_size',
-        id: RANGE_OF_SIZE,
-        // start: 0, // seed - must be connected manually
-        size: iterations,
-        step: 1,
-      },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
@@ -82,19 +76,15 @@ export const buildCanvasTextToImageGraph = (
         scheduler,
         steps,
       },
-      [MODEL_LOADER]: {
+      [PIPELINE_MODEL_LOADER]: {
         type: 'pipeline_model_loader',
-        id: MODEL_LOADER,
+        id: PIPELINE_MODEL_LOADER,
         model,
       },
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
       },
-      [ITERATE]: {
-        type: 'iterate',
-        id: ITERATE,
-      },
     },
     edges: [
       {
@@ -119,7 +109,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -129,7 +119,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -139,7 +129,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -159,7 +149,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'vae',
         },
         destination: {
@@ -167,26 +157,6 @@ export const buildCanvasTextToImageGraph = (
           field: 'vae',
         },
       },
-      {
-        source: {
-          node_id: RANGE_OF_SIZE,
-          field: 'collection',
-        },
-        destination: {
-          node_id: ITERATE,
-          field: 'collection',
-        },
-      },
-      {
-        source: {
-          node_id: ITERATE,
-          field: 'item',
-        },
-        destination: {
-          node_id: NOISE,
-          field: 'seed',
-        },
-      },
       {
         source: {
           node_id: NOISE,
@@ -200,27 +170,10 @@ export const buildCanvasTextToImageGraph = (
     ],
   };
 
-  // handle seed
-  if (shouldRandomizeSeed) {
-    // Random int node to generate the starting seed
-    const randomIntNode: RandomIntInvocation = {
-      id: RANDOM_INT,
-      type: 'rand_int',
-    };
+  // add dynamic prompts, mutating `graph`
+  addDynamicPromptsToGraph(graph, state);
 
-    graph.nodes[RANDOM_INT] = randomIntNode;
-
-    // Connect random int to the start of the range of size so the range starts on the random first seed
-    graph.edges.push({
-      source: { node_id: RANDOM_INT, field: 'a' },
-      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
-    });
-  } else {
-    // User specified seed, so set the start of the range of size to the seed
-    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
-  }
-
-  // add controlnet
+  // add controlnet, mutating `graph`
   addControlNetToLinearGraph(graph, TEXT_TO_LATENTS, state);
 
   return graph;

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts

@@ -1,28 +1,24 @@
 import { RootState } from 'app/store/store';
 import {
   ImageResizeInvocation,
-  RandomIntInvocation,
-  RangeOfSizeInvocation,
+  ImageToLatentsInvocation,
 } from 'services/api/types';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { log } from 'app/logging/useLogger';
 import {
-  ITERATE,
   LATENTS_TO_IMAGE,
-  MODEL_LOADER,
+  PIPELINE_MODEL_LOADER,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
-  RANDOM_INT,
-  RANGE_OF_SIZE,
   IMAGE_TO_IMAGE_GRAPH,
   IMAGE_TO_LATENTS,
   LATENTS_TO_LATENTS,
   RESIZE,
 } from './constants';
-import { set } from 'lodash-es';
 import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
 import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
+import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 
 const moduleLog = log.child({ namespace: 'nodes' });
 
@@ -44,9 +40,6 @@ export const buildLinearImageToImageGraph = (
     shouldFitToWidthHeight,
     width,
     height,
-    iterations,
-    seed,
-    shouldRandomizeSeed,
   } = state.generation;
 
   /**
@@ -79,31 +72,19 @@ export const buildLinearImageToImageGraph = (
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
-      [RANGE_OF_SIZE]: {
-        type: 'range_of_size',
-        id: RANGE_OF_SIZE,
-        // seed - must be connected manually
-        // start: 0,
-        size: iterations,
-        step: 1,
-      },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
       },
-      [MODEL_LOADER]: {
+      [PIPELINE_MODEL_LOADER]: {
         type: 'pipeline_model_loader',
-        id: MODEL_LOADER,
+        id: PIPELINE_MODEL_LOADER,
         model,
       },
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
       },
-      [ITERATE]: {
-        type: 'iterate',
-        id: ITERATE,
-      },
       [LATENTS_TO_LATENTS]: {
         type: 'l2l',
         id: LATENTS_TO_LATENTS,
@@ -124,7 +105,7 @@ export const buildLinearImageToImageGraph = (
     edges: [
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -134,7 +115,7 @@ export const buildLinearImageToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -144,7 +125,7 @@ export const buildLinearImageToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'vae',
         },
         destination: {
@@ -152,26 +133,6 @@ export const buildLinearImageToImageGraph = (
           field: 'vae',
         },
       },
-      {
-        source: {
-          node_id: RANGE_OF_SIZE,
-          field: 'collection',
-        },
-        destination: {
-          node_id: ITERATE,
-          field: 'collection',
-        },
-      },
-      {
-        source: {
-          node_id: ITERATE,
-          field: 'item',
-        },
-        destination: {
-          node_id: NOISE,
-          field: 'seed',
-        },
-      },
       {
         source: {
           node_id: LATENTS_TO_LATENTS,
@@ -204,7 +165,7 @@ export const buildLinearImageToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'vae',
         },
         destination: {
@@ -214,7 +175,7 @@ export const buildLinearImageToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -245,26 +206,6 @@ export const buildLinearImageToImageGraph = (
     ],
   };
 
-  // handle seed
-  if (shouldRandomizeSeed) {
-    // Random int node to generate the starting seed
-    const randomIntNode: RandomIntInvocation = {
-      id: RANDOM_INT,
-      type: 'rand_int',
-    };
-
-    graph.nodes[RANDOM_INT] = randomIntNode;
-
-    // Connect random int to the start of the range of size so the range starts on the random first seed
-    graph.edges.push({
-      source: { node_id: RANDOM_INT, field: 'a' },
-      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
-    });
-  } else {
-    // User specified seed, so set the start of the range of size to the seed
-    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
-  }
-
   // handle `fit`
   if (
     shouldFitToWidthHeight &&
@@ -313,9 +254,9 @@ export const buildLinearImageToImageGraph = (
     });
   } else {
     // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
-    set(graph.nodes[IMAGE_TO_LATENTS], 'image', {
+    (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image = {
       image_name: initialImage.imageName,
-    });
+    };
 
     // Pass the image's dimensions to the `NOISE` node
     graph.edges.push({
@@ -334,7 +275,10 @@ export const buildLinearImageToImageGraph = (
     });
   }
 
-  // add controlnet
+  // add dynamic prompts, mutating `graph`
+  addDynamicPromptsToGraph(graph, state);
+
+  // add controlnet, mutating `graph`
   addControlNetToLinearGraph(graph, LATENTS_TO_LATENTS, state);
 
   return graph;

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts

@@ -1,33 +1,20 @@
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import {
-  BaseModelType,
-  RandomIntInvocation,
-  RangeOfSizeInvocation,
-} from 'services/api/types';
-import {
-  ITERATE,
   LATENTS_TO_IMAGE,
-  MODEL_LOADER,
+  PIPELINE_MODEL_LOADER,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
-  RANDOM_INT,
-  RANGE_OF_SIZE,
   TEXT_TO_IMAGE_GRAPH,
   TEXT_TO_LATENTS,
 } from './constants';
 import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
 import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
-
-type TextToImageGraphOverrides = {
-  width: number;
-  height: number;
-};
+import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 
 export const buildLinearTextToImageGraph = (
-  state: RootState,
-  overrides?: TextToImageGraphOverrides
+  state: RootState
 ): NonNullableGraph => {
   const {
     positivePrompt,
@@ -38,9 +25,6 @@ export const buildLinearTextToImageGraph = (
     steps,
     width,
     height,
-    iterations,
-    seed,
-    shouldRandomizeSeed,
   } = state.generation;
 
   const model = modelIdToPipelineModelField(modelId);
@@ -68,18 +52,11 @@ export const buildLinearTextToImageGraph = (
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
-      [RANGE_OF_SIZE]: {
-        type: 'range_of_size',
-        id: RANGE_OF_SIZE,
-        // start: 0, // seed - must be connected manually
-        size: iterations,
-        step: 1,
-      },
       [NOISE]: {
         type: 'noise',
         id: NOISE,
-        width: overrides?.width || width,
-        height: overrides?.height || height,
+        width,
+        height,
       },
       [TEXT_TO_LATENTS]: {
         type: 't2l',
@@ -88,19 +65,15 @@ export const buildLinearTextToImageGraph = (
         scheduler,
         steps,
       },
-      [MODEL_LOADER]: {
+      [PIPELINE_MODEL_LOADER]: {
         type: 'pipeline_model_loader',
-        id: MODEL_LOADER,
+        id: PIPELINE_MODEL_LOADER,
         model,
       },
       [LATENTS_TO_IMAGE]: {
         type: 'l2i',
         id: LATENTS_TO_IMAGE,
       },
-      [ITERATE]: {
-        type: 'iterate',
-        id: ITERATE,
-      },
     },
     edges: [
       {
@@ -125,7 +98,7 @@ export const buildLinearTextToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -135,7 +108,7 @@ export const buildLinearTextToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'clip',
         },
         destination: {
@@ -145,7 +118,7 @@ export const buildLinearTextToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'unet',
         },
         destination: {
@@ -165,7 +138,7 @@ export const buildLinearTextToImageGraph = (
       },
       {
         source: {
-          node_id: MODEL_LOADER,
+          node_id: PIPELINE_MODEL_LOADER,
           field: 'vae',
         },
         destination: {
@@ -173,26 +146,6 @@ export const buildLinearTextToImageGraph = (
           field: 'vae',
         },
       },
-      {
-        source: {
-          node_id: RANGE_OF_SIZE,
-          field: 'collection',
-        },
-        destination: {
-          node_id: ITERATE,
-          field: 'collection',
-        },
-      },
-      {
-        source: {
-          node_id: ITERATE,
-          field: 'item',
-        },
-        destination: {
-          node_id: NOISE,
-          field: 'seed',
-        },
-      },
       {
         source: {
           node_id: NOISE,
@@ -206,27 +159,10 @@ export const buildLinearTextToImageGraph = (
     ],
   };
 
-  // handle seed
-  if (shouldRandomizeSeed) {
-    // Random int node to generate the starting seed
-    const randomIntNode: RandomIntInvocation = {
-      id: RANDOM_INT,
-      type: 'rand_int',
-    };
+  // add dynamic prompts, mutating `graph`
+  addDynamicPromptsToGraph(graph, state);
 
-    graph.nodes[RANDOM_INT] = randomIntNode;
-
-    // Connect random int to the start of the range of size so the range starts on the random first seed
-    graph.edges.push({
-      source: { node_id: RANDOM_INT, field: 'a' },
-      destination: { node_id: RANGE_OF_SIZE, field: 'start' },
-    });
-  } else {
-    // User specified seed, so set the start of the range of size to the seed
-    (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
-  }
-
-  // add controlnet
+  // add controlnet, mutating `graph`
   addControlNetToLinearGraph(graph, TEXT_TO_LATENTS, state);
 
   return graph;

invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts

@@ -7,12 +7,13 @@ export const NOISE = 'noise';
 export const RANDOM_INT = 'rand_int';
 export const RANGE_OF_SIZE = 'range_of_size';
 export const ITERATE = 'iterate';
-export const MODEL_LOADER = 'pipeline_model_loader';
+export const PIPELINE_MODEL_LOADER = 'pipeline_model_loader';
 export const IMAGE_TO_LATENTS = 'image_to_latents';
 export const LATENTS_TO_LATENTS = 'latents_to_latents';
 export const RESIZE = 'resize_image';
 export const INPAINT = 'inpaint';
 export const CONTROL_NET_COLLECT = 'control_net_collect';
+export const DYNAMIC_PROMPT = 'dynamic_prompt';
 
 // friendly graph ids
 export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph';

invokeai/frontend/web/src/features/nodes/util/nodeBuilders/buildCompelNode.ts

@@ -1,26 +0,0 @@
-import { v4 as uuidv4 } from 'uuid';
-import { RootState } from 'app/store/store';
-import { CompelInvocation } from 'services/api/types';
-import { O } from 'ts-toolbelt';
-
-export const buildCompelNode = (
-  prompt: string,
-  state: RootState,
-  overrides: O.Partial<CompelInvocation, 'deep'> = {}
-): CompelInvocation => {
-  const nodeId = uuidv4();
-  const { generation } = state;
-
-  const { model } = generation;
-
-  const compelNode: CompelInvocation = {
-    id: nodeId,
-    type: 'compel',
-    prompt,
-    model,
-  };
-
-  Object.assign(compelNode, overrides);
-
-  return compelNode;
-};

invokeai/frontend/web/src/features/nodes/util/nodeBuilders/buildImageToImageNode.ts

@@ -1,107 +0,0 @@
-import { v4 as uuidv4 } from 'uuid';
-import { RootState } from 'app/store/store';
-import {
-  Edge,
-  ImageToImageInvocation,
-  TextToImageInvocation,
-} from 'services/api/types';
-import { O } from 'ts-toolbelt';
-import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-
-export const buildImg2ImgNode = (
-  state: RootState,
-  overrides: O.Partial<ImageToImageInvocation, 'deep'> = {}
-): ImageToImageInvocation => {
-  const nodeId = uuidv4();
-  const { generation } = state;
-
-  const activeTabName = activeTabNameSelector(state);
-
-  const {
-    positivePrompt: prompt,
-    negativePrompt: negativePrompt,
-    seed,
-    steps,
-    width,
-    height,
-    cfgScale,
-    scheduler,
-    model,
-    img2imgStrength: strength,
-    shouldFitToWidthHeight: fit,
-    shouldRandomizeSeed,
-    initialImage,
-  } = generation;
-
-  // const initialImage = initialImageSelector(state);
-
-  const imageToImageNode: ImageToImageInvocation = {
-    id: nodeId,
-    type: 'img2img',
-    prompt: `${prompt} [${negativePrompt}]`,
-    steps,
-    width,
-    height,
-    cfg_scale: cfgScale,
-    scheduler,
-    model,
-    strength,
-    fit,
-  };
-
-  // on Canvas tab, we do not manually specific init image
-  if (activeTabName !== 'unifiedCanvas') {
-    if (!initialImage) {
-      // TODO: handle this more better
-      throw 'no initial image';
-    }
-
-    imageToImageNode.image = {
-      image_name: initialImage.imageName,
-    };
-  }
-
-  if (!shouldRandomizeSeed) {
-    imageToImageNode.seed = seed;
-  }
-
-  Object.assign(imageToImageNode, overrides);
-
-  return imageToImageNode;
-};
-
-type hiresReturnType = {
-  node: Record<string, ImageToImageInvocation>;
-  edge: Edge;
-};
-
-export const buildHiResNode = (
-  baseNode: Record<string, TextToImageInvocation>,
-  strength?: number
-): hiresReturnType => {
-  const nodeId = uuidv4();
-  const baseNodeId = Object.keys(baseNode)[0];
-  const baseNodeValues = Object.values(baseNode)[0];
-
-  return {
-    node: {
-      [nodeId]: {
-        ...baseNodeValues,
-        id: nodeId,
-        type: 'img2img',
-        strength,
-        fit: true,
-      },
-    },
-    edge: {
-      source: {
-        field: 'image',
-        node_id: baseNodeId,
-      },
-      destination: {
-        field: 'image',
-        node_id: nodeId,
-      },
-    },
-  };
-};

invokeai/frontend/web/src/features/nodes/util/nodeBuilders/buildInpaintNode.ts

@@ -1,48 +0,0 @@
-import { v4 as uuidv4 } from 'uuid';
-import { RootState } from 'app/store/store';
-import { InpaintInvocation } from 'services/api/types';
-import { O } from 'ts-toolbelt';
-
-export const buildInpaintNode = (
-  state: RootState,
-  overrides: O.Partial<InpaintInvocation, 'deep'> = {}
-): InpaintInvocation => {
-  const nodeId = uuidv4();
-
-  const {
-    positivePrompt: prompt,
-    negativePrompt: negativePrompt,
-    seed,
-    steps,
-    width,
-    height,
-    cfgScale,
-    scheduler,
-    model,
-    img2imgStrength: strength,
-    shouldFitToWidthHeight: fit,
-    shouldRandomizeSeed,
-  } = state.generation;
-
-  const inpaintNode: InpaintInvocation = {
-    id: nodeId,
-    type: 'inpaint',
-    prompt: `${prompt} [${negativePrompt}]`,
-    steps,
-    width,
-    height,
-    cfg_scale: cfgScale,
-    scheduler,
-    model,
-    strength,
-    fit,
-  };
-
-  if (!shouldRandomizeSeed) {
-    inpaintNode.seed = seed;
-  }
-
-  Object.assign(inpaintNode, overrides);
-
-  return inpaintNode;
-};

invokeai/frontend/web/src/features/nodes/util/nodeBuilders/buildIterateNode.ts

@@ -1,13 +0,0 @@
-import { v4 as uuidv4 } from 'uuid';
-
-import { IterateInvocation } from 'services/api/types';
-
-export const buildIterateNode = (): IterateInvocation => {
-  const nodeId = uuidv4();
-  return {
-    id: nodeId,
-    type: 'iterate',
-    // collection: [],
-    // index: 0,
-  };
-};

invokeai/frontend/web/src/features/nodes/util/nodeBuilders/buildRangeNode.ts

@@ -1,26 +0,0 @@
-import { v4 as uuidv4 } from 'uuid';
-
-import { RootState } from 'app/store/store';
-import { RandomRangeInvocation, RangeInvocation } from 'services/api/types';
-
-export const buildRangeNode = (
-  state: RootState
-): RangeInvocation | RandomRangeInvocation => {
-  const nodeId = uuidv4();
-  const { shouldRandomizeSeed, iterations, seed } = state.generation;
-
-  if (shouldRandomizeSeed) {
-    return {
-      id: nodeId,
-      type: 'random_range',
-      size: iterations,
-    };
-  }
-
-  return {
-    id: nodeId,
-    type: 'range',
-    start: seed,
-    stop: seed + iterations,
-  };
-};

invokeai/frontend/web/src/features/nodes/util/nodeBuilders/buildTextToImageNode.ts

@@ -1,45 +0,0 @@
-import { v4 as uuidv4 } from 'uuid';
-import { RootState } from 'app/store/store';
-import { TextToImageInvocation } from 'services/api/types';
-import { O } from 'ts-toolbelt';
-
-export const buildTxt2ImgNode = (
-  state: RootState,
-  overrides: O.Partial<TextToImageInvocation, 'deep'> = {}
-): TextToImageInvocation => {
-  const nodeId = uuidv4();
-  const { generation } = state;
-
-  const {
-    positivePrompt: prompt,
-    negativePrompt: negativePrompt,
-    seed,
-    steps,
-    width,
-    height,
-    cfgScale: cfg_scale,
-    scheduler,
-    shouldRandomizeSeed,
-    model,
-  } = generation;
-
-  const textToImageNode: NonNullable<TextToImageInvocation> = {
-    id: nodeId,
-    type: 'txt2img',
-    prompt: `${prompt} [${negativePrompt}]`,
-    steps,
-    width,
-    height,
-    cfg_scale,
-    scheduler,
-    model,
-  };
-
-  if (!shouldRandomizeSeed) {
-    textToImageNode.seed = seed;
-  }
-
-  Object.assign(textToImageNode, overrides);
-
-  return textToImageNode;
-};

invokeai/frontend/web/src/features/nodes/util/parseSchema.ts

@@ -5,33 +5,48 @@ import {
   InputFieldTemplate,
   InvocationSchemaObject,
   InvocationTemplate,
-  isInvocationSchemaObject,
   OutputFieldTemplate,
 } from '../types/types';
-import {
-  buildInputFieldTemplate,
-  buildOutputFieldTemplates,
-} from './fieldTemplateBuilders';
+import { buildInputFieldTemplate, getFieldType } from './fieldTemplateBuilders';
+import { O } from 'ts-toolbelt';
+
+// recursively exclude all properties of type U from T
+type DeepExclude<T, U> = T extends U
+  ? never
+  : T extends object
+  ? {
+      [K in keyof T]: DeepExclude<T[K], U>;
+    }
+  : T;
+
+// The schema from swagger-parser is dereferenced, and we know `components` and `components.schemas` exist
+type DereferencedOpenAPIDocument = DeepExclude<
+  O.Required<OpenAPIV3.Document, 'schemas' | 'components', 'deep'>,
+  OpenAPIV3.ReferenceObject
+>;
 
 const RESERVED_FIELD_NAMES = ['id', 'type', 'is_intermediate'];
 
 const invocationDenylist = ['Graph', 'InvocationMeta'];
 
-export const parseSchema = (openAPI: OpenAPIV3.Document) => {
-  // filter out non-invocation schemas, plus some tricky invocations for now
-  const filteredSchemas = filter(
-    openAPI.components!.schemas,
-    (schema, key) =>
+const nodeFilter = (
+  schema: DereferencedOpenAPIDocument['components']['schemas'][string],
+  key: string
+) =>
   key.includes('Invocation') &&
   !key.includes('InvocationOutput') &&
-      !invocationDenylist.some((denylistItem) => key.includes(denylistItem))
-  ) as (OpenAPIV3.ReferenceObject | InvocationSchemaObject)[];
+  !invocationDenylist.some((denylistItem) => key.includes(denylistItem));
+
+export const parseSchema = (openAPI: DereferencedOpenAPIDocument) => {
+  // filter out non-invocation schemas, plus some tricky invocations for now
+  const filteredSchemas = filter(openAPI.components.schemas, nodeFilter);
 
   const invocations = filteredSchemas.reduce<
     Record<string, InvocationTemplate>
-  >((acc, schema) => {
-    // only want SchemaObjects
-    if (isInvocationSchemaObject(schema)) {
+  >((acc, s) => {
+    // cast to InvocationSchemaObject, we know the shape
+    const schema = s as InvocationSchemaObject;
+
     const type = schema.properties.type.default;
 
     const title = schema.ui?.title ?? schema.title.replace('Invocation', '');
@@ -41,10 +56,8 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => {
     const inputs: Record<string, InputFieldTemplate> = {};
 
     if (type === 'collect') {
-        const itemProperty = schema.properties[
-          'item'
-        ] as InvocationSchemaObject;
-        // Handle the special Collect node
+      // Special handling for the Collect node
+      const itemProperty = schema.properties['item'] as InvocationSchemaObject;
       inputs.item = {
         type: 'item',
         name: 'item',
@@ -55,6 +68,7 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => {
         default: undefined,
       };
     } else if (type === 'iterate') {
+      // Special handling for the Iterate node
       const itemProperty = schema.properties[
         'collection'
       ] as InvocationSchemaObject;
@@ -91,16 +105,12 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => {
       );
     }
 
-      const rawOutput = (schema as InvocationSchemaObject).output;
-
     let outputs: Record<string, OutputFieldTemplate>;
 
-      // some special handling is needed for collect, iterate and range nodes
     if (type === 'iterate') {
-        // this is guaranteed to be a SchemaObject
-        const iterationOutput = openAPI.components!.schemas![
-          'IterateInvocationOutput'
-        ] as OpenAPIV3.SchemaObject;
+      // Special handling for the Iterate node output
+      const iterationOutput =
+        openAPI.components.schemas['IterateInvocationOutput'];
 
       outputs = {
         item: {
@@ -111,7 +121,25 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => {
         },
       };
     } else {
-        outputs = buildOutputFieldTemplates(rawOutput, openAPI, typeHints);
+      // All other node outputs
+      outputs = reduce(
+        schema.output.properties as OpenAPIV3.SchemaObject,
+        (outputsAccumulator, property, propertyName) => {
+          if (!['type', 'id'].includes(propertyName)) {
+            const fieldType = getFieldType(property, propertyName, typeHints);
+
+            outputsAccumulator[propertyName] = {
+              name: propertyName,
+              title: property.title ?? '',
+              description: property.description ?? '',
+              type: fieldType,
+            };
+          }
+
+          return outputsAccumulator;
+        },
+        {} as Record<string, OutputFieldTemplate>
+      );
     }
 
     const invocation: InvocationTemplate = {
@@ -124,7 +152,6 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => {
     };
 
     Object.assign(acc, { [type]: invocation });
-    }
 
     return acc;
   }, {});

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamIterations.tsx

@@ -1,4 +1,5 @@
 import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAINumberInput from 'common/components/IAINumberInput';
 import IAISlider from 'common/components/IAISlider';
@@ -10,15 +11,15 @@ import { uiSelector } from 'features/ui/store/uiSelectors';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-const selector = createSelector(
-  [generationSelector, configSelector, uiSelector, hotkeysSelector],
-  (generation, config, ui, hotkeys) => {
+const selector = createSelector([stateSelector], (state) => {
   const { initial, min, sliderMax, inputMax, fineStep, coarseStep } =
-      config.sd.iterations;
-    const { iterations } = generation;
-    const { shouldUseSliders } = ui;
+    state.config.sd.iterations;
+  const { iterations } = state.generation;
+  const { shouldUseSliders } = state.ui;
+  const isDisabled =
+    state.dynamicPrompts.isEnabled && state.dynamicPrompts.combinatorial;
 
-    const step = hotkeys.shift ? fineStep : coarseStep;
+  const step = state.hotkeys.shift ? fineStep : coarseStep;
 
   return {
     iterations,
@@ -28,9 +29,9 @@ const selector = createSelector(
     inputMax,
     step,
     shouldUseSliders,
+    isDisabled,
   };
-  }
-);
+});
 
 const ParamIterations = () => {
   const {
@@ -41,6 +42,7 @@ const ParamIterations = () => {
     inputMax,
     step,
     shouldUseSliders,
+    isDisabled,
   } = useAppSelector(selector);
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
@@ -58,6 +60,7 @@ const ParamIterations = () => {
 
   return shouldUseSliders ? (
     <IAISlider
+      isDisabled={isDisabled}
       label={t('parameters.images')}
       step={step}
       min={min}
@@ -72,6 +75,7 @@ const ParamIterations = () => {
     />
   ) : (
     <IAINumberInput
+      isDisabled={isDisabled}
       label={t('parameters.images')}
       step={step}
       min={min}

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

@@ -24,7 +24,7 @@ const ModelSelect = () => {
   );
 
   const { data: pipelineModels } = useListModelsQuery({
-    model_type: 'pipeline',
+    model_type: 'main',
   });
 
   const data = useMemo(() => {

invokeai/frontend/web/src/features/system/store/configSlice.ts

@@ -60,6 +60,14 @@ export const initialConfigState: AppConfig = {
       fineStep: 0.01,
       coarseStep: 0.05,
     },
+    dynamicPrompts: {
+      maxPrompts: {
+        initial: 100,
+        min: 1,
+        sliderMax: 1000,
+        inputMax: 10000,
+      },
+    },
   },
 };
 

invokeai/frontend/web/src/features/system/store/systemSlice.ts

@@ -4,7 +4,6 @@ import * as InvokeAI from 'app/types/invokeai';
 
 import { InvokeLogLevel } from 'app/logging/useLogger';
 import { userInvoked } from 'app/store/actions';
-import { parsedOpenAPISchema } from 'features/nodes/store/nodesSlice';
 import { TFuncKey, t } from 'i18next';
 import { LogLevelName } from 'roarr';
 import {
@@ -26,6 +25,7 @@ import {
 } from 'services/api/thunks/session';
 import { makeToast } from '../../../app/components/Toaster';
 import { LANGUAGES } from '../components/LanguagePicker';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 
 export type CancelStrategy = 'immediate' | 'scheduled';
 
@@ -382,7 +382,7 @@ export const systemSlice = createSlice({
     /**
      * OpenAPI schema was parsed
      */
-    builder.addCase(parsedOpenAPISchema, (state) => {
+    builder.addCase(nodeTemplatesBuilt, (state) => {
       state.wasSchemaParsed = true;
     });
 

invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx

@@ -17,7 +17,6 @@ import { setActiveTab, togglePanels } from 'features/ui/store/uiSlice';
 import { memo, MouseEvent, ReactNode, useCallback, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { MdDeviceHub, MdGridOn } from 'react-icons/md';
-import { GoTextSize } from 'react-icons/go';
 import {
   activeTabIndexSelector,
   activeTabNameSelector,
@@ -33,7 +32,7 @@ import ImageGalleryContent from 'features/gallery/components/ImageGalleryContent
 import TextToImageTab from './tabs/TextToImage/TextToImageTab';
 import UnifiedCanvasTab from './tabs/UnifiedCanvas/UnifiedCanvasTab';
 import NodesTab from './tabs/Nodes/NodesTab';
-import { FaImage } from 'react-icons/fa';
+import { FaFont, FaImage } from 'react-icons/fa';
 import ResizeHandle from './tabs/ResizeHandle';
 import ImageTab from './tabs/ImageToImage/ImageToImageTab';
 import AuxiliaryProgressIndicator from 'app/components/AuxiliaryProgressIndicator';
@@ -47,7 +46,7 @@ export interface InvokeTabInfo {
 const tabs: InvokeTabInfo[] = [
   {
     id: 'txt2img',
-    icon: <Icon as={GoTextSize} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
+    icon: <Icon as={FaFont} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
     content: <TextToImageTab />,
   },
   {

invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTabParameters.tsx

@@ -8,6 +8,7 @@ import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Sym
 import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import ImageToImageTabCoreParameters from './ImageToImageTabCoreParameters';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
+import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
 
 const ImageToImageTabParameters = () => {
   return (
@@ -16,6 +17,7 @@ const ImageToImageTabParameters = () => {
       <ParamNegativeConditioning />
       <ProcessButtons />
       <ImageToImageTabCoreParameters />
+      <ParamDynamicPromptsCollapse />
       <ParamControlNetCollapse />
       <ParamVariationCollapse />
       <ParamNoiseCollapse />

invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTabParameters.tsx

@@ -9,6 +9,7 @@ import ParamHiresCollapse from 'features/parameters/components/Parameters/Hires/
 import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import TextToImageTabCoreParameters from './TextToImageTabCoreParameters';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
+import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
 
 const TextToImageTabParameters = () => {
   return (
@@ -17,6 +18,7 @@ const TextToImageTabParameters = () => {
       <ParamNegativeConditioning />
       <ProcessButtons />
       <TextToImageTabCoreParameters />
+      <ParamDynamicPromptsCollapse />
       <ParamControlNetCollapse />
       <ParamVariationCollapse />
       <ParamNoiseCollapse />

invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx

@@ -8,6 +8,7 @@ import { memo } from 'react';
 import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
+import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
 
 const UnifiedCanvasParameters = () => {
   return (
@@ -16,6 +17,7 @@ const UnifiedCanvasParameters = () => {
       <ParamNegativeConditioning />
       <ProcessButtons />
       <UnifiedCanvasCoreParameters />
+      <ParamDynamicPromptsCollapse />
       <ParamControlNetCollapse />
       <ParamVariationCollapse />
       <ParamSymmetryCollapse />

invokeai/frontend/web/src/services/api/endpoints/images.ts

@@ -15,6 +15,7 @@ export const imagesApi = api.injectEndpoints({
         }
         return tags;
       },
+      keepUnusedDataFor: 86400, // 24 hours
     }),
   }),
 });

invokeai/frontend/web/src/services/api/models/HedImageprocessorInvocation.ts

@@ -0,0 +1,36 @@
+/* istanbul ignore file */
+/* tslint:disable */
+/* eslint-disable */
+
+import type { ImageField } from './ImageField';
+
+/**
+ * Applies HED edge detection to image
+ */
+export type HedImageProcessorInvocation = {
+  /**
+   * The id of this node. Must be unique among all nodes.
+   */
+  id: string;
+  /**
+   * Whether or not this node is an intermediate node.
+   */
+  is_intermediate?: boolean;
+  type?: 'hed_image_processor';
+  /**
+   * The image to process
+   */
+  image?: ImageField;
+  /**
+   * The pixel resolution for detection
+   */
+  detect_resolution?: number;
+  /**
+   * The pixel resolution for the output image
+   */
+  image_resolution?: number;
+  /**
+   * Whether to use scribble mode
+   */
+  scribble?: boolean;
+};

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -648,6 +648,13 @@ export type components = {
        * @default 1
        */
       end_step_percent: number;
+      /**
+       * Control Mode 
+       * @description The contorl mode to use 
+       * @default balanced 
+       * @enum {string}
+       */
+      control_mode?: "balanced" | "more_prompt" | "more_control" | "unbalanced";
     };
     /**
      * ControlNetInvocation 
@@ -701,6 +708,13 @@ export type components = {
        * @default 1
        */
       end_step_percent?: number;
+      /**
+       * Control Mode 
+       * @description The control mode used 
+       * @default balanced 
+       * @enum {string}
+       */
+      control_mode?: "balanced" | "more_prompt" | "more_control" | "unbalanced";
     };
     /** ControlNetModelConfig */
     ControlNetModelConfig: {
@@ -1016,7 +1030,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1059,7 +1073,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -2903,7 +2917,7 @@ export type components = {
     /** ModelsList */
     ModelsList: {
       /** Models */
-      models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"])[];
+      models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"])[];
     };
     /**
      * MultiplyInvocation 
@@ -2979,6 +2993,18 @@ export type components = {
        * @default 512
        */
       height?: number;
+      /**
+       * Perlin 
+       * @description The amount of perlin noise to add to the noise 
+       * @default 0
+       */
+      perlin?: number;
+      /**
+       * Use Cpu 
+       * @description Use CPU for noise generation (for reproducible results across platforms) 
+       * @default true
+       */
+      use_cpu?: boolean;
     };
     /**
      * NoiseOutput 
@@ -4163,18 +4189,18 @@ export type components = {
        */
       image?: components["schemas"]["ImageField"];
     };
-    /**
-     * StableDiffusion1ModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusion2ModelFormat 
      * @description An enumeration. 
      * @enum {string}
      */
     StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion1ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;
@@ -4285,7 +4311,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -4322,7 +4348,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {

invokeai/frontend/web/src/services/api/thunks/schema.ts

@@ -1,20 +1,45 @@
+import SwaggerParser from '@apidevtools/swagger-parser';
 import { createAsyncThunk } from '@reduxjs/toolkit';
 import { log } from 'app/logging/useLogger';
-import { parsedOpenAPISchema } from 'features/nodes/store/nodesSlice';
 import { OpenAPIV3 } from 'openapi-types';
 
 const schemaLog = log.child({ namespace: 'schema' });
 
+function getCircularReplacer() {
+  const ancestors: Record<string, any>[] = [];
+  return function (key: string, value: any) {
+    if (typeof value !== 'object' || value === null) {
+      return value;
+    }
+    // `this` is the object that value is contained in,
+    // i.e., its direct parent.
+    // @ts-ignore
+    while (ancestors.length > 0 && ancestors.at(-1) !== this) {
+      ancestors.pop();
+    }
+    if (ancestors.includes(value)) {
+      return '[Circular]';
+    }
+    ancestors.push(value);
+    return value;
+  };
+}
+
 export const receivedOpenAPISchema = createAsyncThunk(
   'nodes/receivedOpenAPISchema',
-  async (_, { dispatch }): Promise<OpenAPIV3.Document> => {
-    const response = await fetch(`openapi.json`);
-    const openAPISchema = await response.json();
+  async (_, { dispatch, rejectWithValue }) => {
+    try {
+      const dereferencedSchema = (await SwaggerParser.dereference(
+        'openapi.json'
+      )) as OpenAPIV3.Document;
 
-    schemaLog.info({ openAPISchema }, 'Received OpenAPI schema');
+      const schemaJSON = JSON.parse(
+        JSON.stringify(dereferencedSchema, getCircularReplacer())
+      );
 
-    dispatch(parsedOpenAPISchema(openAPISchema as OpenAPIV3.Document));
-
-    return openAPISchema;
+      return schemaJSON;
+    } catch (error) {
+      return rejectWithValue({ error });
+    }
   }
 );

invokeai/frontend/web/src/services/api/types.d.ts

@@ -1,81 +1,92 @@
+import { O } from 'ts-toolbelt';
 import { components } from './schema';
 
+type schemas = components['schemas'];
+
 /**
- * Types from the API, re-exported from the types generated by `openapi-typescript`.
+ * Extracts the schema type from the schema.
  */
+type S<T extends keyof components['schemas']> = components['schemas'][T];
+
+/**
+ * Extracts the node type from the schema.
+ * Also flags the `type` property as required.
+ */
+type N<T extends keyof components['schemas']> = O.Required<
+  components['schemas'][T],
+  'type'
+>;
 
 // Images
-export type ImageDTO = components['schemas']['ImageDTO'];
-export type BoardDTO = components['schemas']['BoardDTO'];
-export type BoardChanges = components['schemas']['BoardChanges'];
-export type ImageChanges = components['schemas']['ImageRecordChanges'];
-export type ImageCategory = components['schemas']['ImageCategory'];
-export type ResourceOrigin = components['schemas']['ResourceOrigin'];
-export type ImageField = components['schemas']['ImageField'];
+export type ImageDTO = S<'ImageDTO'>;
+export type BoardDTO = S<'BoardDTO'>;
+export type BoardChanges = S<'BoardChanges'>;
+export type ImageChanges = S<'ImageRecordChanges'>;
+export type ImageCategory = S<'ImageCategory'>;
+export type ResourceOrigin = S<'ResourceOrigin'>;
+export type ImageField = S<'ImageField'>;
 export type OffsetPaginatedResults_BoardDTO_ =
-  components['schemas']['OffsetPaginatedResults_BoardDTO_'];
+  S<'OffsetPaginatedResults_BoardDTO_'>;
 export type OffsetPaginatedResults_ImageDTO_ =
-  components['schemas']['OffsetPaginatedResults_ImageDTO_'];
+  S<'OffsetPaginatedResults_ImageDTO_'>;
 
 // Models
-export type ModelType = components['schemas']['ModelType'];
-export type BaseModelType = components['schemas']['BaseModelType'];
-export type PipelineModelField = components['schemas']['PipelineModelField'];
-export type ModelsList = components['schemas']['ModelsList'];
+export type ModelType = S<'ModelType'>;
+export type BaseModelType = S<'BaseModelType'>;
+export type PipelineModelField = S<'PipelineModelField'>;
+export type ModelsList = S<'ModelsList'>;
 
 // Graphs
-export type Graph = components['schemas']['Graph'];
-export type Edge = components['schemas']['Edge'];
-export type GraphExecutionState = components['schemas']['GraphExecutionState'];
+export type Graph = S<'Graph'>;
+export type Edge = S<'Edge'>;
+export type GraphExecutionState = S<'GraphExecutionState'>;
 
 // General nodes
-export type CollectInvocation = components['schemas']['CollectInvocation'];
-export type IterateInvocation = components['schemas']['IterateInvocation'];
-export type RangeInvocation = components['schemas']['RangeInvocation'];
-export type RandomRangeInvocation =
-  components['schemas']['RandomRangeInvocation'];
-export type RangeOfSizeInvocation =
-  components['schemas']['RangeOfSizeInvocation'];
-export type InpaintInvocation = components['schemas']['InpaintInvocation'];
-export type ImageResizeInvocation =
-  components['schemas']['ImageResizeInvocation'];
-export type RandomIntInvocation = components['schemas']['RandomIntInvocation'];
-export type CompelInvocation = components['schemas']['CompelInvocation'];
+export type CollectInvocation = N<'CollectInvocation'>;
+export type IterateInvocation = N<'IterateInvocation'>;
+export type RangeInvocation = N<'RangeInvocation'>;
+export type RandomRangeInvocation = N<'RandomRangeInvocation'>;
+export type RangeOfSizeInvocation = N<'RangeOfSizeInvocation'>;
+export type InpaintInvocation = N<'InpaintInvocation'>;
+export type ImageResizeInvocation = N<'ImageResizeInvocation'>;
+export type RandomIntInvocation = N<'RandomIntInvocation'>;
+export type CompelInvocation = N<'CompelInvocation'>;
+export type DynamicPromptInvocation = N<'DynamicPromptInvocation'>;
+export type NoiseInvocation = N<'NoiseInvocation'>;
+export type TextToLatentsInvocation = N<'TextToLatentsInvocation'>;
+export type LatentsToLatentsInvocation = N<'LatentsToLatentsInvocation'>;
+export type ImageToLatentsInvocation = N<'ImageToLatentsInvocation'>;
+export type LatentsToImageInvocation = N<'LatentsToImageInvocation'>;
+export type PipelineModelLoaderInvocation = N<'PipelineModelLoaderInvocation'>;
 
 // ControlNet Nodes
-export type CannyImageProcessorInvocation =
-  components['schemas']['CannyImageProcessorInvocation'];
+export type ControlNetInvocation = N<'ControlNetInvocation'>;
+export type CannyImageProcessorInvocation = N<'CannyImageProcessorInvocation'>;
 export type ContentShuffleImageProcessorInvocation =
-  components['schemas']['ContentShuffleImageProcessorInvocation'];
-export type HedImageProcessorInvocation =
-  components['schemas']['HedImageProcessorInvocation'];
+  N<'ContentShuffleImageProcessorInvocation'>;
+export type HedImageProcessorInvocation = N<'HedImageProcessorInvocation'>;
 export type LineartAnimeImageProcessorInvocation =
-  components['schemas']['LineartAnimeImageProcessorInvocation'];
+  N<'LineartAnimeImageProcessorInvocation'>;
 export type LineartImageProcessorInvocation =
-  components['schemas']['LineartImageProcessorInvocation'];
+  N<'LineartImageProcessorInvocation'>;
 export type MediapipeFaceProcessorInvocation =
-  components['schemas']['MediapipeFaceProcessorInvocation'];
+  N<'MediapipeFaceProcessorInvocation'>;
 export type MidasDepthImageProcessorInvocation =
-  components['schemas']['MidasDepthImageProcessorInvocation'];
-export type MlsdImageProcessorInvocation =
-  components['schemas']['MlsdImageProcessorInvocation'];
+  N<'MidasDepthImageProcessorInvocation'>;
+export type MlsdImageProcessorInvocation = N<'MlsdImageProcessorInvocation'>;
 export type NormalbaeImageProcessorInvocation =
-  components['schemas']['NormalbaeImageProcessorInvocation'];
+  N<'NormalbaeImageProcessorInvocation'>;
 export type OpenposeImageProcessorInvocation =
-  components['schemas']['OpenposeImageProcessorInvocation'];
-export type PidiImageProcessorInvocation =
-  components['schemas']['PidiImageProcessorInvocation'];
+  N<'OpenposeImageProcessorInvocation'>;
+export type PidiImageProcessorInvocation = N<'PidiImageProcessorInvocation'>;
 export type ZoeDepthImageProcessorInvocation =
-  components['schemas']['ZoeDepthImageProcessorInvocation'];
+  N<'ZoeDepthImageProcessorInvocation'>;
 
 // Node Outputs
-export type ImageOutput = components['schemas']['ImageOutput'];
-export type MaskOutput = components['schemas']['MaskOutput'];
-export type PromptOutput = components['schemas']['PromptOutput'];
-export type IterateInvocationOutput =
-  components['schemas']['IterateInvocationOutput'];
-export type CollectInvocationOutput =
-  components['schemas']['CollectInvocationOutput'];
-export type LatentsOutput = components['schemas']['LatentsOutput'];
-export type GraphInvocationOutput =
-  components['schemas']['GraphInvocationOutput'];
+export type ImageOutput = S<'ImageOutput'>;
+export type MaskOutput = S<'MaskOutput'>;
+export type PromptOutput = S<'PromptOutput'>;
+export type IterateInvocationOutput = S<'IterateInvocationOutput'>;
+export type CollectInvocationOutput = S<'CollectInvocationOutput'>;
+export type LatentsOutput = S<'LatentsOutput'>;
+export type GraphInvocationOutput = S<'GraphInvocationOutput'>;

invokeai/frontend/web/tsconfig.node.json

@@ -9,6 +9,7 @@
     "vite.config.ts",
     "./config/vite.app.config.ts",
     "./config/vite.package.config.ts",
-    "./config/vite.common.config.ts"
+    "./config/vite.common.config.ts",
+    "./config/common.ts"
   ]
 }

pyproject.toml

@@ -39,7 +39,7 @@ dependencies = [
   "click",
   "clip_anytorch",          # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
   "compel>=1.2.1",
-  "controlnet-aux>=0.0.4",
+  "controlnet-aux>=0.0.6",
   "timm==0.6.13",           # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
   "datasets",
   "diffusers[torch]~=0.17.1",

tests/conftest.py

@@ -0,0 +1,30 @@
+import pytest
+from invokeai.app.services.invocation_services import InvocationServices
+from invokeai.app.services.invocation_queue import MemoryInvocationQueue
+from invokeai.app.services.sqlite import SqliteItemStorage, sqlite_memory
+from invokeai.app.services.graph import LibraryGraph, GraphExecutionState
+from invokeai.app.services.processor import DefaultInvocationProcessor
+
+# Ignore these files as they need to be rewritten following the model manager refactor
+collect_ignore = ["nodes/test_graph_execution_state.py", "nodes/test_node_graph.py", "test_textual_inversion.py"]
+
+@pytest.fixture(scope="session", autouse=True)
+def mock_services():
+    # NOTE: none of these are actually called by the test invocations
+    return InvocationServices(
+        model_manager = None, # type: ignore
+        events = None, # type: ignore
+        logger = None, # type: ignore
+        images = None, # type: ignore
+        latents = None, # type: ignore
+        board_images=None, # type: ignore
+        boards=None, # type: ignore
+        queue = MemoryInvocationQueue(),
+        graph_library=SqliteItemStorage[LibraryGraph](
+            filename=sqlite_memory, table_name="graphs"
+        ),
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = sqlite_memory, table_name = 'graph_executions'),
+        processor = DefaultInvocationProcessor(),
+        restoration = None, # type: ignore
+        configuration = None, # type: ignore
+    )

tests/nodes/test_graph_execution_state.py

@@ -1,14 +1,18 @@
-from .test_invoker import create_edge
-from .test_nodes import ImageTestInvocation, ListPassThroughInvocation, PromptTestInvocation, PromptCollectionTestInvocation
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
+import pytest
+
+from invokeai.app.invocations.baseinvocation import (BaseInvocation,
+                                                     BaseInvocationOutput,
+                                                     InvocationContext)
 from invokeai.app.invocations.collections import RangeInvocation
 from invokeai.app.invocations.math import AddInvocation, MultiplyInvocation
-from invokeai.app.services.processor import DefaultInvocationProcessor
-from invokeai.app.services.sqlite import SqliteItemStorage, sqlite_memory
-from invokeai.app.services.invocation_queue import MemoryInvocationQueue
+from invokeai.app.services.graph import (CollectInvocation, Graph,
+                                         GraphExecutionState,
+                                         IterateInvocation)
 from invokeai.app.services.invocation_services import InvocationServices
-from invokeai.app.services.graph import Graph, GraphInvocation, InvalidEdgeError, LibraryGraph, NodeAlreadyInGraphError, NodeNotFoundError, are_connections_compatible, EdgeConnection, CollectInvocation, IterateInvocation, GraphExecutionState
-import pytest
+
+from .test_invoker import create_edge
+from .test_nodes import (ImageTestInvocation, PromptCollectionTestInvocation,
+                         PromptTestInvocation)
 
 
 @pytest.fixture
@@ -19,25 +23,6 @@ def simple_graph():
     g.add_edge(create_edge("1", "prompt", "2", "prompt"))
     return g
 
-@pytest.fixture
-def mock_services():
-    # NOTE: none of these are actually called by the test invocations
-    return InvocationServices(
-        model_manager = None, # type: ignore
-        events = None, # type: ignore
-        logger = None, # type: ignore
-        images = None, # type: ignore
-        latents = None, # type: ignore
-        queue = MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](
-            filename=sqlite_memory, table_name="graphs"
-        ),
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = sqlite_memory, table_name = 'graph_executions'),
-        processor = DefaultInvocationProcessor(),
-        restoration = None, # type: ignore
-        configuration = None, # type: ignore
-    )
-
 def invoke_next(g: GraphExecutionState, services: InvocationServices) -> tuple[BaseInvocation, BaseInvocationOutput]:
     n = g.next()
     if n is None:

tests/nodes/test_invoker.py

@@ -1,13 +1,12 @@
-from .test_nodes import ErrorInvocation, ImageTestInvocation, ListPassThroughInvocation, PromptTestInvocation, PromptCollectionTestInvocation, TestEventService, create_edge, wait_until
-from invokeai.app.services.processor import DefaultInvocationProcessor
-from invokeai.app.services.sqlite import SqliteItemStorage, sqlite_memory
-from invokeai.app.services.invocation_queue import MemoryInvocationQueue
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
-from invokeai.app.services.invocation_services import InvocationServices
-from invokeai.app.services.graph import Graph, GraphInvocation, InvalidEdgeError, LibraryGraph, NodeAlreadyInGraphError, NodeNotFoundError, are_connections_compatible, EdgeConnection, CollectInvocation, IterateInvocation, GraphExecutionState
 import pytest
 
+from invokeai.app.services.graph import Graph, GraphExecutionState
+from invokeai.app.services.invocation_services import InvocationServices
+from invokeai.app.services.invoker import Invoker
+
+from .test_nodes import (ErrorInvocation, ImageTestInvocation,
+                         PromptTestInvocation, create_edge, wait_until)
+
 
 @pytest.fixture
 def simple_graph():
@@ -17,25 +16,6 @@ def simple_graph():
     g.add_edge(create_edge("1", "prompt", "2", "prompt"))
     return g
 
-@pytest.fixture
-def mock_services() -> InvocationServices:
-    # NOTE: none of these are actually called by the test invocations
-    return InvocationServices(
-        model_manager = None, # type: ignore
-        events = TestEventService(),
-        logger = None, # type: ignore
-        images = None, # type: ignore
-        latents = None, # type: ignore
-        queue = MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](
-            filename=sqlite_memory, table_name="graphs"
-        ),
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = sqlite_memory, table_name = 'graph_executions'),
-        processor = DefaultInvocationProcessor(),
-        restoration = None, # type: ignore
-        configuration = None, # type: ignore
-    )
-
 @pytest.fixture()
 def mock_invoker(mock_services: InvocationServices) -> Invoker:
     return Invoker(
@@ -57,6 +37,7 @@ def test_can_create_graph_state_from_graph(mock_invoker: Invoker, simple_graph):
     assert isinstance(g, GraphExecutionState)
     assert g.graph == simple_graph
 
+@pytest.mark.xfail(reason = "Requires fixing following the model manager refactor")
 def test_can_invoke(mock_invoker: Invoker, simple_graph):
     g = mock_invoker.create_execution_state(graph = simple_graph)
     invocation_id = mock_invoker.invoke(g)
@@ -72,6 +53,7 @@ def test_can_invoke(mock_invoker: Invoker, simple_graph):
     g = mock_invoker.services.graph_execution_manager.get(g.id)
     assert len(g.executed) > 0
 
+@pytest.mark.xfail(reason = "Requires fixing following the model manager refactor")
 def test_can_invoke_all(mock_invoker: Invoker, simple_graph):
     g = mock_invoker.create_execution_state(graph = simple_graph)
     invocation_id = mock_invoker.invoke(g, invoke_all = True)
@@ -87,6 +69,7 @@ def test_can_invoke_all(mock_invoker: Invoker, simple_graph):
     g = mock_invoker.services.graph_execution_manager.get(g.id)
     assert g.is_complete()
 
+@pytest.mark.xfail(reason = "Requires fixing following the model manager refactor")
 def test_handles_errors(mock_invoker: Invoker):
     g = mock_invoker.create_execution_state()
     g.graph.add_node(ErrorInvocation(id = "1"))