Only kick off on merges to main

.github/pr_labels.yml

@@ -1,40 +0,0 @@
-Root:
-- changed-files:
-  - any-glob-to-any-file: '*'
-
-PythonDeps:
-- changed-files:
-  - any-glob-to-any-file: 'pyproject.toml'
-
-Python:
-- changed-files:
-  - any-glob-to-any-file: 
-    - 'invokeai/**'
-    - '!invokeai/frontend/web/**'
-    - 'tests/**'
-
-Invocations:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/app/invocations/**'
-
-Backend:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/backend/**'
-
-Api:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/app/api/**'
-
-Services:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/app/services/**'
-
-FrontendDeps:
-- changed-files:
-  - any-glob-to-any-file:
-    - '**/*/package.json'
-    - '**/*/pnpm-lock.yaml'
-
-Frontend:
-- changed-files:
-  - any-glob-to-any-file: 'invokeai/frontend/web/**'

.github/workflows/change-monitor.yml

@@ -0,0 +1,29 @@
+name: Trigger Target Workflow
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  trigger:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Trigger Workflow in Another Repository
+        run: |
+          # Set the required variables
+          repo_owner="invoke-ai" 
+          repo_name="Invoke"  
+          event_type="invokeai-pr-merge" 
+          service=${{ github.event.inputs.target_service }}"
+          version="${{ github.event.inputs.target_version }}"
+  
+          curl -L \
+            -X POST \
+            -H "Accept: application/vnd.github+json" \
+            -H "Authorization: Bearer ${{ secrets.PAT }}" \
+            -H "X-GitHub-Api-Version: 2022-11-28" \
+            https://api.github.com/repos/$repo_owner/$repo_name/dispatches \
+            -d "{\"event_type\": \"$event_type\", \"client_payload\": {\"service\": \"$service\", \"version\": \"$version\", \"unit\": false, \"integration\": true}}"

.github/workflows/label-pr.yml

@@ -1,16 +0,0 @@
-name: "Pull Request Labeler"
-on:
-- pull_request_target
-
-jobs:
-  labeler:
-    permissions:
-      contents: read
-      pull-requests: write
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - uses: actions/labeler@v5
-        with:
-          configuration-path: .github/pr_labels.yml

docs/nodes/communityNodes.md

@@ -25,6 +25,7 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
     + [Grid to Gif](#grid-to-gif)
     + [Halftone](#halftone)
+    + [Ideal Size](#ideal-size)
     + [Image and Mask Composition Pack](#image-and-mask-composition-pack)
     + [Image Dominant Color](#image-dominant-color)
     + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
@@ -195,6 +196,13 @@ CMYK Halftone Output:
 
 <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
 
+--------------------------------
+### Ideal Size
+
+**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
+
+**Node Link:** https://github.com/JPPhoto/ideal-size-node
+
 --------------------------------
 ### Image and Mask Composition Pack
 

docs/nodes/defaultNodes.md

@@ -36,7 +36,6 @@ their descriptions.
 | Integer Math                                                  | Perform basic math operations on two integers                                                                                                        |
 | Convert Image Mode                                            | Converts an image to a different mode.                                                                                                               |
 | Crop Image                                                    | Crops an image to a specified box. The box can be outside of the image.                                                                              |
-| Ideal Size                                                    | Calculates an ideal image size for latents for a first pass of a multi-pass upscaling to avoid duplication and other artifacts                       |
 | Image Hue Adjustment                                          | Adjusts the Hue of an image.                                                                                                                         |
 | Inverse Lerp Image                                            | Inverse linear interpolation of all pixels of an image                                                                                               |
 | Image Primitive                                               | An image primitive value                                                                                                                             |

invokeai/app/api/routers/download_queue.py

@@ -24,7 +24,6 @@ download_queue_router = APIRouter(prefix="/v1/download_queue", tags=["download_q
 )
 async def list_downloads() -> List[DownloadJob]:
     """Get a list of active and inactive jobs."""
-    print("test")
     queue = ApiDependencies.invoker.services.download_queue
     return queue.list_jobs()
 

invokeai/app/invocations/latent.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
 from typing import List, Literal, Optional, Union
@@ -75,8 +74,6 @@ from .model import ModelInfo, UNetField, VaeField
 if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
-print("test")
-
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
 SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
@@ -1231,57 +1228,3 @@ class CropLatentsCoreInvocation(BaseInvocation):
         context.services.latents.save(name, cropped_latents)
 
         return build_latents_output(latents_name=name, latents=cropped_latents)
-
-
-@invocation_output("ideal_size_output")
-class IdealSizeOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    width: int = OutputField(description="The ideal width of the image (in pixels)")
-    height: int = OutputField(description="The ideal height of the image (in pixels)")
-
-
-@invocation(
-    "ideal_size",
-    title="Ideal Size",
-    tags=["latents", "math", "ideal_size"],
-    version="1.0.2",
-)
-class IdealSizeInvocation(BaseInvocation):
-    """Calculates the ideal size for generation to avoid duplication"""
-
-    width: int = InputField(default=1024, description="Final image width")
-    height: int = InputField(default=576, description="Final image height")
-    unet: UNetField = InputField(default=None, description=FieldDescriptions.unet)
-    multiplier: float = InputField(
-        default=1.0,
-        description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in initial generation artifacts if too large)",
-    )
-
-    def trim_to_multiple_of(self, *args, multiple_of=LATENT_SCALE_FACTOR):
-        return tuple((x - x % multiple_of) for x in args)
-
-    def invoke(self, context: InvocationContext) -> IdealSizeOutput:
-        aspect = self.width / self.height
-        dimension = 512
-        if self.unet.unet.base_model == BaseModelType.StableDiffusion2:
-            dimension = 768
-        elif self.unet.unet.base_model == BaseModelType.StableDiffusionXL:
-            dimension = 1024
-        dimension = dimension * self.multiplier
-        min_dimension = math.floor(dimension * 0.5)
-        model_area = dimension * dimension  # hardcoded for now since all models are trained on square images
-
-        if aspect > 1.0:
-            init_height = max(min_dimension, math.sqrt(model_area / aspect))
-            init_width = init_height * aspect
-        else:
-            init_width = max(min_dimension, math.sqrt(model_area * aspect))
-            init_height = init_width / aspect
-
-        scaled_width, scaled_height = self.trim_to_multiple_of(
-            math.floor(init_width),
-            math.floor(init_height),
-        )
-
-        return IdealSizeOutput(width=scaled_width, height=scaled_height)

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -23,7 +23,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
         self.__threadLimit = BoundedSemaphore(1)
         self.__invoker = invoker
         self.__stop_event = Event()
-        print("test")
         self.__invoker_thread = Thread(
             name="invoker_processor",
             target=self.__process,

invokeai/backend/model_management/lora.py

@@ -113,7 +113,7 @@ class ModelPatcher:
                     for layer_key, layer in lora.layers.items():
                         if not layer_key.startswith(prefix):
                             continue
-                        print("test")
+
                         # TODO(ryand): A non-negligible amount of time is currently spent resolving LoRA keys. This
                         # should be improved in the following ways:
                         # 1. The key mapping could be more-efficiently pre-computed. This would save time every time a

invokeai/frontend/web/package.json

@@ -20,7 +20,6 @@
   ],
   "scripts": {
     "dev": "concurrently \"vite dev\" \"pnpm run theme:watch\"",
-    "hi": "echo test",
     "dev:host": "concurrently \"vite dev --host\" \"pnpm run theme:watch\"",
     "build": "pnpm run lint && vite build",
     "typegen": "node scripts/typegen.js",

invokeai/frontend/web/src/features/controlAdapters/hooks/useAddControlAdapter.ts

@@ -16,7 +16,7 @@ export const useAddControlAdapter = (type: ControlAdapterType) => {
     const firstCompatibleModel = models.filter((m) =>
       baseModel ? m.base_model === baseModel : true
     )[0];
-    console.log("test")
+
     if (firstCompatibleModel) {
       return firstCompatibleModel;
     }

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "InvokeAI"
-description = "An test implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
+description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
 requires-python = ">=3.10, <3.12"
 readme = { content-type = "text/markdown", file = "README.md" }
 keywords = ["stable-diffusion", "AI"]