[DO NOT MERGE] Label Testing PR

Added ideal size node

.github/pr_labels.yml

@@ -0,0 +1,40 @@
+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/label-pr.yml

@@ -0,0 +1,16 @@
+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

@@ -25,7 +25,6 @@ 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)
@@ -196,13 +195,6 @@ 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

@@ -36,6 +36,7 @@ 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

@@ -24,6 +24,7 @@ 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

@@ -1,5 +1,6 @@
 # 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
@@ -74,6 +75,8 @@ 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())]
@@ -1228,3 +1231,57 @@ 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

@@ -23,6 +23,7 @@ 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

@@ -20,6 +20,7 @@
   ],
   "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 implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
+description = "An test 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"]