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https://github.com/invoke-ai/InvokeAI
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[DO NOT MERGE] Label Testing PR
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@ -24,6 +24,7 @@ download_queue_router = APIRouter(prefix="/v1/download_queue", tags=["download_q
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)
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async def list_downloads() -> List[DownloadJob]:
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"""Get a list of active and inactive jobs."""
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print("test")
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queue = ApiDependencies.invoker.services.download_queue
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return queue.list_jobs()
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@ -75,6 +75,8 @@ from .model import ModelInfo, UNetField, VaeField
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if choose_torch_device() == torch.device("mps"):
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from torch import mps
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print("test")
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DEFAULT_PRECISION = choose_precision(choose_torch_device())
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SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
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@ -23,6 +23,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
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self.__threadLimit = BoundedSemaphore(1)
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self.__invoker = invoker
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self.__stop_event = Event()
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print("test")
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self.__invoker_thread = Thread(
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name="invoker_processor",
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target=self.__process,
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@ -113,7 +113,7 @@ class ModelPatcher:
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for layer_key, layer in lora.layers.items():
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if not layer_key.startswith(prefix):
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continue
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print("test")
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# TODO(ryand): A non-negligible amount of time is currently spent resolving LoRA keys. This
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# should be improved in the following ways:
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# 1. The key mapping could be more-efficiently pre-computed. This would save time every time a
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@ -20,6 +20,7 @@
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],
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"scripts": {
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"dev": "concurrently \"vite dev\" \"pnpm run theme:watch\"",
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"hi": "echo test",
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"dev:host": "concurrently \"vite dev --host\" \"pnpm run theme:watch\"",
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"build": "pnpm run lint && vite build",
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"typegen": "node scripts/typegen.js",
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@ -16,7 +16,7 @@ export const useAddControlAdapter = (type: ControlAdapterType) => {
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const firstCompatibleModel = models.filter((m) =>
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baseModel ? m.base_model === baseModel : true
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)[0];
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console.log("test")
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if (firstCompatibleModel) {
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return firstCompatibleModel;
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}
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@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "InvokeAI"
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description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
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description = "An test implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
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requires-python = ">=3.10, <3.12"
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readme = { content-type = "text/markdown", file = "README.md" }
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keywords = ["stable-diffusion", "AI"]
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