fix merge conflicts with main

invokeai/app/api_app.py

@@ -28,7 +28,7 @@ from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.services.config.config_default import get_config
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
-from invokeai.backend.util.devices import get_torch_device_name
+from invokeai.backend.util.devices import TorchDevice
 
 from ..backend.util.logging import InvokeAILogger
 from .api.dependencies import ApiDependencies
@@ -63,7 +63,7 @@ logger = InvokeAILogger.get_logger(config=app_config)
 mimetypes.add_type("application/javascript", ".js")
 mimetypes.add_type("text/css", ".css")
 
-torch_device_name = get_torch_device_name()
+torch_device_name = TorchDevice.get_torch_device_name()
 logger.info(f"Using torch device: {torch_device_name}")
 
 

invokeai/app/invocations/compel.py

@@ -24,7 +24,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ConditioningFieldData,
     SDXLConditioningInfo,
 )
-from invokeai.backend.util.devices import torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from .model import CLIPField
@@ -99,7 +99,7 @@ class CompelInvocation(BaseInvocation):
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
-                dtype_for_device_getter=torch_dtype,
+                dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,
             )
 
@@ -193,7 +193,7 @@ class SDXLPromptInvocationBase:
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
-                dtype_for_device_getter=torch_dtype,
+                dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,  # TODO:
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,  # TODO: clip skip
                 requires_pooled=get_pooled,

invokeai/app/invocations/latent.py

@@ -72,15 +72,12 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
     image_resized_to_grid_as_tensor,
 )
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.util.devices import choose_precision, choose_torch_device
+from ...backend.util.devices import TorchDevice
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from .controlnet_image_processors import ControlField
 from .model import ModelIdentifierField, UNetField, VAEField
 
-if choose_torch_device() == torch.device("mps"):
+DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()
-    from torch import mps
-
-DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
 
 @invocation_output("scheduler_output")
@@ -959,9 +956,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
             result_latents = result_latents.to("cpu")
-            torch.cuda.empty_cache()
+            TorchDevice.empty_cache()
-            if choose_torch_device() == torch.device("mps"):
-                mps.empty_cache()
 
             name = context.tensors.save(tensor=result_latents)
         return LatentsOutput.build(latents_name=name, latents=result_latents, seed=None)
@@ -1028,9 +1023,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.disable_tiling()
 
             # clear memory as vae decode can request a lot
-            torch.cuda.empty_cache()
+            TorchDevice.empty_cache()
-            if choose_torch_device() == torch.device("mps"):
-                mps.empty_cache()
 
             with torch.inference_mode():
                 # copied from diffusers pipeline
@@ -1042,9 +1035,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
                 image = VaeImageProcessor.numpy_to_pil(np_image)[0]
 
-        torch.cuda.empty_cache()
+        TorchDevice.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
 
         image_dto = context.images.save(image=image)
 
@@ -1083,9 +1074,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.tensors.load(self.latents.latents_name)
-
+        device = TorchDevice.choose_torch_device()
-        # TODO:
-        device = choose_torch_device()
 
         resized_latents = torch.nn.functional.interpolate(
             latents.to(device),
@@ -1096,9 +1085,8 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
-        torch.cuda.empty_cache()
+
-        if device == torch.device("mps"):
+        TorchDevice.empty_cache()
-            mps.empty_cache()
 
         name = context.tensors.save(tensor=resized_latents)
         return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@@ -1125,8 +1113,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        # TODO:
+        device = TorchDevice.choose_torch_device()
-        device = choose_torch_device()
 
         # resizing
         resized_latents = torch.nn.functional.interpolate(
@@ -1138,9 +1125,7 @@ class ScaleLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
-        torch.cuda.empty_cache()
+        TorchDevice.empty_cache()
-        if device == torch.device("mps"):
-            mps.empty_cache()
 
         name = context.tensors.save(tensor=resized_latents)
         return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@@ -1272,8 +1257,7 @@ class BlendLatentsInvocation(BaseInvocation):
         if latents_a.shape != latents_b.shape:
             raise Exception("Latents to blend must be the same size.")
 
-        # TODO:
+        device = TorchDevice.choose_torch_device()
-        device = choose_torch_device()
 
         def slerp(
             t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
@@ -1326,9 +1310,8 @@ class BlendLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
-        torch.cuda.empty_cache()
+
-        if device == torch.device("mps"):
+        TorchDevice.empty_cache()
-            mps.empty_cache()
 
         name = context.tensors.save(tensor=blended_latents)
         return LatentsOutput.build(latents_name=name, latents=blended_latents)

invokeai/app/invocations/noise.py

@@ -9,7 +9,7 @@ from invokeai.app.invocations.fields import FieldDescriptions, InputField, Laten
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.misc import SEED_MAX
 
-from ...backend.util.devices import choose_torch_device, torch_dtype
+from ...backend.util.devices import TorchDevice
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -46,7 +46,7 @@ def get_noise(
             height // downsampling_factor,
             width // downsampling_factor,
         ],
-        dtype=torch_dtype(device),
+        dtype=TorchDevice.choose_torch_dtype(device=device),
         device=noise_device_type,
         generator=generator,
     ).to("cpu")
@@ -111,14 +111,14 @@ class NoiseInvocation(BaseInvocation):
 
     @field_validator("seed", mode="before")
     def modulo_seed(cls, v):
-        """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
+        """Return the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
         return v % (SEED_MAX + 1)
 
     def invoke(self, context: InvocationContext) -> NoiseOutput:
         noise = get_noise(
             width=self.width,
             height=self.height,
-            device=choose_torch_device(),
+            device=TorchDevice.choose_torch_device(),
             seed=self.seed,
             use_cpu=self.use_cpu,
         )

invokeai/app/invocations/upscale.py

@@ -3,7 +3,6 @@ from typing import Literal
 
 import cv2
 import numpy as np
-import torch
 from PIL import Image
 from pydantic import ConfigDict
 
@@ -12,7 +11,7 @@ from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField, WithBoard, WithMetadata
@@ -33,9 +32,6 @@ ESRGAN_MODEL_URLS: dict[str, str] = {
     "RealESRGAN_x2plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
 }
 
-if choose_torch_device() == torch.device("mps"):
-    from torch import mps
-
 
 @invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.2")
 class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -115,9 +111,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
 
-        torch.cuda.empty_cache()
+        TorchDevice.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
 
         image_dto = context.images.save(image=pil_image)
 

invokeai/app/services/config/config_default.py

@@ -27,12 +27,12 @@ DEFAULT_RAM_CACHE = 10.0
 DEFAULT_VRAM_CACHE = 0.25
 DEFAULT_CONVERT_CACHE = 20.0
 DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
-PRECISION = Literal["auto", "float16", "bfloat16", "float32", "autocast"]
+PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
 ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
 ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
 LOG_FORMAT = Literal["plain", "color", "syslog", "legacy"]
 LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
-CONFIG_SCHEMA_VERSION = "4.0.0"
+CONFIG_SCHEMA_VERSION = "4.0.1"
 
 
 def get_default_ram_cache_size() -> float:
@@ -106,7 +106,7 @@ class InvokeAIAppConfig(BaseSettings):
         lazy_offload: Keep models in VRAM until their space is needed.
         log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
         device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
-        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`, `autocast`
+        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
         sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
         attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
         attention_slice_size: Slice size, valid when attention_type=="sliced".<br>Valid values: `auto`, `balanced`, `max`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`
@@ -377,6 +377,9 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
             # `max_vram_cache_size` was renamed to `vram` some time in v3, but both names were used
             if k == "max_vram_cache_size" and "vram" not in category_dict:
                 parsed_config_dict["vram"] = v
+            # autocast was removed in v4.0.1
+            if k == "precision" and v == "autocast":
+                parsed_config_dict["precision"] = "auto"
             if k == "conf_path":
                 parsed_config_dict["legacy_models_yaml_path"] = v
             if k == "legacy_conf_dir":
@@ -399,6 +402,28 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
     return config
 
 
+def migrate_v4_0_0_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
+    """Migrate v4.0.0 config dictionary to a current config object.
+
+    Args:
+        config_dict: A dictionary of settings from a v4.0.0 config file.
+
+    Returns:
+        An instance of `InvokeAIAppConfig` with the migrated settings.
+    """
+    parsed_config_dict: dict[str, Any] = {}
+    for k, v in config_dict.items():
+        # autocast was removed from precision in v4.0.1
+        if k == "precision" and v == "autocast":
+            parsed_config_dict["precision"] = "auto"
+        else:
+            parsed_config_dict[k] = v
+        if k == "schema_version":
+            parsed_config_dict[k] = CONFIG_SCHEMA_VERSION
+    config = DefaultInvokeAIAppConfig.model_validate(parsed_config_dict)
+    return config
+
+
 def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
     """Load and migrate a config file to the latest version.
 
@@ -425,17 +450,21 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
             raise RuntimeError(f"Failed to load and migrate v3 config file {config_path}: {e}") from e
         migrated_config.write_file(config_path)
         return migrated_config
-    else:
+
-        # Attempt to load as a v4 config file
+    if loaded_config_dict["schema_version"] == "4.0.0":
-        try:
+        loaded_config_dict = migrate_v4_0_0_config_dict(loaded_config_dict)
-            # Meta is not included in the model fields, so we need to validate it separately
+        loaded_config_dict.write_file(config_path)
-            config = InvokeAIAppConfig.model_validate(loaded_config_dict)
+
-            assert (
+    # Attempt to load as a v4 config file
-                config.schema_version == CONFIG_SCHEMA_VERSION
+    try:
-            ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        # Meta is not included in the model fields, so we need to validate it separately
-            return config
+        config = InvokeAIAppConfig.model_validate(loaded_config_dict)
-        except Exception as e:
+        assert (
-            raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e
+            config.schema_version == CONFIG_SCHEMA_VERSION
+        ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        return config
+    except Exception as e:
+        raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e
 
 
 @lru_cache(maxsize=1)

invokeai/app/services/model_install/model_install_default.py

@@ -13,6 +13,7 @@ from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
 from typing import Any, Dict, List, Optional, Union
 
+import torch
 import yaml
 from huggingface_hub import HfFolder
 from pydantic.networks import AnyHttpUrl
@@ -42,7 +43,7 @@ from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMet
 from invokeai.backend.model_manager.probe import ModelProbe
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.util import InvokeAILogger
-from invokeai.backend.util.devices import choose_precision, choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 from .model_install_base import (
     MODEL_SOURCE_TO_TYPE_MAP,
@@ -643,11 +644,10 @@ class ModelInstallService(ModelInstallServiceBase):
             self._next_job_id += 1
         return id
 
-    @staticmethod
+    def _guess_variant(self) -> Optional[ModelRepoVariant]:
-    def _guess_variant() -> Optional[ModelRepoVariant]:
         """Guess the best HuggingFace variant type to download."""
-        precision = choose_precision(choose_torch_device())
+        precision = TorchDevice.choose_torch_dtype()
-        return ModelRepoVariant.FP16 if precision == "float16" else None
+        return ModelRepoVariant.FP16 if precision == torch.float16 else None
 
     def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
         return ModelInstallJob(

invokeai/app/services/model_manager/model_manager_default.py

@@ -1,12 +1,14 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 """Implementation of ModelManagerServiceBase."""
 
+from typing import Optional
+
 import torch
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
 from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 from ..config import InvokeAIAppConfig
@@ -67,7 +69,7 @@ class ModelManagerService(ModelManagerServiceBase):
         model_record_service: ModelRecordServiceBase,
         download_queue: DownloadQueueServiceBase,
         events: EventServiceBase,
-        execution_device: torch.device = choose_torch_device(),
+        execution_device: Optional[torch.device] = None,
     ) -> Self:
         """
         Construct the model manager service instance.
@@ -82,7 +84,7 @@ class ModelManagerService(ModelManagerServiceBase):
             max_vram_cache_size=app_config.vram,
             lazy_offloading=app_config.lazy_offload,
             logger=logger,
-            execution_device=execution_device,
+            execution_device=execution_device or TorchDevice.choose_torch_device(),
         )
         convert_cache = ModelConvertCache(cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache)
         loader = ModelLoadService(

invokeai/backend/image_util/depth_anything/__init__.py

@@ -12,7 +12,7 @@ from invokeai.app.services.config.config_default import get_config
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
 from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 config = get_config()
@@ -47,7 +47,7 @@ class DepthAnythingDetector:
         self.context = context
         self.model: Optional[DPT_DINOv2] = None
         self.model_size: Union[Literal["large", "base", "small"], None] = None
-        self.device = choose_torch_device()
+        self.device = TorchDevice.choose_torch_device()
 
     def load_model(self, model_size: Literal["large", "base", "small"] = "small") -> DPT_DINOv2:
         depth_anything_model_path = self.context.models.download_and_cache_ckpt(DEPTH_ANYTHING_MODELS[model_size])
@@ -68,7 +68,7 @@ class DepthAnythingDetector:
             self.model.load_state_dict(torch.load(depth_anything_model_path.as_posix(), map_location="cpu"))
             self.model.eval()
 
-        self.model.to(choose_torch_device())
+        self.model.to(self.device)
         return self.model
 
     def __call__(self, image: Image.Image, resolution: int = 512) -> Image.Image:
@@ -81,7 +81,7 @@ class DepthAnythingDetector:
 
         image_height, image_width = np_image.shape[:2]
         np_image = transform({"image": np_image})["image"]
-        tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(choose_torch_device())
+        tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(self.device)
 
         with torch.no_grad():
             depth = self.model(tensor_image)

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -4,11 +4,10 @@
 
 import numpy as np
 import onnxruntime as ort
-import torch
 
 from invokeai.app.services.config.config_default import get_config
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 from .onnxdet import inference_detector
 from .onnxpose import inference_pose
@@ -23,9 +22,9 @@ config = get_config()
 
 class Wholebody:
     def __init__(self, context: InvocationContext):
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
-        providers = ["CUDAExecutionProvider"] if device == torch.device("cuda") else ["CPUExecutionProvider"]
+        providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
 
         onnx_det = context.models.download_and_cache_ckpt(DWPOSE_MODELS["yolox_l.onnx"])
         onnx_pose = context.models.download_and_cache_ckpt(DWPOSE_MODELS["dw-ll_ucoco_384.onnx"])

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -11,7 +11,7 @@ from tqdm import tqdm
 
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.model_manager.config import AnyModel
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 """
 Adapted from https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
@@ -65,7 +65,7 @@ class RealESRGAN:
         self.pre_pad = pre_pad
         self.mod_scale: Optional[int] = None
         self.half = half
-        self.device = choose_torch_device()
+        self.device = TorchDevice.choose_torch_device()
 
         # prefer to use params_ema
         if "params_ema" in loadnet:

invokeai/backend/image_util/safety_checker.py

@@ -13,7 +13,7 @@ from transformers import AutoFeatureExtractor
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config.config_default import get_config
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
@@ -51,7 +51,7 @@ class SafetyChecker:
         cls._load_safety_checker()
         if cls.safety_checker is None or cls.feature_extractor is None:
             return False
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
         features = cls.feature_extractor([image], return_tensors="pt")
         features.to(device)
         cls.safety_checker.to(device)

invokeai/backend/model_manager/load/load_default.py

@@ -18,7 +18,7 @@ from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoad
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
-from invokeai.backend.util.devices import choose_torch_device, torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 
 # TO DO: The loader is not thread safe!
@@ -37,7 +37,7 @@ class ModelLoader(ModelLoaderBase):
         self._logger = logger
         self._ram_cache = ram_cache
         self._convert_cache = convert_cache
-        self._torch_dtype = torch_dtype(choose_torch_device())
+        self._torch_dtype = TorchDevice.choose_torch_dtype()
 
     def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -31,15 +31,12 @@ import torch
 from invokeai.backend.model_manager import AnyModel, SubModelType
 from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 from .model_cache_base import CacheRecord, CacheStats, ModelCacheBase, ModelLockerBase
 from .model_locker import ModelLocker
 
-if choose_torch_device() == torch.device("mps"):
-    from torch import mps
-
 # Maximum size of the cache, in gigs
 # Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
 DEFAULT_MAX_CACHE_SIZE = 6.0
@@ -245,9 +242,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                     f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
                 )
 
-        torch.cuda.empty_cache()
+        TorchDevice.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
 
     def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
         """Move model into the indicated device.
@@ -417,10 +412,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 self.stats.cleared = models_cleared
             gc.collect()
 
-        torch.cuda.empty_cache()
+        TorchDevice.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
-
         self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
 
     def _delete_cache_entry(self, cache_entry: CacheRecord[AnyModel]) -> None:

invokeai/backend/model_manager/merge.py

@@ -17,7 +17,7 @@ from diffusers.utils import logging as dlogging
 
 from invokeai.app.services.model_install import ModelInstallServiceBase
 from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
-from invokeai.backend.util.devices import choose_torch_device, torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 from . import (
     AnyModelConfig,
@@ -43,6 +43,7 @@ class ModelMerger(object):
         Initialize a ModelMerger object with the model installer.
         """
         self._installer = installer
+        self._dtype = TorchDevice.choose_torch_dtype()
 
     def merge_diffusion_models(
         self,
@@ -68,7 +69,7 @@ class ModelMerger(object):
             warnings.simplefilter("ignore")
             verbosity = dlogging.get_verbosity()
             dlogging.set_verbosity_error()
-            dtype = torch.float16 if variant == "fp16" else torch_dtype(choose_torch_device())
+            dtype = torch.float16 if variant == "fp16" else self._dtype
 
             # Note that checkpoint_merger will not work with downloaded HuggingFace fp16 models
             # until upstream https://github.com/huggingface/diffusers/pull/6670 is merged and released.
@@ -151,7 +152,7 @@ class ModelMerger(object):
         dump_path.mkdir(parents=True, exist_ok=True)
         dump_path = dump_path / merged_model_name
 
-        dtype = torch.float16 if variant == "fp16" else torch_dtype(choose_torch_device())
+        dtype = torch.float16 if variant == "fp16" else self._dtype
         merged_pipe.save_pretrained(dump_path.as_posix(), safe_serialization=True, torch_dtype=dtype, variant=variant)
 
         # register model and get its unique key

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -28,7 +28,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
 from invokeai.backend.util.attention import auto_detect_slice_size
-from invokeai.backend.util.devices import normalize_device
+from invokeai.backend.util.devices import TorchDevice
 
 
 @dataclass
@@ -258,7 +258,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
             mem_free = psutil.virtual_memory().free
         elif self.unet.device.type == "cuda":
-            mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.unet.device))
+            mem_free, _ = torch.cuda.mem_get_info(TorchDevice.normalize(self.unet.device))
         else:
             raise ValueError(f"unrecognized device {self.unet.device}")
         # input tensor of [1, 4, h/8, w/8]

invokeai/backend/util/__init__.py

@@ -2,7 +2,6 @@
 Initialization file for invokeai.backend.util
 """
 
-from .devices import choose_precision, choose_torch_device
 from .logging import InvokeAILogger
 from .util import GIG, Chdir, directory_size
 
@@ -11,6 +10,4 @@ __all__ = [
     "directory_size",
     "Chdir",
     "InvokeAILogger",
-    "choose_precision",
-    "choose_torch_device",
 ]

invokeai/backend/util/devices.py

@@ -1,89 +1,110 @@
-from __future__ import annotations
+from typing import Dict, Literal, Optional, Union
-
-from contextlib import nullcontext
-from typing import Literal, Optional, Union
 
 import torch
-from torch import autocast
+from deprecated import deprecated
 
-from invokeai.app.services.config.config_default import PRECISION, get_config
+from invokeai.app.services.config.config_default import get_config
 
+# legacy APIs
+TorchPrecisionNames = Literal["float32", "float16", "bfloat16"]
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
 
 
+@deprecated("Use TorchDevice.choose_torch_dtype() instead.")  # type: ignore
+def choose_precision(device: torch.device) -> TorchPrecisionNames:
+    """Return the string representation of the recommended torch device."""
+    torch_dtype = TorchDevice.choose_torch_dtype(device)
+    return PRECISION_TO_NAME[torch_dtype]
+
+
+@deprecated("Use TorchDevice.choose_torch_device() instead.")  # type: ignore
 def choose_torch_device() -> torch.device:
-    """Convenience routine for guessing which GPU device to run model on"""
+    """Return the torch.device to use for accelerated inference."""
-    config = get_config()
+    return TorchDevice.choose_torch_device()
-    if config.device == "auto":
+
-        if torch.cuda.is_available():
+
-            return torch.device("cuda")
+@deprecated("Use TorchDevice.choose_torch_dtype() instead.")  # type: ignore
-        if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+def torch_dtype(device: torch.device) -> torch.dtype:
-            return torch.device("mps")
+    """Return the torch precision for the recommended torch device."""
+    return TorchDevice.choose_torch_dtype(device)
+
+
+NAME_TO_PRECISION: Dict[TorchPrecisionNames, torch.dtype] = {
+    "float32": torch.float32,
+    "float16": torch.float16,
+    "bfloat16": torch.bfloat16,
+}
+PRECISION_TO_NAME: Dict[torch.dtype, TorchPrecisionNames] = {v: k for k, v in NAME_TO_PRECISION.items()}
+
+
+class TorchDevice:
+    """Abstraction layer for torch devices."""
+
+    @classmethod
+    def choose_torch_device(cls) -> torch.device:
+        """Return the torch.device to use for accelerated inference."""
+        app_config = get_config()
+        if app_config.device != "auto":
+            device = torch.device(app_config.device)
+        elif torch.cuda.is_available():
+            device = CUDA_DEVICE
+        elif torch.backends.mps.is_available():
+            device = MPS_DEVICE
         else:
-            return CPU_DEVICE
+            device = CPU_DEVICE
-    else:
+        return cls.normalize(device)
-        return torch.device(config.device)
 
+    @classmethod
+    def choose_torch_dtype(cls, device: Optional[torch.device] = None) -> torch.dtype:
+        """Return the precision to use for accelerated inference."""
+        device = device or cls.choose_torch_device()
+        config = get_config()
+        if device.type == "cuda" and torch.cuda.is_available():
+            device_name = torch.cuda.get_device_name(device)
+            if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
+                # These GPUs have limited support for float16
+                return cls._to_dtype("float32")
+            elif config.precision == "auto":
+                # Default to float16 for CUDA devices
+                return cls._to_dtype("float16")
+            else:
+                # Use the user-defined precision
+                return cls._to_dtype(config.precision)
 
-def get_torch_device_name() -> str:
+        elif device.type == "mps" and torch.backends.mps.is_available():
-    device = choose_torch_device()
+            if config.precision == "auto":
-    return torch.cuda.get_device_name(device) if device.type == "cuda" else device.type.upper()
+                # Default to float16 for MPS devices
+                return cls._to_dtype("float16")
+            else:
+                # Use the user-defined precision
+                return cls._to_dtype(config.precision)
+        # CPU / safe fallback
+        return cls._to_dtype("float32")
 
+    @classmethod
+    def get_torch_device_name(cls) -> str:
+        """Return the device name for the current torch device."""
+        device = cls.choose_torch_device()
+        return torch.cuda.get_device_name(device) if device.type == "cuda" else device.type.upper()
 
-def choose_precision(device: torch.device) -> Literal["float32", "float16", "bfloat16"]:
+    @classmethod
-    """Return an appropriate precision for the given torch device."""
+    def normalize(cls, device: Union[str, torch.device]) -> torch.device:
-    app_config = get_config()
+        """Add the device index to CUDA devices."""
-    if device.type == "cuda":
+        device = torch.device(device)
-        device_name = torch.cuda.get_device_name(device)
+        if device.index is None and device.type == "cuda" and torch.cuda.is_available():
-        if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
-            # These GPUs have limited support for float16
-            return "float32"
-        elif app_config.precision == "auto" or app_config.precision == "autocast":
-            # Default to float16 for CUDA devices
-            return "float16"
-        else:
-            # Use the user-defined precision
-            return app_config.precision
-    elif device.type == "mps":
-        if app_config.precision == "auto" or app_config.precision == "autocast":
-            # Default to float16 for MPS devices
-            return "float16"
-        else:
-            # Use the user-defined precision
-            return app_config.precision
-    # CPU / safe fallback
-    return "float32"
-
-
-def torch_dtype(device: Optional[torch.device] = None) -> torch.dtype:
-    device = device or choose_torch_device()
-    precision = choose_precision(device)
-    if precision == "float16":
-        return torch.float16
-    if precision == "bfloat16":
-        return torch.bfloat16
-    else:
-        # "auto", "autocast", "float32"
-        return torch.float32
-
-
-def choose_autocast(precision: PRECISION):
-    """Returns an autocast context or nullcontext for the given precision string"""
-    # float16 currently requires autocast to avoid errors like:
-    # 'expected scalar type Half but found Float'
-    if precision == "autocast" or precision == "float16":
-        return autocast
-    return nullcontext
-
-
-def normalize_device(device: Union[str, torch.device]) -> torch.device:
-    """Ensure device has a device index defined, if appropriate."""
-    device = torch.device(device)
-    if device.index is None:
-        # cuda might be the only torch backend that currently uses the device index?
-        # I don't see anything like `current_device` for cpu or mps.
-        if device.type == "cuda":
             device = torch.device(device.type, torch.cuda.current_device())
-    return device
+        return device
+
+    @classmethod
+    def empty_cache(cls) -> None:
+        """Clear the GPU device cache."""
+        if torch.backends.mps.is_available():
+            torch.mps.empty_cache()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    @classmethod
+    def _to_dtype(cls, precision_name: TorchPrecisionNames) -> torch.dtype:
+        return NAME_TO_PRECISION[precision_name]

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

@@ -770,6 +770,8 @@
         "float": "Float",
         "fullyContainNodes": "Fully Contain Nodes to Select",
         "fullyContainNodesHelp": "Nodes must be fully inside the selection box to be selected",
+        "showEdgeLabels": "Show Edge Labels",
+        "showEdgeLabelsHelp": "Show labels on edges, indicating the connected nodes",
         "hideLegendNodes": "Hide Field Type Legend",
         "hideMinimapnodes": "Hide MiniMap",
         "inputMayOnlyHaveOneConnection": "Input may only have one connection",

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

@@ -9,7 +9,7 @@ import { useHotkeys } from 'react-hotkeys-hook';
 
 export const useGlobalHotkeys = () => {
   const dispatch = useAppDispatch();
-  const isModelManagerEnabled = useFeatureStatus('modelManager').isFeatureEnabled;
+  const isModelManagerEnabled = useFeatureStatus('modelManager');
   const { queueBack, isDisabled: isDisabledQueueBack, isLoading: isLoadingQueueBack } = useQueueBack();
 
   useHotkeys(

invokeai/frontend/web/src/features/gallery/components/Boards/BoardContextMenu.tsx

@@ -32,7 +32,7 @@ const BoardContextMenu = ({ board, board_id, setBoardToDelete, children }: Props
 
   const isSelectedForAutoAdd = useAppSelector(selectIsSelectedForAutoAdd);
   const boardName = useBoardName(board_id);
-  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload').isFeatureEnabled;
+  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload');
 
   const [bulkDownload] = useBulkDownloadImagesMutation();
 

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -54,7 +54,7 @@ const CurrentImageButtons = () => {
   const selection = useAppSelector((s) => s.gallery.selection);
   const shouldDisableToolbarButtons = useAppSelector(selectShouldDisableToolbarButtons);
 
-  const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
+  const isUpscalingEnabled = useFeatureStatus('upscaling');
   const isQueueMutationInProgress = useIsQueueMutationInProgress();
   const toaster = useAppToaster();
   const { t } = useTranslation();

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/MultipleSelectionMenuItems.tsx

@@ -20,7 +20,7 @@ const MultipleSelectionMenuItems = () => {
   const selection = useAppSelector((s) => s.gallery.selection);
   const customStarUi = useStore($customStarUI);
 
-  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload').isFeatureEnabled;
+  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload');
 
   const [starImages] = useStarImagesMutation();
   const [unstarImages] = useUnstarImagesMutation();

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -45,7 +45,7 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
   const toaster = useAppToaster();
-  const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
+  const isCanvasEnabled = useFeatureStatus('unifiedCanvas');
   const customStarUi = useStore($customStarUI);
   const { downloadImage } = useDownloadImage();
 

invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts

@@ -18,7 +18,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
     [imageDTO?.image_name]
   );
   const isSelected = useAppSelector(selectIsSelected);
-  const isMultiSelectEnabled = useFeatureStatus('multiselect').isFeatureEnabled;
+  const isMultiSelectEnabled = useFeatureStatus('multiselect');
 
   const handleClick = useCallback(
     (e: MouseEvent<HTMLDivElement>) => {

invokeai/frontend/web/src/features/hrf/components/HrfSettings.tsx

@@ -8,7 +8,7 @@ import ParamHrfStrength from './ParamHrfStrength';
 import ParamHrfToggle from './ParamHrfToggle';
 
 export const HrfSettings = memo(() => {
-  const isHRFFeatureEnabled = useFeatureStatus('hrf').isFeatureEnabled;
+  const isHRFFeatureEnabled = useFeatureStatus('hrf');
   const hrfEnabled = useAppSelector((s) => s.hrf.hrfEnabled);
 
   if (!isHRFFeatureEnabled) {

invokeai/frontend/web/src/features/modelManagerV2/hooks/useStarterModelsToast.tsx

@@ -10,7 +10,7 @@ const TOAST_ID = 'starterModels';
 
 export const useStarterModelsToast = () => {
   const { t } = useTranslation();
-  const isEnabled = useFeatureStatus('starterModels').isFeatureEnabled;
+  const isEnabled = useFeatureStatus('starterModels');
   const [didToast, setDidToast] = useState(false);
   const [mainModels, { data }] = useMainModels();
   const toast = useToast();

invokeai/frontend/web/src/features/nodes/components/flow/edges/InvocationDefaultEdge.tsx

@@ -1,8 +1,9 @@
+import { Flex, Text } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import type { CSSProperties } from 'react';
 import { memo, useMemo } from 'react';
 import type { EdgeProps } from 'reactflow';
-import { BaseEdge, getBezierPath } from 'reactflow';
+import { BaseEdge, EdgeLabelRenderer, getBezierPath } from 'reactflow';
 
 import { makeEdgeSelector } from './util/makeEdgeSelector';
 
@@ -25,9 +26,10 @@ const InvocationDefaultEdge = ({
     [source, sourceHandleId, target, targetHandleId, selected]
   );
 
-  const { isSelected, shouldAnimate, stroke } = useAppSelector(selector);
+  const { isSelected, shouldAnimate, stroke, label } = useAppSelector(selector);
+  const shouldShowEdgeLabels = useAppSelector((s) => s.nodes.shouldShowEdgeLabels);
 
-  const [edgePath] = getBezierPath({
+  const [edgePath, labelX, labelY] = getBezierPath({
     sourceX,
     sourceY,
     sourcePosition,
@@ -47,7 +49,33 @@ const InvocationDefaultEdge = ({
     [isSelected, shouldAnimate, stroke]
   );
 
-  return <BaseEdge path={edgePath} markerEnd={markerEnd} style={edgeStyles} />;
+  return (
+    <>
+      <BaseEdge path={edgePath} markerEnd={markerEnd} style={edgeStyles} />
+      {label && shouldShowEdgeLabels && (
+        <EdgeLabelRenderer>
+          <Flex
+            className="nodrag nopan"
+            pointerEvents="all"
+            position="absolute"
+            transform={`translate(-50%, -50%) translate(${labelX}px,${labelY}px)`}
+            bg="base.800"
+            borderRadius="base"
+            borderWidth={1}
+            borderColor={isSelected ? 'undefined' : 'transparent'}
+            opacity={isSelected ? 1 : 0.5}
+            py={1}
+            px={3}
+            shadow="md"
+          >
+            <Text size="sm" fontWeight="semibold" color={isSelected ? 'base.100' : 'base.300'}>
+              {label}
+            </Text>
+          </Flex>
+        </EdgeLabelRenderer>
+      )}
+    </>
+  );
 };
 
 export default memo(InvocationDefaultEdge);

invokeai/frontend/web/src/features/nodes/components/flow/edges/util/makeEdgeSelector.ts

@@ -1,7 +1,7 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { colorTokenToCssVar } from 'common/util/colorTokenToCssVar';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectFieldOutputTemplate } from 'features/nodes/store/selectors';
+import { selectFieldOutputTemplate, selectNodeTemplate } from 'features/nodes/store/selectors';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 
 import { getFieldColor } from './getEdgeColor';
@@ -10,6 +10,7 @@ const defaultReturnValue = {
   isSelected: false,
   shouldAnimate: false,
   stroke: colorTokenToCssVar('base.500'),
+  label: '',
 };
 
 export const makeEdgeSelector = (
@@ -19,25 +20,34 @@ export const makeEdgeSelector = (
   targetHandleId: string | null | undefined,
   selected?: boolean
 ) =>
-  createMemoizedSelector(selectNodesSlice, (nodes): { isSelected: boolean; shouldAnimate: boolean; stroke: string } => {
+  createMemoizedSelector(
-    const sourceNode = nodes.nodes.find((node) => node.id === source);
+    selectNodesSlice,
-    const targetNode = nodes.nodes.find((node) => node.id === target);
+    (nodes): { isSelected: boolean; shouldAnimate: boolean; stroke: string; label: string } => {
+      const sourceNode = nodes.nodes.find((node) => node.id === source);
+      const targetNode = nodes.nodes.find((node) => node.id === target);
 
-    const isInvocationToInvocationEdge = isInvocationNode(sourceNode) && isInvocationNode(targetNode);
+      const isInvocationToInvocationEdge = isInvocationNode(sourceNode) && isInvocationNode(targetNode);
 
-    const isSelected = Boolean(sourceNode?.selected || targetNode?.selected || selected);
+      const isSelected = Boolean(sourceNode?.selected || targetNode?.selected || selected);
-    if (!sourceNode || !sourceHandleId) {
+      if (!sourceNode || !sourceHandleId || !targetNode || !targetHandleId) {
-      return defaultReturnValue;
+        return defaultReturnValue;
+      }
+
+      const outputFieldTemplate = selectFieldOutputTemplate(nodes, sourceNode.id, sourceHandleId);
+      const sourceType = isInvocationToInvocationEdge ? outputFieldTemplate?.type : undefined;
+
+      const stroke = sourceType && nodes.shouldColorEdges ? getFieldColor(sourceType) : colorTokenToCssVar('base.500');
+
+      const sourceNodeTemplate = selectNodeTemplate(nodes, sourceNode.id);
+      const targetNodeTemplate = selectNodeTemplate(nodes, targetNode.id);
+
+      const label = `${sourceNodeTemplate?.title || sourceNode.data?.label} -> ${targetNodeTemplate?.title || targetNode.data?.label}`;
+
+      return {
+        isSelected,
+        shouldAnimate: nodes.shouldAnimateEdges && isSelected,
+        stroke,
+        label,
+      };
     }
-
+  );
-    const outputFieldTemplate = selectFieldOutputTemplate(nodes, sourceNode.id, sourceHandleId);
-    const sourceType = isInvocationToInvocationEdge ? outputFieldTemplate?.type : undefined;
-
-    const stroke = sourceType && nodes.shouldColorEdges ? getFieldColor(sourceType) : colorTokenToCssVar('base.500');
-
-    return {
-      isSelected,
-      shouldAnimate: nodes.shouldAnimateEdges && isSelected,
-      stroke,
-    };
-  });

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx

@@ -16,7 +16,7 @@ const props: ChakraProps = { w: 'unset' };
 
 const InvocationNodeFooter = ({ nodeId }: Props) => {
   const hasImageOutput = useHasImageOutput(nodeId);
-  const isCacheEnabled = useFeatureStatus('invocationCache').isFeatureEnabled;
+  const isCacheEnabled = useFeatureStatus('invocationCache');
   return (
     <Flex
       className={DRAG_HANDLE_CLASSNAME}

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopRightPanel/WorkflowEditorSettings.tsx

@@ -24,6 +24,7 @@ import {
   selectNodesSlice,
   shouldAnimateEdgesChanged,
   shouldColorEdgesChanged,
+  shouldShowEdgeLabelsChanged,
   shouldSnapToGridChanged,
   shouldValidateGraphChanged,
 } from 'features/nodes/store/nodesSlice';
@@ -35,12 +36,20 @@ import { SelectionMode } from 'reactflow';
 const formLabelProps: FormLabelProps = { flexGrow: 1 };
 
 const selector = createMemoizedSelector(selectNodesSlice, (nodes) => {
-  const { shouldAnimateEdges, shouldValidateGraph, shouldSnapToGrid, shouldColorEdges, selectionMode } = nodes;
+  const {
+    shouldAnimateEdges,
+    shouldValidateGraph,
+    shouldSnapToGrid,
+    shouldColorEdges,
+    shouldShowEdgeLabels,
+    selectionMode,
+  } = nodes;
   return {
     shouldAnimateEdges,
     shouldValidateGraph,
     shouldSnapToGrid,
     shouldColorEdges,
+    shouldShowEdgeLabels,
     selectionModeIsChecked: selectionMode === SelectionMode.Full,
   };
 });
@@ -52,8 +61,14 @@ type Props = {
 const WorkflowEditorSettings = ({ children }: Props) => {
   const { isOpen, onOpen, onClose } = useDisclosure();
   const dispatch = useAppDispatch();
-  const { shouldAnimateEdges, shouldValidateGraph, shouldSnapToGrid, shouldColorEdges, selectionModeIsChecked } =
+  const {
-    useAppSelector(selector);
+    shouldAnimateEdges,
+    shouldValidateGraph,
+    shouldSnapToGrid,
+    shouldColorEdges,
+    shouldShowEdgeLabels,
+    selectionModeIsChecked,
+  } = useAppSelector(selector);
 
   const handleChangeShouldValidate = useCallback(
     (e: ChangeEvent<HTMLInputElement>) => {
@@ -90,6 +105,13 @@ const WorkflowEditorSettings = ({ children }: Props) => {
     [dispatch]
   );
 
+  const handleChangeShouldShowEdgeLabels = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      dispatch(shouldShowEdgeLabelsChanged(e.target.checked));
+    },
+    [dispatch]
+  );
+
   const { t } = useTranslation();
 
   return (
@@ -137,6 +159,14 @@ const WorkflowEditorSettings = ({ children }: Props) => {
                   <FormHelperText>{t('nodes.fullyContainNodesHelp')}</FormHelperText>
                 </FormControl>
                 <Divider />
+                <FormControl>
+                  <Flex w="full">
+                    <FormLabel>{t('nodes.showEdgeLabels')}</FormLabel>
+                    <Switch isChecked={shouldShowEdgeLabels} onChange={handleChangeShouldShowEdgeLabels} />
+                  </Flex>
+                  <FormHelperText>{t('nodes.showEdgeLabelsHelp')}</FormHelperText>
+                </FormControl>
+                <Divider />
                 <Heading size="sm" pt={4}>
                   {t('common.advanced')}
                 </Heading>

invokeai/frontend/web/src/features/nodes/hooks/useOutputFieldNames.ts

@@ -1,5 +1,5 @@
-import { createSelector } from '@reduxjs/toolkit';
 import { EMPTY_ARRAY } from 'app/store/constants';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
 import { selectNodeTemplate } from 'features/nodes/store/selectors';
@@ -10,7 +10,7 @@ import { useMemo } from 'react';
 export const useOutputFieldNames = (nodeId: string) => {
   const selector = useMemo(
     () =>
-      createSelector(selectNodesSlice, (nodes) => {
+      createMemoizedSelector(selectNodesSlice, (nodes) => {
         const template = selectNodeTemplate(nodes, nodeId);
         if (!template) {
           return EMPTY_ARRAY;

invokeai/frontend/web/src/features/nodes/hooks/useWithFooter.ts

@@ -5,8 +5,7 @@ import { useHasImageOutput } from './useHasImageOutput';
 
 export const useWithFooter = (nodeId: string) => {
   const hasImageOutput = useHasImageOutput(nodeId);
-  const isCacheEnabled = useFeatureStatus('invocationCache').isFeatureEnabled;
+  const isCacheEnabled = useFeatureStatus('invocationCache');
-
   const withFooter = useMemo(() => hasImageOutput || isCacheEnabled, [hasImageOutput, isCacheEnabled]);
   return withFooter;
 };

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

@@ -103,6 +103,7 @@ const initialNodesState: NodesState = {
   shouldAnimateEdges: true,
   shouldSnapToGrid: false,
   shouldColorEdges: true,
+  shouldShowEdgeLabels: false,
   isAddNodePopoverOpen: false,
   nodeOpacity: 1,
   selectedNodes: [],
@@ -549,6 +550,9 @@ export const nodesSlice = createSlice({
     shouldAnimateEdgesChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldAnimateEdges = action.payload;
     },
+    shouldShowEdgeLabelsChanged: (state, action: PayloadAction<boolean>) => {
+      state.shouldShowEdgeLabels = action.payload;
+    },
     shouldSnapToGridChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldSnapToGrid = action.payload;
     },
@@ -831,6 +835,7 @@ export const {
   viewportChanged,
   edgeAdded,
   nodeTemplatesBuilt,
+  shouldShowEdgeLabelsChanged,
 } = nodesSlice.actions;
 
 // This is used for tracking `state.workflow.isTouched`

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

@@ -32,6 +32,7 @@ export type NodesState = {
   isAddNodePopoverOpen: boolean;
   addNewNodePosition: XYPosition | null;
   selectionMode: SelectionMode;
+  shouldShowEdgeLabels: boolean;
 };
 
 export type WorkflowMode = 'edit' | 'view';

invokeai/frontend/web/src/features/parameters/components/Canvas/InfillAndScaling/ParamInfillColorOptions.tsx

@@ -1,24 +1,18 @@
 import { Box, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIColorPicker from 'common/components/IAIColorPicker';
 import { selectGenerationSlice, setInfillColorValue } from 'features/parameters/store/generationSlice';
-import { memo, useCallback, useMemo } from 'react';
+import { memo, useCallback } from 'react';
 import type { RgbaColor } from 'react-colorful';
 import { useTranslation } from 'react-i18next';
 
+const selectInfillColor = createMemoizedSelector(selectGenerationSlice, (generation) => generation.infillColorValue);
+
 const ParamInfillColorOptions = () => {
   const dispatch = useAppDispatch();
 
-  const selector = useMemo(
+  const infillColor = useAppSelector(selectInfillColor);
-    () =>
-      createSelector(selectGenerationSlice, (generation) => ({
-        infillColor: generation.infillColorValue,
-      })),
-    []
-  );
-
-  const { infillColor } = useAppSelector(selector);
 
   const infillMethod = useAppSelector((s) => s.generation.infillMethod);
 

invokeai/frontend/web/src/features/parameters/components/Canvas/InfillAndScaling/ParamInfillMosaicOptions.tsx

@@ -1,35 +1,23 @@
 import { Box, CompositeNumberInput, CompositeSlider, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIColorPicker from 'common/components/IAIColorPicker';
 import {
-  selectGenerationSlice,
   setInfillMosaicMaxColor,
   setInfillMosaicMinColor,
   setInfillMosaicTileHeight,
   setInfillMosaicTileWidth,
 } from 'features/parameters/store/generationSlice';
-import { memo, useCallback, useMemo } from 'react';
+import { memo, useCallback } from 'react';
 import type { RgbaColor } from 'react-colorful';
 import { useTranslation } from 'react-i18next';
 
 const ParamInfillMosaicTileSize = () => {
   const dispatch = useAppDispatch();
 
-  const selector = useMemo(
+  const infillMosaicTileWidth = useAppSelector((s) => s.generation.infillMosaicTileWidth);
-    () =>
+  const infillMosaicTileHeight = useAppSelector((s) => s.generation.infillMosaicTileHeight);
-      createSelector(selectGenerationSlice, (generation) => ({
+  const infillMosaicMinColor = useAppSelector((s) => s.generation.infillMosaicMinColor);
-        infillMosaicTileWidth: generation.infillMosaicTileWidth,
+  const infillMosaicMaxColor = useAppSelector((s) => s.generation.infillMosaicMaxColor);
-        infillMosaicTileHeight: generation.infillMosaicTileHeight,
-        infillMosaicMinColor: generation.infillMosaicMinColor,
-        infillMosaicMaxColor: generation.infillMosaicMaxColor,
-      })),
-    []
-  );
-
-  const { infillMosaicTileWidth, infillMosaicTileHeight, infillMosaicMinColor, infillMosaicMaxColor } =
-    useAppSelector(selector);
-
   const infillMethod = useAppSelector((s) => s.generation.infillMethod);
 
   const { t } = useTranslation();

invokeai/frontend/web/src/features/queue/components/QueueActionsMenuButton.tsx

@@ -27,8 +27,8 @@ export const QueueActionsMenuButton = memo(() => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
   const clearQueueDisclosure = useDisclosure();
-  const isPauseEnabled = useFeatureStatus('pauseQueue').isFeatureEnabled;
+  const isPauseEnabled = useFeatureStatus('pauseQueue');
-  const isResumeEnabled = useFeatureStatus('resumeQueue').isFeatureEnabled;
+  const isResumeEnabled = useFeatureStatus('resumeQueue');
   const { queueSize } = useGetQueueStatusQuery(undefined, {
     selectFromResult: (res) => ({
       queueSize: res.data ? res.data.queue.pending + res.data.queue.in_progress : 0,

invokeai/frontend/web/src/features/queue/components/QueueControls.tsx

@@ -9,7 +9,7 @@ import { InvokeQueueBackButton } from './InvokeQueueBackButton';
 import { QueueActionsMenuButton } from './QueueActionsMenuButton';
 
 const QueueControls = () => {
-  const isPrependEnabled = useFeatureStatus('prependQueue').isFeatureEnabled;
+  const isPrependEnabled = useFeatureStatus('prependQueue');
   return (
     <Flex w="full" position="relative" borderRadius="base" gap={2} pt={2} flexDir="column">
       <ButtonGroup size="lg" isAttached={false}>

invokeai/frontend/web/src/features/queue/components/QueueTabContent.tsx

@@ -8,7 +8,7 @@ import QueueStatus from './QueueStatus';
 import QueueTabQueueControls from './QueueTabQueueControls';
 
 const QueueTabContent = () => {
-  const isInvocationCacheEnabled = useFeatureStatus('invocationCache').isFeatureEnabled;
+  const isInvocationCacheEnabled = useFeatureStatus('invocationCache');
 
   return (
     <Flex borderRadius="base" w="full" h="full" flexDir="column" gap={2}>

invokeai/frontend/web/src/features/queue/components/QueueTabQueueControls.tsx

@@ -8,8 +8,8 @@ import PruneQueueButton from './PruneQueueButton';
 import ResumeProcessorButton from './ResumeProcessorButton';
 
 const QueueTabQueueControls = () => {
-  const isPauseEnabled = useFeatureStatus('pauseQueue').isFeatureEnabled;
+  const isPauseEnabled = useFeatureStatus('pauseQueue');
-  const isResumeEnabled = useFeatureStatus('resumeQueue').isFeatureEnabled;
+  const isResumeEnabled = useFeatureStatus('resumeQueue');
   return (
     <Flex layerStyle="first" borderRadius="base" p={2} gap={2}>
       {isPauseEnabled || isResumeEnabled ? (

invokeai/frontend/web/src/features/queue/hooks/useQueueFront.ts

@@ -13,7 +13,7 @@ export const useQueueFront = () => {
   const [_, { isLoading }] = useEnqueueBatchMutation({
     fixedCacheKey: 'enqueueBatch',
   });
-  const prependEnabled = useFeatureStatus('prependQueue').isFeatureEnabled;
+  const prependEnabled = useFeatureStatus('prependQueue');
 
   const isDisabled = useMemo(() => {
     return !isReady || !prependEnabled;

invokeai/frontend/web/src/features/settingsAccordions/components/ControlSettingsAccordion/ControlSettingsAccordion.tsx

@@ -62,7 +62,7 @@ const selector = createMemoizedSelector(selectControlAdaptersSlice, (controlAdap
 export const ControlSettingsAccordion: React.FC = memo(() => {
   const { t } = useTranslation();
   const { controlAdapterIds, badges } = useAppSelector(selector);
-  const isControlNetDisabled = useFeatureStatus('controlNet').isFeatureDisabled;
+  const isControlNetEnabled = useFeatureStatus('controlNet');
   const { isOpen, onToggle } = useStandaloneAccordionToggle({
     id: 'control-settings',
     defaultIsOpen: true,
@@ -71,7 +71,7 @@ export const ControlSettingsAccordion: React.FC = memo(() => {
   const [addIPAdapter, isAddIPAdapterDisabled] = useAddControlAdapter('ip_adapter');
   const [addT2IAdapter, isAddT2IAdapterDisabled] = useAddControlAdapter('t2i_adapter');
 
-  if (isControlNetDisabled) {
+  if (!isControlNetEnabled) {
     return null;
   }
 

invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsLanguageSelect.tsx

@@ -40,7 +40,7 @@ export const SettingsLanguageSelect = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const language = useAppSelector((s) => s.system.language);
-  const isLocalizationEnabled = useFeatureStatus('localization').isFeatureEnabled;
+  const isLocalizationEnabled = useFeatureStatus('localization');
 
   const value = useMemo(() => options.find((o) => o.value === language), [language]);
 

invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsMenu.tsx

@@ -23,9 +23,9 @@ const SettingsMenu = () => {
   const { isOpen, onOpen, onClose } = useDisclosure();
   useGlobalMenuClose(onClose);
 
-  const isBugLinkEnabled = useFeatureStatus('bugLink').isFeatureEnabled;
+  const isBugLinkEnabled = useFeatureStatus('bugLink');
-  const isDiscordLinkEnabled = useFeatureStatus('discordLink').isFeatureEnabled;
+  const isDiscordLinkEnabled = useFeatureStatus('discordLink');
-  const isGithubLinkEnabled = useFeatureStatus('githubLink').isFeatureEnabled;
+  const isGithubLinkEnabled = useFeatureStatus('githubLink');
 
   return (
     <Menu isOpen={isOpen} onOpen={onOpen} onClose={onClose}>

invokeai/frontend/web/src/features/system/hooks/useFeatureStatus.ts

@@ -1,32 +1,24 @@
+import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
 import type { AppFeature, SDFeature } from 'app/types/invokeai';
+import { selectConfigSlice } from 'features/system/store/configSlice';
 import type { InvokeTabName } from 'features/ui/store/tabMap';
 import { useMemo } from 'react';
 
 export const useFeatureStatus = (feature: AppFeature | SDFeature | InvokeTabName) => {
-  const disabledTabs = useAppSelector((s) => s.config.disabledTabs);
+  const selectIsFeatureEnabled = useMemo(
-
-  const disabledFeatures = useAppSelector((s) => s.config.disabledFeatures);
-
-  const disabledSDFeatures = useAppSelector((s) => s.config.disabledSDFeatures);
-
-  const isFeatureDisabled = useMemo(
     () =>
-      disabledFeatures.includes(feature as AppFeature) ||
+      createSelector(selectConfigSlice, (config) => {
-      disabledSDFeatures.includes(feature as SDFeature) ||
+        return !(
-      disabledTabs.includes(feature as InvokeTabName),
+          config.disabledFeatures.includes(feature as AppFeature) ||
-    [disabledFeatures, disabledSDFeatures, disabledTabs, feature]
+          config.disabledSDFeatures.includes(feature as SDFeature) ||
+          config.disabledTabs.includes(feature as InvokeTabName)
+        );
+      }),
+    [feature]
   );
 
-  const isFeatureEnabled = useMemo(
+  const isFeatureEnabled = useAppSelector(selectIsFeatureEnabled);
-    () =>
-      !(
-        disabledFeatures.includes(feature as AppFeature) ||
-        disabledSDFeatures.includes(feature as SDFeature) ||
-        disabledTabs.includes(feature as InvokeTabName)
-      ),
-    [disabledFeatures, disabledSDFeatures, disabledTabs, feature]
-  );
 
-  return { isFeatureDisabled, isFeatureEnabled };
+  return isFeatureEnabled;
 };

tests/backend/util/test_devices.py

@@ -0,0 +1,132 @@
+"""
+Test abstract device class.
+"""
+
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from invokeai.app.services.config import get_config
+from invokeai.backend.util.devices import TorchDevice, choose_precision, choose_torch_device, torch_dtype
+
+devices = ["cpu", "cuda:0", "cuda:1", "mps"]
+device_types_cpu = [("cpu", torch.float32), ("cuda:0", torch.float32), ("mps", torch.float32)]
+device_types_cuda = [("cpu", torch.float32), ("cuda:0", torch.float16), ("mps", torch.float32)]
+device_types_mps = [("cpu", torch.float32), ("cuda:0", torch.float32), ("mps", torch.float16)]
+
+
+@pytest.mark.parametrize("device_name", devices)
+def test_device_choice(device_name):
+    config = get_config()
+    config.device = device_name
+    torch_device = TorchDevice.choose_torch_device()
+    assert torch_device == torch.device(device_name)
+
+
+@pytest.mark.parametrize("device_dtype_pair", device_types_cpu)
+def test_device_dtype_cpu(device_dtype_pair):
+    with (
+        patch("torch.cuda.is_available", return_value=False),
+        patch("torch.backends.mps.is_available", return_value=False),
+    ):
+        device_name, dtype = device_dtype_pair
+        config = get_config()
+        config.device = device_name
+        torch_dtype = TorchDevice.choose_torch_dtype()
+        assert torch_dtype == dtype
+
+
+@pytest.mark.parametrize("device_dtype_pair", device_types_cuda)
+def test_device_dtype_cuda(device_dtype_pair):
+    with (
+        patch("torch.cuda.is_available", return_value=True),
+        patch("torch.cuda.get_device_name", return_value="RTX4070"),
+        patch("torch.backends.mps.is_available", return_value=False),
+    ):
+        device_name, dtype = device_dtype_pair
+        config = get_config()
+        config.device = device_name
+        torch_dtype = TorchDevice.choose_torch_dtype()
+        assert torch_dtype == dtype
+
+
+@pytest.mark.parametrize("device_dtype_pair", device_types_mps)
+def test_device_dtype_mps(device_dtype_pair):
+    with (
+        patch("torch.cuda.is_available", return_value=False),
+        patch("torch.backends.mps.is_available", return_value=True),
+    ):
+        device_name, dtype = device_dtype_pair
+        config = get_config()
+        config.device = device_name
+        torch_dtype = TorchDevice.choose_torch_dtype()
+        assert torch_dtype == dtype
+
+
+@pytest.mark.parametrize("device_dtype_pair", device_types_cuda)
+def test_device_dtype_override(device_dtype_pair):
+    with (
+        patch("torch.cuda.get_device_name", return_value="RTX4070"),
+        patch("torch.cuda.is_available", return_value=True),
+        patch("torch.backends.mps.is_available", return_value=False),
+    ):
+        device_name, dtype = device_dtype_pair
+        config = get_config()
+        config.device = device_name
+        config.precision = "float32"
+        torch_dtype = TorchDevice.choose_torch_dtype()
+        assert torch_dtype == torch.float32
+
+
+def test_normalize():
+    assert (
+        TorchDevice.normalize("cuda") == torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cuda")
+    )
+    assert (
+        TorchDevice.normalize("cuda:0") == torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cuda")
+    )
+    assert (
+        TorchDevice.normalize("cuda:1") == torch.device("cuda:1") if torch.cuda.is_available() else torch.device("cuda")
+    )
+    assert TorchDevice.normalize("mps") == torch.device("mps")
+    assert TorchDevice.normalize("cpu") == torch.device("cpu")
+
+
+@pytest.mark.parametrize("device_name", devices)
+def test_legacy_device_choice(device_name):
+    config = get_config()
+    config.device = device_name
+    with pytest.deprecated_call():
+        torch_device = choose_torch_device()
+    assert torch_device == torch.device(device_name)
+
+
+@pytest.mark.parametrize("device_dtype_pair", device_types_cpu)
+def test_legacy_device_dtype_cpu(device_dtype_pair):
+    with (
+        patch("torch.cuda.is_available", return_value=False),
+        patch("torch.backends.mps.is_available", return_value=False),
+        patch("torch.cuda.get_device_name", return_value="RTX9090"),
+    ):
+        device_name, dtype = device_dtype_pair
+        config = get_config()
+        config.device = device_name
+        with pytest.deprecated_call():
+            torch_device = choose_torch_device()
+            returned_dtype = torch_dtype(torch_device)
+        assert returned_dtype == dtype
+
+
+def test_legacy_precision_name():
+    config = get_config()
+    config.precision = "auto"
+    with (
+        pytest.deprecated_call(),
+        patch("torch.cuda.is_available", return_value=True),
+        patch("torch.backends.mps.is_available", return_value=True),
+        patch("torch.cuda.get_device_name", return_value="RTX9090"),
+    ):
+        assert "float16" == choose_precision(torch.device("cuda"))
+        assert "float16" == choose_precision(torch.device("mps"))
+        assert "float32" == choose_precision(torch.device("cpu"))