Rename spandrel models to 'Image-to-Image Model' throughout the UI.

invokeai/app/invocations/fields.py

@@ -135,7 +135,7 @@ class FieldDescriptions:
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
-    spandrel_image_to_image_model = "Spandrel Image-to-Image model"
+    spandrel_image_to_image_model = "Image-to-Image model"
     lora_weight = "The weight at which the LoRA is applied to each model"
     compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
     raw_prompt = "Raw prompt text (no parsing)"

invokeai/app/invocations/spandrel_upscale.py

@@ -1,47 +0,0 @@
-import torch
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    InputField,
-    UIType,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
-
-
-@invocation("upscale_spandrel", title="Upscale (spandrel)", tags=["upscale"], category="upscale", version="1.0.0")
-class UpscaleSpandrelInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Upscales an image using any upscaler supported by spandrel (https://github.com/chaiNNer-org/spandrel)."""
-
-    image: ImageField = InputField(description="The input image")
-    spandrel_image_to_image_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.spandrel_image_to_image_model, ui_type=UIType.SpandrelImageToImageModel
-    )
-
-    @torch.inference_mode()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
-
-        # Load the model.
-        spandrel_model_info = context.models.load(self.spandrel_image_to_image_model)
-
-        with spandrel_model_info as spandrel_model:
-            assert isinstance(spandrel_model, SpandrelImageToImageModel)
-
-            # Prepare input image for inference.
-            image_tensor = SpandrelImageToImageModel.pil_to_tensor(image)
-            image_tensor = image_tensor.to(device=spandrel_model.device, dtype=spandrel_model.dtype)
-
-            # Run inference.
-            image_tensor = spandrel_model.run(image_tensor)
-
-        # Convert the output tensor to a PIL image.
-        pil_image = SpandrelImageToImageModel.tensor_to_pil(image_tensor)
-        image_dto = context.images.save(image=pil_image)
-        return ImageOutput.build(image_dto)

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelList.tsx

@@ -155,11 +155,11 @@ const ModelList = () => {
         )}
         {/* Spandrel Image to Image List */}
         {isLoadingSpandrelImageToImageModels && (
-          <FetchingModelsLoader loadingMessage="Loading Spandrel Image to Image Models..." />
+          <FetchingModelsLoader loadingMessage="Loading Image-to-Image Models..." />
         )}
         {!isLoadingSpandrelImageToImageModels && filteredSpandrelImageToImageModels.length > 0 && (
           <ModelListWrapper
-            title="Spandrel Image to Image"
+            title="Image-to-Image"
             modelList={filteredSpandrelImageToImageModels}
             key="spandrel-image-to-image"
           />

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelTypeFilter.tsx

@@ -21,7 +21,7 @@ export const ModelTypeFilter = () => {
       t2i_adapter: t('common.t2iAdapter'),
       ip_adapter: t('common.ipAdapter'),
       clip_vision: 'Clip Vision',
-      spandrel_image_to_image: 'Spandrel Image to Image',
+      spandrel_image_to_image: 'Image-to-Image',
     }),
     [t]
   );