Support passthrough/skipping nodes

backend/src/api/output.py

@@ -27,7 +27,7 @@ def __init__(
         self.never_reason: str | None = None
         self.kind: OutputKind = kind
         self.has_handle: bool = has_handle
-        self.pass_through_of: InputId | None = None
+        self.passthrough_of: InputId | None = None
 
         self.associated_type: Any = associated_type
 
@@ -43,7 +43,7 @@ def to_dict(self):
             "neverReason": self.never_reason,
             "kind": self.kind,
             "hasHandle": self.has_handle,
-            "passThroughOf": self.pass_through_of,
+            "passthroughOf": self.passthrough_of,
             "description": self.description,
             "suggest": self.should_suggest,
         }
@@ -64,8 +64,8 @@ def suggest(self):
         self.should_suggest = True
         return self
 
-    def as_pass_through_of(self, input_id: InputId | int):
-        self.pass_through_of = InputId(input_id)
+    def as_passthrough_of(self, input_id: InputId | int):
+        self.passthrough_of = InputId(input_id)
         return self
 
     def get_broadcast_data(self, _value: T) -> BroadcastData | None:

backend/src/nodes/properties/outputs/numpy_outputs.py

@@ -67,7 +67,7 @@ def __init__(
         self.assume_normalized: bool = assume_normalized
 
         if shape_as is not None:
-            self.as_pass_through_of(shape_as)
+            self.as_passthrough_of(shape_as)
 
     def get_broadcast_data(self, value: np.ndarray) -> BroadcastData:
         h, w, c = get_h_w_c(value)

backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/brightness_and_contrast.py

@@ -33,7 +33,9 @@
             controls_step=1,
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0", assume_normalized=True)],
+    outputs=[
+        ImageOutput(shape_as=0, assume_normalized=True),
+    ],
 )
 def brightness_and_contrast_node(
     img: np.ndarray, brightness: float, contrast: float

backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/clamp.py

@@ -35,10 +35,7 @@
         ),
     ],
     outputs=[
-        ImageOutput(
-            image_type="Input0",
-            assume_normalized=True,
-        )
+        ImageOutput(shape_as=0, assume_normalized=True),
     ],
 )
 def clamp_node(img: np.ndarray, minimum: float, maximum: float) -> np.ndarray:

backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/color_levels.py

@@ -66,7 +66,9 @@
             controls_step=0.01,
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[
+        ImageOutput(shape_as=0),
+    ],
 )
 def color_levels_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/hue_and_saturation.py

@@ -68,7 +68,7 @@ def with_lightness(img: np.ndarray, lightness: float) -> np.ndarray:
         ),
     ],
     outputs=[
-        ImageOutput(image_type="Input0", assume_normalized=True),
+        ImageOutput(shape_as=0, assume_normalized=True),
     ],
 )
 def hue_and_saturation_node(

backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/invert_color.py

@@ -15,7 +15,7 @@
     description="Inverts all colors in an image.",
     icon="MdInvertColors",
     inputs=[ImageInput()],
-    outputs=[ImageOutput(image_type="Input0", assume_normalized=True)],
+    outputs=[ImageOutput(shape_as=0, assume_normalized=True)],
 )
 def invert_color_node(img: np.ndarray) -> np.ndarray:
     c = get_h_w_c(img)[2]

backend/src/packages/chaiNNer_standard/image_adjustment/arithmetic/add.py

@@ -24,7 +24,7 @@
             controls_step=1,
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def add_node(img: np.ndarray, add: float) -> np.ndarray:
     if add == 0:

backend/src/packages/chaiNNer_standard/image_adjustment/arithmetic/divide.py

@@ -25,7 +25,7 @@
             scale="log",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def divide_node(img: np.ndarray, divide: float) -> np.ndarray:
     if divide == 1.0:

backend/src/packages/chaiNNer_standard/image_adjustment/arithmetic/multiply.py

@@ -25,7 +25,7 @@
             scale="log",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def multiply_node(img: np.ndarray, mult: float) -> np.ndarray:
     if mult == 1.0:

backend/src/packages/chaiNNer_standard/image_adjustment/arithmetic/premultiplied_alpha.py

@@ -32,7 +32,7 @@ class AlphaAssociation(Enum):
             },
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def premultiplied_alpha_node(
     img: np.ndarray, alpha_association: AlphaAssociation

backend/src/packages/chaiNNer_standard/image_adjustment/gamma/gamma.py

@@ -27,12 +27,7 @@
         ),
         BoolInput("Invert Gamma", default=False),
     ],
-    outputs=[
-        ImageOutput(
-            image_type="Input0",
-            assume_normalized=True,
-        )
-    ],
+    outputs=[ImageOutput(shape_as=0, assume_normalized=True)],
 )
 def gamma_node(img: np.ndarray, gamma: float, invert_gamma: bool) -> np.ndarray:
     if gamma == 1:

backend/src/packages/chaiNNer_standard/image_adjustment/gamma/log_to_linear.py

@@ -44,7 +44,7 @@
             "When checked this will convert the input image back to the Kodak Cineon Logarithmic encoding"
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def log_to_linear_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_adjustment/threshold/threshold.py

@@ -68,7 +68,7 @@ class ThresholdType(Enum):
         ),
     ],
     outputs=[
-        ImageOutput(image_type="Input0"),
+        ImageOutput(shape_as=0),
     ],
     key_info=KeyInfo.number(1),
     see_also=[

backend/src/packages/chaiNNer_standard/image_adjustment/threshold/threshold_adaptive.py

@@ -74,7 +74,7 @@ class AdaptiveMethod(Enum):
             "Assuming that **Threshold Type** is *Binary*, then higher values will result in more white pixels and lower values will result in more black pixels.",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
     limited_to_8bpc=True,
 )
 def threshold_adaptive_node(

backend/src/packages/chaiNNer_standard/image_filter/blur/box_blur.py

@@ -76,7 +76,7 @@ def get_kernel_2d(radius_x: float, radius_y: float) -> np.ndarray:
             ),
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def box_blur_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/blur/gaussian_blur.py

@@ -40,7 +40,7 @@
             ),
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def gaussian_blur_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/blur/lens_blur.py

@@ -170,7 +170,7 @@ def lens_blur(
             scale="log",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def lens_blur_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/blur/median_blur.py

@@ -19,7 +19,7 @@
         ImageInput(),
         SliderInput("Radius", minimum=0, maximum=1000, default=1, scale="log"),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
     limited_to_8bpc=True,
 )
 def median_blur_node(

backend/src/packages/chaiNNer_standard/image_filter/blur/surface_blur.py

@@ -42,7 +42,7 @@
             maximum=1000,
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def surface_blur_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/correction/average_color_fix.py

@@ -33,7 +33,7 @@
             unit="%",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def average_color_fix_node(
     input_img: np.ndarray, ref_img: np.ndarray, scale_factor: float

backend/src/packages/chaiNNer_standard/image_filter/correction/color_transfer.py

@@ -59,7 +59,7 @@ class TransferColorAlgorithm(Enum):
             BoolInput("Reciprocal Scaling Factor", default=True).with_id(4),
         ),
     ],
-    outputs=[ImageOutput("Image", image_type="Input0")],
+    outputs=[ImageOutput("Image", shape_as=0)],
 )
 def color_transfer_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/miscellaneous/dilate.py

@@ -49,7 +49,7 @@ class MorphShape(Enum):
             scale="log",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def dilate_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/miscellaneous/distance_transform.py

@@ -59,7 +59,7 @@ def binary_sdf(img: np.ndarray, spread: float) -> np.ndarray:
             "Sub-pixel distance transform is implemented using the excellent [ESDF algorithm](https://acko.net/blog/subpixel-distance-transform/) by Steven Wittens.",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def distance_transform_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/miscellaneous/edge_detection.py

@@ -143,7 +143,7 @@ class GradientComponent(Enum):
             SliderInput("Radius 2", minimum=0, default=2, maximum=20, precision=3),
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def edge_detection_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/miscellaneous/erode.py

@@ -49,7 +49,7 @@ class MorphShape(Enum):
             scale="log",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def erode_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/miscellaneous/pixelate.py

@@ -24,7 +24,7 @@
             SliderInput("Size Y", minimum=1, maximum=1024, default=10, scale="log"),
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def pixelate_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/noise/denoise.py

@@ -42,7 +42,7 @@
         NumberInput("Patch radius", minimum=1, default=3, maximum=30, precision=0),
         NumberInput("Search radius", minimum=1, default=10, maximum=30, precision=0),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
     limited_to_8bpc=True,
 )
 def denoise_node(

backend/src/packages/chaiNNer_standard/image_filter/quantize/dither.py

@@ -82,7 +82,7 @@ class UniformDitherAlgorithm(Enum):
             ).with_id(5),
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def dither_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/quantize/dither_palette.py

@@ -66,7 +66,7 @@ class PaletteDitherAlgorithm(Enum):
             ).with_id(4),
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def dither_palette_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/sharpen/high_boost_filter.py

@@ -57,7 +57,7 @@ class KernelType(Enum):
             )
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def high_boost_filter_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_filter/sharpen/unsharp_mask.py

@@ -47,7 +47,7 @@
             scale="log",
         ),
     ],
-    outputs=[ImageOutput(image_type="Input0")],
+    outputs=[ImageOutput(shape_as=0)],
 )
 def unsharp_mask_node(
     img: np.ndarray,

backend/src/packages/chaiNNer_standard/image_utility/modification/flip.py

@@ -19,7 +19,7 @@
         ImageInput("Image"),
         EnumInput(FlipAxis),
     ],
-    outputs=[ImageOutput(image_type="Input0", assume_normalized=True)],
+    outputs=[ImageOutput(shape_as=0, assume_normalized=True)],
     key_info=KeyInfo.enum(1),
 )
 def flip_node(img: np.ndarray, axis: FlipAxis) -> np.ndarray:

backend/src/packages/chaiNNer_standard/material_textures/conversion/metal_to_specular.py

@@ -62,7 +62,7 @@ def metal_to_spec(
         ImageInput("Roughness", channels=1).make_optional(),
     ],
     outputs=[
-        ImageOutput("Diffuse", image_type="Input0"),
+        ImageOutput("Diffuse", shape_as=0),
         ImageOutput(
             "Specular",
             image_type=navi.Image(size_as="Input1"),

backend/src/packages/chaiNNer_standard/material_textures/conversion/specular_to_metal.py

@@ -88,7 +88,7 @@ def spec_to_metal(
         ),
     ],
     outputs=[
-        ImageOutput("Albedo", image_type="Input0"),
+        ImageOutput("Albedo", shape_as=0),
         ImageOutput(
             "Metal",
             image_type=navi.Image(size_as="Input1"),

docs/CONTRIBUTING.md

@@ -132,7 +132,7 @@ from .. import blur_group
         NumberInput("Radius", minimum=0, default=2, precision=1),
     ],
     outputs=[
-        ImageOutput(image_type="Input0"),
+        ImageOutput(shape_as=0),
     ],
 )
 def gaussian_blur_node(img: np.ndarray, radius: float) -> np.ndarray:
@@ -144,7 +144,7 @@ Let's go through the metadata:
 -   `schema_id` is a unique identifier for the node. The schema ID currently doesn't have a defined format, but we typically use `chainner:<category>:<node_name>`. In our example, we use `chainner:image_filter:gaussian_blur`.
 -   `name`, `description`, and `icon` are all displayed in the UI.
 -   `inputs` declares all arguments of the python function. In our case, we have 2 inputs: `img` and `radius`. `ImageInput` and `NumberInput` are classes that define the type of the input. `ImageInput` is used for images, and `NumberInput` is used for numbers. `NumberInput` takes a few arguments. `minimum` and `maximum` define the range of the number, `default` defines the default value, and `precision` defines the number of decimal places.
--   `outputs` declares all return values of the python function. In our case, we return one image. `ImageOutput` is used for images. `image_type="Input0"` means that the output image has the same size and number of channels as the input image.
+-   `outputs` declares all return values of the python function. In our case, we return one image. `ImageOutput` is used for images. `shape_as=0` means that the output image has the same size and number of channels as the input image.
 
 For more information about metadata, see the [Node Metadata docs](nodes.md#node-metadata).