mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-27 08:16:44 +00:00
restore nodes order as it in the V1 version for smaller git diff
This commit is contained in:
bigcat88
parent
40abe9647c
commit
675e9fd788
@ -57,6 +57,161 @@ def composite(destination, source, x, y, mask=None, multiplier=8, resize_source=
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return destination
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class LatentCompositeMasked(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="LatentCompositeMasked_V3",
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display_name="Latent Composite Masked _V3",
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category="latent",
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inputs=[
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io.Latent.Input("destination"),
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io.Latent.Input("source"),
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io.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
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io.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
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io.Boolean.Input("resize_source", default=False),
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io.Mask.Input("mask", optional=True),
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],
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outputs=[io.Latent.Output()],
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)
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@classmethod
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def execute(cls, destination, source, x, y, resize_source, mask=None) -> io.NodeOutput:
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output = destination.copy()
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destination_samples = destination["samples"].clone()
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source_samples = source["samples"]
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output["samples"] = composite(destination_samples, source_samples, x, y, mask, 8, resize_source)
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return io.NodeOutput(output)
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class ImageCompositeMasked(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="ImageCompositeMasked_V3",
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display_name="Image Composite Masked _V3",
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category="image",
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inputs=[
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io.Image.Input("destination"),
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io.Image.Input("source"),
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io.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Boolean.Input("resize_source", default=False),
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io.Mask.Input("mask", optional=True),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, destination, source, x, y, resize_source, mask=None) -> io.NodeOutput:
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destination, source = node_helpers.image_alpha_fix(destination, source)
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destination = destination.clone().movedim(-1, 1)
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output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
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return io.NodeOutput(output)
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class MaskToImage(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="MaskToImage_V3",
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display_name="Convert Mask to Image _V3",
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category="mask",
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inputs=[
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io.Mask.Input("mask"),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, mask) -> io.NodeOutput:
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return io.NodeOutput(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3))
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class ImageToMask(io.ComfyNode):
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CHANNELS = ["red", "green", "blue", "alpha"]
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="ImageToMask_V3",
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display_name="Convert Image to Mask _V3",
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category="mask",
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inputs=[
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io.Image.Input("image"),
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io.Combo.Input("channel", options=cls.CHANNELS),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, image, channel) -> io.NodeOutput:
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return io.NodeOutput(image[:, :, :, cls.CHANNELS.index(channel)])
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class ImageColorToMask(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="ImageColorToMask_V3",
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display_name="Image Color to Mask _V3",
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category="mask",
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inputs=[
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io.Image.Input("image"),
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io.Int.Input("color", default=0, min=0, max=0xFFFFFF),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, image, color) -> io.NodeOutput:
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temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
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temp = (
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torch.bitwise_left_shift(temp[:, :, :, 0], 16)
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+ torch.bitwise_left_shift(temp[:, :, :, 1], 8)
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+ temp[:, :, :, 2]
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)
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return io.NodeOutput(torch.where(temp == color, 1.0, 0).float())
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class SolidMask(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="SolidMask_V3",
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display_name="Solid Mask _V3",
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category="mask",
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inputs=[
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io.Float.Input("value", default=1.0, min=0.0, max=1.0, step=0.01),
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io.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION),
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io.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, value, width, height) -> io.NodeOutput:
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return io.NodeOutput(torch.full((1, height, width), value, dtype=torch.float32, device="cpu"))
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class InvertMask(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="InvertMask_V3",
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display_name="Invert Mask _V3",
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category="mask",
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inputs=[
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io.Mask.Input("mask"),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, mask) -> io.NodeOutput:
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return io.NodeOutput(1.0 - mask)
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class CropMask(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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@ -80,6 +235,66 @@ class CropMask(io.ComfyNode):
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return io.NodeOutput(mask[:, y : y + height, x : x + width])
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class MaskComposite(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="MaskComposite_V3",
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display_name="Mask Composite _V3",
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category="mask",
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inputs=[
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io.Mask.Input("destination"),
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io.Mask.Input("source"),
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io.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Combo.Input("operation", options=["multiply", "add", "subtract", "and", "or", "xor"]),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, destination, source, x, y, operation) -> io.NodeOutput:
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output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
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source = source.reshape((-1, source.shape[-2], source.shape[-1]))
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left, top = (
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x,
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y,
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)
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right, bottom = (
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min(left + source.shape[-1], destination.shape[-1]),
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min(top + source.shape[-2], destination.shape[-2]),
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)
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visible_width, visible_height = (
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right - left,
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bottom - top,
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)
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source_portion = source[:, :visible_height, :visible_width]
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destination_portion = output[:, top:bottom, left:right]
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if operation == "multiply":
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output[:, top:bottom, left:right] = destination_portion * source_portion
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elif operation == "add":
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output[:, top:bottom, left:right] = destination_portion + source_portion
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elif operation == "subtract":
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output[:, top:bottom, left:right] = destination_portion - source_portion
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elif operation == "and":
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output[:, top:bottom, left:right] = torch.bitwise_and(
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destination_portion.round().bool(), source_portion.round().bool()
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).float()
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elif operation == "or":
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output[:, top:bottom, left:right] = torch.bitwise_or(
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destination_portion.round().bool(), source_portion.round().bool()
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).float()
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elif operation == "xor":
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output[:, top:bottom, left:right] = torch.bitwise_xor(
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destination_portion.round().bool(), source_portion.round().bool()
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).float()
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return io.NodeOutput(torch.clamp(output, 0.0, 1.0))
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class FeatherMask(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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@ -158,183 +373,28 @@ class GrowMask(io.ComfyNode):
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return io.NodeOutput(torch.stack(out, dim=0))
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class ImageColorToMask(io.ComfyNode):
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class ThresholdMask(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="ImageColorToMask_V3",
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display_name="Image Color to Mask _V3",
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category="mask",
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inputs=[
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io.Image.Input("image"),
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io.Int.Input("color", default=0, min=0, max=0xFFFFFF),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, image, color) -> io.NodeOutput:
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temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
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temp = (
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torch.bitwise_left_shift(temp[:, :, :, 0], 16)
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+ torch.bitwise_left_shift(temp[:, :, :, 1], 8)
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+ temp[:, :, :, 2]
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)
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return io.NodeOutput(torch.where(temp == color, 1.0, 0).float())
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class ImageCompositeMasked(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="ImageCompositeMasked_V3",
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display_name="Image Composite Masked _V3",
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category="image",
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inputs=[
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io.Image.Input("destination"),
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io.Image.Input("source"),
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io.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Boolean.Input("resize_source", default=False),
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io.Mask.Input("mask", optional=True),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, destination, source, x, y, resize_source, mask=None) -> io.NodeOutput:
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destination, source = node_helpers.image_alpha_fix(destination, source)
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destination = destination.clone().movedim(-1, 1)
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output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
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return io.NodeOutput(output)
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class ImageToMask(io.ComfyNode):
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CHANNELS = ["red", "green", "blue", "alpha"]
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="ImageToMask_V3",
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display_name="Convert Image to Mask _V3",
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category="mask",
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inputs=[
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io.Image.Input("image"),
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io.Combo.Input("channel", options=cls.CHANNELS),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, image, channel) -> io.NodeOutput:
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return io.NodeOutput(image[:, :, :, cls.CHANNELS.index(channel)])
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class InvertMask(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="InvertMask_V3",
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display_name="Invert Mask _V3",
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node_id="ThresholdMask_V3",
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display_name="Threshold Mask _V3",
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category="mask",
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inputs=[
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io.Mask.Input("mask"),
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io.Float.Input("value", default=0.5, min=0.0, max=1.0, step=0.01),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, mask) -> io.NodeOutput:
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return io.NodeOutput(1.0 - mask)
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class LatentCompositeMasked(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="LatentCompositeMasked_V3",
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display_name="Latent Composite Masked _V3",
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category="latent",
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inputs=[
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io.Latent.Input("destination"),
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io.Latent.Input("source"),
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io.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
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io.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
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io.Boolean.Input("resize_source", default=False),
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io.Mask.Input("mask", optional=True),
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],
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outputs=[io.Latent.Output()],
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)
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@classmethod
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def execute(cls, destination, source, x, y, resize_source, mask=None) -> io.NodeOutput:
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output = destination.copy()
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destination_samples = destination["samples"].clone()
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source_samples = source["samples"]
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output["samples"] = composite(destination_samples, source_samples, x, y, mask, 8, resize_source)
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return io.NodeOutput(output)
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class MaskComposite(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="MaskComposite_V3",
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display_name="Mask Composite _V3",
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category="mask",
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inputs=[
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io.Mask.Input("destination"),
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io.Mask.Input("source"),
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io.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Combo.Input("operation", options=["multiply", "add", "subtract", "and", "or", "xor"]),
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],
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outputs=[io.Mask.Output()],
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)
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@classmethod
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def execute(cls, destination, source, x, y, operation) -> io.NodeOutput:
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output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
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source = source.reshape((-1, source.shape[-2], source.shape[-1]))
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left, top = (
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x,
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y,
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)
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right, bottom = (
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min(left + source.shape[-1], destination.shape[-1]),
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min(top + source.shape[-2], destination.shape[-2]),
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)
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visible_width, visible_height = (
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right - left,
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bottom - top,
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)
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source_portion = source[:, :visible_height, :visible_width]
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destination_portion = output[:, top:bottom, left:right]
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if operation == "multiply":
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output[:, top:bottom, left:right] = destination_portion * source_portion
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elif operation == "add":
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output[:, top:bottom, left:right] = destination_portion + source_portion
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elif operation == "subtract":
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output[:, top:bottom, left:right] = destination_portion - source_portion
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elif operation == "and":
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output[:, top:bottom, left:right] = torch.bitwise_and(
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destination_portion.round().bool(), source_portion.round().bool()
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).float()
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elif operation == "or":
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output[:, top:bottom, left:right] = torch.bitwise_or(
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destination_portion.round().bool(), source_portion.round().bool()
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).float()
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elif operation == "xor":
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output[:, top:bottom, left:right] = torch.bitwise_xor(
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destination_portion.round().bool(), source_portion.round().bool()
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).float()
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return io.NodeOutput(torch.clamp(output, 0.0, 1.0))
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def execute(cls, mask, value) -> io.NodeOutput:
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return io.NodeOutput((mask > value).float())
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# Mask Preview - original implement from
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# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
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# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes
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class MaskPreview(io.ComfyNode):
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"""Mask Preview - original implement in ComfyUI_essentials.
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@ -360,63 +420,6 @@ class MaskPreview(io.ComfyNode):
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return io.NodeOutput(ui=ui.PreviewMask(masks))
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class MaskToImage(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="MaskToImage_V3",
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display_name="Convert Mask to Image _V3",
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category="mask",
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inputs=[
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io.Mask.Input("mask"),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, mask) -> io.NodeOutput:
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return io.NodeOutput(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3))
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class SolidMask(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="SolidMask_V3",
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display_name="Solid Mask _V3",
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category="mask",
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inputs=[
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io.Float.Input("value", default=1.0, min=0.0, max=1.0, step=0.01),
|
||||
io.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION),
|
||||
io.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION),
|
||||
],
|
||||
outputs=[io.Mask.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, value, width, height) -> io.NodeOutput:
|
||||
return io.NodeOutput(torch.full((1, height, width), value, dtype=torch.float32, device="cpu"))
|
||||
|
||||
|
||||
class ThresholdMask(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ThresholdMask_V3",
|
||||
display_name="Threshold Mask _V3",
|
||||
category="mask",
|
||||
inputs=[
|
||||
io.Mask.Input("mask"),
|
||||
io.Float.Input("value", default=0.5, min=0.0, max=1.0, step=0.01),
|
||||
],
|
||||
outputs=[io.Mask.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, mask, value) -> io.NodeOutput:
|
||||
return io.NodeOutput((mask > value).float())
|
||||
|
||||
|
||||
NODES_LIST: list[type[io.ComfyNode]] = [
|
||||
CropMask,
|
||||
FeatherMask,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user