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ComfyUI/comfy_extras/nodes_mask.py
Christian Byrne bbb8864778
add search aliases to all nodes (#12035) 
* feat: Add search_aliases field to node schema

Adds `search_aliases` field to improve node discoverability. Users can define alternative search terms for nodes (e.g., "text concat" → StringConcatenate).

Changes:
- Add `search_aliases: list[str]` to V3 Schema
- Add `SEARCH_ALIASES` support for V1 nodes
- Include field in `/object_info` response
- Add aliases to high-priority core nodes

V1 usage:
```python
class MyNode:
    SEARCH_ALIASES = ["alt name", "synonym"]
```

V3 usage:
```python
io.Schema(
    node_id="MyNode",
    search_aliases=["alt name", "synonym"],
    ...
)
```

## Related PRs
- Frontend: Comfy-Org/ComfyUI_frontend#XXXX (draft - merge after this)
- Docs: Comfy-Org/docs#XXXX (draft - merge after stable)

* Propagate search_aliases through V3 Schema.get_v1_info to NodeInfoV1

* feat: add SEARCH_ALIASES for core nodes (#12016)

Add search aliases to 22 core nodes in nodes.py to improve node discoverability:
- Checkpoint/model loaders: CheckpointLoader, DiffusersLoader
- Conditioning nodes: ConditioningAverage, ConditioningSetArea, ConditioningSetMask, ConditioningZeroOut
- Style nodes: StyleModelApply
- Image nodes: LoadImageMask, LoadImageOutput, ImageBatch, ImageInvert, ImagePadForOutpaint
- Latent nodes: LoadLatent, SaveLatent, LatentBlend, LatentComposite, LatentCrop, LatentFlip, LatentFromBatch, LatentUpscale, LatentUpscaleBy, RepeatLatentBatch

* feat: add SEARCH_ALIASES for image, mask, and string nodes (#12017)

Add search aliases to nodes in comfy_extras for better discoverability:
- nodes_mask.py: mask manipulation nodes
- nodes_images.py: image processing nodes
- nodes_post_processing.py: post-processing effect nodes
- nodes_string.py: string manipulation nodes
- nodes_compositing.py: compositing nodes
- nodes_morphology.py: morphological operation nodes
- nodes_latent.py: latent space nodes

Uses search_aliases parameter in io.Schema() for v3 nodes.

* feat: add SEARCH_ALIASES for audio and video nodes (#12018)

Add search aliases to audio and video nodes for better discoverability:
- nodes_audio.py: audio loading, saving, and processing nodes
- nodes_video.py: video loading and processing nodes
- nodes_wan.py: WAN model nodes

Uses search_aliases parameter in io.Schema() for v3 nodes.

* feat: add SEARCH_ALIASES for model and misc nodes (#12019)

Add search aliases to model-related and miscellaneous nodes:
- Model nodes: nodes_model_merging.py, nodes_model_advanced.py, nodes_lora_extract.py
- Sampler nodes: nodes_custom_sampler.py, nodes_align_your_steps.py
- Control nodes: nodes_controlnet.py, nodes_attention_multiply.py, nodes_hooks.py
- Training nodes: nodes_train.py, nodes_dataset.py
- Utility nodes: nodes_logic.py, nodes_canny.py, nodes_differential_diffusion.py
- Architecture-specific: nodes_sd3.py, nodes_pixart.py, nodes_lumina2.py, nodes_kandinsky5.py, nodes_hidream.py, nodes_fresca.py, nodes_hunyuan3d.py
- Media nodes: nodes_load_3d.py, nodes_webcam.py, nodes_preview_any.py, nodes_wanmove.py

Uses search_aliases parameter in io.Schema() for v3 nodes, SEARCH_ALIASES class attribute for legacy nodes.
2026-01-22 18:36:58 -08:00

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import numpy as np
import scipy.ndimage
import torch
import comfy.utils
import node_helpers
from typing_extensions import override
from comfy_api.latest import ComfyExtension, IO, UI
import nodes
def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
source = source.to(destination.device)
if resize_source:
source = torch.nn.functional.interpolate(source, size=(destination.shape[-2], destination.shape[-1]), mode="bilinear")
source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
x = max(-source.shape[-1] * multiplier, min(x, destination.shape[-1] * multiplier))
y = max(-source.shape[-2] * multiplier, min(y, destination.shape[-2] * multiplier))
left, top = (x // multiplier, y // multiplier)
right, bottom = (left + source.shape[-1], top + source.shape[-2],)
if mask is None:
mask = torch.ones_like(source)
else:
mask = mask.to(destination.device, copy=True)
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[-2], source.shape[-1]), mode="bilinear")
mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
# calculate the bounds of the source that will be overlapping the destination
# this prevents the source trying to overwrite latent pixels that are out of bounds
# of the destination
visible_width, visible_height = (destination.shape[-1] - left + min(0, x), destination.shape[-2] - top + min(0, y),)
mask = mask[:, :, :visible_height, :visible_width]
if mask.ndim < source.ndim:
mask = mask.unsqueeze(1)
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[..., :visible_height, :visible_width]
destination_portion = inverse_mask * destination[..., top:bottom, left:right]
destination[..., top:bottom, left:right] = source_portion + destination_portion
return destination
class LatentCompositeMasked(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="LatentCompositeMasked",
search_aliases=["overlay latent", "layer latent", "paste latent", "inpaint latent"],
category="latent",
inputs=[
IO.Latent.Input("destination"),
IO.Latent.Input("source"),
IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
IO.Boolean.Input("resize_source", default=False),
IO.Mask.Input("mask", optional=True),
],
outputs=[IO.Latent.Output()],
)
@classmethod
def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
output = destination.copy()
destination = destination["samples"].clone()
source = source["samples"]
output["samples"] = composite(destination, source, x, y, mask, 8, resize_source)
return IO.NodeOutput(output)
composite = execute # TODO: remove
class ImageCompositeMasked(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ImageCompositeMasked",
search_aliases=["paste image", "overlay", "layer"],
category="image",
inputs=[
IO.Image.Input("destination"),
IO.Image.Input("source"),
IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Boolean.Input("resize_source", default=False),
IO.Mask.Input("mask", optional=True),
],
outputs=[IO.Image.Output()],
)
@classmethod
def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
destination, source = node_helpers.image_alpha_fix(destination, source)
destination = destination.clone().movedim(-1, 1)
output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
return IO.NodeOutput(output)
composite = execute # TODO: remove
class MaskToImage(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MaskToImage",
search_aliases=["convert mask"],
display_name="Convert Mask to Image",
category="mask",
inputs=[
IO.Mask.Input("mask"),
],
outputs=[IO.Image.Output()],
)
@classmethod
def execute(cls, mask) -> IO.NodeOutput:
result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
return IO.NodeOutput(result)
mask_to_image = execute # TODO: remove
class ImageToMask(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ImageToMask",
search_aliases=["extract channel", "channel to mask"],
display_name="Convert Image to Mask",
category="mask",
inputs=[
IO.Image.Input("image"),
IO.Combo.Input("channel", options=["red", "green", "blue", "alpha"]),
],
outputs=[IO.Mask.Output()],
)
@classmethod
def execute(cls, image, channel) -> IO.NodeOutput:
channels = ["red", "green", "blue", "alpha"]
mask = image[:, :, :, channels.index(channel)]
return IO.NodeOutput(mask)
image_to_mask = execute # TODO: remove
class ImageColorToMask(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ImageColorToMask",
search_aliases=["color keying", "chroma key"],
category="mask",
inputs=[
IO.Image.Input("image"),
IO.Int.Input("color", default=0, min=0, max=0xFFFFFF, step=1, display_mode=IO.NumberDisplay.number),
],
outputs=[IO.Mask.Output()],
)
@classmethod
def execute(cls, image, color) -> IO.NodeOutput:
temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]
mask = torch.where(temp == color, 1.0, 0).float()
return IO.NodeOutput(mask)
image_to_mask = execute # TODO: remove
class SolidMask(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="SolidMask",
category="mask",
inputs=[
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, step=1),
IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
],
outputs=[IO.Mask.Output()],
)
@classmethod
def execute(cls, value, width, height) -> IO.NodeOutput:
out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu")
return IO.NodeOutput(out)
solid = execute # TODO: remove
class InvertMask(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="InvertMask",
search_aliases=["reverse mask", "flip mask"],
category="mask",
inputs=[
IO.Mask.Input("mask"),
],
outputs=[IO.Mask.Output()],
)
@classmethod
def execute(cls, mask) -> IO.NodeOutput:
out = 1.0 - mask
return IO.NodeOutput(out)
invert = execute # TODO: remove
class CropMask(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="CropMask",
search_aliases=["cut mask", "extract mask region", "mask slice"],
category="mask",
inputs=[
IO.Mask.Input("mask"),
IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
],
outputs=[IO.Mask.Output()],
)
@classmethod
def execute(cls, mask, x, y, width, height) -> IO.NodeOutput:
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
out = mask[:, y:y + height, x:x + width]
return IO.NodeOutput(out)
crop = execute # TODO: remove
class MaskComposite(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MaskComposite",
search_aliases=["combine masks", "blend masks", "layer masks"],
category="mask",
inputs=[
IO.Mask.Input("destination"),
IO.Mask.Input("source"),
IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Combo.Input("operation", options=["multiply", "add", "subtract", "and", "or", "xor"]),
],
outputs=[IO.Mask.Output()],
)
@classmethod
def execute(cls, destination, source, x, y, operation) -> IO.NodeOutput:
output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
source = source.reshape((-1, source.shape[-2], source.shape[-1]))
left, top = (x, y,)
right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
visible_width, visible_height = (right - left, bottom - top,)
source_portion = source[:, :visible_height, :visible_width]
destination_portion = output[:, top:bottom, left:right]
if operation == "multiply":
output[:, top:bottom, left:right] = destination_portion * source_portion
elif operation == "add":
output[:, top:bottom, left:right] = destination_portion + source_portion
elif operation == "subtract":
output[:, top:bottom, left:right] = destination_portion - source_portion
elif operation == "and":
output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
elif operation == "or":
output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()
elif operation == "xor":
output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()
output = torch.clamp(output, 0.0, 1.0)
return IO.NodeOutput(output)
combine = execute # TODO: remove
class FeatherMask(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="FeatherMask",
search_aliases=["soft edge mask", "blur mask edges", "gradient mask edge"],
category="mask",
inputs=[
IO.Mask.Input("mask"),
IO.Int.Input("left", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("top", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("right", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("bottom", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
],
outputs=[IO.Mask.Output()],
)
@classmethod
def execute(cls, mask, left, top, right, bottom) -> IO.NodeOutput:
output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()
left = min(left, output.shape[-1])
right = min(right, output.shape[-1])
top = min(top, output.shape[-2])
bottom = min(bottom, output.shape[-2])
for x in range(left):
feather_rate = (x + 1.0) / left
output[:, :, x] *= feather_rate
for x in range(right):
feather_rate = (x + 1) / right
output[:, :, -x] *= feather_rate
for y in range(top):
feather_rate = (y + 1) / top
output[:, y, :] *= feather_rate
for y in range(bottom):
feather_rate = (y + 1) / bottom
output[:, -y, :] *= feather_rate
return IO.NodeOutput(output)
feather = execute # TODO: remove
class GrowMask(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="GrowMask",
search_aliases=["expand mask", "shrink mask"],
display_name="Grow Mask",
category="mask",
inputs=[
IO.Mask.Input("mask"),
IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
IO.Boolean.Input("tapered_corners", default=True),
],
outputs=[IO.Mask.Output()],
)
@classmethod
def execute(cls, mask, expand, tapered_corners) -> IO.NodeOutput:
c = 0 if tapered_corners else 1
kernel = np.array([[c, 1, c],
[1, 1, 1],
[c, 1, c]])
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
out = []
for m in mask:
output = m.numpy()
for _ in range(abs(expand)):
if expand < 0:
output = scipy.ndimage.grey_erosion(output, footprint=kernel)
else:
output = scipy.ndimage.grey_dilation(output, footprint=kernel)
output = torch.from_numpy(output)
out.append(output)
return IO.NodeOutput(torch.stack(out, dim=0))
expand_mask = execute # TODO: remove
class ThresholdMask(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ThresholdMask",
search_aliases=["binary mask"],
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:
mask = (mask > value).float()
return IO.NodeOutput(mask)
image_to_mask = execute # TODO: remove
# Mask Preview - original implement from
# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes
class MaskPreview(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MaskPreview",
search_aliases=["show mask", "view mask", "inspect mask", "debug mask"],
display_name="Preview Mask",
category="mask",
description="Saves the input images to your ComfyUI output directory.",
inputs=[
IO.Mask.Input("mask"),
],
hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
is_output_node=True,
)
@classmethod
def execute(cls, mask, filename_prefix="ComfyUI") -> IO.NodeOutput:
return IO.NodeOutput(ui=UI.PreviewMask(mask))
class MaskExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
LatentCompositeMasked,
ImageCompositeMasked,
MaskToImage,
ImageToMask,
ImageColorToMask,
SolidMask,
InvertMask,
CropMask,
MaskComposite,
FeatherMask,
GrowMask,
ThresholdMask,
MaskPreview,
]
async def comfy_entrypoint() -> MaskExtension:
return MaskExtension()
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