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Make multi-input node inputs optional

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kijai 2025-07-16 16:54:06 +03:00
parent ab8cac5396
commit ad37ce656c
3 changed files with 34 additions and 16 deletions
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38
nodes/image_nodes.py
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@ -1351,15 +1351,19 @@ class CrossFadeImagesMulti:
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE",),
"image_2": ("IMAGE",),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
},
"optional": {
"image_2": ("IMAGE",),
}
}
def crossfadeimages(self, inputcount, transitioning_frames, interpolation, **kwargs):
image_1 = kwargs["image_1"]
first_image_shape = image_1.shape
first_image_device = image_1.device
height = image_1.shape[1]
width = image_1.shape[2]
@ -1367,7 +1371,7 @@ class CrossFadeImagesMulti:
for c in range(1, inputcount):
frames = []
new_image = kwargs[f"image_{c + 1}"]
new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).to(first_image_device)
new_image_height = new_image.shape[1]
new_image_width = new_image.shape[2]
@ -1483,14 +1487,16 @@ Creates transitions between images.
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE",),
"image_2": ("IMAGE",),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
"transitioning_frames": ("INT", {"default": 2,"min": 2, "max": 4096, "step": 1}),
"blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
"reverse": ("BOOLEAN", {"default": False}),
"device": (["CPU", "GPU"], {"default": "CPU"}),
},
},
"optional": {
"image_2": ("IMAGE",),
}
}
def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse, **kwargs):
@ -1500,12 +1506,14 @@ Creates transitions between images.
image_1 = kwargs["image_1"]
height = image_1.shape[1]
width = image_1.shape[2]
first_image_shape = image_1.shape
first_image_device = image_1.device
easing_function = easing_functions[interpolation]
for c in range(1, inputcount):
frames = []
new_image = kwargs[f"image_{c + 1}"]
new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).to(first_image_device)
new_image_height = new_image.shape[1]
new_image_width = new_image.shape[2]
@ -2106,8 +2114,11 @@ class ImageBatchMulti:
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE", ),
"image_2": ("IMAGE", ),
},
"optional": {
"image_2": ("IMAGE", ),
}
}
RETURN_TYPES = ("IMAGE",)
@ -2123,9 +2134,10 @@ with the **inputcount** and clicking update.
def combine(self, inputcount, **kwargs):
from nodes import ImageBatch
image_batch_node = ImageBatch()
image = kwargs["image_1"]
image = kwargs["image_1"].cpu()
first_image_shape = image.shape
for c in range(1, inputcount):
new_image = kwargs[f"image_{c + 1}"]
new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).cpu()
image, = image_batch_node.batch(image, new_image)
return (image,)
@ -2139,7 +2151,6 @@ class ImageTensorList:
}}
RETURN_TYPES = ("IMAGE",)
#OUTPUT_IS_LIST = (True,)
FUNCTION = "append"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
@ -2210,7 +2221,7 @@ class ImageConcatMulti:
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE", ),
"image_2": ("IMAGE", ),
"direction": (
[ 'right',
'down',
@ -2222,6 +2233,9 @@ class ImageConcatMulti:
}),
"match_image_size": ("BOOLEAN", {"default": False}),
},
"optional": {
"image_2": ("IMAGE", ),
},
}
RETURN_TYPES = ("IMAGE",)
@ -2240,7 +2254,7 @@ with the **inputcount** and clicking update.
if first_image_shape is None:
first_image_shape = image.shape
for c in range(1, inputcount):
new_image = kwargs[f"image_{c + 1}"]
new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape))
image, = ImageConcanate.concatenate(self, image, new_image, direction, match_image_size, first_image_shape=first_image_shape)
first_image_shape = None
return (image,)

8
nodes/nodes.py
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@ -270,10 +270,12 @@ class JoinStringMulti:
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"string_1": ("STRING", {"default": '', "forceInput": True}),
"string_2": ("STRING", {"default": '', "forceInput": True}),
"delimiter": ("STRING", {"default": ' ', "multiline": False}),
"return_list": ("BOOLEAN", {"default": False}),
},
"optional": {
"string_2": ("STRING", {"default": '', "forceInput": True}),
}
}
RETURN_TYPES = ("STRING",)
@ -292,7 +294,9 @@ with the **inputcount** and clicking update.
return_list = kwargs["return_list"]
strings = [string] # Initialize a list with the first string
for c in range(1, inputcount):
new_string = kwargs[f"string_{c + 1}"]
new_string = kwargs.get(f"string_{c + 1}", "")
if not new_string:
continue
if return_list:
strings.append(new_string) # Add new string to the list
else:

4
web/js/jsnodes.js
View File

@ -56,7 +56,7 @@ app.registerExtension({
}
else{
for(let i = num_inputs+1; i <= target_number_of_inputs; ++i)
this.addInput(`image_${i}`, this._type)
this.addInput(`image_${i}`, this._type, {shape: 7});
}
});
@ -247,7 +247,7 @@ app.registerExtension({
}
else{
for(let i = num_inputs+1; i <= target_number_of_inputs; ++i)
this.addInput(`string_${i}`, this._type)
this.addInput(`string${i}`, this._type, {shape: 7});
}
});
}