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import torch
import time
import asyncio
from comfy.utils import ProgressBar
from .tools import VariantSupport
from comfy_execution.graph_utils import GraphBuilder
from comfy.comfy_types.node_typing import ComfyNodeABC
from comfy.comfy_types import IO
class TestLazyMixImages:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image1": ("IMAGE",{"lazy": True}),
"image2": ("IMAGE",{"lazy": True}),
"mask": ("MASK",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "mix"
CATEGORY = "Testing/Nodes"
def check_lazy_status(self, mask, image1, image2):
mask_min = mask.min()
mask_max = mask.max()
needed = []
if image1 is None and (mask_min != 1.0 or mask_max != 1.0):
needed.append("image1")
if image2 is None and (mask_min != 0.0 or mask_max != 0.0):
needed.append("image2")
return needed
# Not trying to handle different batch sizes here just to keep the demo simple
def mix(self, mask, image1, image2):
mask_min = mask.min()
mask_max = mask.max()
if mask_min == 0.0 and mask_max == 0.0:
return (image1,)
elif mask_min == 1.0 and mask_max == 1.0:
return (image2,)
if len(mask.shape) == 2:
mask = mask.unsqueeze(0)
if len(mask.shape) == 3:
mask = mask.unsqueeze(3)
if mask.shape[3] < image1.shape[3]:
mask = mask.repeat(1, 1, 1, image1.shape[3])
result = image1 * (1. - mask) + image2 * mask,
return (result[0],)
class TestVariadicAverage:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input1": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "variadic_average"
CATEGORY = "Testing/Nodes"
def variadic_average(self, input1, **kwargs):
inputs = [input1]
while 'input' + str(len(inputs) + 1) in kwargs:
inputs.append(kwargs['input' + str(len(inputs) + 1)])
return (torch.stack(inputs).mean(dim=0),)
class TestCustomIsChanged:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
},
"optional": {
"should_change": ("BOOL", {"default": False}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "custom_is_changed"
CATEGORY = "Testing/Nodes"
def custom_is_changed(self, image, should_change=False):
return (image,)
@classmethod
def IS_CHANGED(cls, should_change=False, *args, **kwargs):
if should_change:
return float("NaN")
else:
return False
class TestIsChangedWithConstants:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "custom_is_changed"
CATEGORY = "Testing/Nodes"
def custom_is_changed(self, image, value):
return (image * value,)
@classmethod
def IS_CHANGED(cls, image, value):
if image is None:
return value
else:
return image.mean().item() * value
class TestCustomValidation1:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input1": ("IMAGE,FLOAT",),
"input2": ("IMAGE,FLOAT",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "custom_validation1"
CATEGORY = "Testing/Nodes"
def custom_validation1(self, input1, input2):
if isinstance(input1, float) and isinstance(input2, float):
result = torch.ones([1, 512, 512, 3]) * input1 * input2
else:
result = input1 * input2
return (result,)
@classmethod
def VALIDATE_INPUTS(cls, input1=None, input2=None):
if input1 is not None:
if not isinstance(input1, (torch.Tensor, float)):
return f"Invalid type of input1: {type(input1)}"
if input2 is not None:
if not isinstance(input2, (torch.Tensor, float)):
return f"Invalid type of input2: {type(input2)}"
return True
class TestCustomValidation2:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input1": ("IMAGE,FLOAT",),
"input2": ("IMAGE,FLOAT",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "custom_validation2"
CATEGORY = "Testing/Nodes"
def custom_validation2(self, input1, input2):
if isinstance(input1, float) and isinstance(input2, float):
result = torch.ones([1, 512, 512, 3]) * input1 * input2
else:
result = input1 * input2
return (result,)
@classmethod
def VALIDATE_INPUTS(cls, input_types, input1=None, input2=None):
if input1 is not None:
if not isinstance(input1, (torch.Tensor, float)):
return f"Invalid type of input1: {type(input1)}"
if input2 is not None:
if not isinstance(input2, (torch.Tensor, float)):
return f"Invalid type of input2: {type(input2)}"
if 'input1' in input_types:
if input_types['input1'] not in ["IMAGE", "FLOAT"]:
return f"Invalid type of input1: {input_types['input1']}"
if 'input2' in input_types:
if input_types['input2'] not in ["IMAGE", "FLOAT"]:
return f"Invalid type of input2: {input_types['input2']}"
return True
@VariantSupport()
class TestCustomValidation3:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input1": ("IMAGE,FLOAT",),
"input2": ("IMAGE,FLOAT",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "custom_validation3"
CATEGORY = "Testing/Nodes"
def custom_validation3(self, input1, input2):
if isinstance(input1, float) and isinstance(input2, float):
result = torch.ones([1, 512, 512, 3]) * input1 * input2
else:
result = input1 * input2
return (result,)
class TestCustomValidation4:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input1": ("FLOAT",),
"input2": ("FLOAT",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "custom_validation4"
CATEGORY = "Testing/Nodes"
def custom_validation4(self, input1, input2):
result = torch.ones([1, 512, 512, 3]) * input1 * input2
return (result,)
@classmethod
def VALIDATE_INPUTS(cls, input1, input2):
if input1 is not None:
if not isinstance(input1, float):
return f"Invalid type of input1: {type(input1)}"
if input2 is not None:
if not isinstance(input2, float):
return f"Invalid type of input2: {type(input2)}"
return True
class TestCustomValidation5:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input1": ("FLOAT", {"min": 0.0, "max": 1.0}),
"input2": ("FLOAT", {"min": 0.0, "max": 1.0}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "custom_validation5"
CATEGORY = "Testing/Nodes"
def custom_validation5(self, input1, input2):
value = input1 * input2
return (torch.ones([1, 512, 512, 3]) * value,)
@classmethod
def VALIDATE_INPUTS(cls, **kwargs):
if kwargs['input2'] == 7.0:
return "7s are not allowed. I've never liked 7s."
return True
class TestDynamicDependencyCycle:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input1": ("IMAGE",),
"input2": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "dynamic_dependency_cycle"
CATEGORY = "Testing/Nodes"
def dynamic_dependency_cycle(self, input1, input2):
g = GraphBuilder()
mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
mix1 = g.node("TestLazyMixImages", image1=input1, mask=mask.out(0))
mix2 = g.node("TestLazyMixImages", image1=mix1.out(0), image2=input2, mask=mask.out(0))
# Create the cyle
mix1.set_input("image2", mix2.out(0))
return {
"result": (mix2.out(0),),
"expand": g.finalize(),
}
class TestMixedExpansionReturns:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input1": ("FLOAT",),
},
}
RETURN_TYPES = ("IMAGE","IMAGE")
FUNCTION = "mixed_expansion_returns"
CATEGORY = "Testing/Nodes"
def mixed_expansion_returns(self, input1):
white_image = torch.ones([1, 512, 512, 3])
if input1 <= 0.1:
return (torch.ones([1, 512, 512, 3]) * 0.1, white_image)
elif input1 <= 0.2:
return {
"result": (torch.ones([1, 512, 512, 3]) * 0.2, white_image),
}
else:
g = GraphBuilder()
mask = g.node("StubMask", value=0.3, height=512, width=512, batch_size=1)
black = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
white = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
mix = g.node("TestLazyMixImages", image1=black.out(0), image2=white.out(0), mask=mask.out(0))
return {
"result": (mix.out(0), white_image),
"expand": g.finalize(),
}
class TestSamplingInExpansion:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 100}),
"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 30.0}),
"prompt": ("STRING", {"multiline": True, "default": "a beautiful landscape with mountains and trees"}),
"negative_prompt": ("STRING", {"multiline": True, "default": "blurry, bad quality, worst quality"}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "sampling_in_expansion"
CATEGORY = "Testing/Nodes"
def sampling_in_expansion(self, model, clip, vae, seed, steps, cfg, prompt, negative_prompt):
g = GraphBuilder()
# Create a basic image generation workflow using the input model, clip and vae
# 1. Setup text prompts using the provided CLIP model
positive_prompt = g.node("CLIPTextEncode",
text=prompt,
clip=clip)
negative_prompt = g.node("CLIPTextEncode",
text=negative_prompt,
clip=clip)
# 2. Create empty latent with specified size
empty_latent = g.node("EmptyLatentImage", width=512, height=512, batch_size=1)
# 3. Setup sampler and generate image latent
sampler = g.node("KSampler",
model=model,
positive=positive_prompt.out(0),
negative=negative_prompt.out(0),
latent_image=empty_latent.out(0),
seed=seed,
steps=steps,
cfg=cfg,
sampler_name="euler_ancestral",
scheduler="normal")
# 4. Decode latent to image using VAE
output = g.node("VAEDecode", samples=sampler.out(0), vae=vae)
return {
"result": (output.out(0),),
"expand": g.finalize(),
}
class TestSleep(ComfyNodeABC):
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": (IO.ANY, {}),
"seconds": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 9999.0, "step": 0.01, "tooltip": "The amount of seconds to sleep."}),
},
"hidden": {
"unique_id": "UNIQUE_ID",
},
}
RETURN_TYPES = (IO.ANY,)
FUNCTION = "sleep"
CATEGORY = "_for_testing"
async def sleep(self, value, seconds, unique_id):
pbar = ProgressBar(seconds, node_id=unique_id)
start = time.time()
expiration = start + seconds
now = start
while now < expiration:
now = time.time()
pbar.update_absolute(now - start)
await asyncio.sleep(0.01)
return (value,)
class TestParallelSleep(ComfyNodeABC):
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image1": ("IMAGE", ),
"image2": ("IMAGE", ),
"image3": ("IMAGE", ),
"sleep1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}),
"sleep2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}),
"sleep3": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}),
},
"hidden": {
"unique_id": "UNIQUE_ID",
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "parallel_sleep"
CATEGORY = "_for_testing"
OUTPUT_NODE = True
def parallel_sleep(self, image1, image2, image3, sleep1, sleep2, sleep3, unique_id):
# Create a graph dynamically with three TestSleep nodes
g = GraphBuilder()
# Create sleep nodes for each duration and image
sleep_node1 = g.node("TestSleep", value=image1, seconds=sleep1)
sleep_node2 = g.node("TestSleep", value=image2, seconds=sleep2)
sleep_node3 = g.node("TestSleep", value=image3, seconds=sleep3)
# Blend the results using TestVariadicAverage
blend = g.node("TestVariadicAverage",
input1=sleep_node1.out(0),
input2=sleep_node2.out(0),
input3=sleep_node3.out(0))
return {
"result": (blend.out(0),),
"expand": g.finalize(),
}
class TestOutputNodeWithSocketOutput:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "process"
CATEGORY = "_for_testing"
OUTPUT_NODE = True
def process(self, image, value):
# Apply value scaling and return both as output and socket
result = image * value
return (result,)
TEST_NODE_CLASS_MAPPINGS = {
"TestLazyMixImages": TestLazyMixImages,
"TestVariadicAverage": TestVariadicAverage,
"TestCustomIsChanged": TestCustomIsChanged,
"TestIsChangedWithConstants": TestIsChangedWithConstants,
"TestCustomValidation1": TestCustomValidation1,
"TestCustomValidation2": TestCustomValidation2,
"TestCustomValidation3": TestCustomValidation3,
"TestCustomValidation4": TestCustomValidation4,
"TestCustomValidation5": TestCustomValidation5,
"TestDynamicDependencyCycle": TestDynamicDependencyCycle,
"TestMixedExpansionReturns": TestMixedExpansionReturns,
"TestSamplingInExpansion": TestSamplingInExpansion,
"TestSleep": TestSleep,
"TestParallelSleep": TestParallelSleep,
"TestOutputNodeWithSocketOutput": TestOutputNodeWithSocketOutput,
}
TEST_NODE_DISPLAY_NAME_MAPPINGS = {
"TestLazyMixImages": "Lazy Mix Images",
"TestVariadicAverage": "Variadic Average",
"TestCustomIsChanged": "Custom IsChanged",
"TestIsChangedWithConstants": "IsChanged With Constants",
"TestCustomValidation1": "Custom Validation 1",
"TestCustomValidation2": "Custom Validation 2",
"TestCustomValidation3": "Custom Validation 3",
"TestCustomValidation4": "Custom Validation 4",
"TestCustomValidation5": "Custom Validation 5",
"TestDynamicDependencyCycle": "Dynamic Dependency Cycle",
"TestMixedExpansionReturns": "Mixed Expansion Returns",
"TestSamplingInExpansion": "Sampling In Expansion",
"TestSleep": "Test Sleep",
"TestParallelSleep": "Test Parallel Sleep",
"TestOutputNodeWithSocketOutput": "Test Output Node With Socket Output",
}
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