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Removed nodes_v3_test.py

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Jedrzej Kosinski
2025-07-30 19:51:25 -07:00
parent 22cbb96c63
commit 040e062800
1 changed files with 0 additions and 276 deletions
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comfy_extras/nodes_v3_test.py
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@@ -1,276 +0,0 @@
import torch
import time
from comfy_api.latest import io, ui, _io
from comfy_api.latest import ComfyExtension
import logging # noqa
import comfy.utils
import asyncio
from typing_extensions import override
@io.comfytype(io_type="XYZ")
class XYZ(io.ComfyTypeIO):
Type = tuple[int,str]
class V3TestNode(io.ComfyNode):
# NOTE: this is here just to test that state is not leaking
def __init__(self):
super().__init__()
self.hahajkunless = ";)"
@classmethod
def define_schema(cls):
return io.Schema(
node_id="V3_01_TestNode1",
display_name="V3 Test Node",
category="v3 nodes",
description="This is a funky V3 node test.",
inputs=[
io.Image.Input("image", display_name="new_image"),
XYZ.Input("xyz", optional=True),
io.Custom("JKL").Input("jkl", optional=True),
io.Mask.Input("mask", display_name="mask haha", optional=True),
io.Int.Input("some_int", display_name="new_name", min=0, max=127, default=42,
tooltip="My tooltip 😎", display_mode=io.NumberDisplay.slider),
io.Combo.Input("combo", options=["a", "b", "c"], tooltip="This is a combo input"),
io.MultiCombo.Input("combo2", options=["a","b","c"]),
io.MultiType.Input(io.Int.Input("int_multitype", display_name="haha"), types=[io.Float]),
io.MultiType.Input("multitype", types=[io.Mask, io.Float, io.Int], optional=True),
],
outputs=[
io.Int.Output(),
io.Image.Output(display_name="img🖼️", tooltip="This is an image"),
],
hidden=[
io.Hidden.prompt,
io.Hidden.auth_token_comfy_org,
io.Hidden.unique_id,
],
is_output_node=True,
)
@classmethod
def validate_inputs(cls, image: io.Image.Type, some_int: int, combo: io.Combo.Type, combo2: io.MultiCombo.Type, xyz: XYZ.Type=None, mask: io.Mask.Type=None, **kwargs):
if some_int < 0:
raise Exception("some_int must be greater than 0")
if combo == "c":
raise Exception("combo must be a or b")
return True
@classmethod
def execute(cls, image: io.Image.Type, some_int: int, combo: io.Combo.Type, combo2: io.MultiCombo.Type, xyz: XYZ.Type=None, mask: io.Mask.Type=None, **kwargs):
if hasattr(cls, "hahajkunless"):
raise Exception("The 'cls' variable leaked instance state between runs!")
if hasattr(cls, "doohickey"):
raise Exception("The 'cls' variable leaked state on class properties between runs!")
try:
cls.doohickey = "LOLJK"
except AttributeError:
pass
return io.NodeOutput(some_int, image, ui=ui.PreviewImage(image, cls=cls))
# class V3LoraLoader(io.ComfyNode):
# @classmethod
# def define_schema(cls):
# return io.Schema(
# node_id="V3_LoraLoader",
# display_name="V3 LoRA Loader",
# category="v3 nodes",
# description="LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together.",
# inputs=[
# io.Model.Input("model", tooltip="The diffusion model the LoRA will be applied to."),
# io.Clip.Input("clip", tooltip="The CLIP model the LoRA will be applied to."),
# io.Combo.Input(
# "lora_name",
# options=folder_paths.get_filename_list("loras"),
# tooltip="The name of the LoRA."
# ),
# io.Float.Input(
# "strength_model",
# default=1.0,
# min=-100.0,
# max=100.0,
# step=0.01,
# tooltip="How strongly to modify the diffusion model. This value can be negative."
# ),
# io.Float.Input(
# "strength_clip",
# default=1.0,
# min=-100.0,
# max=100.0,
# step=0.01,
# tooltip="How strongly to modify the CLIP model. This value can be negative."
# ),
# ],
# outputs=[
# io.Model.Output(),
# io.Clip.Output(),
# ],
# )
# @classmethod
# def execute(cls, model: io.Model.Type, clip: io.Clip.Type, lora_name: str, strength_model: float, strength_clip: float, **kwargs):
# if strength_model == 0 and strength_clip == 0:
# return io.NodeOutput(model, clip)
# lora = cls.resources.get(resources.TorchDictFolderFilename("loras", lora_name))
# model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
# return io.NodeOutput(model_lora, clip_lora)
class NInputsTest(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="V3_NInputsTest",
display_name="V3 N Inputs Test",
inputs=[
_io.AutogrowDynamic.Input("nmock", template_input=io.Image.Input("image"), min=1, max=3),
_io.AutogrowDynamic.Input("nmock2", template_input=io.Int.Input("int"), optional=True, min=1, max=4),
],
outputs=[
io.Image.Output(),
],
)
@classmethod
def validate_inputs(cls, nmock, nmock2):
return True
@classmethod
def fingerprint_inputs(cls, nmock, nmock2):
return time.time()
@classmethod
def check_lazy_status(cls, **kwargs) -> list[str]:
need = [name for name in kwargs if kwargs[name] is None]
return need
@classmethod
def execute(cls, nmock, nmock2):
first_image = nmock[0]
all_images = []
for img in nmock:
if img.shape != first_image.shape:
img = img.movedim(-1,1)
img = comfy.utils.common_upscale(img, first_image.shape[2], first_image.shape[1], "lanczos", "center")
img = img.movedim(1,-1)
all_images.append(img)
combined_image = torch.cat(all_images, dim=0)
return io.NodeOutput(combined_image)
class V3TestSleep(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="V3_TestSleep",
display_name="V3 Test Sleep",
category="_for_testing",
description="Test async sleep functionality.",
inputs=[
io.AnyType.Input("value", display_name="Value"),
io.Float.Input("seconds", display_name="Seconds", default=1.0, min=0.0, max=9999.0, step=0.01, tooltip="The amount of seconds to sleep."),
],
outputs=[
io.AnyType.Output(),
],
hidden=[
io.Hidden.unique_id,
],
is_experimental=True,
)
@classmethod
async def execute(cls, value: io.AnyType.Type, seconds: io.Float.Type, **kwargs):
logging.info(f"V3TestSleep: {cls.hidden.unique_id}")
pbar = comfy.utils.ProgressBar(seconds, node_id=cls.hidden.unique_id)
start = time.time()
expiration = start + seconds
now = start
while now < expiration:
now = time.time()
pbar.update_absolute(now - start)
await asyncio.sleep(0.02)
return io.NodeOutput(value)
class V3DummyStart(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="V3_DummyStart",
display_name="V3 Dummy Start",
category="v3 nodes",
description="This is a dummy start node.",
inputs=[],
outputs=[
io.Custom("XYZ").Output(),
],
)
@classmethod
def execute(cls):
return io.NodeOutput(None)
class V3DummyEnd(io.ComfyNode):
COOL_VALUE = 123
@classmethod
def define_schema(cls):
return io.Schema(
node_id="V3_DummyEnd",
display_name="V3 Dummy End",
category="v3 nodes",
description="This is a dummy end node.",
inputs=[
io.Custom("XYZ").Input("xyz"),
],
outputs=[],
is_output_node=True,
)
@classmethod
def custom_action(cls):
return 456
@classmethod
def execute(cls, xyz: io.Custom("XYZ").Type):
logging.info(f"V3DummyEnd: {cls.COOL_VALUE}")
logging.info(f"V3DummyEnd: {cls.custom_action()}")
return
class V3DummyEndInherit(V3DummyEnd):
@classmethod
def define_schema(cls):
schema = super().define_schema()
schema.node_id = "V3_DummyEndInherit"
schema.display_name = "V3 Dummy End Inherit"
return schema
@classmethod
def execute(cls, xyz: io.Custom("XYZ").Type):
logging.info(f"V3DummyEndInherit: {cls.COOL_VALUE}")
return super().execute(xyz)
NODES_LIST: list[type[io.ComfyNode]] = [
V3TestNode,
# V3LoraLoader,
NInputsTest,
V3TestSleep,
V3DummyStart,
V3DummyEnd,
V3DummyEndInherit,
]
class v3TestExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return NODES_LIST
async def comfy_entrypoint() -> v3TestExtension:
return v3TestExtension()