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removed passthru

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re-arrange of route and delay nodes
This commit is contained in:
Alexander G. Morano
2024-12-12 12:32:00 -05:00
parent 5c2ad75195
commit 759f309c11
5 changed files with 105 additions and 133 deletions
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core/calc.py
+1 -64
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@@ -3,7 +3,6 @@ Jovimetrix - http://www.github.com/amorano/jovimetrix
Calculation
"""
import os
import sys
import math
import random
@@ -17,10 +16,9 @@ from scipy.special import gamma
from loguru import logger
from comfy.utils import ProgressBar
from nodes import interrupt_processing
from Jovimetrix import JOV_TYPE_ANY, JOV_TYPE_FULL, JOV_TYPE_NUMBER, \
JOV_TYPE_VECTOR, Lexicon, JOVBaseNode, ComfyAPIMessage, TimedOutException, \
JOV_TYPE_VECTOR, Lexicon, JOVBaseNode, \
comfy_message, deep_merge, parse_reset
from Jovimetrix.sup.util import EnumConvertType, EnumSwizzle, parse_dynamic, \
@@ -32,17 +30,6 @@ from Jovimetrix.sup.anim import EnumWave, EnumEase, ease_op, wave_op
JOV_CATEGORY = "CALC"
# min amount of time before showing the cancel dialog
JOV_DELAY_MIN = 5
try: JOV_DELAY_MIN = int(os.getenv("JOV_DELAY_MIN", JOV_DELAY_MIN))
except: pass
JOV_DELAY_MIN = max(1, JOV_DELAY_MIN)
# max 10 minutes to start
JOV_DELAY_MAX = 600
try: JOV_DELAY_MAX = int(os.getenv("JOV_DELAY_MAX", JOV_DELAY_MAX))
except: pass
# ==============================================================================
# === LAMBDA ===
# ==============================================================================
@@ -593,56 +580,6 @@ Evaluates two inputs (A and B) with a specified comparison operators and optiona
outs = list(outs)
return outs, *vals,
class DelayNode(JOVBaseNode):
NAME = "DELAY (JOV) ✋🏽"
CATEGORY = f"JOVIMETRIX 🔺🟩🔵/{JOV_CATEGORY}"
RETURN_TYPES = (JOV_TYPE_ANY,)
RETURN_NAMES = (Lexicon.PASS_OUT,)
SORT = 240
DESCRIPTION = """
Introduce pauses in the workflow that accept an optional input to pass through and a timer parameter to specify the duration of the delay. If no timer is provided, it defaults to a maximum delay. During the delay, it periodically checks for messages to interrupt the delay. Once the delay is completed, it returns the input passed to it. You can disable the screensaver with the `ENABLE` option
"""
@classmethod
def INPUT_TYPES(cls) -> dict:
d = super().INPUT_TYPES()
d = deep_merge(d, {
"optional": {
Lexicon.PASS_IN: (JOV_TYPE_ANY, {"default": None}),
Lexicon.TIMER: ("INT", {"default" : 0, "mij": -1}),
Lexicon.ENABLE: ("BOOLEAN", {"default": True, "tooltips":"Enable or disable the screensaver"})
},
"outputs": {
0: (Lexicon.PASS_OUT, {"tooltips":"Pass through data when the delay ends"})
}
})
return Lexicon._parse(d, cls)
def run(self, ident, **kw) -> Tuple[Any]:
delay = parse_param(kw, Lexicon.TIMER, EnumConvertType.INT, -1, 0, JOV_DELAY_MAX)[0]
if delay < 0:
delay = JOV_DELAY_MAX
if delay > JOV_DELAY_MIN:
comfy_message(ident, "jovi-delay-user", {"id": ident, "timeout": delay})
# enable = parse_param(kw, Lexicon.ENABLE, EnumConvertType.BOOLEAN, True)
step = 1
pbar = ProgressBar(delay)
while step <= delay:
try:
data = ComfyAPIMessage.poll(ident, timeout=1)
if data.get('id', None) == ident:
if data.get('cmd', False) == False:
interrupt_processing(True)
logger.warning(f"delay [cancelled] ({step}): {ident}")
break
except TimedOutException as _:
if step % 10 == 0:
logger.info(f"delay [continue] ({step}): {ident}")
pbar.update_absolute(step)
step += 1
return kw[Lexicon.PASS_IN],
class LerpNode(JOVBaseNode):
NAME = "LERP (JOV) 🔰"
CATEGORY = f"JOVIMETRIX 🔺🟩🔵/{JOV_CATEGORY}"
core/utility/info.py
+1 -62
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@@ -14,7 +14,7 @@ import matplotlib.pyplot as plt
from loguru import logger
from Jovimetrix import JOV_TYPE_ANY, JOV_TYPE_IMAGE, DynamicInputType, \
from Jovimetrix import JOV_TYPE_ANY, JOV_TYPE_IMAGE, \
Lexicon, JOVBaseNode, deep_merge, parse_reset
from Jovimetrix.sup.util import EnumConvertType, decode_tensor, parse_dynamic, \
@@ -233,64 +233,3 @@ Exports and Displays immediate information about images.
count, height, width = image.shape
cc = 1
return count, width, height, cc, (width, height), (width, height, cc)
class Passthru(JOVBaseNode):
NAME = "PASSTHRU (JOV) 🚌"
CATEGORY = f"JOVIMETRIX 🔺🟩🔵/{JOV_CATEGORY}"
RETURN_TYPES = ()
RETURN_NAMES = ()
SORT = 860
DESCRIPTION = """
Passes the data into python so it can be probed.
"""
OUTPUT_NODE = True
@classmethod
def INPUT_TYPES(cls) -> dict:
d = super().INPUT_TYPES()
d = deep_merge(d, {
"optional": {
Lexicon.UNKNOWN: (JOV_TYPE_ANY, {"default": None, "tooltips":"Pass through data."}),
}
})
return Lexicon._parse(d, cls)
def run(self, **kw) -> Tuple[Any, ...]:
inout = parse_param(kw, Lexicon.UNKNOWN, EnumConvertType.ANY, [None])
for x in inout:
logger.info(f"{type(x)}")
# logger.info(dir(x))
return ()
class RouteNode(JOVBaseNode):
NAME = "ROUTE (JOV) 🚌"
CATEGORY = f"JOVIMETRIX 🔺🟩🔵/{JOV_CATEGORY}"
RETURN_TYPES = ("BUS",) + (JOV_TYPE_ANY,) * 127
RETURN_NAMES = (Lexicon.ROUTE,)
SORT = 850
DESCRIPTION = """
Routes the input data from the optional input ports to the output port, preserving the order of inputs. The `PASS_IN` optional input is directly passed through to the output, while other optional inputs are collected and returned as tuples, preserving the order of insertion.
"""
@classmethod
def INPUT_TYPES(cls) -> dict:
d = super().INPUT_TYPES()
d = deep_merge(d, {
"optional": DynamicInputType(JOV_TYPE_ANY),
"""
"optional": {
Lexicon.ROUTE: ("BUS", {"default": None, "tooltips":"Pass through another route node to pre-populate the outputs."}),
},
"""
"outputs": {
0: (Lexicon.ROUTE, {"tooltips":"Pass through for Route node"})
}
})
return Lexicon._parse(d, cls)
def run(self, **kw) -> Tuple[Any, ...]:
inout = parse_param(kw, Lexicon.ROUTE, EnumConvertType.ANY, [None])
vars = kw.copy()
vars.pop(Lexicon.ROUTE, None)
vars.pop('ident', None)
return inout, *vars.values(),
core/utility/io.py
+99 -2
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@@ -7,7 +7,7 @@ import os
import json
from uuid import uuid4
from pathlib import Path
from typing import Any
from typing import Any, Tuple
import torch
import numpy as np
@@ -18,8 +18,11 @@ from loguru import logger
from comfy.utils import ProgressBar
from folder_paths import get_output_directory
from nodes import interrupt_processing
from Jovimetrix import JOV_TYPE_IMAGE, Lexicon, JOVBaseNode, deep_merge
from Jovimetrix import JOV_TYPE_ANY, JOV_TYPE_IMAGE, Lexicon, JOVBaseNode, \
ComfyAPIMessage, TimedOutException, DynamicInputType, \
comfy_message, deep_merge
from Jovimetrix.sup.util import EnumConvertType, path_next, parse_param, \
zip_longest_fill
@@ -30,6 +33,17 @@ from Jovimetrix.sup.image import tensor2cv, tensor2pil
JOV_CATEGORY = "UTILITY"
# min amount of time before showing the cancel dialog
JOV_DELAY_MIN = 5
try: JOV_DELAY_MIN = int(os.getenv("JOV_DELAY_MIN", JOV_DELAY_MIN))
except: pass
JOV_DELAY_MIN = max(1, JOV_DELAY_MIN)
# max 10 minutes to start
JOV_DELAY_MAX = 600
try: JOV_DELAY_MAX = int(os.getenv("JOV_DELAY_MAX", JOV_DELAY_MAX))
except: pass
FORMATS = ["gif", "png", "jpg"]
if (JOV_GIFSKI := os.getenv("JOV_GIFSKI", None)) is not None:
if not os.path.isfile(JOV_GIFSKI):
@@ -43,6 +57,56 @@ else:
# ==============================================================================
class DelayNode(JOVBaseNode):
NAME = "DELAY (JOV) ✋🏽"
CATEGORY = f"JOVIMETRIX 🔺🟩🔵/{JOV_CATEGORY}"
RETURN_TYPES = (JOV_TYPE_ANY,)
RETURN_NAMES = (Lexicon.PASS_OUT,)
SORT = 240
DESCRIPTION = """
Introduce pauses in the workflow that accept an optional input to pass through and a timer parameter to specify the duration of the delay. If no timer is provided, it defaults to a maximum delay. During the delay, it periodically checks for messages to interrupt the delay. Once the delay is completed, it returns the input passed to it. You can disable the screensaver with the `ENABLE` option
"""
@classmethod
def INPUT_TYPES(cls) -> dict:
d = super().INPUT_TYPES()
d = deep_merge(d, {
"optional": {
Lexicon.PASS_IN: (JOV_TYPE_ANY, {"default": None}),
Lexicon.TIMER: ("INT", {"default" : 0, "mij": -1}),
Lexicon.ENABLE: ("BOOLEAN", {"default": True, "tooltips":"Enable or disable the screensaver"})
},
"outputs": {
0: (Lexicon.PASS_OUT, {"tooltips":"Pass through data when the delay ends"})
}
})
return Lexicon._parse(d, cls)
def run(self, ident, **kw) -> Tuple[Any]:
delay = parse_param(kw, Lexicon.TIMER, EnumConvertType.INT, -1, 0, JOV_DELAY_MAX)[0]
if delay < 0:
delay = JOV_DELAY_MAX
if delay > JOV_DELAY_MIN:
comfy_message(ident, "jovi-delay-user", {"id": ident, "timeout": delay})
# enable = parse_param(kw, Lexicon.ENABLE, EnumConvertType.BOOLEAN, True)
step = 1
pbar = ProgressBar(delay)
while step <= delay:
try:
data = ComfyAPIMessage.poll(ident, timeout=1)
if data.get('id', None) == ident:
if data.get('cmd', False) == False:
interrupt_processing(True)
logger.warning(f"delay [cancelled] ({step}): {ident}")
break
except TimedOutException as _:
if step % 10 == 0:
logger.info(f"delay [continue] ({step}): {ident}")
pbar.update_absolute(step)
step += 1
return kw[Lexicon.PASS_IN],
class ExportNode(JOVBaseNode):
NAME = "EXPORT (JOV) 📽"
CATEGORY = f"JOVIMETRIX 🔺🟩🔵/{JOV_CATEGORY}"
@@ -140,6 +204,39 @@ Responsible for saving images or animations to disk. It supports various output
img.save(output(format), optimize=optimize)
return ()
class RouteNode(JOVBaseNode):
NAME = "ROUTE (JOV) 🚌"
CATEGORY = f"JOVIMETRIX 🔺🟩🔵/{JOV_CATEGORY}"
RETURN_TYPES = ("BUS",) + (JOV_TYPE_ANY,) * 127
RETURN_NAMES = (Lexicon.ROUTE,)
SORT = 850
DESCRIPTION = """
Routes the input data from the optional input ports to the output port, preserving the order of inputs. The `PASS_IN` optional input is directly passed through to the output, while other optional inputs are collected and returned as tuples, preserving the order of insertion.
"""
@classmethod
def INPUT_TYPES(cls) -> dict:
d = super().INPUT_TYPES()
d = deep_merge(d, {
"optional": DynamicInputType(JOV_TYPE_ANY),
"""
"optional": {
Lexicon.ROUTE: ("BUS", {"default": None, "tooltips":"Pass through another route node to pre-populate the outputs."}),
},
"""
"outputs": {
0: (Lexicon.ROUTE, {"tooltips":"Pass through for Route node"})
}
})
return Lexicon._parse(d, cls)
def run(self, **kw) -> Tuple[Any, ...]:
inout = parse_param(kw, Lexicon.ROUTE, EnumConvertType.ANY, [None])
vars = kw.copy()
vars.pop(Lexicon.ROUTE, None)
vars.pop('ident', None)
return inout, *vars.values(),
class SaveOutput(JOVBaseNode):
NAME = "SAVE OUTPUT (JOV) 💾"
CATEGORY = f"JOVIMETRIX 🔺🟩🔵/{JOV_CATEGORY}"
node_list.json
-1
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@@ -39,7 +39,6 @@
"MIDI READER (JOV) \ud83c\udfb9": "Captures MIDI messages from an external MIDI device or controller",
"OP BINARY (JOV) \ud83c\udf1f": "Execute binary operations like addition, subtraction, multiplication, division, and bitwise operations on input values, supporting various data types and vector sizes",
"OP UNARY (JOV) \ud83c\udfb2": "Perform single function operations like absolute value, mean, median, mode, magnitude, normalization, maximum, or minimum on input values",
"PASSTHRU (JOV) \ud83d\ude8c": "Passes the data into python so it can be probed",
"PIXEL MERGE (JOV) \ud83e\udec2": "Combines individual color channels (red, green, blue) along with an optional mask channel to create a composite image",
"PIXEL SPLIT (JOV) \ud83d\udc94": "Takes an input image and splits it into its individual color channels (red, green, blue), along with a mask channel",
"PIXEL SWAP (JOV) \ud83d\udd03": "Swap pixel values between two input images based on specified channel swizzle operations",
sup/image/__init__.py
+4 -4
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@@ -11,7 +11,7 @@ from io import BytesIO
import math
import base64
from enum import Enum
from typing import List, Tuple
from typing import List, Tuple, Union
import cv2
import torch
@@ -45,9 +45,9 @@ TYPE_iRGBA = Tuple[int, int, int, int]
TYPE_fRGB = Tuple[float, float, float]
TYPE_fRGBA = Tuple[float, float, float, float]
TYPE_PIXEL = int | float | TYPE_iRGB | TYPE_iRGBA | TYPE_fRGB | TYPE_fRGBA
TYPE_IMAGE = np.ndarray | torch.Tensor
TYPE_VECTOR = TYPE_IMAGE | TYPE_PIXEL
TYPE_PIXEL = Union[int, float, TYPE_iRGB, TYPE_iRGBA, TYPE_fRGB, TYPE_fRGBA]
TYPE_IMAGE = Union[np.ndarray, torch.Tensor]
TYPE_VECTOR = Union[TYPE_IMAGE, TYPE_PIXEL]
# ==============================================================================
# === ENUMERATION ===