proper type of INPUT_TYPES return

2025-03-17 18:37:30 -04:00
parent 81bf0a201a
commit 00a4b79fd3
10 changed files with 169 additions and 117 deletions
@@ -47,7 +47,7 @@ import importlib
 from pathlib import Path
 from string import Template
 from types import ModuleType
-from typing import Any, Dict, List, Literal, Tuple
+from typing import Any, Dict, List, Literal, Tuple, TypeAlias

 try:
    from markdownify import markdownify
@@ -121,6 +121,8 @@ class AnyType(str):

 JOV_TYPE_ANY = AnyType("*")

+InputType: TypeAlias = Dict[str, Tuple[str|List[str], Dict[str, Any]]]
+
 # want to make explicit entries; comfy only looks for single type
 JOV_TYPE_NUMBER = "BOOLEAN,FLOAT,INT"
 JOV_TYPE_VECTOR = "VEC2,VEC3,VEC4,VEC2INT,VEC3INT,VEC4INT,COORD2D,COORD3D"
@@ -7,7 +7,7 @@ import sys
 import math
 import random
 from enum import Enum
-from typing import Any, Dict, List, Tuple
+from typing import Any, List, Tuple
 from collections import Counter

 import torch
@@ -17,14 +17,18 @@ from loguru import logger

 from comfy.utils import ProgressBar

-from .. import JOV_TYPE_ANY, JOV_TYPE_FULL, JOV_TYPE_NUMBER, JOV_TYPE_NUMERICAL, \
-    Lexicon, JOVBaseNode, \
+from .. import \
+    JOV_TYPE_ANY, JOV_TYPE_FULL, JOV_TYPE_NUMBER, JOV_TYPE_NUMERICAL, \
+    InputType, Lexicon, JOVBaseNode, \
    comfy_api_post, deep_merge, parse_reset

-from ..sup.util import EnumConvertType, EnumSwizzle, \
+from ..sup.util import \
+    EnumConvertType, EnumSwizzle, \
    parse_dynamic, parse_param, parse_value, vector_swap, zip_longest_fill

-from ..sup.anim import EnumWave, EnumEase, ease_op, wave_op
+from ..sup.anim import \
+    EnumWave, EnumEase, \
+    ease_op, wave_op

 # ==============================================================================

@@ -217,7 +221,7 @@ STRING is treated as a list of CHARACTER.
 IMAGE and MASK will return a TRUE bit for any non-black pixel, as a stream of bits for all pixels in the image.
 """
    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -270,7 +274,7 @@ Perform single function operations like absolute value, mean, median, mode, magn
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -372,7 +376,7 @@ Execute binary operations like addition, subtraction, multiplication, division,
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        names_convert = EnumConvertType._member_names_[:10]
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
@@ -514,7 +518,7 @@ Evaluates two inputs (A and B) with a specified comparison operators and optiona
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -636,7 +640,7 @@ Additionally, you can specify the easing function (EASE) and the desired output
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        names_convert = EnumConvertType._member_names_[:10]
        d = deep_merge(d, {
@@ -721,7 +725,7 @@ Manipulate strings through filtering
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -786,7 +790,7 @@ Swap components between two vectors based on specified swizzle patterns and valu
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        names_convert = EnumConvertType._member_names_[3:10]
        d = deep_merge(d, {
@@ -841,7 +845,7 @@ A timer and frame counter, emitting pulses or signals based on time intervals. I
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -931,7 +935,7 @@ Supplies raw or default values for various data types, supporting vector input w
    UPDATE = False

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()

        typ = EnumConvertType._member_names_
@@ -1049,7 +1053,7 @@ Produce waveforms like sine, square, or sawtooth with adjustable frequency, ampl
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -1103,7 +1107,7 @@ Outputs a VEC2 or VEC2INT.
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -1141,7 +1145,7 @@ Outputs a VEC3 or VEC3INT.
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -1182,7 +1186,7 @@ Outputs a VEC4 or VEC4INT.
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -1223,7 +1227,7 @@ class ParameterNode(JOVBaseNode):
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -3,7 +3,7 @@ Jovimetrix - Composition
 """

 from enum import Enum
-from typing import Any, Dict, List, Tuple
+from typing import Any, List, Tuple

 import cv2
 import torch
@@ -13,38 +13,46 @@ from loguru import logger

 from comfy.utils import ProgressBar

-from .. import JOV_TYPE_IMAGE, \
-    JOVBaseNode, JOVImageNode, Lexicon, \
+from .. import \
+    JOV_TYPE_IMAGE, \
+    JOVBaseNode, JOVImageNode, Lexicon, InputType, \
    deep_merge

-from ..sup.util import EnumConvertType, \
+from ..sup.util import \
+    EnumConvertType, \
    parse_dynamic, parse_param, zip_longest_fill

-from ..sup.image import MIN_IMAGE_SIZE, \
+from ..sup.image import \
+    MIN_IMAGE_SIZE, \
    EnumImageType, \
    image_mask, image_mask_add, image_matte, image_minmax, image_convert, \
    cv2tensor, cv2tensor_full, tensor2cv

-from ..sup.image.color import EnumCBDeficiency, EnumCBSimulator, EnumColorMap, EnumColorTheory, \
+from ..sup.image.color import \
+    EnumCBDeficiency, EnumCBSimulator, EnumColorMap, EnumColorTheory, \
    color_lut_full, color_lut_match, color_lut_palette, \
    color_lut_tonal, color_lut_visualize, color_match_reinhard, color_theory, color_blind, \
    color_top_used, image_gradient_expand, image_gradient_map, pixel_eval

-from ..sup.image.adjust import EnumEdge, EnumMirrorMode, EnumScaleMode, \
+from ..sup.image.adjust import \
+    EnumEdge, EnumMirrorMode, EnumScaleMode, \
    EnumInterpolation, EnumThreshold, EnumThresholdAdapt, \
    image_contrast, image_edge_wrap, image_equalize, image_filter, image_gamma,  \
    image_hsv, image_invert, image_mirror, image_pixelate, image_posterize, \
    image_quantize, image_scalefit, image_sharpen, image_swap_channels, \
    image_transform, image_flatten, image_threshold, morph_edge_detect, morph_emboss

-from ..sup.image.channel import EnumPixelSwizzle, \
+from ..sup.image.channel import \
+    EnumPixelSwizzle, \
    channel_merge, channel_solid

-from ..sup.image.compose import EnumAdjustOP, EnumBlendType, EnumOrientation, \
+from ..sup.image.compose import \
+    EnumAdjustOP, EnumBlendType, EnumOrientation, \
    image_levels, image_split, image_stack, image_blend, \
    image_crop, image_crop_center, image_crop_polygonal

-from ..sup.image.mapping import EnumProjection, \
+from ..sup.image.mapping import \
+    EnumProjection, \
    remap_fisheye, remap_perspective, remap_polar, remap_sphere

 # ==============================================================================
@@ -77,7 +85,7 @@ Enhance and modify images with various effects such as blurring, sharpening, col
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -225,7 +233,7 @@ Combine two input images using various blending modes, such as normal, screen, m
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -314,7 +322,7 @@ Simulate color blindness effects on images. You can select various types of colo
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -351,7 +359,7 @@ Adjust the color scheme of one image to match another with the Color Match Node.
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -439,7 +447,7 @@ The top-k colors ordered from most->least used as a strip, tonal palette and 3D
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -497,7 +505,7 @@ Generate a color harmony based on the selected scheme. Supported schemes include
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -536,7 +544,7 @@ Extract a portion of an input image or resize it. It supports various cropping m
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -600,7 +608,7 @@ Create masks based on specific color ranges within an image. Specify the color r
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -645,7 +653,7 @@ Combine multiple input images into a single image by summing their pixel values.
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -688,7 +696,7 @@ Remaps an input image using a gradient lookup table (LUT). The gradient image wi
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -740,7 +748,7 @@ Combines individual color channels (red, green, blue) along with an optional mas
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -825,7 +833,7 @@ Takes an input image and splits it into its individual color channels (red, gree
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -853,7 +861,7 @@ Swap pixel values between two input images based on specified channel swizzle op
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -918,7 +926,7 @@ Merge multiple input images into a single composite image by stacking them along
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -962,7 +970,7 @@ Define a range and apply it to an image for segmentation and feature extraction.
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -1005,7 +1013,7 @@ Apply various geometric transformations to images, including translation, rotati
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -1109,7 +1117,7 @@ The Histogram Node generates a histogram representation of the input image, show
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -2,7 +2,7 @@
 Jovimetrix - Creation
 """

-from typing import Dict, Tuple
+from typing import Tuple

 import torch
 import numpy as np
@@ -11,27 +11,34 @@ from skimage.filters import gaussian

 from comfy.utils import ProgressBar

-from .. import JOV_TYPE_IMAGE, \
-    JOVBaseNode, JOVImageNode, Lexicon, \
+from .. import \
+    JOV_TYPE_IMAGE, \
+    InputType, JOVBaseNode, JOVImageNode, Lexicon, \
    deep_merge

-from ..sup.util import EnumConvertType, \
+from ..sup.util import \
+    EnumConvertType, \
    parse_param, zip_longest_fill

-from ..sup.image import MIN_IMAGE_SIZE, EnumImageType, image_convert, image_mask, \
-    image_mask_add, image_matte, cv2tensor, cv2tensor_full, tensor2cv, pil2cv
+from ..sup.image import \
+    MIN_IMAGE_SIZE, \
+    EnumImageType, \
+    image_convert, image_mask_add, image_matte, cv2tensor, cv2tensor_full, tensor2cv, pil2cv

 from ..sup.image.channel import channel_solid

-from ..sup.image.compose import EnumShapes, \
+from ..sup.image.compose import \
+    EnumShapes, \
    shape_ellipse, shape_polygon, shape_quad, image_mask_binary

-from ..sup.image.adjust import EnumEdge, EnumScaleMode, EnumInterpolation, \
+from ..sup.image.adjust import \
+    EnumEdge, EnumScaleMode, EnumInterpolation, \
    image_invert, image_rotate, image_scalefit, image_transform, image_translate

 from ..sup.image.mapping import image_stereogram

-from ..sup.text import EnumAlignment, EnumJustify, \
+from ..sup.text import \
+    EnumAlignment, EnumJustify, \
    font_names, text_autosize, text_draw

 # ==============================================================================
@@ -48,7 +55,7 @@ Generate a constant image or mask of a specified size and color. It can be used
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -103,7 +110,7 @@ Create n-sided polygons. These shapes can be customized by adjusting parameters
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -174,7 +181,7 @@ Generates false perception 3D images from 2D input. Set tile divisions, noise, g
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -220,7 +227,7 @@ class StereoscopicNode(JOVBaseNode):
 Simulates depth perception in images by generating stereoscopic views. It accepts an optional input image for color matte. Adjust baseline and focal length for customized depth effects.
 """
    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -258,7 +265,7 @@ Generates images containing text based on parameters such as font, size, alignme
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -4,7 +4,7 @@ Jovimetrix GLSL Creation

 import sys
 from pathlib import Path
-from typing import Any, Dict, Tuple
+from typing import Any, Tuple

 import torch
 from loguru import logger
@@ -16,21 +16,25 @@ except:
    pass
 from comfy.utils import ProgressBar

-from .. import JOV_TYPE_IMAGE, \
-    Lexicon, JOVImageNode, \
+from .. import \
+    JOV_TYPE_IMAGE, \
+    InputType, Lexicon, JOVImageNode, \
    comfy_api_post, deep_merge

-from ..sup.util import EnumConvertType, \
+from ..sup.util import \
+    EnumConvertType, \
    parse_param, parse_value

-from ..sup.image.adjust import EnumInterpolation, EnumScaleMode, \
+from ..sup.image.adjust import \
+    EnumInterpolation, EnumScaleMode, \
    image_scalefit

-from ..sup.image import MIN_IMAGE_SIZE, \
+from ..sup.image import \
+    MIN_IMAGE_SIZE, \
    image_convert, tensor2cv, cv2tensor_full

-from ..sup.shader import JOV_ROOT_GLSL, GLSL_PROGRAMS, PROG_FRAGMENT, \
-    PROG_VERTEX, PTYPE, \
+from ..sup.shader import \
+    JOV_ROOT_GLSL, GLSL_PROGRAMS, PROG_FRAGMENT, PROG_VERTEX, PTYPE, \
    CompileException, EnumGLSLEdge, GLSLShader, \
    shader_meta, load_file_glsl

@@ -85,7 +89,7 @@ class GLSLNodeBase(JOVImageNode):
    CATEGORY = f"JOVIMETRIX 🔺🟩🔵/GLSL"

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -180,7 +184,7 @@ Execute custom GLSL (OpenGL Shading Language) fragment shaders to generate image
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        opts = d.get('optional', {})
        opts.update({
@@ -202,7 +206,7 @@ class GLSLNodeDynamic(GLSLNodeBase):
    PARAM = None

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        original_params = super().INPUT_TYPES()
        opts = original_params.get('optional', {})
        opts.update({
@@ -6,18 +6,23 @@ Jovimetrix - Device -- MIDI
    type 2 (asynchronous): each track is independent of the others
 """

-from typing import Dict, Tuple
+from typing import Tuple
 from math import isclose
 from queue import Queue

 from comfy.utils import ProgressBar

-from .. import JOVBaseNode, Lexicon, deep_merge
+from .. import \
+    InputType, JOVBaseNode, Lexicon, \
+    deep_merge

-from ..sup.util import EnumConvertType, parse_param
+from ..sup.util import \
+    EnumConvertType, \
+    parse_param

-from ..sup.midi import MIDIMessage, MIDINoteOnFilter, MIDIServerThread,\
-      midi_device_names
+from ..sup.midi import \
+    MIDIMessage, MIDINoteOnFilter, MIDIServerThread, \
+    midi_device_names

 # ==============================================================================

@@ -47,7 +52,7 @@ Processes MIDI messages received from an external MIDI controller or device. It
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -80,7 +85,7 @@ Captures MIDI messages from an external MIDI device or controller. It monitors M
    CHANGED = False

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -151,7 +156,7 @@ Provides advanced filtering capabilities for MIDI messages based on various crit
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -256,7 +261,7 @@ Filter MIDI messages based on various criteria, including MIDI mode (such as not
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -5,7 +5,7 @@ Jovimetrix - Device -- WEBCAM, REMOTE URLS, SPOUT
 import sys
 import time
 import uuid
-from typing import Dict, Tuple
+from typing import Tuple
 from enum import Enum

 import cv2
@@ -14,21 +14,25 @@ from loguru import logger

 from comfy.utils import ProgressBar

-from .. import JOV_DOCKERENV, JOV_TYPE_IMAGE, \
-    JOVBaseNode, JOVImageNode, Lexicon, \
+from .. import \
+    JOV_DOCKERENV, JOV_TYPE_IMAGE, \
+    InputType, JOVBaseNode, JOVImageNode, Lexicon, \
    deep_merge

-from ..sup.util import EnumConvertType, \
+from ..sup.util import \
+    EnumConvertType, \
    parse_param, zip_longest_fill

-from ..sup.stream import camera_list, monitor_list, window_list, \
-    monitor_capture, StreamingServer, StreamManager, \
-    MediaStreamDevice, JOV_SPOUT
+from ..sup.stream import \
+    JOV_SPOUT, \
+    StreamingServer, StreamManager, MediaStreamDevice, \
+    camera_list, monitor_list, window_list, monitor_capture

 if not JOV_DOCKERENV:
    from ..sup.stream import window_capture

-from ..sup.image.adjust import EnumScaleMode, EnumInterpolation, \
+from ..sup.image.adjust import \
+    EnumScaleMode, EnumInterpolation, \
    image_scalefit

 from ..sup.image.channel import channel_solid
@@ -36,7 +40,8 @@ from ..sup.image.channel import channel_solid
 if JOV_SPOUT:
    from ..sup.stream import SpoutSender, MediaStreamSpout

-from ..sup.image import MIN_IMAGE_SIZE, \
+from ..sup.image import \
+    MIN_IMAGE_SIZE, \
    EnumImageType, \
    image_convert, cv2tensor_full, tensor2cv

@@ -75,7 +80,7 @@ Capture frames from various sources such as URLs, cameras, monitors, windows, or
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()

        if cls.CAMERAS is None:
@@ -296,7 +301,7 @@ Sends frames to a specified route, typically for live streaming or recording pur
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -367,7 +372,7 @@ Sends frames to a specified Spout receiver application for real-time video shari
 """

        @classmethod
-        def INPUT_TYPES(cls) -> Dict[str, str]:
+        def INPUT_TYPES(cls) -> InputType:
            d = super().INPUT_TYPES()
            d = deep_merge(d, {
                "optional": {
@@ -10,7 +10,7 @@ import random
 from enum import Enum
 from pathlib import Path
 from itertools import zip_longest
-from typing import Any, Dict, List, Literal, Tuple
+from typing import Any, List, Literal, Tuple

 import torch
 import numpy as np
@@ -20,15 +20,21 @@ from loguru import logger
 from comfy.utils import ProgressBar
 from nodes import interrupt_processing

-from ... import JOV_TYPE_ANY, ROOT, Lexicon, JOVBaseNode, deep_merge, \
-    comfy_api_post, parse_reset
+from ... import \
+    JOV_TYPE_ANY, ROOT, \
+    InputType, Lexicon, JOVBaseNode, \
+    deep_merge, comfy_api_post, parse_reset

-from ...sup.util import EnumConvertType, parse_dynamic, parse_param
+from ...sup.util import \
+    EnumConvertType, \
+    parse_dynamic, parse_param

-from ...sup.image import MIN_IMAGE_SIZE, IMAGE_FORMATS, \
+from ...sup.image import \
+    MIN_IMAGE_SIZE, IMAGE_FORMATS, \
    image_convert, image_matte, image_load, cv2tensor, cv2tensor_full, tensor2cv

-from ...sup.image.adjust import EnumScaleMode, EnumInterpolation, \
+from ...sup.image.adjust import \
+    EnumScaleMode, EnumInterpolation, \
    image_scalefit

 # ==============================================================================
@@ -69,7 +75,7 @@ Processes a batch of data based on the selected mode, such as merging, picking,
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -238,7 +244,7 @@ class QueueBaseNode(JOVBaseNode):
        return float('nan')

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -478,7 +484,7 @@ Manage a queue of specific items: media files. Supports various image and video
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -4,16 +4,25 @@ Jovimetrix - Utility

 import io
 import json
-from typing import Any, Dict, Tuple
+from typing import Any, Tuple

 import torch
 import numpy as np
 from PIL import Image
 import matplotlib.pyplot as plt

-from ... import JOV_TYPE_IMAGE, Lexicon, JOVBaseNode, deep_merge, parse_reset
-from ...sup.util import EnumConvertType, parse_dynamic, parse_param
-from ...sup.image import MIN_IMAGE_SIZE, pil2tensor
+from ... import \
+    JOV_TYPE_IMAGE, \
+    InputType, Lexicon, JOVBaseNode, \
+    deep_merge, parse_reset
+
+from ...sup.util import \
+    EnumConvertType, \
+    parse_dynamic, parse_param
+
+from ...sup.image import \
+    MIN_IMAGE_SIZE, \
+    pil2tensor

 # ==============================================================================

@@ -139,7 +148,7 @@ Visualize a series of data points over time. It accepts a dynamic number of valu
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -220,7 +229,7 @@ Exports and Displays immediate information about images.
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -6,7 +6,7 @@ import os
 import json
 from uuid import uuid4
 from pathlib import Path
-from typing import Any, Dict, Tuple
+from typing import Any, Tuple

 import torch
 import numpy as np
@@ -19,12 +19,14 @@ from comfy.utils import ProgressBar
 from folder_paths import get_output_directory
 from nodes import interrupt_processing

-from ... import JOV_TYPE_ANY, JOV_TYPE_IMAGE, \
-    Lexicon, JOVBaseNode, ComfyAPIMessage, TimedOutException, \
+from ... import \
+    JOV_TYPE_ANY, JOV_TYPE_IMAGE, \
+    InputType, Lexicon, JOVBaseNode, ComfyAPIMessage, TimedOutException, \
    comfy_api_post, deep_merge

-from ...sup.util import EnumConvertType, path_next, parse_param, \
-    zip_longest_fill
+from ...sup.util import \
+    EnumConvertType, \
+    path_next, parse_param, zip_longest_fill

 from ...sup.image import tensor2cv, tensor2pil

@@ -70,7 +72,7 @@ Introduce pauses in the workflow that accept an optional input to pass through a
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -124,7 +126,7 @@ Responsible for saving images or animations to disk. It supports various output
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        d = deep_merge(d, {
            "optional": {
@@ -234,7 +236,7 @@ Routes the input data from the optional input ports to the output port, preservi
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES()
        e = {
            "optional": {
@@ -262,7 +264,7 @@ Save the output image along with its metadata to the specified path. Supports sa
 """

    @classmethod
-    def INPUT_TYPES(cls) -> Dict[str, str]:
+    def INPUT_TYPES(cls) -> InputType:
        d = super().INPUT_TYPES(True, True)
        d = deep_merge(d, {
            "optional": {