* cleared serialization bug

* better "docker" environment check
* cleaner type hints

README.md

@@ -94,11 +94,16 @@ You can colorize nodes via their `title background`, `node body` or `title text`
 
 ## UPDATES
 
-**2024/02/07** @1.7.9:
+**2024/02/10** @1.7.12:
+* cleared serialization bug
+* better "docker" environment check
+* cleaner type hints
+
+**2024/02/08** @1.7.9:
 * moved to proper semantic version numbers
 * PyOpenGL-accelerate updated to 3.1.9
 * better versioning in requirements around numpy
-
+* cleaner
 **2024/02/07** @1.7.08:
 * `JOV_SCAN_DEVICES` reset to default off.
 

__init__.py

@@ -87,7 +87,16 @@
 # direct the documentation output -- used to build jovimetrix-examples
 JOV_INTERNAL_DOC = os.getenv("JOV_INTERNAL_DOC", str(ROOT / "_doc"))
 
-JOV_DOCKERENV = os.path.exists('/.dockerenv')
+JOV_DOCKERENV = False
+try:
+    with open('/proc/1/cgroup', 'rt') as f:
+        content = f.read()
+        JOV_DOCKERENV = any(x in content for x in ['docker', 'kubepods', 'containerd'])
+except FileNotFoundError:
+    pass
+
+if JOV_DOCKERENV:
+    logger.info("RUNNING IN A DOCKER")
 
 # The object_info route data -- cached
 COMFYUI_OBJ_DATA = {}

core/calc.py

@@ -699,7 +699,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw)  -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         # turn any all inputs into the
         data_list = parse_dynamic(kw, Lexicon.UNKNOWN, EnumConvertType.ANY, [""])
         if data_list is None:
@@ -768,7 +768,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw)  -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         pA = parse_param(kw, Lexicon.IN_A, EnumConvertType.VEC4, [(0,0,0,0)])
         pB = parse_param(kw, Lexicon.IN_B, EnumConvertType.VEC4, [(0,0,0,0)])
         swap_x = parse_param(kw, Lexicon.SWAP_X, EnumSwizzle, EnumSwizzle.A_X.name)

core/compose.py

@@ -245,7 +245,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         pA = parse_param(kw, Lexicon.PIXEL_A, EnumConvertType.IMAGE, None)
         pB = parse_param(kw, Lexicon.PIXEL_B, EnumConvertType.IMAGE, None)
         mask = parse_param(kw, Lexicon.MASK, EnumConvertType.MASK, None)
@@ -760,7 +760,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw)  -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw)  -> Tuple[torch.Tensor, ...]:
         rgba = parse_param(kw, Lexicon.PIXEL, EnumConvertType.IMAGE, None)
         R = parse_param(kw, Lexicon.R, EnumConvertType.MASK, None)
         G = parse_param(kw, Lexicon.G, EnumConvertType.MASK, None)
@@ -832,7 +832,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         images = []
         pA = parse_param(kw, Lexicon.PIXEL, EnumConvertType.IMAGE, None)
         pbar = ProgressBar(len(pA))
@@ -870,7 +870,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw)  -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw)  -> Tuple[torch.Tensor, ...]:
         pA = parse_param(kw, Lexicon.PIXEL_A, EnumConvertType.IMAGE, None)
         pB = parse_param(kw, Lexicon.PIXEL_B, EnumConvertType.IMAGE, None)
         swap_r = parse_param(kw, Lexicon.SWAP_R, EnumPixelSwizzle, EnumPixelSwizzle.RED_A.name)
@@ -932,7 +932,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         images = parse_dynamic(kw, Lexicon.IMAGE, EnumConvertType.IMAGE, None)
         if len(images) == 0:
             logger.warning("no images to stack")
@@ -1027,7 +1027,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         pA = parse_param(kw, Lexicon.PIXEL, EnumConvertType.IMAGE, None)
         offset = parse_param(kw, Lexicon.XY, EnumConvertType.VEC2, [(0, 0)], -2.5, 2.5)
         angle = parse_param(kw, Lexicon.ANGLE, EnumConvertType.FLOAT, 0)

core/create.py

@@ -115,7 +115,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         shape = parse_param(kw, Lexicon.SHAPE, EnumShapes, EnumShapes.CIRCLE.name)
         sides = parse_param(kw, Lexicon.SIDES, EnumConvertType.INT, 3, 3, 100)
         angle = parse_param(kw, Lexicon.ANGLE, EnumConvertType.FLOAT, 0)
@@ -183,7 +183,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         pA = parse_param(kw, Lexicon.PIXEL, EnumConvertType.IMAGE, None)
         depth = parse_param(kw, Lexicon.DEPTH, EnumConvertType.IMAGE, None)
         divisions = parse_param(kw, Lexicon.TILE, EnumConvertType.INT, 1, 1, 8)
@@ -225,7 +225,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor]:
         pA = parse_param(kw, Lexicon.PIXEL, EnumConvertType.IMAGE, None)
         baseline = parse_param(kw, Lexicon.INT, EnumConvertType.FLOAT, 0, 0.1, 1)
         focal_length = parse_param(kw, Lexicon.VALUE, EnumConvertType.FLOAT, 500, 0)
@@ -283,7 +283,7 @@ def INPUT_TYPES(cls) -> dict:
         })
         return Lexicon._parse(d)
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, **kw) -> Tuple[torch.Tensor, ...]:
         full_text = parse_param(kw, Lexicon.STRING, EnumConvertType.STRING, "jovimetrix")
         font_idx = parse_param(kw, Lexicon.FONT, EnumConvertType.STRING, self.FONT_NAMES[0])
         autosize = parse_param(kw, Lexicon.AUTOSIZE, EnumConvertType.BOOLEAN, False)

core/device_stream.py

@@ -29,7 +29,6 @@
 if not JOV_DOCKERENV:
     from ..sup.stream import window_capture
 
-
 from ..sup.image.adjust import EnumScaleMode, EnumInterpolation, \
     image_scalefit
 
@@ -150,7 +149,7 @@ def __init__(self, *arg, **kw) -> None:
         self.__empty = (a, e, m,)
         self.__last = [(a, e, m,)]
 
-    def run(self, **kw) -> Tuple[torch.Tensor, torch.Tensor]:
+    def run(self, *arg, **kw) -> Tuple[torch.Tensor, ...]:
         wait = parse_param(kw, Lexicon.WAIT, EnumConvertType.BOOLEAN, False)[0]
         if wait:
             return self.__last

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "jovimetrix"
 description = "Integrates Webcam, MIDI, Spout and GLSL shader support. Animation via tick. Parameter manipulation with wave generator. Math operations with Unary and Binary support. Value conversion for all major types (int, string, list, dict, Image, Mask). Shape mask generation, image stacking and channel ops, batch splitting, merging and randomizing, load images and video from anywhere, dynamic bus routing with a single node, export support for GIPHY, save output anywhere! flatten, crop, transform; check colorblindness, make stereogram or stereoscopic images, or liner interpolate values and more."
-version = "1.7.9"
+version = "1.7.12"
 license = { file = "LICENSE" }
 readme = "README.md"
 authors = [{ name = "Alexander G. Morano", email = "[email protected]" }]
@@ -27,12 +27,12 @@ dependencies = [
     "mido[ports-rtmidi]",
     "mss",
     "numba",
-    "numpy>=1.26.4,<2.0.0; python_version < '3.12'",
-    "numpy>=2.0.0; python_version > '3.11'",
+    "numpy>=1.26.4,<2.0.0; python_version <= '3.11'",
+    "numpy>=2.0.0; python_version >= '3.12'",
     "opencv-contrib-python",
     "Pillow",
     "PyOpenGL",
-    "PyOpenGL-accelerate; python_version < '3.12'",
+    "PyOpenGL-accelerate",
     "pywin32; platform_system==\"Windows\"",
     "requests",
     "scikit-image",

web/nodes/queue.js

@@ -9,8 +9,7 @@ import { app } from "../../../scripts/app.js";
 import { ComfyWidgets } from "../../../scripts/widgets.js"
 import { apiJovimetrix } from '../util/util_api.js'
 import { flashBackgroundColor } from '../util/util_fun.js'
-import { nodeFitHeight, TypeSlotEvent, TypeSlot } from '../util/util_node.js'
-import { widgetHide, widgetShow } from '../util/util_widget.js'
+import { TypeSlotEvent, TypeSlot } from '../util/util_node.js'
 import { widgetSizeModeHook } from '../util/util_jov.js'
 
 const _id1 = "QUEUE (JOV) 🗃";
@@ -60,45 +59,8 @@ app.registerExtension({
 
             const widget_queue = this.widgets.find(w => w.name == 'Q');
             const widget_batch = this.widgets.find(w => w.name == 'BATCH');
-            const widget_value = this.widgets.find(w => w.name == 'VAL');
             const widget_hold = this.widgets.find(w => w.name == '✋🏽');
             const widget_reset = this.widgets.find(w => w.name == 'RESET');
-            const widget_stop = this.widgets.find(w => w.name == 'STOP');
-            const widget_loop = this.widgets.find(w => w.name == '🔄');
-            /*
-            widget_batch.callback = () => {
-                widgetHide(this, widget_value);
-                widgetHide(this, widget_hold);
-                widgetHide(this, widget_stop);
-                widgetHide(this, widget_loop);
-                widgetHide(this, widget_reset);
-                if (widget_batch.value == false) {
-                    widgetShow(widget_value);
-                    widgetShow(widget_hold);
-                    if (widget_hold.value == false) {
-                        widgetShow(widget_stop);
-                        widgetShow(widget_loop);
-                        widgetShow(widget_reset);
-                    }
-                }
-                nodeFitHeight(this);
-            }
-
-            widget_hold.callback = () => {
-                if (widget_batch.value == true) {
-                    return;
-                }
-                widgetHide(this, widget_stop);
-                widgetHide(this, widget_loop);
-                widgetHide(this, widget_reset);
-                if (widget_hold.value == false) {
-                    widgetShow(widget_stop);
-                    widgetShow(widget_loop);
-                    widgetShow(widget_reset);
-                }
-                nodeFitHeight(this);
-            }
-            */
 
             widget_queue.inputEl.addEventListener('input', function () {
                 const value = widget_queue.value.split('\n');

web/nodes/stream_reader.js

@@ -76,7 +76,7 @@ app.registerExtension({
                 }
                 nodeFitHeight(self);
             }
-            setTimeout(() => { source.callback(); }, 10);
+            setTimeout(source.callback(), 10);
             return me;
         }
     }

web/util/util_widget.js

@@ -94,20 +94,6 @@ export function widgetHide(node, widget, suffix = '') {
     widget.origComputeSize = widget.computeSize;
     widget.computeSize = () => [0, -4];
 
-    widget.origSerializeValue = widget.serializeValue;
-    widget.serializeValue = async () => {
-        // Prevent serializing the widget if we have no input linked
-        if (!node.inputs) {
-            return undefined;
-        }
-
-        let node_input = node.inputs.find((i) => i.widget?.name == widget.name);
-        if (!node_input || !node_input.link) {
-            return undefined;
-        }
-        return widget.origSerializeValue ? widget.origSerializeValue() : widget.value;
-    }
-
     // Hide any linked widgets, e.g. seed+seedControl
     if (widget.linkedWidgets) {
         for (const w of widget.linkedWidgets) {