allow slice reversal in ARRAY node

GLSL nodes allow for IMAGE or MASK input for RGB(A)

README.md

@@ -62,25 +62,40 @@ If those nodes have descriptions written in HTML or Markdown, they will be conve
 
 ## UPDATES
 
-**2024/09/04**:
+**2024/09/05** @1.2.35:
+* `VALUE` Node defaults fixed on save
+* `Colorizer` Panel is a undergoing major re-constructive surgery
+* Allow slice reversal in `ARRAY` Node
+* `GLSL` Nodes allow for `IMAGE or MASK` input for RGB(A)
+* NOTE ADJUSTED VERSION NUMBERS TO SHOW OLDEST COMFYUI and FRONTEND VERSIONS SUPPORTED:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
+
+**2024/09/04** @1.2.34:
 * Import change for chromium tab crash
 * Added ComfyUI default "tooltips" as last fallback for help docs
-* Supports ComfyUI 0.2.1+, frontend 1.2.45+
+* Supports Versions:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
 
-**2024/09/03**:
+**2024/09/03** @1.2.33:
 * New `QUEUE TOO` Node focused on efficient image media loading.
 * Better reporting in `AKASHIC` Node for core ComfyUI types.
 * `MODE` setting for most nodes has been defaulted to `MATTE`. The older `NONE` setting has been removed.
 * Thanks to [christian-byrne](https://github.com/christian-byrne) for squashing a bug in [the help sidebar!](https://github.com/Amorano/Jovimetrix/pull/55)
 * Thanks to [Ainaemaet](https://github.com/Ainaemaet) for cleaning up the `STREAM READER` Node device list [when no devices are present](https://github.com/Amorano/Jovimetrix/pull/53)!
-* Supports ComfyUI 0.2.0+, frontend 1.2.45+
+* Supports Versions:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
 
-**2024/08/31**:
+**2024/08/31** @1.2.32:
 * Better MASK/ALPHA support for `BLEND`, `ADJUST` and `QUEUE`
 * Cleaner Markdown outputs
-* Supports ComfyUI 0.1.3+, frontend 1.2.41+
+* Supports Versions:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
 
-**2024/08/28**:
+**2024/08/28** @1.2.31:
 
 * New `STRINGER` Node for string operations: Split, Join, Replace and Slice.
 
@@ -94,25 +109,35 @@ If those nodes have descriptions written in HTML or Markdown, they will be conve
 
 ![QUEUE NODE](https://github.com/user-attachments/assets/9686b900-24a2-46ab-88ba-9e3c929b439c)
 
-* Supports ComfyUI 0.1.3+, frontend 1.2.39+
+* Supports Versions:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
 
-**2024/08/25**:
+**2024/08/25** @1.2.30:
 * Added conversion coercion for Mixlab Layer types
 ![Mixlab supports](https://github.com/user-attachments/assets/05a53b98-b620-4743-b7b5-26da4140d443)
-* Supports ComfyUI 0.1.2+, frontend 1.2.34+
+* Supports Versions:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
 
-**2024/08/24**:
+**2024/08/24** @1.2.29:
 * All node dynamic help
 * Array node -- updated to return single values as their single value, not a list
-* Supports ComfyUI 0.1.2+, frontend 1.2.30+
+* Supports Versions:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
 
-**2024/08/23**:
+**2024/08/23** @1.2.28:
 * Colorization and Help panel only work on new frontend
-* Supports ComfyUI 0.1.1+, frontend 1.2.29+
+* Supports Versions:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
 
-**2024/08/20**:
+**2024/08/20** @1.2.14:
 * Complete Wiki and Examples revamp.
-* Supports ComfyUI 0.0.8+, frontend 1.2.30+
+* Supports Versions:
+  * ComfyUI 0.1.3+
+  * ComfyUI Frontend 1.2.30+
 
 # INSTALLATION
 

__init__.py

@@ -34,7 +34,7 @@
     StreamReaderNode, StreamWriterNode, SpoutWriter,
     AkashicNode, ArrayNode, BatchLoadNode, DynamicNode, ValueGraphNode, ExportNode, QueueNode,
     RouteNode, SaveOutputNode
-@version: 1.2.32
+@version: 1.2.35
 """
 
 import os

core/calc.py

@@ -969,7 +969,6 @@ def run(self, **kw) -> Tuple[bool]:
         pbar = ProgressBar(len(params))
         old_seed = -1
         for idx, (raw, r_x, r_y, r_z, r_w, typ, xyzw, seed, yyzw, x_str) in enumerate(params):
-            # logger.debug((raw, r_x, r_y, r_z, r_w, typ, xyzw, seed, yyzw, x_str))
             typ = EnumConvertType[typ]
             default = [x_str]
             default2 = None
@@ -1016,6 +1015,8 @@ def run(self, **kw) -> Tuple[bool]:
             ret.extend(extra)
             results.append(ret)
             pbar.update_absolute(idx)
+        if len(results) < 2:
+            return results[0]
         return [x for x in zip(*results)]
 
 class WaveGeneratorNode(JOVBaseNode):

core/create_glsl.py

@@ -18,7 +18,7 @@
     pass
 from comfy.utils import ProgressBar
 
-from Jovimetrix import JOVImageNode, comfy_message, deep_merge, Lexicon, ROOT
+from Jovimetrix import JOVImageNode, comfy_message, deep_merge, Lexicon, ROOT, JOV_TYPE_ANY
 from Jovimetrix.sup.util import load_file, parse_param, EnumConvertType, parse_value
 from Jovimetrix.sup.image import EnumInterpolation, EnumScaleMode, cv2tensor_full, image_convert, image_scalefit, tensor2cv, MIN_IMAGE_SIZE
 from Jovimetrix.sup.shader import PTYPE, shader_meta, CompileException, GLSLShader
@@ -135,19 +135,24 @@ def run(self, ident, **kw) -> tuple[torch.Tensor]:
         step = 1. / self.__glsl.fps
 
         images = []
+        vars = {}
         batch = max(1, batch)
-        pbar = ProgressBar(batch)
-        for idx in range(batch):
-            vars = {}
-            firstImage = None
-            for k, v in variables.items():
-                var = v if not isinstance(v, (list, tuple,)) else v[idx % len(v)]
+        firstImage = None
+        # check if the input(s) have more than a single entry, get the max...
+        if batch == 1:
+            for k, var in variables.items():
                 if isinstance(var, (torch.Tensor)):
-                    var = tensor2cv(var)
-                    var = image_convert(var, 4)
+                    batch = max(batch, var.shape[0])
+                    var = [image_convert(tensor2cv(v), 4) for v in var]
                     if firstImage is None:
-                        firstImage = var
-                vars[k] = var
+                        firstImage = var[0]
+
+                variables[k] = var if isinstance(var, (list, tuple,)) else [var]
+
+        pbar = ProgressBar(batch)
+        for idx in range(batch):
+            for k, val in variables.items():
+                vars[k] = val[idx % len(val)]
 
             w, h = wihi
             if firstImage is not None and mode == EnumScaleMode.MATTE:
@@ -219,24 +224,26 @@ def INPUT_TYPES(cls) -> dict:
                 params = {"default": None}
 
                 d = None
+                type_name = typ.name
                 if glsl_type != 'sampler2D':
                     if default is not None:
                         d = default.split(',')
                     params['default'] = parse_value(d, typ, 0)
 
                     if val_min is not None:
-                        params['val_min'] = parse_value(val_min, EnumConvertType.FLOAT, -sys.maxsize)
+                        params['mij'] = parse_value(val_min, EnumConvertType.FLOAT, -sys.maxsize)
 
                     if val_max is not None:
-                        params['val_max'] = parse_value(val_max, EnumConvertType.FLOAT, sys.maxsize)
+                        params['maj'] = parse_value(val_max, EnumConvertType.FLOAT, sys.maxsize)
 
                     if val_step is not None:
                         d = 1 if typ.name.endswith('INT') else 0.01
-                        params['val_step'] = parse_value(val_step, EnumConvertType.FLOAT, d)
-
+                        params['step'] = parse_value(val_step, EnumConvertType.FLOAT, d)
+                else:
+                    type_name = JOV_TYPE_ANY
                 if tooltip is not None:
                     params["tooltips"] = tooltip
-                data[name] = (typ.name, params,)
+                data[name] = (type_name, params,)
 
         data.update(opts)
         original_params['optional'] = data

core/utility.py

@@ -257,6 +257,11 @@ def run(self, **kw) -> Tuple[int, list]:
         elif mode == EnumBatchMode.SLICE:
             start, end, step = slice_range
             end = len(data) if end == 0 else end
+            if step == 0:
+                step = 1
+            elif step < 0:
+                data = data[::-1]
+                step = abs(step)
             data = data[start:end:step]
         elif mode == EnumBatchMode.RANDOM:
             if self.__seed is None or self.__seed != seed:

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 = "1.2.34"
+version = "1.2.35"
 license = { file = "LICENSE" }
 dependencies = [
     "aenum>=3.1.15,<4",

sup/image.py

@@ -462,11 +462,11 @@ def pil2tensor(image: Image.Image) -> torch.Tensor:
 def tensor2cv(tensor: torch.Tensor) -> TYPE_IMAGE:
     """Convert a torch Tensor to a numpy ndarray."""
     tensor = tensor.cpu().squeeze().numpy()
-    if tensor.ndim == 1:
+    if tensor.ndim < 3:
         tensor = np.expand_dims(tensor, -1)
     image = np.clip(255.0 * tensor, 0, 255).astype(np.uint8)
     if image.shape[2] == 4:
-        image_flatten_mask
+        # image_flatten_mask
         mask = image_mask(image)
         image = image_blend(image, image, mask)
         image = image_mask_add(image, mask)