I think this is something env dependent

README.md

@@ -12,5 +12,5 @@ Run from the repo root:
 
 ## Notes
 
-- Auto-install is opt-in via `COMFYUI_LOGICUTILS_AUTO_INSTALL=1`.
-- To force-disable the install hook, set `COMFYUI_LOGICUTILS_SKIP_INSTALL=1`.
+- Auto-install runs when loaded in ComfyUI. To disable it, set `COMFYUI_LOGICUTILS_SKIP_INSTALL=1`.
+- To force CPU/GPU tagger dependency selection, set `COMFYUI_LOGICUTILS_IMGUTILS_VARIANT=cpu` or `COMFYUI_LOGICUTILS_IMGUTILS_VARIANT=gpu`.

install.py

@@ -32,45 +32,62 @@ def process_wrap(cmd_str, cwd=None, handler=None):
     return process.wait()
 
 if "python_embeded" in sys.executable or "python_embedded" in sys.executable: #standalone python version
-    pip_install = [sys.executable, '-s', '-m', 'pip', 'install', "-U"]
+    pip_install = [sys.executable, '-s', '-m', 'pip', 'install', "-U", "--default-timeout=1000"]
 else:
-    pip_install = [sys.executable, '-m', 'pip', 'install', "-U"]
+    pip_install = [sys.executable, '-m', 'pip', 'install', "-U", "--default-timeout=1000"]
+
+def _imgutils_install_candidates() -> list[str]:
+    """
+    Decide which dghs-imgutils package variant to install first.
+
+    - `dghs-imgutils[gpu]` includes optional GPU-related dependencies.
+    - If GPU detection fails, default to CPU variant first.
+    """
+    variant = os.environ.get("COMFYUI_LOGICUTILS_IMGUTILS_VARIANT", "").strip().lower()
+    if variant in {"gpu", "cuda"}:
+        return ["dghs-imgutils[gpu]", "dghs-imgutils"]
+    if variant in {"cpu"}:
+        return ["dghs-imgutils", "dghs-imgutils[gpu]"]
+
+    # auto
+    try:
+        import torch
+
+        if getattr(torch, "cuda", None) is not None and torch.cuda.is_available():
+            return ["dghs-imgutils[gpu]", "dghs-imgutils"]
+    except Exception:
+        pass
+    return ["dghs-imgutils", "dghs-imgutils[gpu]"]
 
 def initialization():
-    auto_install = os.environ.get("COMFYUI_LOGICUTILS_AUTO_INSTALL", "").strip().lower() in {
-        "1",
-        "true",
-        "yes",
-    }
-    if not auto_install:
-        return
-
     try:
         import piexif
     except Exception:
+        print("piexif not found, installing...")
         run_installation("piexif")
     try:
         import chardet
     except Exception:
+        print("chardet not found, installing...")
         run_installation("chardet")
     try:
         from imgutils.tagging import get_wd14_tags
     except Exception:
-        # dghs-imgutils currently pins numpy<2, which typically won't have wheels for
-        # the latest Python releases right away (e.g. Python 3.13 in ComfyUI portable).
-        if sys.version_info >= (3, 13):
-            print(
-                "Skipping auto-install of dghs-imgutils on Python >= 3.13 "
-                "(tagger nodes will be disabled unless installed manually)."
-            )
-        else:
-            run_installation("dghs-imgutils[gpu]")
+        print("imgutils not found, installing...")
+        for candidate in _imgutils_install_candidates():
+            print(f"Trying to install {candidate}...")
+            run_installation(candidate)
+            try:
+                from imgutils.tagging import get_wd14_tags  # noqa: F401
+                break
+            except Exception:
+                continue
     try:
         from Crypto.PublicKey import RSA
     except Exception:
+        print("pycryptodome not found, installing...")
         run_installation("pycryptodome")
 
-
 def run_installation(pkg_name: str):
     print(f"Installing {pkg_name}...")
     try:

io_node.py

@@ -1,8 +1,10 @@
-import base64
-import json
-import math
-import numpy as np
-import torch
+import base64
+import json
+import math
+import random
+from pathlib import Path
+import numpy as np
+import torch
 
 try:
     import piexif.helper
@@ -311,7 +313,7 @@ class SaveImageCustomNode:
 
 
 @fundamental_node
-class SaveTextCustomNode:
+class SaveTextCustomNode:
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
         self.type = "output"
@@ -353,11 +355,40 @@ class SaveTextCustomNode:
             f.write(text)
         results.append({"filename": file, "subfolder": subfolder, "type": self.type})
 
-        return {"ui": {"texts": results}, "outputs": {"images": file.rstrip(".txt")}}
-
-
-@fundamental_node
-class DumpTextJsonlNode:
+        return {"ui": {"texts": results}, "outputs": {"images": file.rstrip(".txt")}}
+
+
+@fundamental_node
+class CommaRejoinNode:
+    FUNCTION = "comma_rejoin"
+    RETURN_TYPES = ("STRING",)
+    CATEGORY = "text"
+    custom_name = "Comma Rejoin"
+
+    @staticmethod
+    def comma_rejoin(text, split_separator=",", join_separator=", "):
+        # Split (by comma or given separator), strip items, then join using the
+        # join separator verbatim (e.g. ", ").
+        parts = str(text).split(split_separator) if split_separator else [str(text)]
+        stripped = [part.strip() for part in parts]
+        stripped = [part for part in stripped if part != ""]
+        return (join_separator.join(stripped),)
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", {"default": ""}),
+            },
+            "optional": {
+                "split_separator": ("STRING", {"default": ","}),
+                "join_separator": ("STRING", {"default": ", "}),
+            },
+        }
+
+
+@fundamental_node
+class DumpTextJsonlNode:
     """
     Appends text to a JSONL file (one JSON object per line).
     Each line will have the structure: { "<keyname>": "<text_item>" }
@@ -1275,7 +1306,7 @@ class Base64DecodeNode:
 
 
 @fundamental_node
-class ImageFromURLNode:
+class ImageFromURLNode:
     FUNCTION = "url_download"
     RETURN_TYPES = ("IMAGE",)
     CATEGORY = "image"
@@ -1292,16 +1323,84 @@ class ImageFromURLNode:
         return (image,)
 
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "url": ("STRING",),
-            }
-        }
-
-
-@fundamental_node
-class Base64EncodeNode:
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "url": ("STRING",),
+            }
+        }
+
+
+@fundamental_node
+class RandomImageFromFolderNode:
+    FUNCTION = "random_image_from_folder"
+    RETURN_TYPES = ("IMAGE", "STRING")
+    RETURN_NAMES = ("image", "path")
+    CATEGORY = "image"
+    custom_name = "Random Image From Folder"
+
+    @classmethod
+    def IS_CHANGED(cls, *args, **kwargs):
+        return float("nan")
+
+    @staticmethod
+    @PILHandlingHodes.output_wrapper
+    def random_image_from_folder(folder, extension, recursive, seed=0):
+        folder = str(folder).strip()
+        if not folder:
+            raise ValueError("folder must be a non-empty path")
+
+        folder_path = Path(folder).expanduser()
+        if not folder_path.is_dir():
+            raise ValueError(f"folder is not a directory: {folder_path}")
+
+        ext = str(extension).strip().lower().lstrip(".")
+        if ext == "all":
+            extensions = {".jpg", ".jpeg", ".png", ".webp"}
+        elif ext == "jpg":
+            extensions = {".jpg", ".jpeg"}
+        else:
+            extensions = {f".{ext}"}
+
+        candidates = []
+        if recursive:
+            for suffix in extensions:
+                candidates.extend(folder_path.rglob(f"*{suffix}"))
+        else:
+            for suffix in extensions:
+                candidates.extend(folder_path.glob(f"*{suffix}"))
+
+        candidates = [p for p in candidates if p.is_file()]
+        if not candidates:
+            raise FileNotFoundError(
+                f"No images found in '{folder_path}' with extension '{extension}'"
+            )
+
+        candidates = sorted(set(candidates), key=lambda p: str(p).lower())
+        rng = random.Random(seed)
+        chosen = candidates[rng.randrange(len(candidates))]
+
+        with Image.open(chosen) as img:
+            img = img.convert("RGB")
+
+        return (img, str(chosen))
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "folder": ("STRING", {"default": ""}),
+                "extension": (["all", "jpg", "png", "webp"], {"default": "all"}),
+                "recursive": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "seed": ("INT", {"default": 0, "min": 0, "max": 2**63 - 1}),
+            },
+        }
+
+
+@fundamental_node
+class Base64EncodeNode:
     FUNCTION = "base64_encode"
     RETURN_TYPES = ("STRING",)
     CATEGORY = "image"

nodes.py

@@ -20,7 +20,9 @@ _SKIP_INSTALL = os.environ.get("COMFYUI_LOGICUTILS_SKIP_INSTALL", "").strip().lo
 
 if _IN_COMFYUI and not _SKIP_INSTALL:
     initialization()
-
+else:
+    print("Skipping ComfyUI-LogicUtils installation.")
+
 from .logic_gates import CLASS_MAPPINGS as LogicMapping, CLASS_NAMES as LogicNames
 from .randomness import CLASS_MAPPINGS as RandomMapping, CLASS_NAMES as RandomNames
 from .conversion import CLASS_MAPPINGS as ConversionMapping, CLASS_NAMES as ConversionNames
@@ -32,33 +34,33 @@ else:
     IONames = {}
 from .auxilary import CLASS_MAPPINGS as AuxilaryMapping, CLASS_NAMES as AuxilaryNames
 from .external import CLASS_MAPPINGS as ExternalMapping, CLASS_NAMES as ExternalNames
-
-
-
-NODE_CLASS_MAPPINGS = {
-}
-NODE_CLASS_MAPPINGS.update(IOMapping)
-NODE_CLASS_MAPPINGS.update(LogicMapping)
-NODE_CLASS_MAPPINGS.update(RandomMapping)
-NODE_CLASS_MAPPINGS.update(ConversionMapping)
-NODE_CLASS_MAPPINGS.update(MathMapping)
-NODE_CLASS_MAPPINGS.update(ExternalMapping)
-NODE_CLASS_MAPPINGS.update(AuxilaryMapping)
-
-
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-
-}
-NODE_DISPLAY_NAME_MAPPINGS.update(IONames)
-NODE_DISPLAY_NAME_MAPPINGS.update(LogicNames)
-NODE_DISPLAY_NAME_MAPPINGS.update(RandomNames)
-NODE_DISPLAY_NAME_MAPPINGS.update(ConversionNames)
-NODE_DISPLAY_NAME_MAPPINGS.update(MathNames)
-NODE_DISPLAY_NAME_MAPPINGS.update(ExternalNames)
-NODE_DISPLAY_NAME_MAPPINGS.update(AuxilaryNames)
-
-
+
+
+
+NODE_CLASS_MAPPINGS = {
+}
+NODE_CLASS_MAPPINGS.update(IOMapping)
+NODE_CLASS_MAPPINGS.update(LogicMapping)
+NODE_CLASS_MAPPINGS.update(RandomMapping)
+NODE_CLASS_MAPPINGS.update(ConversionMapping)
+NODE_CLASS_MAPPINGS.update(MathMapping)
+NODE_CLASS_MAPPINGS.update(ExternalMapping)
+NODE_CLASS_MAPPINGS.update(AuxilaryMapping)
+
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+
+}
+NODE_DISPLAY_NAME_MAPPINGS.update(IONames)
+NODE_DISPLAY_NAME_MAPPINGS.update(LogicNames)
+NODE_DISPLAY_NAME_MAPPINGS.update(RandomNames)
+NODE_DISPLAY_NAME_MAPPINGS.update(ConversionNames)
+NODE_DISPLAY_NAME_MAPPINGS.update(MathNames)
+NODE_DISPLAY_NAME_MAPPINGS.update(ExternalNames)
+NODE_DISPLAY_NAME_MAPPINGS.update(AuxilaryNames)
+
+
 try:
     from .pystructure import CLASS_MAPPINGS as PyStructureMapping, CLASS_NAMES as PyStructureNames
     NODE_CLASS_MAPPINGS.update(PyStructureMapping)

tests/test_io_nodes_extras.py

@@ -0,0 +1,57 @@
+import tempfile
+import unittest
+from pathlib import Path
+
+import torch
+from PIL import Image
+
+from import_utils import import_local
+
+
+class TestIoNodesExtras(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.io_node = import_local("io_node")
+
+    def test_comma_rejoin_defaults(self):
+        Node = self.io_node.CLASS_MAPPINGS["CommaRejoinNode"]
+        self.assertEqual(Node.comma_rejoin("a,b , c"), ("a, b, c",))
+        self.assertEqual(Node.comma_rejoin("  a ,  b  ,c "), ("a, b, c",))
+
+    def test_comma_rejoin_custom_separators(self):
+        Node = self.io_node.CLASS_MAPPINGS["CommaRejoinNode"]
+        self.assertEqual(
+            Node.comma_rejoin("a| b |c", split_separator="|", join_separator="| "),
+            ("a| b| c",),
+        )
+
+    def test_random_image_from_folder(self):
+        Node = self.io_node.CLASS_MAPPINGS["RandomImageFromFolderNode"]
+
+        with tempfile.TemporaryDirectory() as tmp:
+            root = Path(tmp)
+            sub = root / "sub"
+            sub.mkdir()
+
+            img1 = root / "a.png"
+            img2 = root / "b.jpg"
+            img3 = sub / "c.png"
+
+            Image.new("RGB", (8, 6), (10, 20, 30)).save(img1)
+            Image.new("RGB", (8, 6), (40, 50, 60)).save(img2)
+            Image.new("RGB", (8, 6), (70, 80, 90)).save(img3)
+
+            # Non-recursive should only pick from root
+            tensor, path = Node.random_image_from_folder(
+                str(root), "all", False, seed=0
+            )
+            self.assertTrue(torch.is_tensor(tensor))
+            self.assertEqual(tuple(tensor.shape), (1, 6, 8, 3))
+            self.assertIn(Path(path), {img1, img2})
+
+            # Recursive should include subfolder images
+            tensor, path = Node.random_image_from_folder(str(root), "png", True, seed=0)
+            self.assertTrue(torch.is_tensor(tensor))
+            self.assertEqual(tuple(tensor.shape), (1, 6, 8, 3))
+            self.assertIn(Path(path), {img1, img3})
+