feat: ✨ yet another repl node for comfy

Still unsure how to expose it

README.md

@@ -1,10 +1,4 @@
 # MTB Nodes
-
-> [!NOTE]
-> master/main is outdated for now to keep backward compatibility, the next version is being worked on in
-> [`dev/0.6.0`](https://github.com/melMass/comfy_mtb/tree/dev/0.6.0)
-
-
 [![embedded test](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml/badge.svg)](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml)
 
 ![home](https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873)

__init__.py

@@ -7,7 +7,7 @@
 #
 ###
 
-__version__ = "0.5.4"
+__version__ = "0.6.0"
 
 import os
 
@@ -265,6 +265,10 @@ def register_routes():
 
     from PIL import Image
 
+    from .repl import setup_custom_web_routes
+
+    setup_custom_web_routes(PromptServer.instance.app)
+
     with contextlib.suppress(ImportError):
         from cachetools import TTLCache
 

env.nu

@@ -1,85 +1,175 @@
 # NOTE: This file is only use for development you can ignore it
 
-use private/log.nu
+use log.nu
+use nssm.nu *
+use nutils.nu [ make-id upsert-all fwd-slash backup-file ]
+use os.nu [ link ]
 
-def get_root [--clean] {
-    if $clean {
-        $env.COMFY_CLEAN_ROOT
-    } else {
-        $env.COMFY_ROOT
-    }
+# --- utilities ---
+def get_root [ --clean] {
+  if $clean {
+    $env.COMFY.ROOTS.clean
+  } else {
+    $env.COMFY.ROOTS.main
+  }
 }
 
-export def "comfy build-web" [] {
-    cd $env.COMFY_MTB
-    cd web_source
-    npm run build
-    cp dist/*.js ../web/dist
-}
-
-export def "comfy dev-web" [] {
-    cd $env.COMFY_MTB
-    cd web_source
-    npm run dev
-}
-
-export def "daily run"  [] {
-  let res = (comfy update --rebase)
-  comfy update --clean
-  comfy update_extensions
-
-  daily commit $res.from $res.to
+def --env path-add [pth] {
+  $env.PATH = ($env.PATH | append ($pth | path expand))
 }
 
 def short-date [] {
   format date "%Y-%m-%d"
 }
 
+export def spawn-for [timeout: duration task: closure] {
+  let input = $in
+  let parent_id = job id
+  let task_id = job spawn {
+    $input | do $task | job send --tag (job id) $parent_id
+  }
+  try {
+    job recv --tag $task_id --timeout $timeout
+  } catch {
+    job kill $task_id
+    error make {
+      msg: "Task timed out."
+      label: {
+        text: "timed out"
+        span: (metadata $task).span
+      }
+    }
+  }
+}
+
+# --- exports --
+export def "comfy profile" [timeout = 60sec] {
+  let to_match = "To see the GUI go to"
+
+  pyinstrument -r html main.py ...($env.COMFY.ARGS)
+  | tee -e {
+    each {
+      let stde = $in
+      print -ne $stde
+      if $to_match in $stde {
+        print $"(ansi gb)Profiling Done!(ansi reset)"
+        let process = (ps -l | where name =~ python | where command =~ pyinstrument | last)
+        kill -f $process.pid
+      }
+    }
+  }
+  | complete
+  | get stdout
+  | save $"profiled_(date now | format date '%s').html"
+}
+
+export def "comfy profile-plus" [] {
+
+  let timestamp = (date now | format date "%s")
+  let log_name = $"cprofile_run_($timestamp)"
+  let profiled = (python -m cProfile main.py --port 3000 --preview-method auto | tee -e { print -ne } | complete)
+
+  let out = (
+    $profiled.stdout
+    | lines
+    # skip summary
+    | skip 4
+    | str join "\n"
+  )
+  # save result
+  $out | save $"raw_($log_name).txt"
+
+  # process
+  $out
+  | from ssv
+  | upsert-all { into float } tottime percall cumtime
+  | save $"($log_name).nuon"
+}
+
+export def restart-server [] {
+  nssm restart -c comfy
+}
+
+# build the web components of mtb
+export def "comfy build-web" [] {
+  cd $env.COMFY.ROOTS.mtb
+  if ("./web/dist" | path exists) {
+    rm -rt ./web/dist
+  }
+
+  cd web_source
+  ^$env.NPM_BINARY run build
+  cp -r dist ../web/dist
+}
+
+# start the dev server for web components
+export def "comfy dev-web" [] {
+  cd $env.COMFY.ROOTS.mtb
+  cd web_source
+  ^$env.NPM_BINARY run dev
+}
+
+# daily check / update
+export def "daily run" [] {
+  let res = (comfy update --rebase)
+  comfy update --clean
+  comfy update_extensions
+
+  daily commit $res.from_commit $res.to_commit
+}
+
 # was daily run today?
 export def "daily was-run" [] {
 
-  let daily = ($env.COMFY_MTB | path join daily.nuon)
+  let daily = ($env.COMFY.ROOTS.mtb | path join daily.nuon)
 
   if ($daily | path exists) {
-    let last = (open $daily | sort-by date | get date  | last | short-date)
+    let last = (open $daily | sort-by date | get date | last | short-date)
     let today = (date now | short-date)
     return ($last == $today)
   }
   return false
 }
 
-export def "daily commit"  [from:string, to:string] {
-  let daily = ($env.COMFY_MTB | path join daily.nuon)
-  let commit = [{date: (date now) from:$from to:$to}]
+export def "daily commit" [from_commit: string to_commit: string] {
+  let daily = ($env.COMFY.ROOTS.mtb | path join daily.nuon)
+  let commit = [{date: (date now) from_commit: $from_commit to_commit: $to_commit}]
 
-  let dailies = (if ($daily | path exists) {
-    open $daily | append $commit
-  } else {
+  let dailies = (
+    if ($daily | path exists) {
+      open $daily | append $commit
+    } else {
       $commit
-  })
+    }
+  )
 
   $dailies | save -f $daily
   log success "Commited daily check"
 }
 
 # start the comfy server
-export def "comfy start" [--clean,--old-ui, --listen, --skip-daily(-s)] {
-  if (not (daily was-run)) and not $skip_daily {
-      log info "Running daily checks"
-      daily run
-    }
-    let root = get_root --clean=($clean)
-    cd $root
+export def "comfy start" [
+  --clean
+  --old-ui
+  --listen
+  --skip-daily (-s)
+] {
+  if not (daily was-run) and not $skip_daily {
+    log info "Running daily checks"
+    daily run
+  }
+  let root = (get_root --clean=$clean)
+  cd $root
 
-    log info "Running Server"
+  log info "Running Server"
 
-    MTB_DEBUG=true python main.py --port 3000 ...(if $old_ui { ["--front-end-version", "Comfy-Org/ComfyUI_legacy_frontend@latest"]} else {[ --front-end-version Comfy-Org/ComfyUI_frontend@latest]}) --preview-method auto ...(if $listen {["--listen"]} else {[]})
+  MTB_DEBUG=true python main.py --port 3000 ...(if $old_ui { ["--front-end-version" "Comfy-Org/ComfyUI_legacy_frontend@latest"] } else { [--front-end-version Comfy-Org/ComfyUI_frontend@latest] }) --preview-method auto ...(if $listen { ["--listen"] } else { [] })
 }
 
 # update comfy itself and merge master in current branch
 export def "comfy update" [
-    --clean # ??
-    --rebase # Rebase instead of merge
+  --clean # comfy clean instance
+  --rebase # Rebase instead of merge
 ] {
   let root = get_root --clean=$clean
 
@@ -94,21 +184,28 @@ export def "comfy update" [
   log info "Backing up and removing models symlinks"
 
   # preparing root for pull
-  if not $clean {
+  let pyproject = if not $clean {
+    log info "Backing up the pyproject.toml..."
+    let proj = (backup-file --root pyproject.toml)
+
+    log info "Restoring the original pyproject"
     git checkout pyproject.toml
     cd $models
     # find and store all symlinks
-    let links = (ls -la |
-      where not ($it.target | is-empty) |
-      select name target |
-      sort-by name)
-
+    log info "Checking for links in models..."
+    let links = (
+      ls -la | where not ($it.target | is-empty) | select name target | sort-by name
+    )
+    log info $"Found links: ($links)"
 
     if not ($links | is-empty) {
+      log info "Backing up the symlinks..."
+      backup-file --root links.nuon
       $links | save -f links.nuon
       # remove them
       open links.nuon | each {|p| rm $p.name }
     }
+    $proj
   } else {
     # just remove symlinks
     rm $models
@@ -141,7 +238,6 @@ export def "comfy update" [
     if $rebase {
       log info "Rebasing changes"
       git rebase master
-
     } else {
       log info "Merging changes"
       git merge master
@@ -152,9 +248,10 @@ export def "comfy update" [
 
   if not $clean {
     rm pyproject.toml
-    cp pyproject-mel.toml pyproject.toml
+    log info "Using our own pyproject..."
+    cp $pyproject pyproject.toml
     cd $models
-
+    log info "Relinking models..."
     # resymlink them
     open links.nuon | each {|p| link -a $p.target $p.name }
   } else {
@@ -167,72 +264,82 @@ export def "comfy update" [
 
   log success $"Update successful \(($commit_count) new commits\)"
 
-  return {from:$current_commit to:$new_commit}
-
-
+  return {from_commit: $current_commit to_commit: $new_commit}
 }
 
-export def "comfy toggle_extensions" [--clean] {
-    let root = get_root --clean=($clean)
-    cd $root
-    cd custom_nodes
-    let exts = (ls | where type in ["dir","symlink"] | get name)
-    let choices = ($exts | input list -m "choose extension to toggle")
-    if ($choices | is-empty) {
-        return
-    }
+export def "comfy toggle_extensions" [
+  --clean
+] {
+  let root = get_root --clean=$clean
+  cd $root
+  cd custom_nodes
+  let exts = (ls | where type in ["dir" "symlink"] | get name)
+  let choices = ($exts | input list -m "choose extension to toggle")
+  if ($choices | is-empty) {
+    return
+  }
 
-    log info "Choices" $choices
+  log info "Choices" $choices
 
-    let filtered = $choices | wrap name | upsert enabled {|p| not ($p.name | str ends-with ".disabled")}
+  let filtered = $choices | wrap name | upsert enabled {|p| not ($p.name | str ends-with ".disabled") }
 
-    log info "Filtered" $filtered
-    $filtered | each {|f|
-        let new_name = ($f.name | str replace ".disabled" "")
+  log info "Filtered" $filtered
+  $filtered | each {|f|
+    let new_name = ($f.name | str replace ".disabled" "")
 
-        let new_name = if $f.enabled {
-            $"($new_name).disabled"
-        } else {
-            $new_name
-        }
-        log info $"Moving ($f.name) to ($new_name)"
-        mv $f.name $new_name
+    let new_name = if $f.enabled {
+      $"($new_name).disabled"
+    } else {
+      $new_name
     }
+    log info $"Moving ($f.name) to ($new_name)"
+    mv $f.name $new_name
+  }
 }
 
 # git pull all extensions
-export def "comfy update_extensions" [--clean] {
-    let root = get_root --clean=($clean)
-    cd $root
-    cd custom_nodes
-    git multipull . -s -q
+export def "comfy update_extensions" [ --clean] {
+  let root = get_root --clean=$clean
+  cd $root
+  cd custom_nodes
+  git multipull . -s -q
 }
 
-def --env path-add [pth] {
-    $env.PATH = ($env.PATH | append ($pth | path expand))
-
+# manual set version of mtb
+export def "comfy-mtb set-version" [version: string] {
+  # let pyproject = open pyproject.toml
+  # let current_version = $pyproject.project.version
+  # $pyproject | upsert project.version $version | save -f pyproject.toml
+  # taplo format pyproject.toml
+  sd "(__version__ = )\"(.*)\"" $"${1}\"($version)\"" __init__.py
+  sd "(version = )(.*)" $"${1}\"($version)\"" pyproject.toml
+  # log info $"⬆️ Bump version: ($current_version) → ($version)"
 }
 
-
+# -- env
 export-env {
   $env.PYTHONUTF8 = 1
-  $env.COMFY_MTB = ("." | path expand)
-  # $env.CUDA_ROOT =  'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\'
-
-  $env.CUDA_HOME = $env.CUDA_ROOT
-
-  $env.COMFY_ROOT = ("../.." | path expand)
-  $env.COMFY_CLEAN_ROOT =  ($env.COMFY_ROOT | path dirname | path join ComfyClean)
-
-  path-add 'C:/Portable/TensorRT-8.6.0.12/lib'
-
-  if $nu.os-info.family == 'windows' {
-    path-add 'G:\BIN\TensorRT-10.7.0.23\lib'
-    path-add 'G:\BIN\cudnn-windows-x86_64-9.6.0.74_cuda12-archive\bin'
+  $env.COMFY = {
+    base_url : "https://mel-pc.tail3c8eb.ts.net"
+    ARGS: [--port 3000 --preview-method auto]
+    ROOTS: {
+      mtb: ("." | path expand | fwd-slash)
+      main: ("../.." | path expand | fwd-slash)
+      clean: ($env.COMFY_ROOT | path dirname | path join ComfyClean | fwd-slash)
+    }
   }
 
+  $env.NPM_BINARY = "bun"
+  $env.CUDA_HOME = $env.CUDA_ROOT
+  #
+  path-add 'C:/Portable/TensorRT-8.6.0.12/lib'
+  #
+  if $nu.os-info.family == 'windows' {
+    path-add "G:/BIN/TensorRT-10.7.0.23/lib"
+    path-add "G:/BIN/cudnn-windows-x86_64-9.6.0.74_cuda12-archive/bin"
+  }
+  #
   path-add ($env.CUDA_ROOT | path join bin)
-  overlay use ../../.venv/Scripts/activate.nu
+
+  overlay use "../../.venv/Scripts/activate.nu"
 }
-
-

features.json

@@ -0,0 +1,3 @@
+{
+  "use_repl": false
+}

nodes/audio.py

@@ -277,14 +277,14 @@ class MTB_AudioToText(MtbAudio):
                 f"Processing chunk {chunk_offset:.1f}s - {chunk_end / sample_rate:.1f}s"
             )
 
-            max_length = getattr(model.config, "max_length", None) or 448
+            max_length = model.config.max_length or 448
             attention_mask = torch.ones((1, max_length))
 
             input_features = processor(
                 chunk_waveform,
                 sampling_rate=sample_rate,
                 return_tensors="pt",
-            ).input_features.to(device=device, dtype=model.dtype)
+            ).input_features.to(device)
 
             with torch.no_grad():
                 predicted_ids = model.generate(

nodes/batch.py

@@ -335,9 +335,9 @@ class MTB_BatchShape:
                 "image_width": ("INT", {"default": 512}),
                 "image_height": ("INT", {"default": 512}),
                 "shape_size": ("INT", {"default": 100}),
-                "color": ("COLOR", {"default": "#ffffff","widgetType": "MTB_COLOR"}),
-                "bg_color": ("COLOR", {"default": "#000000","widgetType": "MTB_COLOR"}),
-                "shade_color": ("COLOR", {"default": "#000000","widgetType": "MTB_COLOR"}),
+                "color": ("COLOR", {"default": "#ffffff"}),
+                "bg_color": ("COLOR", {"default": "#000000"}),
+                "shade_color": ("COLOR", {"default": "#000000"}),
                 "thickness": ("INT", {"default": 5}),
                 "shadex": ("FLOAT", {"default": 0.0}),
                 "shadey": ("FLOAT", {"default": 0.0}),
@@ -842,7 +842,7 @@ class MTB_Batch2dTransform:
                     ["edge", "constant", "reflect", "symmetric"],
                     {"default": "edge"},
                 ),
-                "constant_color": ("COLOR", {"default": "#000000","widgetType": "MTB_COLOR"}),
+                "constant_color": ("COLOR", {"default": "#000000"}),
             },
             "optional": {
                 "x": ("FLOATS",),

nodes/debug.py

@@ -1,33 +1,70 @@
 import base64
 import io
-import json
-from pathlib import Path
+import textwrap
+from collections.abc import Callable
+from functools import wraps
+from typing import Any, Literal, Protocol, TypedDict, runtime_checkable
 
-import folder_paths
 import torch
+from rich import inspect
+from rich.console import Console
 
 from ..log import log
-from ..utils import tensor2pil
+from ..utils import LazyProxyTensor, get_torch_tensor_info, tensor2pil
+
+try:
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    plt.style.use("dark_background")
+    MATPLOTLIB_AVAILABLE = True
+except ImportError:
+    MATPLOTLIB_AVAILABLE = False
 
 
-def get_detailed_type_info(obj):
-    type_info = []
+# region Decorator
+def metadata(**meta_kwargs: Any) -> Callable[[Any], Any]:
+    """Add metadata to method (`__meta__` dict)."""
+
+    def decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]:
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            return func(*args, **kwargs)
+
+        wrapper.__meta__ = meta_kwargs
+        return wrapper
+
+    return decorator
+
+
+# endregion
+class UIResult(TypedDict):
+    kind: Literal["text", "b64_images"]
+    data: str
+
+
+def indent_results(results: list[UIResult], by: str = "  "):
+    for res in results:
+        if res["kind"] == "text":
+            log.debug(f"Indenting: {res['data']}")
+            res["data"] = textwrap.indent(res["data"], by)
+
+    return results
+
+
+ProcessorResult = list[UIResult]
+
+
+def _get_detailed_type_info(obj) -> str:
+    type_info: list[str] = []
 
     type_name = type(obj).__name__
     type_info.append(f"Type: {type_name}")
 
     if isinstance(obj, torch.Tensor):
-        type_info.extend(
-            [
-                f"Shape: {obj.shape}",
-                f"Dtype: {obj.dtype}",
-                f"Device: {obj.device}",
-                f"Requires grad: {obj.requires_grad}",
-                f"Stride: {obj.stride()}",
-                f"Contiguous: {obj.is_contiguous()}",
-            ]
-        )
-    elif isinstance(obj, (list, tuple)):
+        return get_torch_tensor_info(obj)
+
+    elif isinstance(obj, list | tuple):
         type_info.extend(
             [
                 f"Length: {len(obj)}",
@@ -47,122 +84,184 @@ def get_detailed_type_info(obj):
         attributes = [attr for attr in dir(obj) if not attr.startswith("_")]
         type_info.append(f"Attributes: {attributes}")
 
-    return type_info
+    return "\n".join(type_info)
+
+
+def _apply_rich_results(processed, mode="none", title=""):
+    processing_text = False
+    acc = ""
+    reshaped: list[UIResult] = []
+    for i in range(len(processed)):
+        if processed[i]["kind"] == "text":
+            if not processing_text:
+                processing_text = True
+            acc += processed[i]["data"] + "\n"
+            if len(processed) == (i + 1):
+                reshaped.append(
+                    UIResult(
+                        kind="text", data=_apply_rich(acc, mode, title=title)
+                    )
+                )
+        else:
+            if processing_text:
+                processing_text = False
+                reshaped.append(
+                    UIResult(
+                        kind="text", data=_apply_rich(acc, mode, title=title)
+                    )
+                )
+                acc = ""
+            reshaped.append(processed[i])
+
+    return reshaped
+    # for item in processed:
 
 
 # region processors
-def process_tensor(tensor: torch.Tensor, as_type=False):
-    log.debug(f"Tensor: {tensor.shape}")
-
-    if as_type:
-        return {
-            "text": [f"Tensor of shape {tensor.shape} of type {tensor.dtype}"]
-        }
-
-    is_mask = len(tensor.shape) == 3
-
-    if is_mask:
-        tensor = tensor.unsqueeze(-1).repeat(1, 1, 1, 3)
-
-    image = tensor2pil(tensor)
-    b64_imgs = []
-    for im in image:
-        if is_mask:
-            im = im.convert("L")
-
-        buffered = io.BytesIO()
-        im.save(buffered, format="PNG")
-        b64_imgs.append(
-            "data:image/png;base64,"
-            + base64.b64encode(buffered.getvalue()).decode("utf-8")
+def _apply_rich(
+    formatted: str | list[str], rich_mode: str | None = None, *, title=""
+) -> str:
+    if rich_mode is None:
+        return (
+            formatted if isinstance(formatted, str) else "\n".join(formatted)
         )
 
-    return {"b64_images": b64_imgs}
+    from rich.console import Console
 
+    console = Console(record=True)
 
-def process_list(anything, as_type=False):
-    text = []
-    if not anything:
-        return {"text": []}
-
-    if as_type:
-        type_info = get_detailed_type_info(anything)
-        type_info.extend(get_detailed_type_info(anything[0]))
-        return {"text": type_info}
-
-    first_element = anything[0]
-    if (
-        isinstance(first_element, list)
-        and first_element
-        and isinstance(first_element[0], torch.Tensor)
-    ):
-        text.append(
-            "List of List of Tensors: "
-            f"{first_element[0].shape} (x{len(anything)})"
-        )
-
-    elif isinstance(first_element, torch.Tensor):
-        text.append(
-            f"List of Tensors: {first_element.shape} (x{len(anything)})"
-        )
+    if isinstance(formatted, list):
+        for line in formatted:
+            console.print(line)
     else:
-        text.append(f"Array ({len(anything)}): {anything}")
+        console.print(formatted)
 
-    return {"text": text}
+    CSV_CODE_FORMAT = """
+<svg class="rich-terminal" viewBox="0 0 {width} {height}" xmlns="http://www.w3.org/2000/svg">
+    <!-- Generated with Rich https://www.textualize.io -->
+    <style>
 
+    @font-face {{
+        font-family: "Fira Code";
+        src: local("FiraCode-Regular"),
+                url("https://cdnjs.cloudflare.com/ajax/libs/firacode/6.2.0/woff2/FiraCode-Regular.woff2") format("woff2"),
+                url("https://cdnjs.cloudflare.com/ajax/libs/firacode/6.2.0/woff/FiraCode-Regular.woff") format("woff");
+        font-style: normal;
+        font-weight: 400;
+    }}
+    @font-face {{
+        font-family: "Fira Code";
+        src: local("FiraCode-Bold"),
+                url("https://cdnjs.cloudflare.com/ajax/libs/firacode/6.2.0/woff2/FiraCode-Bold.woff2") format("woff2"),
+                url("https://cdnjs.cloudflare.com/ajax/libs/firacode/6.2.0/woff/FiraCode-Bold.woff") format("woff");
+        font-style: bold;
+        font-weight: 700;
+    }}
 
-def process_dict(anything, as_type=False):
-    text = []
-    if as_type:
-        return {"text": get_detailed_type_info(anything)}
+    .{unique_id}-matrix {{
+        font-family: Fira Code, monospace;
+        font-size: {char_height}px;
+        line-height: {line_height}px;
+        font-variant-east-asian: full-width;
+    }}
 
-    if "samples" in anything:
-        is_empty = (
-            "(empty)" if torch.count_nonzero(anything["samples"]) == 0 else ""
-        )
-        text.append(f"Latent Samples: {anything['samples'].shape} {is_empty}")
+    .{unique_id}-title {{
+        font-size: 18px;
+        font-weight: bold;
+        font-family: arial;
+    }}
 
-    elif "waveform" in anything:
-        is_empty = (
-            "(empty) " if torch.count_nonzero(anything["samples"]) == 0 else ""
+    {styles}
+    </style>
+
+    <defs>
+    <clipPath id="{unique_id}-clip-terminal">
+      <rect x="0" y="0" width="{terminal_width}" height="{terminal_height}" />
+    </clipPath>
+    {lines}
+    </defs>
+
+    {chrome}
+    <g  clip-path="url(#{unique_id}-clip-terminal)">
+    {backgrounds}
+    <g class="{unique_id}-matrix">
+    {matrix}
+    </g>
+    </g>
+</svg>
+"""
+
+    if rich_mode == "svg-window":
+        return console.export_svg(title=title, code_format=CSV_CODE_FORMAT)
+    elif rich_mode == "svg":
+        return console.export_svg(
+            title=title,
+            code_format=CSV_CODE_FORMAT.replace("{chrome}", ""),
         )
 
-        text.append(
-            f"Audio Samples: {anything['waveform'].shape}{is_empty} | sample rate {anything['sample_rate']}"
+    elif rich_mode == "html":
+        CONSOLE_HTML_FORMAT = textwrap.dedent("""
+        <div style="color:{foreground};">
+            <code style="font-family:inherit">{code}</code>
+        </div>
+        """).strip()
+
+        import rich.terminal_theme
+
+        return console.export_html(
+            inline_styles=True,
+            code_format=CONSOLE_HTML_FORMAT,
+            theme=rich.terminal_theme.MONOKAI,
         )
 
-    else:
-        log.debug(f"Unhandled dict: {anything.keys()}")
-        text.append(json.dumps(anything, indent=2))
-
-    return {"text": text}
-
-
-def process_bool(anything, as_type=False):
-    return {"text": ["True" if anything else "False"]}
-
-
-def process_text(anything, as_type=False):
-    if as_type:
-        return {"text": get_detailed_type_info(anything)}
-
-    return {"text": [str(anything)]}
+    log.error(f"Unknown rich mode: {rich_mode}")
+    return formatted if isinstance(formatted, str) else "\n".join(formatted)
 
 
 # endregion
 
 
-class MTB_Debug:
-    """Experimental node to debug any Comfy values.
+# region conditions
 
-    support for more types and widgets is planned.
-    """
+
+# those are pretty dumb there is now probably a better way..
+def is_condition(item):
+    return (
+        isinstance(item, list)
+        and all(isinstance(i, list) for i in item)
+        and isinstance(item[0][0], torch.Tensor)
+    )
+
+
+# endregion
+
+RICH_MODE = Literal["none", "html", "svg", "svg-window"]
+
+
+@runtime_checkable
+class Processor(Protocol):
+    """Generic protocol for processor functions."""
+
+    def __call__(
+        self, item: Any, *, as_type: bool = False, deep: bool = False
+    ) -> ProcessorResult: ...
+
+
+class MTB_Debug:
+    """A debug node."""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {"output_to_console": ("BOOLEAN", {"default": False})},
-            "optional": {"as_detailed_types": ("BOOLEAN", {"default": False})},
+            "optional": {
+                "as_detailed_types": ("BOOLEAN", {"default": False}),
+                "deep_inspect": ("BOOLEAN", {"default": False}),
+                "rich_mode": (
+                    ("none", "html", "svg", "svg-window"),
+                    {"default": "none"},
+                ),
+            },
         }
 
     RETURN_TYPES = ()
@@ -170,99 +269,426 @@ class MTB_Debug:
     CATEGORY = "mtb/debug"
     OUTPUT_NODE = True
 
+    _processors: dict[type, Processor]
+
+    def __init__(self):
+        self._condition_processors = {is_condition: self._process_condition}
+        self._class_name_processors = {
+            "CLIP": self._process_clip,
+            "VAE": self._process_vae,
+        }
+        self._processors = {
+            torch.nn.Module: self._process_module,
+            torch.Tensor: self._process_tensor,
+            LazyProxyTensor: self._process_repr,
+            list: self._process_container,
+            tuple: self._process_container,
+            dict: self._process_dict,
+            bool: self._process_bool,
+            str: self._process_primitive,
+            int: self._process_primitive,
+            float: self._process_primitive,
+            type(None): self._process_primitive,
+        }
+
+    # - Dispatchers ------------------------------------------------------------
+    def _dispatch_processor(
+        self, item: Any, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        """Find and calls the appropriate processor for the given item."""
+        # first conditions
+        for c, process in self._condition_processors.items():
+            if c(item):
+                return process(item, as_type=as_type, deep=deep)
+
+        # named class
+        class_name = type(item).__name__
+        if class_name in self._class_name_processors:
+            return self._class_name_processors[class_name](
+                item, as_type=as_type, deep=deep
+            )
+
+        # type based or unknown
+        processor = self._processors.get(type(item), self._process_unknown)
+        res = processor(item, as_type=as_type, deep=deep)
+
+        return res
+
     def do_debug(
-        self, output_to_console: bool, as_detailed_types: bool, **kwargs
+        self,
+        **kwargs,
     ):
         output = {"ui": {"items": []}}
 
-        if output_to_console:
-            for k, v in kwargs.items():
-                log.info(f"{k}: {v}")
+        settings = {k: kwargs.pop(k) for k in self.INPUT_TYPES()["optional"]}
+        output_to_console = kwargs.pop("output_to_console")
+        as_type = settings.get("as_detailed_types", False)
+        deep = settings.get("deep_inspect", False)
+        rich_mode = settings.get("rich_mode", "none")
 
-        for input_name, anything in kwargs.items():
-            processor = processors.get(type(anything), process_text)
+        for input_name, item in kwargs.items():
+            processed = self._dispatch_processor(
+                item, as_type=as_type, deep=deep
+            )
+            if processed is None:
+                continue
 
-            processed = processor(anything, as_detailed_types)
+            if rich_mode != "none":
+                title = f"{input_name} ({type(item).__name__})"
+                processed = _apply_rich_results(processed, rich_mode, title)
 
-            item = {
-                "input": input_name,
-                **processed,
-            }
-            output["ui"]["items"].append(item)
+            if output_to_console:
+                log.info(f"- Input '{input_name}':")
+                for p in processed:
+                    if p["kind"] == "text":
+                        log.info(f"  {p['data']}")
+                    if p["kind"] == "b64_image":
+                        log.info(f"  (contains {len(p['data'])} images)")
 
+            output["ui"]["items"].append(
+                {"input": input_name, "items": processed}
+            )
         return output
 
+    def _process_unknown(
+        self, item: Any, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        console = Console(
+            record=True,
+            width=120,
+        )
 
-class MTB_SaveTensors:
-    """Save torch tensors (image, mask or latent) to disk.
+        console.print(f"Generic {type(item).__name__}", emoji=True)
+        if as_type:
+            inspect(item, console=console, all=deep, methods=deep, docs=deep)
+        else:
+            console.print(item, emoji=True)
 
-    useful to debug things outside comfy.
-    """
+        text_output = console.export_text(clear=True)
 
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "mtb/debug"
+        return [UIResult(kind="text", data=text_output.strip())]
 
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "filename_prefix": ("STRING", {"default": "ComfyPickle"}),
-            },
-            "optional": {
-                "image": ("IMAGE",),
-                "mask": ("MASK",),
-                "latent": ("LATENT",),
-            },
-        }
+    def _process_repr(
+        self, item: Any, as_type=False, deep=False
+    ) -> ProcessorResult:
+        return [{"kind": "text", "data": item.__repr__()}]
 
-    FUNCTION = "save"
-    OUTPUT_NODE = True
-    RETURN_TYPES = ()
-    CATEGORY = "mtb/debug"
+    def _process_primitive(
+        self, item: Any, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        if as_type:
+            return self._process_unknown(item, as_type=as_type, deep=deep)
 
-    def save(
-        self,
-        filename_prefix,
-        image: torch.Tensor | None = None,
-        mask: torch.Tensor | None = None,
-        latent: torch.Tensor | None = None,
-    ):
-        (
-            full_output_folder,
-            filename,
-            counter,
-            subfolder,
-            filename_prefix,
-        ) = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
-        full_output_folder = Path(full_output_folder)
-        if image is not None:
-            image_file = f"{filename}_image_{counter:05}.pt"
-            torch.save(image, full_output_folder / image_file)
-            # np.save(full_output_folder/ image_file, image.cpu().numpy())
+        return [UIResult(kind="text", data=str(item))]
 
-        if mask is not None:
-            mask_file = f"{filename}_mask_{counter:05}.pt"
-            torch.save(mask, full_output_folder / mask_file)
-            # np.save(full_output_folder/ mask_file, mask.cpu().numpy())
+    def _process_bool(
+        self, item: bool, *, as_type=False, deep=False
+    ) -> ProcessorResult:  # noqa: FBT001
+        return [{"kind": "text", "data": "True" if item else "False"}]
 
-        if latent is not None:
-            # for latent we must use pickle
-            latent_file = f"{filename}_latent_{counter:05}.pt"
-            torch.save(latent, full_output_folder / latent_file)
-            # pickle.dump(latent, open(full_output_folder/ latent_file, "wb"))
+    def _process_clip(
+        self, item: Any, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        try:
+            clip_model = getattr(item, "cond_stage_model", None)
+            tokenizer = getattr(item, "tokenizer", None)
 
-            # np.save(full_output_folder / latent_file,
-            # latent[""].cpu().numpy())
+            text = [UIResult(kind="text", data="CLIP")]
+            if clip_model:
+                text.append(UIResult(kind="text", data="CLIP Model:"))
+                model_summary = self._process_module(
+                    clip_model, as_type=as_type
+                )
+                if model_summary:
+                    text.extend(indent_results(model_summary, "  "))
+                else:
+                    text.append(
+                        UIResult(
+                            kind="text",
+                            data="[error] failed to get informations about clip model",
+                        )
+                    )
 
-        return f"{filename_prefix}_{counter:05}"
+            if tokenizer:
+                text.append(UIResult(kind="text", data="Tokenizer:"))
+                vocab_size = getattr(tokenizer, "vocab_size", "N/A")
+                text.append(
+                    UIResult(
+                        kind="text",
+                        data=f"  Class: {type(tokenizer).__name__}\n  Vocab Size: {vocab_size}",
+                    )
+                )
+
+            return text
+
+        except Exception as e:
+            log.error(f"Failed to process CLIP object: {e}")
+            return self._process_unknown(item, as_type=as_type, deep=deep)
+
+    def _process_condition(
+        self, item: Any, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        count = len(item)
+        result = [UIResult(kind="text", data=f"Conditions: {count}")]
+
+        for cond in item:
+            result.extend(self._preview_conditioning_tensor(cond[0]))
+
+        return result
+
+    def _process_vae(
+        self, item: Any, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        try:
+            vae_model = getattr(
+                item, "first_stage_model", getattr(item, "vae", item)
+            )
+            text = [
+                UIResult(kind="text", data="VAE"),
+                UIResult(kind="text", data="Internal Model:"),
+            ]
+
+            model_summary = self._process_module(
+                vae_model, as_type=as_type, deep=deep
+            )
+            text.extend(indent_results(model_summary, "  "))
+
+            return text
+        except Exception as e:
+            log.error(f"Failed to process VAE object: {e}")
+            return self._process_unknown(item, as_type=as_type, deep=deep)
+
+    def _process_module(
+        self, item: torch.nn.Module, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        if as_type and deep:
+            return self._process_unknown(item, as_type=as_type, deep=deep)
+
+        total_params = sum(p.numel() for p in item.parameters())
+        trainable_params = sum(
+            p.numel() for p in item.parameters() if p.requires_grad
+        )
+        try:
+            device = next(item.parameters()).device
+        except StopIteration:
+            device = "cpu (no parameters)"
+
+        train_percent = (
+            f"{trainable_params / total_params:.2%}"
+            if total_params > 0
+            else "0.00%"
+        )
+
+        text = [
+            f"Model: {type(item).__name__} on {device}",
+            textwrap.dedent(f"""
+            - Parameters: {total_params:,}
+            - Trainable: {trainable_params:,} ({train_percent})
+        """).strip(),
+        ]
+        return [{"kind": "text", "data": d} for d in text]
+
+    def _process_tensor(
+        self, item: torch.Tensor, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        is_latent = item.ndim == 4 and item.shape[1] == 4
+        is_image = (
+            not is_latent and item.ndim == 4 and item.shape[3] in [1, 3, 4]
+        )
+        is_conditioning = item.ndim == 3 and item.shape[2] in [
+            768,
+            1024,
+            1152,
+            1280,
+            2048,
+            4096,
+        ]
+        is_mask = (item.ndim == 2) or (item.ndim == 3 and not is_conditioning)
+
+        if as_type:
+            type_name = "Unknown Tensor"
+            if is_latent:
+                type_name = "Latent Tensor"
+            elif is_image:
+                type_name = "Image Tensor"
+            elif is_conditioning:
+                type_name = "CLIP Conditioning Tensor"
+            elif is_mask:
+                type_name = "Mask Tensor"
+            return [
+                {
+                    "kind": "text",
+                    "data": get_torch_tensor_info(item, name=type_name),
+                }
+            ]
+
+        if is_image or is_mask:
+            return self._render_image_tensor(item)
+        if is_latent:
+            return self._preview_latent_tensor(item)
+        if is_conditioning:
+            return self._preview_conditioning_tensor(item)
+        return self._process_unknown(item, as_type=as_type, deep=deep)
+
+    def _visualize_tensor_heatmap(
+        self, tensor_2d: torch.Tensor, title: str
+    ) -> str | None:
+        if not MATPLOTLIB_AVAILABLE:
+            log.warning("Matplotlib not found. Skipping tensor visualization.")
+            return None
+        if tensor_2d.ndim != 2:
+            log.warning(
+                f"Cannot visualize tensor with {tensor_2d.ndim} dimensions. Requires 2."
+            )
+            return None
+
+        fig, ax = plt.subplots(figsize=(6, 4), dpi=100)
+        im = ax.imshow(tensor_2d.cpu().numpy(), cmap="viridis", aspect="auto")
+        fig.colorbar(im, ax=ax)
+        ax.set_title(title)
+        fig.tight_layout()
+
+        buf = io.BytesIO()
+        fig.savefig(buf, format="png", bbox_inches="tight", pad_inches=0.1)
+        plt.close(fig)
+        buf.seek(0)
+        return "data:image/png;base64," + base64.b64encode(buf.read()).decode(
+            "utf-8"
+        )
+
+    def _render_image_tensor(self, item: torch.Tensor) -> ProcessorResult:
+        is_mask = (item.ndim == 2) or (item.ndim == 3 and item.shape[-1] != 3)
+        img_tensor = (
+            item.unsqueeze(0) if item.ndim == 3 and not is_mask else item
+        )
+        img_tensor = item.unsqueeze(0) if item.ndim == 2 else img_tensor
+
+        images = tensor2pil(img_tensor)
+        b64_imgs = []
+        for im in images:
+            if is_mask:
+                im = im.convert("L")
+            buffered = io.BytesIO()
+            im.save(buffered, format="PNG")
+            b64_imgs.append(
+                "data:image/png;base64,"
+                + base64.b64encode(buffered.getvalue()).decode("utf-8")
+            )
+        return [UIResult(kind="b64_images", data=b64_imgs)]
+
+    def _preview_latent_tensor(self, item: torch.Tensor) -> ProcessorResult:
+        is_empty = "(empty)" if torch.count_nonzero(item) == 0 else ""
+        stats = [
+            f"Min: {item.min():.4f}",
+            f"Max: {item.max():.4f}",
+            f"Mean: {item.mean():.4f}",
+        ]
+        text = [
+            get_torch_tensor_info(item, name="Latent Tensor"),
+            is_empty,
+        ] + stats
+
+        result = [UIResult(kind="text", data=t) for t in text]
+        vis_tensor = item[0].mean(dim=0)
+        heatmap_b64 = self._visualize_tensor_heatmap(
+            vis_tensor, "Latent Energy (Channel Mean)"
+        )
+        if heatmap_b64:
+            result.append(UIResult(kind="b64_images", data=[heatmap_b64]))
+        return result
+
+    def _preview_conditioning_tensor(
+        self, item: torch.Tensor
+    ) -> ProcessorResult:
+        _batch, tokens, embed_dim = item.shape
+        text = [
+            get_torch_tensor_info(item, name="CLIP Conditioning Tensor"),
+            f"Token Count: {tokens}",
+            f"Embedding Dim: {embed_dim}",
+        ]
+
+        result = [UIResult(kind="text", data=d) for d in text]
+        heatmap_b64 = self._visualize_tensor_heatmap(
+            item[0], "Token Embeddings (approx)"
+        )
+        if heatmap_b64:
+            result.append(UIResult(kind="b64_images", data=[heatmap_b64]))
+        return result
+
+    def _process_container(
+        self, item: list | tuple, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        if not item:
+            return [UIResult(kind="text", data=f"Empty {type(item).__name__}")]
+
+        container_type = type(item).__name__
+        element_type = type(item[0]).__name__
+
+        all_match = all(type(i) is type(item[0]) for i in item)
+
+        result = [
+            UIResult(
+                kind="text",
+                data=f"{container_type} of {len(item)} x {element_type}",
+            ),
+            UIResult(kind="text", data=f"(mixed types: {not all_match})"),
+        ]
+
+        if not as_type or (as_type and deep):
+            for i, sub_item in enumerate(item):
+                res = self._dispatch_processor(
+                    sub_item, as_type=as_type, deep=deep
+                )
+                if res:
+                    text = res[0].get("data", "Unknown")
+                    res[0]["data"] = f"[{i}]: {text}"
+
+                result.extend(res)
+
+            return result
+
+        first_item_result = self._dispatch_processor(
+            item[0], as_type=as_type, deep=deep
+        )
+        if not first_item_result:
+            return result
+
+        return (
+            result
+            + [UIResult(kind="text", data="Preview of first element:")]
+            + indent_results(first_item_result, "  - ")
+        )
+
+    def _process_dict(
+        self, item: dict, *, as_type=False, deep=False
+    ) -> ProcessorResult:
+        if "pooled_output" in item and isinstance(
+            item["pooled_output"], torch.Tensor
+        ):
+            return self._dispatch_processor(
+                item["pooled_output"], as_type=as_type, deep=deep
+            )
+
+        if "samples" in item and isinstance(item.get("samples"), torch.Tensor):
+            return self._dispatch_processor(
+                item["samples"], as_type=as_type, deep=deep
+            )
+
+        if "waveform" in item and isinstance(
+            item.get("waveform"), torch.Tensor
+        ):
+            waveform = item["waveform"]
+            is_empty = "(empty) " if torch.count_nonzero(waveform) == 0 else ""
+            text = textwrap.dedent(f"""
+                Audio Waveform: {waveform.shape}{is_empty}
+                Sample Rate: {item.get("sample_rate", "N/A")}
+            """).strip()
+            return [{"kind": "text", "data": text}]
+
+        log.debug(
+            f"Processing generic dict with rich inspector: {item.keys()}"
+        )
+        return self._process_unknown(item, as_type=as_type, deep=deep)
 
 
-processors = {
-    torch.Tensor: process_tensor,
-    list: process_list,
-    dict: process_dict,
-    bool: process_bool,
-}
-
-__nodes__ = [MTB_Debug, MTB_SaveTensors]
+__nodes__ = [MTB_Debug]

nodes/dev.py

@@ -0,0 +1,70 @@
+import folder_paths
+import torch
+
+
+class MTB_SaveTensors:
+    """Save torch tensors (image, mask or latent) to disk.
+
+    useful to debug things outside comfy.
+    """
+
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "mtb/debug"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "filename_prefix": ("STRING", {"default": "ComfyPickle"}),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+                "mask": ("MASK",),
+                "latent": ("LATENT",),
+            },
+        }
+
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    RETURN_TYPES = ()
+    CATEGORY = "mtb/debug"
+
+    def save(
+        self,
+        filename_prefix,
+        image: torch.Tensor | None = None,
+        mask: torch.Tensor | None = None,
+        latent: torch.Tensor | None = None,
+    ):
+        (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            filename_prefix,
+        ) = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        full_output_folder = Path(full_output_folder)
+        if image is not None:
+            image_file = f"{filename}_image_{counter:05}.pt"
+            torch.save(image, full_output_folder / image_file)
+            # np.save(full_output_folder/ image_file, image.cpu().numpy())
+
+        if mask is not None:
+            mask_file = f"{filename}_mask_{counter:05}.pt"
+            torch.save(mask, full_output_folder / mask_file)
+            # np.save(full_output_folder/ mask_file, mask.cpu().numpy())
+
+        if latent is not None:
+            # for latent we must use pickle
+            latent_file = f"{filename}_latent_{counter:05}.pt"
+            torch.save(latent, full_output_folder / latent_file)
+            # pickle.dump(latent, open(full_output_folder/ latent_file, "wb"))
+
+            # np.save(full_output_folder / latent_file,
+            # latent[""].cpu().numpy())
+
+        return f"{filename_prefix}_{counter:05}"
+
+
+__nodes__ = [MTB_SaveTensors]

nodes/generate.py

@@ -7,6 +7,13 @@ from PIL import Image, ImageDraw, ImageFont
 from ..log import log
 from ..utils import comfy_dir, font_path, pil2tensor
 
+# try:
+#     from cairosvg import svg2png
+#     HAS_CAIRO = True
+# except ImportError:
+#     HAS_CAIRO = False
+
+
 # class MtbExamples:
 #     """MTB Example Images"""
 
@@ -193,11 +200,11 @@ by default it fallsback to a default font.
                 ),
                 "color": (
                     "COLOR",
-                    {"default": "black", "widgetType": "MTB_COLOR"},
+                    {"default": "black"},
                 ),
                 "background": (
                     "COLOR",
-                    {"default": "white", "widgetType": "MTB_COLOR"},
+                    {"default": "white"},
                 ),
                 "h_align": (("left", "center", "right"), {"default": "left"}),
                 "v_align": (("top", "center", "bottom"), {"default": "top"}),
@@ -299,7 +306,7 @@ by default it fallsback to a default font.
 
     def text_to_image(
         self,
-        text: str,
+        text: str | list[str],
         font,
         wrap,
         trim,
@@ -341,7 +348,7 @@ by default it fallsback to a default font.
             color = (255, 255, 255, 255)
             background = (0, 0, 0, 255)
 
-        def render_text(text_to_render, alpha=None):
+        def render_text(text_to_render: str, alpha=None) -> Image.Image:
             if trim:
                 text_to_render = text_to_render.strip()
             if wrap:
@@ -426,9 +433,16 @@ by default it fallsback to a default font.
             frame_tensors = [pil2tensor(frame) for frame in frames]
             return (torch.cat(frame_tensors, dim=0),)
         else:
-            text_img = render_text(text)
-            result = Image.alpha_composite(base_img, text_img)
-            return (pil2tensor(result),)
+            results = []
+            if not isinstance(text, list):
+                text = [text]
+
+            for t in text:
+                text_img = render_text(t)
+                result = Image.alpha_composite(base_img, text_img)
+                results.append(result)
+
+            return (pil2tensor(results),)
 
 
 __nodes__ = [

nodes/graph_utils.py

@@ -6,7 +6,7 @@ import urllib.request
 from math import pi
 from typing import Any
 
-import comfy.model_management as model_management
+import comfy.model_management as mm
 import comfy.utils
 import numpy as np
 import torch
@@ -16,8 +16,10 @@ from PIL import Image
 from ..log import log
 from ..utils import (
     EASINGS,
+    LazyProxyTensor,
     apply_easing,
     get_server_info,
+    get_torch_tensor_info,
     numpy_NFOV,
     pil2tensor,
     tensor2np,
@@ -134,12 +136,64 @@ class MTB_ApplyTextTemplate:
     CATEGORY = "mtb/utils"
     FUNCTION = "execute"
 
-    def execute(self, *, template: str, **kwargs):
-        res = f"{template}"
-        for k, v in kwargs.items():
-            res = res.replace(f"{{{k}}}", f"{v}")
+    def execute(self, *, template: str, **kwargs) -> tuple[str | list[str]]:
+        keys = list(kwargs.keys())
+        values = list(kwargs.values())
 
-        return (res,)
+        has_list = any(isinstance(v, list) for v in values)
+        target_length = -1
+
+        if has_list:
+            first_list = next(x for x in values if isinstance(x, list))
+
+            # all_list = all(isinstance(x, list) for x in kwargs.values())
+            # if not all_list:
+            #     raise ValueError(
+            #         "Text template supports either str or list[str] but not a mix of the two (yet?)"
+            #     )
+            target_length = len(first_list)
+            same_length = all(
+                len(v) == target_length for v in values if isinstance(v, list)
+            )
+            if not same_length:
+                raise ValueError(
+                    "Text template received multiple list[str] but their size is varying, they should match..."
+                )
+
+        if has_list:
+            results = []
+
+            # do a padded loop, not the most efficient but easy
+            # to handle for now
+            for it in range(target_length):
+                res = f"{template}"
+                for k, v in kwargs.items():
+                    if isinstance(v, list):
+                        res = self.apply_res(res, k, v[it])
+                    else:
+                        res = self.apply_res(res, k, v)
+                results.append(res)
+
+            return (results,)
+
+        else:
+            res = f"{template}"
+            for k, v in kwargs.items():
+                res = self.apply_res(res, k, v)
+
+            return (res,)
+
+    def apply_res(self, res, key, value):
+        if isinstance(value, float):
+            value = f"{value:.3f}"
+        elif isinstance(value, torch.Tensor):
+            value = get_torch_tensor_info(value)
+        else:
+            log.debug(
+                f"Falling back to default string conversion for {key} of type {type(value).__name__}"
+            )
+
+        return res.replace(f"{{{key}}}", f"{value}")
 
 
 class MTB_MatchDimensions:
@@ -343,7 +397,7 @@ class MTB_AutoPanEquilateral:
 
             frames.append(frame)
 
-            model_management.throw_exception_if_processing_interrupted()
+            mm.throw_exception_if_processing_interrupted()
             pbar.update(1)
 
         return (pil2tensor(frames),)
@@ -916,6 +970,61 @@ class MTB_BooleanNot:
         return (not bool_in,)
 
 
+class MTB_ProxyTensor:
+    """Wraps an input tensor into a LazyProxyTensor.
+
+    builds upon an idea by @AustinMroz
+    """
+
+    NODE_NAME = "ProxyTensor"
+    NODE_DISPLAY_NAME = "Proxy Tensor"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "tensor": ("IMAGE",),
+                "target_dtype": (
+                    ["float32", "float16", "bfloat16"],
+                    {"default": "float32"},
+                ),
+                "target_device": (
+                    ["keep", "cpu", "gpu"],
+                    {
+                        "default": "keep",
+                        "tooltip": "CAUTION: This isn't compatible with most nodes for now",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("proxy_tensor",)
+    FUNCTION = "execute"
+    CATEGORY = "mtb/utils"
+
+    def execute(
+        self,
+        tensor: torch.Tensor,
+        target_dtype: str = "float32",
+        target_device: str = "keep",
+    ):
+        torch_dtype: torch.dtype = getattr(torch, target_dtype)
+
+        if target_device == "gpu":
+            torch_device = mm.get_torch_device()
+        elif target_device == "cpu":
+            torch_device = torch.device("cpu")
+        else:
+            torch_device = tensor.device
+
+        proxy = LazyProxyTensor(tensor, torch_dtype, torch_device)
+
+        log.info(f"Created Proxy Tensor: \n{proxy}")
+
+        return (proxy,)
+
+
 __nodes__ = [
     MTB_StringReplace,
     MTB_FitNumber,
@@ -933,4 +1042,5 @@ __nodes__ = [
     MTB_TensorOps,
     MTB_BooleanNot,
     MTB_GetItem,
+    MTB_ProxyTensor,
 ]

nodes/image_processing.py

@@ -683,7 +683,6 @@ class MTB_ImageCompare:
 
 
 import requests
-import time
 
 
 class MTB_LoadImageFromUrl:
@@ -699,14 +698,6 @@ class MTB_LoadImageFromUrl:
                         "default": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a7/Example.jpg/800px-Example.jpg"
                     },
                 ),
-                "retry_count": (
-                    "INT",
-                    {"default": 3, "min": 1, "max": 20, "step": 1},
-                ),
-                "retry_interval": (
-                    "FLOAT",
-                    {"default": 1.0, "min": 0.0, "max": 60.0, "step": 0.1},
-                ),
             }
         }
 
@@ -714,27 +705,11 @@ class MTB_LoadImageFromUrl:
     FUNCTION = "load"
     CATEGORY = "mtb/IO"
 
-    def load(self, url, retry_count, retry_interval):
-        # get the image from the url with retry + exponential backoff
-        last_error = None
-        for attempt in range(retry_count):
-            try:
-                response = requests.get(url, stream=True)
-                response.raise_for_status()
-                image = Image.open(response.raw)
-                image = ImageOps.exif_transpose(image)
-                return (pil2tensor(image),)
-            except Exception as e:
-                last_error = e
-                if attempt == retry_count - 1:
-                    raise
-                wait_seconds = retry_interval * (2**attempt)
-                if wait_seconds > 0:
-                    time.sleep(wait_seconds)
-
-        if last_error is not None:
-            raise last_error
-        raise RuntimeError("Failed to load image from URL without captured exception")
+    def load(self, url):
+        # get the image from the url
+        image = Image.open(requests.get(url, stream=True).raw)
+        image = ImageOps.exif_transpose(image)
+        return (pil2tensor(image),)
 
 
 class MTB_Blur:
@@ -904,11 +879,8 @@ class MTB_MaskToImage:
         return {
             "required": {
                 "mask": ("MASK",),
-                "color": ("COLOR", {"widgetType": "MTB_COLOR"}),
-                "background": (
-                    "COLOR",
-                    {"default": "#000000", "widgetType": "MTB_COLOR"},
-                ),
+                "color": ("COLOR",),
+                "background": ("COLOR", {"default": "#000000"}),
             },
             "optional": {
                 "invert": ("BOOLEAN", {"default": False}),

nodes/legacy.py

@@ -1,17 +0,0 @@
-# from ..utils import hex_to_rgb
-class MTB_ColorInput:
-    RETURN_TYPES = ("COLOR",)
-    FUNCTION = "color"
-    CATEGORY = "mtb/color"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {"color": ("MTB_COLOR", {"default": "#ffffff"})},
-        }
-
-    def color(self, color):
-        return (color,)
-
-
-__nodes__ = [MTB_ColorInput]

nodes/mask.py

@@ -34,7 +34,7 @@ class MTB_ImageRemoveBackgroundRembg:
                 ),
                 "bgcolor": (
                     "COLOR",
-                    {"default": "#000000","widgetType": "MTB_COLOR"},
+                    {"default": "#000000"},
                 ),
             },
         }

nodes/model.py

@@ -145,7 +145,7 @@ class MTB_ModelPatchSeamless:
         tilingX,
         tilingY,
     ):
-        hacked_model = model.clone()
+        hacked_model = copy.deepcopy(model)
         self.apply_circular(
             hacked_model.model, startStep, stopStep, tilingX, tilingY
         )

nodes/transform.py

@@ -43,10 +43,7 @@ class MTB_TransformImage:
                     ["edge", "constant", "reflect", "symmetric"],
                     {"default": "edge"},
                 ),
-                "constant_color": (
-                    "COLOR",
-                    {"default": "#000000", "widgetType": "MTB_COLOR"},
-                ),
+                "constant_color": ("COLOR", {"default": "#000000"}),
             },
             "optional": {
                 "filter_type": (

nodes/vitmatte.py

@@ -27,7 +27,7 @@ class MTB_LoadVitMatteModel:
     def execute(self, *, kind: str, autodownload: bool):
         dest = models_dir / "vitmatte"
         dest.mkdir(exist_ok=True)
-        name = "dis" if kind == "Distinctions-646" else "com"
+        name = "dist" if kind == "Distinctions-646" else "com"
 
         file = hf_hub_download(
             repo_id="melmass/pytorch-scripts",

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "comfy-mtb"
-version = "0.5.4"
+version = "0.6.0"
 description = "Animation oriented nodes pack for ComfyUI."
 license = { text = "MIT" }
 readme = "README.md"
@@ -62,39 +62,6 @@ PublisherId = "mel"
 DisplayName = "comfy-mtb"
 Icon = "https://avatars.githubusercontent.com/u/7041726?v=4"
 
-[tool.bumpversion]
-current_version = "0.5.1"
-parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
-serialize = ["{major}.{minor}.{patch}"]
-search = "{current_version}"
-replace = "{new_version}"
-regex = false
-ignore_missing_version = false
-ignore_missing_files = false
-tag = true
-sign_tags = true
-tag_name = "v{new_version}"
-tag_message = "⬆️ Bump version: {current_version} → {new_version}"
-allow_dirty = true
-commit = true
-message = "⬆️ Bump version: {current_version} → {new_version}"
-commit_args = ""
-
-[[tool.bumpversion.files]]
-filename = "__init__.py"
-search = "__version__ = \"{current_version}\""
-replace = "__version__ = \"{new_version}\""
-
-[[tool.bumpversion.files]]
-filename = "pyproject.toml"
-search = "version = \"{current_version}\""
-replace = "version = \"{new_version}\""
-
-# [[tool.bumpversion.files]]
-# filename = "your_package/__init__.py"
-# search = "__version__ = '{current_version}'"
-# replace = "__version__ = '{new_version}'"
-
 # INFO: All those remaining keys are meant for local dev
 [tool.pyright]
 include = ["."]
@@ -111,18 +78,18 @@ stubPath = "src/stubs"
 
 reportMissingImports = true
 reportMissingTypeStubs = false
+reportExplicitAny = false
 typeCheckingMode = "basic"
-
-pythonVersion = "3.10"
+pythonVersion = "3.11"
 pythonPlatform = "Windows"
 
+
 [tool.pytest.ini_options]
 log_level = "DEBUG"
 log_cli = true
 markers = [
   "wip: tests that aren't fully finished yet",
-  "heavy: marks tests as heavy (deselect with '-m \"not heavy\"')",
-
+  '''heavy: marks tests as heavy (deselect with '-m "not heavy"')''',
 ]
 filterwarnings = ["ignore::UserWarning", 'ignore::DeprecationWarning']
 

pyrightconfig.json

@@ -1,18 +0,0 @@
-{
-    "exclude": [
-        "**/node_modules",
-        "**/__pycache__",
-    ],
-    "ignore": [
-        "extern"
-    ],
-    "defineConstant": {
-        "DEBUG": true
-    },
-    "venvPath": "../../../.venv/",
-    "reportMissingImports": true,
-    "reportMissingTypeStubs": false,
-    "pythonVersion": "3.10",
-    "pythonPlatform": "All",
-    "reportOptionalMemberAccess": "none"
-}

repl.py

@@ -0,0 +1,637 @@
+import base64
+import code
+import io
+import re
+import sys
+from contextlib import redirect_stderr, redirect_stdout
+
+# import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from aiohttp import web
+from PIL import Image
+from rich.console import Console
+from rich.traceback import Traceback
+
+from .log import log
+
+try:
+    import pyflakes.api
+    import pyflakes.reporter
+
+    _HAS_LINT = True
+except ImportError:
+    print(
+        "ComfyREPL: pyflakes not found. Linting will be disabled. Install with 'pip install pyflakes'."
+    )
+    _HAS_LINT = False
+
+# --- Linting Library ---
+# try:
+#     import ruff
+#     import ruff.lint
+#     import ruff.lint.linter
+#     import ruff.settings
+#
+#     _HAS_LINT = True
+# except ImportError:
+#     print(
+#         "ComfyREPL: ruff not found. Linting will be disabled. Install with 'pip install ruff'."
+#     )
+#     _HAS_LINT = False
+
+# --- Audio/Video Libraries ---
+try:
+    import scipy.io.wavfile
+
+    _HAS_SCIPY = True
+except ImportError:
+    print(
+        "ComfyREPL: SciPy not found. Audio display will be disabled. Install with 'pip install scipy'."
+    )
+    _HAS_SCIPY = False
+
+try:
+    import imageio
+    import imageio.plugins.ffmpeg  # Ensure ffmpeg plugin is available
+
+    _HAS_IMAGEIO = True
+except ImportError:
+    print(
+        "ComfyREPL: Imageio or imageio-ffmpeg not found. Video display will be disabled. Install with 'pip install imageio imageio-ffmpeg'."
+    )
+    _HAS_IMAGEIO = False
+
+
+# --- Audio Display ---
+class AudioDisplay:
+    def __init__(self, samples, sample_rate):
+        if not _HAS_SCIPY:
+            raise ImportError("Audio display requires scipy and numpy.")
+        if not isinstance(samples, (np.ndarray, torch.Tensor)):
+            raise TypeError(
+                "Audio samples must be a numpy array or torch tensor."
+            )
+        if isinstance(samples, torch.Tensor):
+            samples = samples.detach().cpu().numpy()
+
+        # Ensure samples are in a format scipy.io.wavfile can handle (e.g., int16, float32)
+        if samples.dtype == np.float64:
+            samples = samples.astype(np.float32)
+        elif samples.dtype == np.int64:
+            # Or scale to int32 if range requires
+            samples = samples.astype(np.int16)
+
+        self.samples = samples
+        self.sample_rate = sample_rate
+
+    def _to_wav_base64(self):
+        buffer = io.BytesIO()
+        try:
+            scipy.io.wavfile.write(buffer, self.sample_rate, self.samples)
+            audio_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+            return audio_base64
+        except Exception as e:
+            return f"<div style='color: red;'>Error encoding audio: {e}</div>"
+
+    def _repr_html_(self):
+        base64_data = self._to_wav_base64()
+        if base64_data.startswith("<div"):
+            return base64_data
+        return f'<audio controls src="data:audio/wav;base64,{base64_data}" style="margin: 5px 0;"/>'
+
+
+def render_audio(samples, sample_rate):
+    """
+    Render audio samples as an HTML audio player.
+
+    Args:
+        samples (np.ndarray or torch.Tensor): Audio samples.
+        sample_rate (int): Sample rate in Hz.
+
+    Returns
+    -------
+        AudioDisplay: An object that will render as an HTML audio player.
+    """
+    return AudioDisplay(samples, sample_rate)
+
+
+# --- Display Classes ---
+class VideoDisplay:
+    def __init__(self, frames, fps=24, options=None):
+        if not _HAS_IMAGEIO:  # numpy/PIL/torch needed for frames
+            raise ImportError(
+                "Video display requires imageio, imageio-ffmpeg, and image libraries (numpy, Pillow, torch)."
+            )
+
+        self.frames = []
+        for frame in frames:
+            if isinstance(frame, Image.Image):
+                self.frames.append(np.array(frame))
+            elif isinstance(frame, np.ndarray):
+                # Ensure HWC and uint8
+                if frame.ndim == 3 and frame.shape[0] in [1, 3, 4]:  # CHW
+                    frame = np.transpose(frame, (1, 2, 0))
+                if frame.dtype != np.uint8:
+                    frame = (
+                        (frame * 255).astype(np.uint8)
+                        if frame.max() <= 1.0
+                        else frame.astype(np.uint8)
+                    )
+                self.frames.append(frame)
+            elif isinstance(frame, torch.Tensor):
+                np_frame = frame.detach().cpu().numpy()
+                if np_frame.ndim == 3 and np_frame.shape[0] in [
+                    1,
+                    3,
+                    4,
+                ]:  # CHW
+                    np_frame = np.transpose(np_frame, (1, 2, 0))
+                if np_frame.dtype != np.uint8:
+                    np_frame = (
+                        (np_frame * 255).astype(np.uint8)
+                        if np_frame.max() <= 1.0
+                        else np_frame.astype(np.uint8)
+                    )
+                self.frames.append(np_frame)
+            else:
+                raise TypeError(
+                    f"Unsupported frame type: {type(frame)}. Must be PIL.Image, numpy.ndarray, or torch.Tensor."
+                )
+
+        self.fps = fps
+        self.options = options if options is not None else {}
+
+    def _to_mp4_base64(self):
+        buffer = io.BytesIO()
+        try:
+            # Use imageio to write frames to an in-memory MP4 file
+            imageio.mimwrite(
+                buffer,
+                self.frames,
+                format="mp4",
+                fps=self.fps,
+                codec="libx264",
+                quality=8,
+            )  # quality 1-10
+            video_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+            return video_base64
+        except Exception as e:
+            return f"<div style='color: red;'>Error encoding video: {e}</div>"
+
+    def _repr_html_(self):
+        base64_data = self._to_mp4_base64()
+        if base64_data.startswith("<div"):  # Check if it's an error message
+            return base64_data
+
+        # Build HTML options string
+        option_str = ""
+        for key, value in self.options.items():
+            if isinstance(value, bool) and value:
+                option_str += f" {key}"
+            elif isinstance(value, str):
+                option_str += f' {key}="{value}"'
+            else:
+                option_str += f' {key}="{value}"'  # Fallback for numbers etc.
+
+        return f'<video controls src="data:video/mp4;base64,{base64_data}" style="max-width: 100%; height: auto; border: 1px solid #555; margin: 5px 0;"{option_str}/>'
+
+
+def render_video(batch_tensor_or_array_of_pil_images, fps=24, options=None):
+    """
+    Render video frames as an HTML video player.
+
+    Args:
+        batch_tensor_or_array_of_pil_images (list of PIL.Image, np.ndarray, or torch.Tensor):
+            A list of frames, or a single batch tensor/array (B, H, W, C) or (B, C, H, W).
+        fps (int): Frames per second.
+        options (dict): Dictionary of HTML <video> tag attributes (e.g., {"loop": True, "autoplay": True}).
+
+    Returns
+    -------
+        VideoDisplay: An object that will render as an HTML video player.
+    """
+    frames_list = []
+    if isinstance(
+        batch_tensor_or_array_of_pil_images, (np.ndarray, torch.Tensor)
+    ):
+        # Assume it's a batch tensor/array
+        for i in range(batch_tensor_or_array_of_pil_images.shape[0]):
+            frames_list.append(batch_tensor_or_array_of_pil_images[i])
+    elif isinstance(batch_tensor_or_array_of_pil_images, list):
+        frames_list = batch_tensor_or_array_of_pil_images
+    else:
+        raise TypeError(
+            "Input for render_video must be a list of frames or a batch tensor/array."
+        )
+
+    return VideoDisplay(frames_list, fps, options)
+
+
+class ComfyREPLBackend:
+    def __init__(self):
+        self.repl_consoles: dict[str, code.InteractiveConsole] = {}
+        # self.repl_console = None
+        self.image_outputs = []
+        self.audio_outputs = []
+        self.video_outputs = []
+        self._original_displayhook = sys.displayhook
+        # self._init_repl_console()
+
+    @staticmethod
+    def _init_repl_console():
+        """Define the globals that will be available in the REPL session."""
+        repl_globals = {"__builtins__": __builtins__}
+        # repl_globals["plt"] = plt
+        repl_globals["np"] = np
+        repl_globals["Image"] = Image
+        repl_globals["torch"] = torch
+
+        repl_globals["repl_display"] = _repl_display_image
+        if _HAS_SCIPY:
+            repl_globals["render_audio"] = render_audio
+        if _HAS_IMAGEIO:
+            repl_globals["render_video"] = render_video
+
+        return code.InteractiveConsole(locals=repl_globals)
+
+    def _custom_displayhook(self, value):
+        """
+        Displayhook that capture and process image, audio, video objects.
+
+        For other objects, it fallsback to the original displayhook.
+        """
+        if value is None:
+            return
+
+        # Attempt to handle as an image
+        if (
+            isinstance(value, (Image.Image, np.ndarray, torch.Tensor))
+            # or (
+            #     hasattr(value, "figure")
+            #     and isinstance(value.figure, plt.Figure)
+            # )
+            # or isinstance(value, plt.Figure)
+        ):
+            img_html = _repl_display_image(value)
+            self.image_outputs.append(img_html)
+            return
+
+        # Attempt to handle as audio
+        elif isinstance(value, AudioDisplay):
+            audio_html = value._repr_html_()
+            self.audio_outputs.append(audio_html)
+            return
+
+        # Attempt to handle as video
+        elif isinstance(value, VideoDisplay):
+            video_html = value._repr_html_()
+            self.video_outputs.append(video_html)
+            return
+
+        else:
+            # If not a special media type, let the original displayhook handle it.
+            self._original_displayhook(value)
+
+    def _console_to_html(
+        self, stream: io.StringIO | Traceback, width: int = 120
+    ) -> str:
+        if isinstance(stream, io.StringIO):
+            captured_text_output = stream.getvalue()
+        else:
+            captured_text_output = Traceback
+
+        html_console = Console(
+            file=io.StringIO(), record=True, force_terminal=True, width=width
+        )
+        html_console.print(captured_text_output)
+
+        return html_console.export_html(inline_styles=True)
+
+    def get_console(self, node_name: str):
+        console = self.repl_consoles.get(node_name)
+        if console:
+            return console
+
+        console = self._init_repl_console()
+        self.repl_consoles[node_name] = console
+        return self.repl_consoles[node_name]
+
+    def execute_code(self, node_name: str, code: str):
+        # Clear outputs from previous execution
+        self.image_outputs = []
+        self.audio_outputs = []
+        self.video_outputs = []
+
+        output_html = ""
+        error_message = None
+
+        repl_console = self.get_console(node_name)
+
+        string_io = io.StringIO()
+
+        # Temporarily patch sys.displayhook
+        sys.displayhook = self._custom_displayhook
+
+        try:
+            with redirect_stdout(string_io), redirect_stderr(string_io):
+                for line in code.splitlines():
+                    repl_console.push(line)
+
+            full_rich_html = self._console_to_html(string_io)
+            match = re.search(
+                r"<body.*?>(.*?)</body>", full_rich_html, re.DOTALL
+            )
+            if match:
+                output_html = match.group(1)
+            else:
+                output_html = full_rich_html
+
+            # Append any captured media HTML *after* the rich text output
+            for img_html in self.image_outputs:
+                output_html += img_html
+            for audio_html in self.audio_outputs:
+                output_html += audio_html
+            for video_html in self.video_outputs:
+                output_html += video_html
+
+        except Exception as e:
+            exc_type, exc_value, exc_traceback = sys.exc_info()
+            rich_traceback = Traceback.from_exception(
+                exc_type,
+                exc_value,
+                exc_traceback,
+                show_locals=True,
+                suppress=[__file__],
+            )
+            # error_console = Console(
+            # file=io.StringIO(), record=True, force_terminal=True, width=120
+            # )
+            # error_console.print(rich_traceback)
+            # full_error_html = error_console.export_html(inline_styles=True)
+
+            full_error_html = self._console_to_html(rich_traceback)
+
+            match = re.search(
+                r"<body.*?>(.*?)</body>", full_error_html, re.DOTALL
+            )
+            output_html = match.group(1) if match else full_error_html
+
+            error_message = str(e)
+        finally:
+            sys.displayhook = (
+                self._original_displayhook
+            )  # Always restore original displayhook
+
+        return {"output_html": output_html, "error": error_message}
+
+    def lint_code(self, node_name: str, code: str):
+        diagnostics = []
+
+        if not _HAS_LINT:
+            diagnostics.append(
+                {
+                    "row": 0,
+                    "column": 0,
+                    "text": "Pyflakes not installed. Linting disabled. Install with 'uv add pyflakes'.",
+                    "type": "warning",
+                }
+            )
+            return web.json_response({"diagnostics": diagnostics})
+
+        # Use a custom reporter to capture messages
+        class PyflakesReporter(pyflakes.reporter.Reporter):
+            def __init__(self):
+                self.messages = []
+                # Suppress stdout/stderr from pyflakes itself
+                self._stdout = io.StringIO()
+                self._stderr = io.StringIO()
+                super().__init__(self._stdout, self._stderr)
+
+            def flake(self, message):
+                # Ace editor expects 0-indexed row, pyflakes gives 1-indexed lineno
+                self.messages.append(
+                    {
+                        "row": message.lineno - 1,
+                        "column": message.col,
+                        "text": str(message),
+                        "type": "warning",  # pyflakes usually gives warnings
+                    }
+                )
+
+            def unexpectedError(self, filename, msg):
+                self.messages.append(
+                    {
+                        "row": 0,
+                        "column": 0,
+                        "text": f"Pyflakes internal error: {msg}",
+                        "type": "error",
+                    }
+                )
+
+            def syntaxError(self, filename, msg, lineno, offset, text):
+                log.info(f"Received {text} to syntax error")
+                self.messages.append(
+                    {
+                        "row": lineno - 1,  # Ace is 0-indexed
+                        "column": offset,
+                        "text": f"Syntax Error: {msg}",
+                        "type": "error",
+                    }
+                )
+
+        reporter = PyflakesReporter()
+        pyflakes.api.check(code, node_name, reporter)
+
+        return {"diagnostics": reporter.messages}
+
+    def lint_code_ruff(self, code: str):
+        diagnostics = []
+
+        if not _HAS_LINT:
+            diagnostics.append(
+                {
+                    "row": 0,
+                    "column": 0,
+                    "text": "Ruff not installed. Linting disabled. Install with 'pip install ruff'.",
+                    "type": "warning",
+                }
+            )
+            return {"diagnostics": diagnostics}
+
+        # Define the builtins/globals that Ruff should recognize
+        # These are the names we inject into the REPL's scope
+        repl_builtins = [
+            "repl_display",
+            "render_audio",
+            "render_video",
+            # "plt",
+            "np",
+            "Image",
+            "torch",
+        ]
+
+        try:
+            # Lint the code using Ruff's programmatic API
+            result = ruff.lint.linter.lint_stdin(
+                code.encode("utf-8"),
+                path="<stdin>",
+                builtins=repl_builtins,
+            )
+
+            for diagnostic in result.diagnostics:
+                diag_type = "warning"  # Default
+                # Ruff's error codes: F (Pyflakes), E (Pycodestyle), W (Pycodestyle warning), I (isort), N (naming), etc.
+                # F821: Undefined name (often an error)
+                if (
+                    diagnostic.kind.code.startswith("E")
+                    or diagnostic.kind.code == "F821"
+                ):
+                    diag_type = "error"
+                elif diagnostic.kind.code.startswith("W"):
+                    diag_type = "warning"
+
+                diagnostics.append(
+                    {
+                        "row": diagnostic.location.row - 1,  # Ace is 0-indexed
+                        "column": diagnostic.location.column
+                        - 1,  # Ace is 0-indexed
+                        "text": diagnostic.message,
+                        "type": diag_type,
+                    }
+                )
+
+        except Exception as e:
+            diagnostics.append(
+                {
+                    "row": 0,
+                    "column": 0,
+                    "text": f"Ruff internal error: {e}",
+                    "type": "error",
+                }
+            )
+
+        return {"diagnostics": diagnostics}
+
+
+def _repl_display_image(img_data):
+    """
+    Internal function to convert image data (PIL, numpy, torch, matplotlib) to base64 HTML.
+    """
+    pil_img = None
+    # fig = None
+
+    if isinstance(img_data, Image.Image):
+        pil_img = img_data
+    elif isinstance(img_data, np.ndarray):
+        # Handle different numpy array shapes (HWC, CHW)
+        if img_data.ndim == 3:
+            if img_data.shape[0] in [1, 3, 4]:  # Likely CHW
+                if img_data.shape[0] == 1:  # Grayscale
+                    img_data = img_data.squeeze(0)
+                else:  # Color
+                    img_data = np.transpose(img_data, (1, 2, 0))  # CHW to HWC
+            # Ensure it's uint8 for PIL, assuming float [0,1] or int [0,255]
+            if img_data.dtype != np.uint8:
+                img_data = (
+                    (img_data * 255).astype(np.uint8)
+                    if img_data.max() <= 1.0
+                    else img_data.astype(np.uint8)
+                )
+        pil_img = Image.fromarray(img_data)
+    elif isinstance(img_data, torch.Tensor):
+        # Move to CPU, convert to numpy
+        np_img = img_data.detach().cpu().numpy()
+        # Handle different tensor shapes (CHW, HWC)
+        if np_img.ndim == 3:
+            if np_img.shape[0] in [1, 3, 4]:  # Likely CHW
+                if np_img.shape[0] == 1:  # Grayscale
+                    np_img = np_img.squeeze(0)
+                else:  # Color
+                    np_img = np.transpose(np_img, (1, 2, 0))  # CHW to HWC
+            # Ensure it's uint8 for PIL, assuming float [0,1] or int [0,255]
+            if np_img.dtype != np.uint8:
+                np_img = (
+                    (np_img * 255).astype(np.uint8)
+                    if np_img.max() <= 1.0
+                    else np_img.astype(np.uint8)
+                )
+        pil_img = Image.fromarray(np_img)
+    # elif hasattr(img_data, "figure") and isinstance(
+    #     img_data.figure, plt.Figure
+    # ):
+    #     # If it's a matplotlib Axes object, get its figure
+    #     fig = img_data.figure
+    # elif isinstance(img_data, plt.Figure):
+    #     fig = img_data
+    else:
+        return f"<div style='color: red;'>Unsupported image type for display: {type(img_data)}</div>"
+
+    buffer = io.BytesIO()
+    try:
+        if pil_img:
+            pil_img.save(buffer, format="PNG")
+        # elif fig:
+        #     fig.savefig(
+        #         buffer, format="PNG", bbox_inches="tight", pad_inches=0.1
+        #     )
+        #     plt.close(
+        #         fig
+        #     )  # Close the figure to prevent it from showing up in other contexts
+        else:
+            return (
+                "<div style='color: red;'>Could not process image data.</div>"
+            )
+    except Exception as e:
+        return f"<div style='color: red;'>Error saving image: {e}</div>"
+
+    img_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+    return f'<img src="data:image/png;base64,{img_base64}" style="max-width: 100%; height: auto; border: 1px solid #555; margin: 5px 0;"/>'
+
+
+# Instantiate the backend class globally
+_comfy_repl_backend = ComfyREPLBackend()
+
+
+# Update aiohttp handlers to use the backend instance
+async def repl_execute_code_handler(request):
+    data = await request.json()
+
+    name = data.get("name")
+
+    if name is None:  # we send an error
+        return web.Response(
+            status=417, reason="Expectation Failed", text="Missing name key"
+        )
+    code = data.get("code", "")
+    result = _comfy_repl_backend.execute_code(name, code)
+    return web.json_response(result)
+
+
+async def repl_lint_code_handler(request):
+    data = await request.json()
+    name = data.get("name")
+
+    if name is None:  # we send an error
+        return web.Response(
+            status=417, reason="Expectation Failed", text="Missing name key"
+        )
+        # raise web.HTTPExpectationFailed(
+        # reason="Missing name key (reason)", text="Missing name key (text)"
+        # )
+
+    code = data.get("code", "")
+    result = _comfy_repl_backend.lint_code(name, code)
+    return web.json_response(result)
+
+
+def setup_custom_web_routes(app: web.Application):
+    """
+    Function to register our custom web routes with the ComfyUI server.
+    """
+    log.info("ComfyREPL: Registering /mtb/execute route...")
+    app.router.add_post("/mtb/execute", repl_execute_code_handler)
+    app.router.add_post("/mtb/lint", repl_lint_code_handler)
+
+
+# You can add more routes here if needed, e.g., for clearing state.

utils.py

@@ -8,11 +8,14 @@ import shutil
 import socket
 import subprocess
 import sys
+import textwrap
 import uuid
+import warnings
 from collections.abc import Callable, Sequence
 from enum import Enum
 from functools import reduce
 from pathlib import Path
+from types import EllipsisType
 from typing import TypeVar
 from urllib.parse import urlparse
 
@@ -461,8 +464,6 @@ def _run_command(shell_cmd, ignored_lines_start):
     print("Command executed successfully!")
 
 
-
-
 # endregion
 
 
@@ -526,6 +527,196 @@ PIL_FILTER_MAP = {
 
 
 # region TENSOR Utilities
+
+
+class LazyProxyTensor:
+    """Memory-efficient proxy that wrap a tensor but presents itself as a different dtype (e.g., float32).
+
+    It mimics a torch.Tensor's read-only attributes and methods. Data conversion
+    and normalization happen lazily on access (e.g., via slicing), avoiding
+    the high memory cost of a full conversion.
+
+    Supported source dtypes:
+    - torch.uint8 (normalized from [0, 255])
+    - torch.uint16 (normalized from [0, 65535])
+    - All float types (passed through, assumed to be in [0, 1] range)"
+    """
+
+    _source_tensor: torch.Tensor
+    _target_dtype: torch.dtype
+    _target_element_size: int
+    _scale_divisor: float
+    _warned_inefficient_access: bool
+
+    def __init__(
+        self, source_tensor, target_dtype=torch.float32, target_device=None
+    ):
+        if not isinstance(source_tensor, torch.Tensor):
+            raise ValueError("Input must be a torch.Tensor.")
+
+        self._source_tensor = source_tensor
+        self._target_dtype = target_dtype
+        self._target_device = (
+            target_device
+            if target_device is not None
+            else source_tensor.device
+        )
+
+        # Determine the normalization divisor based on source dtype
+        # fmt: off
+        if source_tensor.dtype == torch.uint8: self._scale_divisor = 255.0
+        elif source_tensor.dtype == torch.uint16: self._scale_divisor = 65535.0
+        elif torch.is_floating_point(source_tensor): self._scale_divisor = 1.0
+        else: raise ValueError(f"Unsupported source dtype for LazyProxyTensor: {source_tensor.dtype}")
+        # fmt: on
+
+        self._target_element_size = torch.empty(
+            (), dtype=self._target_dtype
+        ).element_size()
+        self._warned_inefficient_access = False
+
+    def is_contiguous(self, *args, **kwargs):
+        return self._source_tensor.is_contiguous(*args, **kwargs)
+
+    def stride(self, *args, **kwargs):
+        return self._source_tensor.stride(*args, **kwargs)
+
+    @property
+    def shape(self):
+        return self._source_tensor.shape
+
+    @property
+    def requires_grad(self):
+        return False
+
+    def nelement(self):
+        """Return the total number of elements in the (pretend) tensor."""
+        return self._source_tensor.nelement()
+
+    def element_size(self):
+        """Return the size in bytes of an individual (pretend) float element."""
+        return self._target_element_size
+
+    @property
+    def dtype(self):
+        return self._target_dtype
+
+    @property
+    def device(self):
+        return self._target_device
+
+    def __len__(self):
+        return self._source_tensor.shape[0]
+
+    def __getitem__(self, key):
+        if (
+            self._source_tensor.device != self._target_device
+            and not self._warned_inefficient_access
+        ):
+            warnings.warn(
+                "Inefficient access pattern detected for LazyProxyTensor. "
+                "You are slicing a device-proxied tensor, which causes slow, "
+                "repeated data transfers. For performance, use the .iter_chunks() method."
+            )
+            self._warned_inefficient_access = True
+
+        subset = self._source_tensor[key]
+
+        return (
+            subset.to(self._target_device).to(self._target_dtype)
+            / self._scale_divisor
+        )
+
+    # def __iter__(self):
+    #     for i in range(len(self)):
+    #         yield self[i]
+
+    def iter_chunks(self, chunk_size=16):
+        for i in range(0, len(self), chunk_size):
+            chunk = self._source_tensor[i : i + chunk_size]
+            yield (
+                chunk.to(self._target_device, non_blocking=True).to(
+                    self._target_dtype
+                )
+                / self._scale_divisor
+            )
+
+    def squeeze(self, dim: str | EllipsisType | None = None):
+        squeezed = self._source_tensor.squeeze(dim)
+        return LazyProxyTensor(squeezed, self._target_dtype)
+
+    def unsqueeze(self, dim: int = 0):
+        unsqueezed = self._source_tensor.unsqueeze(dim)
+        return LazyProxyTensor(unsqueezed, self._target_dtype)
+
+    def repeat(self, *sizes):
+        repeated = self._source_tensor.repeat(*sizes)
+        return LazyProxyTensor(repeated, self._target_dtype)
+
+    def _format_mem_size(self, mem_bytes):
+        if mem_bytes > 1e9:
+            return f"{mem_bytes / 1e9:.2f} GB"
+        if mem_bytes > 1e6:
+            return f"{mem_bytes / 1e6:.2f} MB"
+        if mem_bytes > 1e3:
+            return f"{mem_bytes / 1e3:.2f} KB"
+        return f"{mem_bytes} B"
+
+    def __repr__(self):
+
+        actual_info = get_torch_tensor_info(self._source_tensor, name="Source")
+        target_info = get_torch_tensor_info(self, name="Target")
+
+        info = f"""
+        {target_info}
+
+        {actual_info}
+        """
+        return textwrap.dedent(info).strip()
+
+
+def get_torch_tensor_info(
+    tensor: torch.Tensor | LazyProxyTensor | np.ndarray,
+    *,
+    name: str | None = None,
+):
+    mem_str = "N/A"
+
+    is_tensor = isinstance(tensor, torch.Tensor | LazyProxyTensor)
+    if is_tensor:
+        mem_bytes = tensor.element_size() * tensor.nelement()
+    else:
+        mem_bytes = tensor.itemsize * tensor.size
+
+    if mem_bytes > 1e9:
+        mem_str = f"{mem_bytes / 1e9:.2f} GB"
+    elif mem_bytes > 1e6:
+        mem_str = f"{mem_bytes / 1e6:.2f} MB"
+    elif mem_bytes > 1e3:
+        mem_str = f"{mem_bytes / 1e3:.2f} KB"
+    else:
+        mem_str = f"{mem_bytes} B"
+
+    device = "N/A"
+    grad = "False"
+    type_name = name or "Tensor" if is_tensor else "Numpy Array"
+
+    if is_tensor:
+        device = tensor.device
+        grad = str(tensor.requires_grad)
+
+    text = f"""
+    {type_name}
+    shape: {tensor.shape}
+    dtype: {str(tensor.dtype).replace("torch.", "")}
+    device: {device}
+    requires grad: {grad}
+    memory: {mem_str}
+    """
+
+    return textwrap.dedent(text).strip()
+
+
 def to_numpy(image: torch.Tensor) -> npt.NDArray[np.uint8]:
     """Converts a tensor to a ndarray with proper scaling and type conversion."""
     np_array = np.clip(255.0 * image.cpu().numpy(), 0, 255).astype(np.uint8)
@@ -536,12 +727,12 @@ def handle_batch(
     tensor: torch.Tensor,
     func: Callable[[torch.Tensor], Image.Image | npt.NDArray[np.uint8]],
 ) -> list[Image.Image] | list[npt.NDArray[np.uint8]]:
-    """Handles batch processing for a given tensor and conversion function."""
+    """Handle batch processing for a given tensor and conversion function."""
     return [func(tensor[i]) for i in range(tensor.shape[0])]
 
 
 def tensor2pil(tensor: torch.Tensor) -> list[Image.Image]:
-    """Converts a batch of tensors to a list of PIL Images."""
+    """Convert a batch of tensors to a list of PIL Images."""
 
     def single_tensor2pil(t: torch.Tensor) -> Image.Image:
         np_array = to_numpy(t)
@@ -558,7 +749,7 @@ def tensor2pil(tensor: torch.Tensor) -> list[Image.Image]:
 
 
 def pil2tensor(images: Image.Image | list[Image.Image]) -> torch.Tensor:
-    """Converts a PIL Image or a list of PIL Images to a tensor."""
+    """Convert a PIL Image or a list of PIL Images to a tensor."""
 
     def single_pil2tensor(image: Image.Image) -> torch.Tensor:
         np_image = np.array(image).astype(np.float32) / 255.0

web/comfy_shared.js

@@ -14,6 +14,51 @@ import { api } from '../../scripts/api.js'
 
 // #region base utils
 
+/**
+ * Computes the convex hull of a set of points using the Monotone Chain algorithm.
+ *
+ * @param {Array<Array<number>>} points An array of points, where each point is an array of two numbers [x, y].
+ * @returns {Array<Array<number>>} The points forming the convex hull, in counter-clockwise order.
+ */
+export const getConvexHull = (points) => {
+  if (points.length <= 3) {
+    return points
+  }
+
+  points.sort((a, b) => a[0] - b[0] || a[1] - b[1])
+
+  const lower = []
+  for (const p of points) {
+    while (
+      lower.length >= 2 &&
+      cross_product(lower[lower.length - 2], lower[lower.length - 1], p) <= 0
+    ) {
+      lower.pop()
+    }
+    lower.push(p)
+  }
+
+  const upper = []
+  for (let i = points.length - 1; i >= 0; i--) {
+    const p = points[i]
+    while (
+      upper.length >= 2 &&
+      cross_product(upper[upper.length - 2], upper[upper.length - 1], p) <= 0
+    ) {
+      upper.pop()
+    }
+    upper.push(p)
+  }
+
+  function cross_product(o, a, b) {
+    return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
+  }
+
+  return lower
+    .slice(0, lower.length - 1)
+    .concat(upper.slice(0, upper.length - 1))
+}
+
 // - crude uuid
 export function makeUUID() {
   let dt = new Date().getTime()
@@ -290,6 +335,10 @@ export const getNamedWidget = (node, ...names) => {
  * @returns {{to:LGraphNode, from:LGraphNode, type:'error' | 'incoming' | 'outgoing'}}
  */
 export const nodesFromLink = (node, link) => {
+  if (typeof link === 'number') {
+    link = app.graph.getLink(link)
+  }
+
   const fromNode = app.graph.getNodeById(link.origin_id)
   const toNode = app.graph.getNodeById(link.target_id)
 
@@ -380,12 +429,54 @@ export function getWidgetType(config) {
 
 // #endregion
 
+// function to test if input is a dynamic one
+const isDynamicInput = (input) => {
+  infoLogger('Checking if input dynamic', { input })
+  // return input.name.startsWith(connectionPrefix)
+  return input._isDynamic === true
+}
+
+// Add a dynamic input, update node properties and slot colors!
+const addDynamicInput = (node, name, kind) => {
+  const input = node.addInput(name, kind)
+  input._isDynamic = true
+
+  update_dynamic_properties(node)
+  set_slot_colors(node, ['cyan', undefined], isDynamicInput)
+
+  return input
+}
+
+const set_slot_colors = (node, colors, condition) => {
+  if (!condition) {
+    condition = (_s) => true
+  }
+
+  for (const slot of node.slots) {
+    infoLogger('Candidate', { slot, accepted: condition(slot) })
+    if (condition(slot)) {
+      slot.color_off = colors[0]
+      slot.color_on = colors[1]
+    }
+  }
+}
+
+const update_dynamic_properties = (node) => {
+  const dyn = []
+  for (const input of node.inputs) {
+    if (isDynamicInput(input)) {
+      dyn.push(input.name)
+    }
+  }
+  node.setProperty('dynamic_connections', dyn)
+}
+
 // #region dynamic connections
 /**
  * @param {NodeType} nodeType The nodetype to attach the documentation to
  * @param {str} prefix A prefix added to each dynamic inputs
  * @param {str | [str]} inputType The datatype(s) of those dynamic inputs
- * @param {{separator?:string, start_index?:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} [opts] Extra options
+ * @param {{separator?:string,rename_menu?:'label'|'name', start_index?:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} [opts] Extra options
  * @returns
  */
 export const setupDynamicConnections = (
@@ -399,20 +490,115 @@ export const setupDynamicConnections = (
     Object.getOwnPropertyDescriptors(nodeType).title.value,
   )
 
-  /** @type {{separator:string, start_index:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} */
+  /** @type {{separator:string,rename_menu?:"label"|"name" start_index:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} */
   const options = Object.assign(
     {
       separator: '_',
       start_index: 1,
+      rename_menu: 'label',
     },
     opts || {},
   )
+  const is_valid_name = (node, val) => {
+    return true
+  }
+  nodeType.prototype.getSlotMenuOptions = (slot) => {
+    if (!slot.input) {
+      return
+    }
+    infoLogger('Slot Menu', { slot })
+    return [
+      {
+        content: `Rename Input (${options.rename_menu})`,
+        callback: () => {
+          const dialog = app.canvas.createDialog(
+            "<span class='name'>Name</span><input autofocus type='text'/><button>OK</button>",
+            {},
+          )
+          const dialogInput = dialog.querySelector('input')
+          if (dialogInput) {
+            if (options.rename_menu === 'label') {
+              dialogInput.value = slot.input.label || slot.input.name || ''
+            } else if (options.rename_menu === 'name') {
+              dialogInput.value = slot.input.name || ''
+            }
+          }
+          const inner = () => {
+            // TODO: check if name exists or other guards
+            const val = dialogInput.value
+            if (!is_valid_name(slot.node, val)) {
+              dialog.close()
+              return
+            }
+
+            app.graph.beforeChange()
+            if (options.rename_menu === 'label') {
+              slot.input.label = val
+            } else if (options.rename_menu === 'name') {
+              slot.input.name = val
+              slot.input.label = val
+            }
+
+            app.graph.afterChange()
+
+            dialog.close()
+          }
+          dialog.querySelector('button').addEventListener('click', inner)
+          dialogInput.addEventListener('keydown', (e) => {
+            dialog.is_modified = true
+            if (e.keyCode === 27) {
+              dialog.close()
+            } else if (e.keyCode === 13) {
+              inner()
+            } else if (e.keyCode !== 13 && e.target?.localName !== 'textarea') {
+              return
+            }
+            e.preventDefault()
+            e.stopPropagation()
+          })
+          dialogInput.focus()
+        },
+      },
+    ]
+  }
+
+  const onConfigure = nodeType.prototype.onConfigure
+
+  nodeType.prototype.onConfigure = function (data) {
+    const r = onConfigure ? onConfigure.apply(this, data) : undefined
+
+    // Set or restore serialized properties, lt seems to auto serialize/deserialize to/from string
+    if (!('dynamic_connections' in this.properties)) {
+      // this.addProperty('dynamic_connections', [], 'string')
+      this.setProperty('dynamic_connections', [])
+    } else {
+      const dynamic_connections = this.properties.dynamic_connections
+      if (typeof dynamic_connections !== 'object') {
+        return r
+      }
+      for (const name of dynamic_connections) {
+        infoLogger(`Would dynamize: ${name}`)
+        const input = this.inputs.find((i) => i.name === name)
+        if (input) {
+          infoLogger('Input found', { input })
+          input._isDynamic = true
+        }
+      }
+    }
+    // set color
+    set_slot_colors(this, ['cyan', undefined], isDynamicInput)
+
+    return r
+  }
+
   const onNodeCreated = nodeType.prototype.onNodeCreated
   const inputList = typeof inputType === 'object'
 
   nodeType.prototype.onNodeCreated = function () {
     const r = onNodeCreated ? onNodeCreated.apply(this, []) : undefined
-    this.addInput(
+
+    const input = addDynamicInput(
+      this,
       `${prefix}${options.separator}${options.start_index}`,
       inputList ? '*' : inputType,
     )
@@ -484,10 +670,7 @@ export const dynamic_connection = (
     opts || {},
   )
 
-  // function to test if input is a dynamic one
-  const isDynamicInput = (inputName) => inputName.startsWith(connectionPrefix)
-
-  if (node.inputs.length > 0 && !isDynamicInput(node.inputs[index].name)) {
+  if (node.inputs.length > 0 && !isDynamicInput(node.inputs[index])) {
     return
   }
 
@@ -497,6 +680,7 @@ export const dynamic_connection = (
   const nameArray = options.nameArray || []
 
   const clean_inputs = () => {
+    if (node.id < 0) return // being duplicated
     if (node.inputs.length === 0) return
 
     let w_count = node.widgets?.length || 0
@@ -506,7 +690,7 @@ export const dynamic_connection = (
     const to_remove = []
     for (let n = 1; n < node.inputs.length; n++) {
       const element = node.inputs[n]
-      if (!element.link && isDynamicInput(element.name)) {
+      if (!element.link && isDynamicInput(element)) {
         if (node.widgets) {
           const w = node.widgets.find((w) => w.name === element.name)
           if (w) {
@@ -520,9 +704,12 @@ export const dynamic_connection = (
     }
     for (let i = 0; i < to_remove.length; i++) {
       const id = to_remove[i]
-
-      node.removeInput(id)
-      i_count -= 1
+      try {
+        node.removeInput(id)
+        i_count -= 1
+      } catch (err) {
+        errorLogger('Cannot remove input', err)
+      }
     }
     node.inputs.length = i_count
 
@@ -536,7 +723,7 @@ export const dynamic_connection = (
     for (let i = 0; i < node.inputs.length; i++) {
       let name = ''
       // rename only prefixed inputs
-      if (isDynamicInput(node.inputs[i].name)) {
+      if (node.inputs[i].name.startsWith(connectionPrefix)) {
         // prefixed => rename and increase index
         name = `${connectionPrefix}${prefixed_idx}`
         prefixed_idx += 1
@@ -590,9 +777,8 @@ export const dynamic_connection = (
     if (node.inputs.length === 0) return
     // add an extra input
     if (node.inputs[node.inputs.length - 1].link !== null) {
-      // count only the prefixed inputs
       const nextIndex = node.inputs.reduce(
-        (acc, cur) => (isDynamicInput(cur.name) ? ++acc : acc),
+        (acc, cur) => (isDynamicInput(cur) ? ++acc : acc),
         0,
       )
 
@@ -602,7 +788,7 @@ export const dynamic_connection = (
           : `${connectionPrefix}${nextIndex + options.start_index}`
 
       infoLogger(`Adding input ${nextIndex + 1} (${name})`)
-      node.addInput(name, conType)
+      addDynamicInput(node, name, conType)
     }
   }
 }

web/debug.js

@@ -11,7 +11,12 @@
 /// <reference path="../types/typedefs.js" />
 
 import { app } from '../../scripts/app.js'
-import * as shared from './comfy_shared.js'
+
+import {
+  setupDynamicConnections,
+  cleanupNode,
+  infoLogger,
+} from './comfy_shared.js'
 import * as mtb_ui from './mtb_ui.js'
 
 function escapeHtml(unsafe) {
@@ -45,25 +50,25 @@ function createDebugSection(title) {
   return section
 }
 
-function createDebugContent(content, type) {
+function createDebugContent(item) {
   const wrapper = mtb_ui.makeElement('div', {
     margin: '4px 0',
   })
 
-  if (type === 'text') {
-    const text = mtb_ui.makeElement('p', {
+  if (item.kind === 'text') {
+    const text = mtb_ui.makeElement('div', {
       margin: '2px 0',
       fontFamily: 'monospace',
       whiteSpace: 'pre-wrap',
     })
-    text.innerHTML = content
+    text.innerHTML = item.data
     wrapper.appendChild(text)
-  } else if (type === 'image') {
+  } else if (item.kind === 'b64_images') {
     const img = mtb_ui.makeElement('img', {
       width: '100%',
       borderRadius: '2px',
     })
-    img.src = content
+    img.src = item.data
     wrapper.appendChild(img)
   }
 
@@ -80,110 +85,85 @@ app.registerExtension({
    */
   async beforeRegisterNodeDef(nodeType, nodeData, app) {
     if (nodeData.name === 'Debug (mtb)') {
-      const onNodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function (...args) {
-        this.options = {}
-        const r = onNodeCreated ? onNodeCreated.apply(this, args) : undefined
-        this.addInput('anything_1', '*')
-        return r
+      const clear_widgets = (target) => {
+        if (target.widgets) {
+          let tgt_len = target.widgets.length
+          for (let i = 0; i < target.widgets.length; i++) {
+            if (
+              ![
+                'output_to_console',
+                'deep_inspect',
+                'as_detailed_types',
+                'rich_mode',
+              ].includes(target.widgets[i].name)
+            ) {
+              target.widgets[i].onRemove?.()
+              target.widgets[i].onRemoved?.()
+              tgt_len -= 1
+            }
+          }
+          target.widgets.length = tgt_len
+        }
       }
 
-      const onConnectionsChange = nodeType.prototype.onConnectionsChange
-      /**
-       * @param {OnConnectionsChangeParams} args
-       */
-      nodeType.prototype.onConnectionsChange = function (...args) {
-        const [_type, index, connected, link_info, ioSlot] = args
-        const r = onConnectionsChange
-          ? onConnectionsChange.apply(this, args)
-          : undefined
-        // TODO: remove all widgets on disconnect once computed
-        shared.dynamic_connection(this, index, connected, 'anything_', '*', {
-          link: link_info,
-          ioSlot: ioSlot,
+      const original_getExtraMenuOptions =
+        nodeType.prototype.getExtraMenuOptions
+      nodeType.prototype.getExtraMenuOptions = function (_, options) {
+        original_getExtraMenuOptions?.apply(this, arguments)
+        options.push({
+          content: '🐛 Clear Outputs',
+          callback: async () => {
+            clear_widgets(this)
+          },
         })
-
-        //- infer type
-        if (link_info) {
-          // const fromNode = this.graph._nodes.find(
-          // (otherNode) => otherNode.id === link_info.origin_id,
-          // )
-          // const fromNode = app.graph.getNodeById(link_info.origin_id)
-          const { from } = shared.nodesFromLink(this, link_info)
-          if (!from || this.inputs.length === 0) return
-          const type = from.outputs[link_info.origin_slot].type
-          this.inputs[index].type = type
-          // this.inputs[index].label = type.toLowerCase()
-        }
-        //- restore dynamic input
-        if (!connected) {
-          this.inputs[index].type = '*'
-          this.inputs[index].label = `anything_${index + 1}`
-        }
-        return r
       }
 
+      setupDynamicConnections(nodeType, 'var', '*')
+
       const onExecuted = nodeType.prototype.onExecuted
       nodeType.prototype.onExecuted = function (...args) {
         onExecuted?.apply(this, args)
         const [data, ..._rest] = args
 
-        if (this.widgets) {
-          let tgt_len = this.widgets.length
-          for (let i = 0; i < this.widgets.length; i++) {
-            if (
-              this.widgets[i].name !== 'output_to_console' &&
-              this.widgets[i].name !== 'as_detailed_types'
-            ) {
-              this.widgets[i].onRemove?.()
-              this.widgets[i].onRemoved?.()
-              tgt_len -= 1
-            }
-          }
-          this.widgets.length = tgt_len
-        }
+        clear_widgets(this)
 
         const inputData = {}
 
         const uiData = data.ui || data
 
+        const name_to_label = this.inputs.reduce((acc, input) => {
+          acc[input.name] = input.label || input.name
+          return acc
+        }, {})
+
+
         if (uiData.items) {
           uiData.items.forEach((item) => {
             const inputName = item.input
-            if (!inputData[inputName]) {
-              inputData[inputName] = { text: [], b64_images: [] }
-            }
-            if (item.text) {
-              inputData[inputName].text.push(...item.text)
-            }
-            if (item.b64_images) {
-              inputData[inputName].b64_images.push(...item.b64_images)
-            }
+            inputData[inputName] = item.items
           })
         }
-
-        let widgetI = 1
+        const mainDebugContainer = mtb_ui.makeElement('div', {
+          width: '100%',
+        })
+        let hasContent = false
         for (const [inputName, content] of Object.entries(inputData)) {
-          if (content.text.length === 0 && content.b64_images.length === 0) {
+          if (!content || content?.length === 0) {
             continue
           }
+          hasContent = true
 
-          const section = createDebugSection(inputName)
+          const section = createDebugSection(name_to_label[inputName])
 
-          if (content.text.length > 0) {
-            content.text.forEach((text) => {
-              section.appendChild(createDebugContent(text, 'text'))
-            })
+          for (const item of content) {
+            section.appendChild(createDebugContent(item))
           }
-
-          if (content.b64_images.length > 0) {
-            content.b64_images.forEach((img) => {
-              section.appendChild(createDebugContent(img, 'image'))
-            })
-          }
-
-          this.addDOMWidget(`debug_section_${widgetI}`, 'CUSTOM', section, {})
-          widgetI++
+          mainDebugContainer.appendChild(section)
+        }
+        if (hasContent) {
+          this.addDOMWidget('debug_output', 'CUSTOM', mainDebugContainer, {
+            hideOnZoom: false,
+          })
         }
 
         this.onRemoved = function () {
@@ -194,8 +174,9 @@ app.registerExtension({
             widget.onRemoved?.()
             widget.onRemove?.()
           }
-          shared.cleanupNode(this)
+          cleanupNode(this)
         }
+        this.setDirtyCanvas(true, true)
       }
     }
   },

web/mtb_repl.js

@@ -0,0 +1,529 @@
+/** Python REPL for the frontend (uses rich)*/
+
+import { app } from '../../scripts/app.js'
+import * as shared from './comfy_shared.js'
+import * as mtb_ui from './mtb_ui.js'
+
+class ComfyREPL extends LiteGraph.LGraphNode {
+  constructor() {
+    super()
+
+    this.shape = LiteGraph.BOX_SHAPE
+    this.isVirtualNode = true
+    this.category = 'mtb/repl'
+    this.title = '🐍 REPL (mtb)'
+
+    this.uuid = shared.makeUUID()
+
+    this.size = [600, 400]
+
+    // Create a container for our custom widgets
+    this.widget = this.addDOMWidget('HTML', 'html', this.createREPLWidget())
+
+    this.loadAceEditor()
+
+    // Store input and output for persistence
+    this.properties = {
+      inputCode: '',
+      outputHistory: '',
+    }
+
+    this.outputArea.innerHTML = this.properties.outputHistory
+    this.outputArea.scrollTop = this.outputArea.scrollHeight
+
+    // Debounced linting function
+    this.debouncedLint = shared.debounce(this.lintCode.bind(this), 500)
+
+    // Resizing state variables
+    this.isResizing = false
+    this.initialMouseY = 0
+    this.initialInputHeight = 0
+    this.initialOutputHeight = 0
+  }
+
+  loadAceEditor() {
+    if (window.MTB?.ace_loaded) {
+      return
+    }
+    let NEED_PATCH = false
+    if (window.ace) {
+      shared.infoLogger(
+        'A global ace was found in scope, to avoid issues with it we will patch it',
+      )
+      NEED_PATCH = true
+      // window._backupAce = window.ace
+      // window.ace = null
+    }
+
+    shared
+      .loadScript('/mtb_async/ace/ace.js')
+      .then((m) => {
+        shared.infoLogger('ACE was loaded', m)
+        // window.MTB_ACE = window.ace
+        window.MTB.ace_loaded = true
+        this.initAceEditor()
+
+        this.aceEditor.setValue(this.properties.inputCode, -1)
+      })
+      .catch((e) => {
+        shared.errorLogger(e)
+      })
+      .finally(() => {
+        if (NEED_PATCH) {
+          console.log('Patching back window object')
+          window.ace = window._backupAce
+        }
+      })
+  }
+
+  initAceEditor() {
+    if (!window.MTB.ace_loaded) {
+      console.error('ACE editor not loaded. Cannot set up editors.')
+      return
+    }
+
+    if (!this.inputDiv) {
+      console.error('Input div not found for Ace editor initialization.')
+      return
+    }
+    this.aceEditor = ace.edit(this.inputDiv)
+    this.aceEditor.setTheme('ace/theme/monokai') //"ace/theme/dracula", "ace/theme/github"
+    this.aceEditor.session.setMode('ace/mode/python')
+    this.aceEditor.setOptions({
+      enableBasicAutocompletion: true,
+      enableLiveAutocompletion: true,
+      enableSnippets: true,
+      fontSize: '14px',
+      fontFamily: 'monospace',
+      showPrintMargin: false,
+      wrap: true,
+      tabSize: 4,
+      useSoftTabs: true,
+      highlightActiveLine: true,
+      highlightSelectedWord: true,
+      cursorStyle: 'ace', // "ace" | "slim" | "smooth" | "wide"
+      behavioursEnabled: true,
+      displayIndentGuides: true,
+      fixedWidthGutter: true,
+      scrollPastEnd: 0.5,
+    })
+
+    // Custom keybinding for Ctrl+Enter
+    this.aceEditor.commands.addCommand({
+      name: 'runCode',
+      bindKey: { win: 'Ctrl-Enter', mac: 'Command-Enter' },
+      exec: () => this.executeCode(),
+    })
+
+    // Listen for changes to trigger linting
+    let lintDisabled = false
+    this.aceEditor.session.on('change', () => {
+      if (!lintDisabled) {
+        this.debouncedLint()
+      }
+    })
+
+    this.outputArea.scrollTop = this.outputArea.scrollHeight
+  }
+
+  addOutput(html) {
+    this.outputArea.innerHTML += html
+    this.properties.outputHistory += html
+    this.outputArea.scrollTop = this.outputArea.scrollHeight
+  }
+
+  createREPLWidget() {
+    const container = mtb_ui.makeElement('div', {
+      display: 'flex',
+      flexDirection: 'column',
+      width: '100%',
+      height: '100%',
+      boxSizing: 'border-box',
+      padding: '5px',
+    })
+
+    this.inputDiv = mtb_ui.makeElement(
+      'div',
+      {
+        width: 'calc(100% - 10px)',
+        height: '100px',
+        backgroundColor: '#333',
+        color: '#eee',
+        border: '1px solid #555',
+        borderRadius: '4px',
+        marginBottom: '5px',
+        boxSizing: 'border-box',
+        overflow: 'hidden',
+      },
+      container,
+    )
+    // Resizable Handle
+    this.handleDiv = mtb_ui.makeElement(
+      'div',
+      {
+        width: '100%',
+        height: '5px',
+        backgroundColor: '#666',
+        cursor: 'ns-resize',
+        marginBottom: '5px',
+        borderRadius: '2px',
+      },
+      container,
+    )
+    this.handleDiv.addEventListener('mousedown', this.startResizing.bind(this))
+
+    // Run Button
+    this.runButton = mtb_ui.makeElement(
+      'button',
+      {
+        width: '100%',
+        padding: '8px',
+        backgroundColor: '#555',
+        color: '#fff',
+        border: 'none',
+        borderRadius: '4px',
+        cursor: 'pointer',
+        marginBottom: '5px',
+        fontSize: '14px',
+      },
+      container,
+    )
+    this.runButton.textContent = 'Run Code (Ctrl+Enter)'
+    this.runButton.onclick = () => this.executeCode()
+
+    // Clear Button
+    this.clearButton = mtb_ui.makeElement(
+      'button',
+      {
+        width: '100%',
+        padding: '8px',
+        backgroundColor: '#555',
+        color: '#fff',
+        border: 'none',
+        borderRadius: '4px',
+        cursor: 'pointer',
+        marginBottom: '5px',
+        fontSize: '14px',
+      },
+      container,
+    )
+    this.clearButton.textContent = 'Clear Output'
+    this.clearButton.onclick = () => {
+      this.outputArea.innerHTML = ''
+      this.properties.outputHistory = ''
+    }
+
+    // Output Area
+    this.outputArea = mtb_ui.makeElement(
+      'div',
+      {
+        flexGrow: '1',
+        width: 'calc(100% - 10px)',
+        backgroundColor: '#222',
+        color: '#ddd',
+        border: '1px solid #555',
+        borderRadius: '4px',
+        padding: '5px',
+        fontFamily: 'monospace',
+        fontSize: '14px',
+        overflowY: 'auto',
+        whiteSpace: 'pre-wrap',
+        boxSizing: 'border-box',
+      },
+      container,
+    )
+
+    return container
+  }
+
+  // --- Resizing Logic ---
+  startResizing(e) {
+    if (!this.inputDiv) {
+      shared.infoLogger("The input div isn't ready", this)
+      shared.errorLogger("The input div isn't ready")
+      return
+    }
+    this.isResizing = true
+    this.initialMouseY = e.clientY
+    this.initialInputHeight = this.inputDiv.offsetHeight
+    this.initialOutputHeight = this.outputArea.offsetHeight
+
+    document.addEventListener('mousemove', this.doResize.bind(this))
+    document.addEventListener('mouseup', this.stopResizing.bind(this))
+    document.body.style.cursor = 'ns-resize' // Change cursor globally
+  }
+  doResize(e) {
+    if (!this.isResizing) return
+
+    const deltaY = e.clientY - this.initialMouseY
+
+    let new_input_height = this.initialInputHeight + deltaY
+    let new_output_height = this.initialOutputHeight - deltaY
+
+    const minInputHeight = 50 // Minimum height for Ace editor
+    const minOutputHeight = 50 // Minimum height for output area
+
+    // Clamp heights to minimums
+    if (new_input_height < minInputHeight) {
+      new_input_height = minInputHeight
+      new_output_height =
+        this.initialInputHeight + this.initialOutputHeight - minInputHeight
+    }
+    if (new_output_height < minOutputHeight) {
+      new_output_height = minOutputHeight
+      new_input_height =
+        this.initialInputHeight + this.initialOutputHeight - minOutputHeight
+    }
+
+    this.inputDiv.style.height = `${new_input_height}px`
+    this.outputArea.style.height = `${new_output_height}px`
+
+    // Update the stored ratio for persistence
+    const totalDynamicHeight =
+      this.inputDiv.offsetHeight + this.outputArea.offsetHeight
+    if (totalDynamicHeight > 0) {
+      this.properties.inputHeightRatio = new_input_height / totalDynamicHeight
+    }
+
+    this.aceEditor.resize() // Important for Ace to redraw
+  }
+
+  stopResizing() {
+    this.isResizing = false
+    document.removeEventListener('mousemove', this.doResize)
+    document.removeEventListener('mouseup', this.stopResizing)
+    document.body.style.cursor = '' // Restore default cursor
+  }
+  // --- End Resizing Logic ---
+
+  async executeCode() {
+    const code = this.aceEditor.getValue()
+
+    if (!code.trim()) {
+      return
+    }
+
+    const inputPrompt = `<div style="color:#888; margin-top: 10px;">>>> ${code}</div>`
+
+    this.addOutput(inputPrompt)
+
+    try {
+      const response = await fetch('/mtb/execute', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({ code: code, name: this.uuid }),
+      })
+
+      if (!response.ok) {
+        throw new Error(`HTTP error! status: ${response.status}`)
+      }
+
+      const result = await response.json()
+      console.debug('Received from backend', result)
+      const outputHtml = result.output_html || ''
+      const error = result.error
+
+      if (error) {
+        this.addOutput(
+          `<div style="color: #f00; font-weight: bold;">Error:</div>${outputHtml}`,
+        )
+      } else {
+        this.addOutput(outputHtml)
+      }
+    } catch (e) {
+      const errorMessage = `<div style="color: #f00;">Frontend Error: ${e.message}</div>`
+
+      this.addOutput(errorMessage)
+      console.error('ComfyREPL Frontend Error:', e)
+    } finally {
+      // Not clearing
+      // this.inputArea.value = '' // Clear input after execution
+      // this.properties.inputCode = '' // Clear persisted input
+    }
+  }
+
+  async lintCode() {
+    if (!this.aceEditor) {
+      return
+    }
+    const code = this.aceEditor.getValue()
+    if (!code.trim()) {
+      this.aceEditor.session.setAnnotations([]) // Clear annotations if empty
+      return
+    }
+
+    try {
+      const response = await fetch('/mtb/lint', {
+        // New linting endpoint
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({ code: code, name: this.uuid }),
+      })
+
+      if (!response.ok) {
+        console.log(response)
+        throw new Error(
+          `HTTP error! status: ${response.status} ${response.statusText}`,
+        )
+      }
+
+      const result = await response.json()
+      // result.diagnostics should be an array of {row, column, text, type}
+      this.aceEditor.session.setAnnotations(result.diagnostics)
+    } catch (e) {
+      console.error('ComfyREPL Linting Error:', e)
+      this.aceEditor.session.setAnnotations([
+        {
+          row: 0,
+          column: 0,
+          text: `Linting failed: ${e.message}`,
+          type: 'error',
+        },
+      ])
+    }
+  }
+
+  // Restore properties when loading a graph
+  onConfigure() {
+    if (this.properties.inputCode && this.aceEditor) {
+      this.aceEditor.setValue(this.properties.inputCode, -1)
+    }
+    // if (this.properties.inputCode) {
+    //   this.inputArea.value = this.properties.inputCode
+    // }
+    if (this.properties.outputHistory) {
+      this.outputArea.innerHTML = this.properties.outputHistory
+      this.outputArea.scrollTop = this.outputArea.scrollHeight
+    }
+    if (this.properties.uuid) {
+      this.uuid = this.properties.uuid
+    }
+    this.debouncedLint()
+    this.onResize(this.size)
+  }
+
+  // Save properties when saving a graph
+  onSerialize(o) {
+    if (this.aceEditor) {
+      o.properties.inputCode = this.aceEditor.getValue() //this.inputArea.value
+    }
+    o.properties.outputHistory = this.outputArea.innerHTML
+    o.properties.uuid = this.uuid
+    o.properties.inputHeightRatio = this.properties.inputHeightRatio
+  }
+
+  onRemoved() {
+    // Clean up DOM elements when node is removed
+    if (this.widget?.element?.parentNode) {
+      this.widget.element.parentNode.removeChild(this.widget.element)
+    }
+    // Destroy Ace editor instance to prevent memory leaks
+    if (this.aceEditor) {
+      this.aceEditor.destroy()
+      this.aceEditor.container.remove() // Remove the Ace container div from DOM
+    }
+    // Clean up global event listeners if node is removed while resizing
+    document.removeEventListener('mousemove', this.doResize)
+    document.removeEventListener('mouseup', this.stopResizing)
+    document.body.style.cursor = ''
+  }
+  // LiteGraph method to handle node resizing
+  onResize(size) {
+    // Call parent method if it exists (important for LiteGraph's internal sizing)
+    if (super.onResize) {
+      super.onResize(size)
+    }
+
+    // Adjust container size
+    const container = this.widget.element
+    container.style.width = `${size[0] - 10}px` // Account for padding
+    container.style.height = `${size[1] - 10}px`
+
+    // Adjust input and output area widths
+    this.inputDiv.style.width = 'calc(100% - 10px)'
+    this.outputArea.style.width = 'calc(100% - 10px)'
+    //
+    // const old = () => {
+    //   // Calculate remaining height for output area
+    //   // Ace editor manages its own height within this.inputDiv, so we use offsetHeight
+    //   const inputHeight = this.inputDiv.offsetHeight
+    //   const runButtonHeight = this.runButton.offsetHeight
+    //   const clearButtonHeight = this.clearButton.offsetHeight
+    //   const totalFixedHeight =
+    //     inputHeight + runButtonHeight + clearButtonHeight + 15 // 15 for margins/padding
+    //
+    //   const remainingHeight = size[1] - 10 - totalFixedHeight
+    //   this.outputArea.style.height = `${Math.max(50, remainingHeight)}px` // Min height 50px
+    // }
+    // Calculate dynamic heights
+    const containerHeight = size[1] - 10
+    const handleHeight = this.handleDiv.offsetHeight
+    const buttonHeights =
+      this.runButton.offsetHeight + this.clearButton.offsetHeight + 15 // Sum of button heights + margins
+
+    const dynamicContentHeight = containerHeight - buttonHeights - handleHeight
+
+    const minInputHeight = 50
+    const minOutputHeight = 50
+
+    let inputHeight = Math.max(
+      minInputHeight,
+      dynamicContentHeight * (this.properties.inputHeightRatio || 1.0),
+    )
+    let outputHeight = Math.max(
+      minOutputHeight,
+      dynamicContentHeight - inputHeight,
+    )
+    //
+    // // Re-distribute if one hits its minimum
+    // if (
+    //   inputHeight === minInputHeight &&
+    //   dynamicContentHeight - minInputHeight > minOutputHeight
+    // ) {
+    //   outputHeight = dynamicContentHeight - minInputHeight
+    // } else if (
+    //   outputHeight === minOutputHeight &&
+    //   dynamicContentHeight - minOutputHeight > minInputHeight
+    // ) {
+    //   inputHeight = dynamicContentHeight - minOutputHeight
+    // }
+    //
+    // // Final check to ensure total height matches available dynamic space
+    // const currentTotal = inputHeight + outputHeight
+    // if (currentTotal !== dynamicContentHeight) {
+    //   // Adjust one of them if there's a small discrepancy due to rounding
+    //   if (inputHeight > minInputHeight) {
+    //     inputHeight += dynamicContentHeight - currentTotal
+    //   } else if (outputHeight > minOutputHeight) {
+    //     outputHeight += dynamicContentHeight - currentTotal
+    //   }
+    // }
+
+    this.inputDiv.style.height = `${inputHeight}px`
+    this.outputArea.style.height = `${outputHeight}px`
+
+    // Update the ratio based on the actual heights set
+    if (dynamicContentHeight > 0) {
+      this.properties.inputHeightRatio = inputHeight / dynamicContentHeight
+    }
+
+    // Inform Ace editor about the resize so it can redraw its content
+    if (this.aceEditor) {
+      this.aceEditor.resize()
+    }
+  }
+}
+
+const repl = {
+  name: 'mtb.repl',
+
+  registerCustomNodes() {
+    LiteGraph.registerNodeType('Python REPL', ComfyREPL)
+  },
+}
+
+app.registerExtension(repl)

web/mtb_widgets.js

@@ -21,7 +21,7 @@ import { infoLogger } from './comfy_shared.js'
 import { NumberInputWidget } from './numberInput.js'
 
 // NOTE: new widget types registered by MTB Widgets
-const newTypes = [/*'BOOL'*/ 'COLOR','MTB_COLOR', 'BBOX']
+const newTypes = [/*'BOOL'*/ 'COLOR', 'BBOX']
 
 const deprecated_nodes = {
   //  'Animation Builder':
@@ -739,6 +739,7 @@ const mtb_widgets = {
       // },
 
       COLOR: (node, inputName, inputData, _app) => {
+        console.debug('Registering color')
         return {
           widget: node.addCustomWidget(
             MtbWidgets.COLOR(inputName, inputData[1]?.default || '#ff0000'),
@@ -747,16 +748,6 @@ const mtb_widgets = {
           minHeight: 30,
         }
       },
-      MTB_COLOR: (node, inputName, inputData, _app) => {
-        return {
-          widget: node.addCustomWidget(
-            MtbWidgets.COLOR(inputName, inputData[1]?.default || '#ff0000'),
-          ),
-          minWidth: 150,
-          minHeight: 30,
-        }
-      },
-
       // BBOX: (node, inputName, inputData, app) => {
       //     console.debug("Registering bbox")
       //     return {
@@ -1060,7 +1051,6 @@ const mtb_widgets = {
               const currentChunkSize = Math.min(chunkSize, totalPrompts - i)
 
               await app.queuePrompt(0, currentChunkSize)
-
             }
             if (!cancelQueue) {
               window.MTB?.notify?.(
@@ -1206,7 +1196,9 @@ const mtb_widgets = {
 
       //NOTE: dynamic nodes
       case 'Apply Text Template (mtb)': {
-        shared.setupDynamicConnections(nodeType, 'var', '*')
+        shared.setupDynamicConnections(nodeType, 'var', '*', {
+          rename_menu: 'name',
+        })
         break
       }
       case 'Save Data Bundle (mtb)': {
@@ -1338,47 +1330,54 @@ const mtb_widgets = {
             const related = new Set([this.id])
             const visited = new Set()
             if (this.outputs[0].links) {
-              const initLink = this.outputs[0].links[0]
-              const { to: loopEnd } = shared.nodesFromLink(this, initLink)
-              const canReachEnd = (node, visited = new Set()) => {
-                if (node === loopEnd) return true
-                if (visited.has(node.id)) return false
-                visited.add(node.id)
-                for (const output of node.outputs || []) {
-                  if (!output.links) continue
-                  for (const linkId of output.links) {
-                    const { to: nextNode } = shared.nodesFromLink(node, linkId)
-                    if (!nextNode) continue
-                    if (canReachEnd(nextNode, visited)) {
-                      return true
+              for (const linkId of this.outputs[0].links) {
+                const { to: loopEnd } = shared.nodesFromLink(this, linkId)
+                const canReachEnd = (node, visited = new Set()) => {
+                  if (node === loopEnd) return true
+                  if (visited.has(node.id)) return false
+                  visited.add(node.id)
+                  for (const output of node.outputs || []) {
+                    if (!output.links) continue
+                    for (const linkId of output.links) {
+                      const { to: nextNode } = shared.nodesFromLink(
+                        node,
+                        linkId,
+                      )
+                      if (!nextNode) continue
+                      if (canReachEnd(nextNode, visited)) {
+                        return true
+                      }
+                    }
+                  }
+                  return false
+                }
+                const traverseNodes = (node) => {
+                  if (visited.has(node.id)) return
+                  visited.add(node.id)
+
+                  // can reach the end
+                  if (node !== this && node !== loopEnd && !canReachEnd(node)) {
+                    return
+                  }
+
+                  related.add(node.id)
+                  for (const output of node.outputs || []) {
+                    if (!output.links) continue
+
+                    for (const linkId of output.links) {
+                      const { to: nextNode } = shared.nodesFromLink(
+                        node,
+                        linkId,
+                      )
+                      if (!nextNode) continue
+
+                      traverseNodes(nextNode)
                     }
                   }
                 }
-                return false
+
+                traverseNodes(this)
               }
-              const traverseNodes = (node) => {
-                if (visited.has(node.id)) return
-                visited.add(node.id)
-
-                // can reach the end
-                if (node !== this && node !== loopEnd && !canReachEnd(node)) {
-                  return
-                }
-
-                related.add(node.id)
-                for (const output of node.outputs || []) {
-                  if (!output.links) continue
-
-                  for (const linkId of output.links) {
-                    const { to: nextNode } = shared.nodesFromLink(node, linkId)
-                    if (!nextNode) continue
-
-                    traverseNodes(nextNode)
-                  }
-                }
-              }
-
-              traverseNodes(this)
             }
             this.related_to_flow = Array.from(related)
             this.computed_flow = true