fix: 🐛 category for settings

- Fixes the "edit icon cannot be clicked"
- Changed the parser to add support for more non std markdown
- Markdown links now always open a new tab instead of replacing current
- New optional shiki support for code blocks (check #211 for details)

.github/workflows/publish_action.yml

@@ -12,8 +12,6 @@ jobs:
     steps:
       - name: ♻️ Check out code
         uses: actions/checkout@v4
-        with:
-          submodules: true
       - name: 📦 Publish Custom Node
         uses: Comfy-Org/publish-node-action@main
         with:

.gitignore

@@ -6,6 +6,3 @@ node_modules/
 compose.yaml
 comfy_mtb.wsb
 Dockerfile
-
-# I store the gh-pages worktrees (src & build) there
-.worktrees

__init__.py

@@ -7,12 +7,10 @@
 #
 ###
 
-__version__ = "0.2.1"
+__version__ = "0.1.6"
 
 import os
 
-from aiohttp.web_request import Request
-
 # TODO: don't override this if the user has that setup already
 if not os.environ.get("TF_FORCE_GPU_ALLOW_GROWTH"):
     os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
@@ -33,21 +31,24 @@ from pathlib import Path
 from aiohttp import web
 from server import PromptServer
 
+import nodes
+
 from .endpoint import endlog
-from .install import get_node_dependencies
 from .log import blue_text, cyan_text, get_label, get_summary, log
 from .utils import comfy_dir, here
 
-NODE_CLASS_MAPPINGS: dict[str, type] = {}
+NODE_CLASS_MAPPINGS = {}
-NODE_DISPLAY_NAME_MAPPINGS: dict[str, str] = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
-NODE_CLASS_MAPPINGS_DEBUG: dict[str, str | None] = {}
+NODE_CLASS_MAPPINGS_DEBUG = {}
 WEB_DIRECTORY = "./web"
 
 
 def extract_nodes_from_source(filename: Path):
     source_code = ""
+
     source_code = filename.read_text(encoding="utf-8")
-    nodes: list[str] = []
+
+    nodes = []
 
     try:
         parsed = ast.parse(source_code)
@@ -56,15 +57,14 @@ def extract_nodes_from_source(filename: Path):
                 target = node.targets[0]
                 if isinstance(target, ast.Name) and target.id == "__nodes__":
                     value = ast.get_source_segment(source_code, node.value)
-                    if value:
+                    node_value = ast.parse(value).body[0].value
-                        node_value = ast.parse(value).body[0].value
+                    if isinstance(node_value, (ast.List, ast.Tuple)):
-                        if isinstance(node_value, ast.List | ast.Tuple):
+                        nodes.extend(
-                            nodes.extend(
+                            element.id
-                                str(element.id)
+                            for element in node_value.elts
-                                for element in node_value.elts
+                            if isinstance(element, ast.Name)
-                                if isinstance(element, ast.Name)
+                        )
-                            )
+                    break
-                        break
     except SyntaxError:
         log.error("Failed to parse")
     return nodes
@@ -72,8 +72,8 @@ def extract_nodes_from_source(filename: Path):
 
 def load_nodes():
     errors: list[str] = []
-    nodes: list[type] = []
+    nodes = []
-    nodes_failed: list[str] = []
+    nodes_failed = []
 
     for filename in (here / "nodes").iterdir():
         if filename.suffix == ".py":
@@ -124,8 +124,7 @@ def uninstall_old_web_extensions():
                 shutil.rmtree(web_mtb)
         except Exception as e:
             log.warning(
-                f"""Failed to remove web mtb directory: {e}
+                f"Failed to remove web mtb directory: {e}\nPlease manually remove it from disk ({web_mtb}) and restart the server."
-Please manually remove it from disk ({web_mtb}) and restart the server."""
             )
 
 
@@ -142,7 +141,7 @@ def wiki_to_classname(s: str):
 
 def classname_to_wiki(s: str):
     classname = s.replace("MTB_", "")
-    parts: list[str] = []
+    parts = []
     start = 0
     for i in range(1, len(classname)):
         if classname[i].isupper():
@@ -162,6 +161,8 @@ if wiki.exists() and wiki.is_dir():
 
 
 # - REGISTER NODES
+
+
 MTB_EXPORT = os.environ.get("MTB_EXPORT")
 
 nodes, failed = load_nodes()
@@ -178,7 +179,7 @@ for node_class in nodes:
             node_class.DESCRIPTION = node_class.__doc__
             if MTB_EXPORT:
                 wiki_name = classname_to_wiki(class_name)
-                _ = (wiki / "nodes" / (wiki_name + ".md")).write_text(
+                (wiki / "nodes" / (wiki_name + ".md")).write_text(
                     node_class.__doc__, encoding="utf-8"
                 )
 
@@ -191,15 +192,12 @@ for node_class in nodes:
     NODE_CLASS_MAPPINGS[node_label] = node_class
     NODE_DISPLAY_NAME_MAPPINGS[class_name] = node_label
     NODE_CLASS_MAPPINGS_DEBUG[node_label] = node_class.__doc__
-
+    # TODO: I removed this, I find it more convenient to write without spaces, but it breaks every of my workflows
-    # TODO: I removed this, I find it more convenient to write without spaces
+    # TODO (cont): and until I find a way to automate the conversion, I'll leave it like this
-    # but it breaks every of my workflows
-    # TODO (cont): and until I find a way to automate the conversion
-    # I'll leave it like this
 
     if os.environ.get("MTB_EXPORT"):
         with open(here / "node_list.json", "w") as f:
-            _ = f.write(
+            f.write(
                 json.dumps(
                     {
                         k: NODE_CLASS_MAPPINGS_DEBUG[k]
@@ -217,31 +215,29 @@ log.debug(
     )
 )
 
-log.info(f"loaded {cyan_text(str(len(nodes)))} nodes successfuly")
+log.info(f"loaded {cyan_text(len(nodes))} nodes successfuly")
-
 if failed:
     with contextlib.suppress(Exception):
         base_url, port = utils.get_server_info()
         log.info(
             f"Some nodes ({len(failed)}) could not be loaded. This can be ignored, but go to http://{base_url}:{port}/mtb if you want more information."
         )
-        log.debug(failed)
 
 
 # - ENDPOINT
 
 
 if hasattr(PromptServer, "instance"):
-    img_cache = None
+    restore_deps = ["basicsr"]
-    prompt_cache = None
+    onnx_deps = ["onnxruntime"]
-
+    swap_deps = ["insightface"] + onnx_deps
-    with contextlib.suppress(ImportError):
+    node_dependency_mapping = {
-        from cachetools import TTLCache
+        "QrCode": ["qrcode"],
-
+        "DeepBump": onnx_deps,
-        img_cache = TTLCache(maxsize=100, ttl=5)  # 1 min TTL
+        "FaceSwap": swap_deps,
-        prompt_cache = TTLCache(maxsize=100, ttl=5)  # 1 min TTL
+        "LoadFaceSwapModel": swap_deps,
-
+        "LoadFaceAnalysisModel": restore_deps,
-    node_dependency_mapping = get_node_dependencies()
+    }
 
     PromptServer.instance.app.router.add_static(
         "/mtb-assets/", path=(here / "html").as_posix()
@@ -310,10 +306,10 @@ if hasattr(PromptServer, "instance"):
             }
         )
 
-    @PromptServer.instance.routes.post("/mtb/server-info")
+    @PromptServer.instance.routes.post("/mtb/debug")
-    async def set_server_info(request: Request):
+    async def set_debug(request):
-        json_data: dict[str, bool] = await request.json()
+        json_data = await request.json()
-        enabled = json_data.get("debug")
+        enabled = json_data.get("enabled")
         if enabled:
             os.environ["MTB_DEBUG"] = "true"
             log.setLevel(logging.DEBUG)
@@ -321,7 +317,7 @@ if hasattr(PromptServer, "instance"):
 
         elif "MTB_DEBUG" in os.environ:
             # del os.environ["MTB_DEBUG"]
-            _ = os.environ.pop("MTB_DEBUG")
+            os.environ.pop("MTB_DEBUG")
             log.setLevel(logging.INFO)
 
         return web.json_response(
@@ -329,17 +325,17 @@ if hasattr(PromptServer, "instance"):
         )
 
     @PromptServer.instance.routes.get("/mtb")
-    async def get_home(request: Request):
+    async def get_home(request):
         from . import endpoint
 
-        _ = reload(endpoint)
+        reload(endpoint)
         # Check if the request prefers HTML content
         if "text/html" in request.headers.get("Accept", ""):
             # # Return an HTML page
             html_response = """
             <div class="flex-container menu">
                 <a href="/mtb/manage">manage</a>
-                <a href="/mtb/server-info">Server Info</a>
+                <a href="/mtb/debug">debug</a>
                 <a href="/mtb/status">status</a>
             </div>
             """
@@ -351,176 +347,28 @@ if hasattr(PromptServer, "instance"):
         # Return JSON for other requests
         return web.json_response({"message": "Welcome to MTB!"})
 
-    import asyncio
+    @PromptServer.instance.routes.get("/mtb/debug")
-    import os
+    async def get_debug(request):
-    from io import BytesIO
-
-    from aiohttp import web
-    from PIL import Image
-
-    def get_cached_image(file_path: str, preview_params=None, channel=None):
-        cache_key = (file_path, preview_params, channel)
-        if img_cache and (cache_key in img_cache):
-            return img_cache[cache_key]
-
-        with Image.open(file_path) as img:
-            info = img.info
-            if preview_params:
-                img = process_preview(img, preview_params)
-            if channel:
-                img = process_channel(img, channel)
-            if prompt_cache:
-                prompt_cache[cache_key] = info
-            if img_cache:
-                img_cache[cache_key] = img.getvalue()
-                return img_cache[cache_key]
-
-            return img.getvalue()
-
-    def process_preview(img: Image.Image, preview_params):
-        image_format, quality, width = preview_params
-        quality = int(quality)
-
-        if width:
-            width = int(width)
-            img.thumbnail((width, int(width * img.height / img.width)))
-
-        buffer = BytesIO()
-        img.save(
-            buffer, format=image_format, quality=quality, metadata=img.info
-        )
-        buffer.seek(0)
-        return buffer
-
-    def process_channel(img: Image.Image, channel: str):
-        if channel == "rgb":
-            if img.mode == "RGBA":
-                r, g, b, _ = img.split()
-                img = Image.merge("RGB", (r, g, b))
-            else:
-                img = img.convert("RGB")
-        elif channel == "a":
-            if img.mode == "RGBA":
-                _, _, _, a = img.split()
-            else:
-                a = Image.new("L", img.size, 255)
-            img = Image.new("RGBA", img.size)
-            img.putalpha(a)
-
-        buffer = BytesIO()
-        img.save(buffer, format="PNG")
-        _ = buffer.seek(0)
-        return buffer
-
-    async def get_image_response(
-        file, filename: str, preview_info=None, channel=None
-    ):
-        img = await asyncio.to_thread(
-            get_cached_image, file, preview_info, channel
-        )
-        return web.Response(
-            body=img,
-            content_type="image/webp" if preview_info else "image/png",
-            headers={"Content-Disposition": f'filename="{filename}"'},
-        )
-
-    # TODO: Embed the metadatas somehow so we can drag and drop
-    #       to load workflows in the sidebar
-    @PromptServer.instance.routes.get("/mtb/view")
-    async def view_image(request: Request):
-        import folder_paths
-
-        filename = request.rel_url.query.get("filename")
-        if not filename:
-            return web.Response(status=404)
-
-        filename, output_dir = folder_paths.annotated_filepath(filename)
-        if filename[0] == "/" or ".." in filename:
-            return web.Response(status=400)
-
-        if output_dir is None:
-            rtype = request.rel_url.query.get("type", "output")
-            output_dir = folder_paths.get_directory_by_type(rtype)
-
-        if output_dir is None:
-            return web.Response(status=400)
-
-        if "subfolder" in request.rel_url.query:
-            full_output_dir = os.path.join(
-                output_dir, request.rel_url.query["subfolder"]
-            )
-            if (
-                os.path.commonpath(
-                    (os.path.abspath(full_output_dir), output_dir)
-                )
-                != output_dir
-            ):
-                return web.Response(status=403)
-            output_dir = full_output_dir
-
-        filename = os.path.basename(filename)
-        file = os.path.join(output_dir, filename)
-
-        if not os.path.isfile(file):
-            return web.Response(status=404)
-
-        preview_info = None
-        if "preview" in request.rel_url.query:
-            preview_params = request.rel_url.query["preview"].split(";")
-            image_format = (
-                preview_params[0]
-                if preview_params[0] in ["webp", "jpeg"]
-                else "webp"
-            )
-            quality = (
-                int(preview_params[1])
-                if len(preview_params) > 1 and preview_params[1].isdigit()
-                else 90
-            )
-            width = request.rel_url.query.get("width")
-            preview_info = (image_format, quality, width)
-
-        channel = request.rel_url.query.get("channel")
-
-        return await get_image_response(file, filename, preview_info, channel)
-
-    @PromptServer.instance.routes.get("/mtb/server-info")
-    async def get_debug(request: Request):
         from . import endpoint
 
-        _ = reload(endpoint)
+        reload(endpoint)
-        isdebug = "MTB_DEBUG" in os.environ
+        enabled = "MTB_DEBUG" in os.environ
-        exposed = "MTB_EXPOSE" in os.environ
-
-        def render_property(name: str, val: str):
-            return f"""<strong>{name}:</strong>
-                <p>
-                    {val}
-                </p>"""
-
         # Check if the request prefers HTML content
         if "text/html" in request.headers.get("Accept", ""):
             # # Return an HTML page
-            html_response = ""
+            html_response = f"""
-
+                <h1>MTB Debug Status: {'Enabled' if enabled else 'Disabled'}</h1>
-            html_response += render_property(
+            """
-                "Debug", "Enabled" if isdebug else "Disabled"
-            )
-
-            html_response += render_property("Exposed", str(exposed))
-
             return web.Response(
-                text=endpoint.render_base_template(
+                text=endpoint.render_base_template("Debug", html_response),
-                    "Server Info", html_response
-                ),
                 content_type="text/html",
             )
 
         # Return JSON for other requests
-        return web.json_response({"exposed": exposed, "debug": isdebug})
+        return web.json_response({"enabled": enabled})
 
     @PromptServer.instance.routes.get("/mtb/actions")
-    async def no_route(request: Request):
+    async def no_route(request):
         from . import endpoint
 
         if "text/html" in request.headers.get("Accept", ""):
@@ -534,7 +382,7 @@ if hasattr(PromptServer, "instance"):
         return web.json_response({"message": "actions has no get for now"})
 
     @PromptServer.instance.routes.post("/mtb/actions")
-    async def do_action(request: Request):
+    async def do_action(request):
         from . import endpoint
 
         reload(endpoint)

endpoint.py

@@ -1,20 +1,10 @@
 import csv
-import secrets
-import sys
-import urllib.parse
-from pathlib import Path
-from typing import Any, Literal
 
-import folder_paths
 from aiohttp import web
 
-from .install import get_node_dependencies
 from .log import mklog
 from .utils import (
-    SortMode,
     backup_file,
-    build_glob_patterns,
-    glob_multiple,
     import_install,
     reqs_map,
     run_command,
@@ -24,25 +14,18 @@ from .utils import (
 endlog = mklog("mtb endpoint")
 
 # - ACTIONS
+import sys
+from pathlib import Path
+
 import_install("requirements")
 
 
-def ACTIONS_installDependency(dependency_names: list[str] | None = None):
+def ACTIONS_installDependency(dependency_names=None):
     if dependency_names is None:
-        # return web.Response(text="No dependency name provided", status=400)
         return {"error": "No dependency name provided"}
-
     endlog.debug(f"Received Install Dependency request for {dependency_names}")
     # reqs = []
     resolved_names = [reqs_map.get(name, name) for name in dependency_names]
-    allowed_deps = list(
-        {d for dep in get_node_dependencies().values() for d in dep}
-    )
-    for dep in dependency_names:
-        if dep not in allowed_deps:
-            return {
-                "error": f"Unknown dependency: {dep}, you can only use this endpoint to install {allowed_deps}"
-            }
     try:
         run_command(
             [Path(sys.executable), "-m", "pip", "install"] + resolved_names
@@ -67,106 +50,6 @@ def ACTIONS_installDependency(dependency_names: list[str] | None = None):
     #             break
 
 
-def ACTIONS_getUserImageFolders():
-    input_dir = Path(folder_paths.get_input_directory())
-    output_dir = Path(folder_paths.get_output_directory())
-
-    input_subdirs = [x.name for x in input_dir.iterdir() if x.is_dir()]
-    output_subdirs = [x.name for x in output_dir.iterdir() if x.is_dir()]
-
-    return {"input": input_subdirs, "output": output_subdirs}
-
-
-def ACTIONS_getUserVideos(
-    size=256, count=200, offset=0, sort: str | None = None
-):
-    count = count or 1000
-    video_extensions = ["webm", "mp4", "mkv", "mov"]
-    entries = {}
-    patterns = build_glob_patterns(video_extensions)
-    input_dir = Path(folder_paths.get_input_directory())
-    entries = glob_multiple(input_dir, patterns)
-
-    sort_mode = SortMode.from_str(sort)
-
-    if sort_mode:
-        sort_key = {
-            SortMode.MODIFIED: lambda x: x.stat().st_mtime,
-            SortMode.MODIFIED_REVERSE: lambda x: x.stat().st_mtime,
-            SortMode.NAME: lambda x: x.name,
-            SortMode.NAME_REVERSE: lambda x: x.name,
-        }.get(sort_mode)
-        if sort_key:
-            reverse = sort_mode in (SortMode.MODIFIED, SortMode.NAME_REVERSE)
-            entries = sorted(entries, key=sort_key, reverse=reverse)
-
-    videos = {
-        video.name: (
-            f"/view?force_rate=0&frame_load_cap=0&skip_first_frames=0&select_every_nth=1&filename={urllib.parse.quote_plus(video.name)}&type=input&format=video&force_size={size}x?"
-        )
-        for i, video in enumerate(entries)
-        if offset <= i < offset + count
-    }
-    return videos
-
-
-def ACTIONS_getUserImages(
-    mode: Literal["input", "output"],
-    count=1000,
-    offset=0,
-    sort: str | None = None,
-    include_subfolders: bool = False,
-    subfolder=None,
-):
-    # enabled = "MTB_EXPOSE" in os.environ
-    # if not enabled:
-    #     return {"error": "Session not authorized to getInputs"}
-
-    imgs = {}
-    count = count or 1000
-
-    input_dir = Path(folder_paths.get_input_directory())
-    output_dir = Path(folder_paths.get_output_directory())
-
-    entry_dir = input_dir if mode == "input" else output_dir
-    if subfolder:
-        entry_dir = entry_dir / subfolder
-
-        if not entry_dir.exists():
-            return {
-                "error": f"Subfolder {entry_dir.name} doesn't exists in {entry_dir.parent.as_posix()}"
-            }
-    supported = ["png", "jpg", "jpeg", "webp", "gif"]
-
-    entries = {}
-    patterns = build_glob_patterns(supported, recursive=include_subfolders)
-    entries = glob_multiple(entry_dir, patterns)
-
-    sort_mode = SortMode.from_str(sort)
-
-    if sort_mode:
-        sort_key = {
-            SortMode.MODIFIED: lambda x: x.stat().st_mtime,
-            SortMode.MODIFIED_REVERSE: lambda x: x.stat().st_mtime,
-            SortMode.NAME: lambda x: x.name,
-            SortMode.NAME_REVERSE: lambda x: x.name,
-        }.get(sort_mode)
-        if sort_key:
-            reverse = sort_mode in (SortMode.MODIFIED, SortMode.NAME_REVERSE)
-            entries = sorted(entries, key=sort_key, reverse=reverse)
-
-    imgs = {
-        img.name: (
-            f"/mtb/view?filename={img.name}&width=512&type={mode}&subfolder={subfolder or ''}"
-            f"{img.parent.relative_to(entry_dir) if include_subfolders else ''}"
-            f"&preview=&rand={secrets.randbelow(424242)}"
-        )
-        for i, img in enumerate(entries)
-        if offset <= i < offset + count
-    }
-    return imgs
-
-
 def ACTIONS_getStyles(style_name=None):
     from .nodes.conditions import MTB_StylesLoader
 
@@ -214,7 +97,7 @@ def ACTIONS_saveStyle(data):
             csv_writer.writerow(row)
 
 
-async def do_action(request: web.Request) -> web.Response:
+async def do_action(request) -> web.Response:
     endlog.debug("Init action request")
     request_data = await request.json()
     name = request_data.get("name")
@@ -226,12 +109,7 @@ async def do_action(request: web.Request) -> web.Response:
     method = globals().get(method_name)
 
     if callable(method):
-        result = None
+        result = method(args) if args else method()
-        if args:
-            result = method(*args) if isinstance(args, list) else method(args)
-        else:
-            result = method()
-
         endlog.debug(f"Action result: {result}")
         return web.json_response({"result": result})
 
@@ -252,13 +130,10 @@ async def do_action(request: web.Request) -> web.Response:
 # - HTML UTILS
 
 
-def dependencies_button(name: str, dependencies: list[str]) -> str:
+def dependencies_button(name, dependencies):
     deps = ",".join([f"'{x}'" for x in dependencies])
     return f"""
-        <button
+        <button class="dependency-button" onclick="window.mtb_action('installDependency',[{deps}])">Install {name} deps</button>
-            class="dependency-button"
-            onclick="window.mtb_action('installDependency',[{deps}])"
-        >Install {name} deps</button>
         """
 
 
@@ -278,7 +153,7 @@ def csv_editor():
     html_out = """
             <div id="style-editor">
              <h1>Style Editor</h1>
-
+            
             """
     for current, styles in style_files.items():
         current_out = f"<h3>{current}</h3>"
@@ -340,14 +215,11 @@ def render_tab_view(**kwargs):
     """
 
 
-def add_foldable_region(title: str, content: str):
+def add_foldable_region(title, content):
     symbol_id = f"{title}-symbol"
     return f"""
     <div class='foldable'>
-        <div
+        <div class='foldable-title' onclick="toggleFoldable('{title}', '{symbol_id}')">
-            class='foldable-title'
-            onclick="toggleFoldable('{title}', '{symbol_id}')"
-        >
             <span id='{symbol_id}' class='foldable-symbol'>&#9655;</span>
             {title}
         </div>
@@ -359,9 +231,7 @@ def add_foldable_region(title: str, content: str):
     """
 
 
-def add_split_pane(
+def add_split_pane(left_content, right_content, vertical=True):
-    left_content: str, right_content: str, *, vertical: bool = True
-):
     orientation = "vertical" if vertical else "horizontal"
     return f"""
     <div class="split-pane {orientation}">
@@ -380,7 +250,7 @@ def add_split_pane(
     """
 
 
-def add_dropdown(title: str, options: list[str]):
+def add_dropdown(title, options):
     option_str = "\n".join(
         [f"<option value='{opt}'>{opt}</option>" for opt in options]
     )
@@ -392,13 +262,13 @@ def add_dropdown(title: str, options: list[str]):
     """
 
 
-def render_table(table_dict: dict[str, Any], sort=True, title=None):
+def render_table(table_dict, sort=True, title=None):
-    table_list = sorted(
+    table_dict = sorted(
         table_dict.items(), key=lambda item: item[0]
     )  # Sort the dictionary by keys
 
     table_rows = ""
-    for name, item in table_list:
+    for name, item in table_dict:
         if isinstance(item, dict):
             if "dependencies" in item:
                 table_rows += f"<tr><td>{name}</td><td>"
@@ -429,12 +299,12 @@ def render_table(table_dict: dict[str, Any], sort=True, title=None):
             <tbody>
                 {table_rows}
             </tbody>
-        </table>
+        </table>      
         </div>
         """
 
 
-def render_base_template(title: str, content: str):
+def render_base_template(title, content):
     github_icon_svg = """<svg xmlns="http://www.w3.org/2000/svg" fill="whitesmoke" height="3em" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>"""
     return f"""
     <!DOCTYPE html>
@@ -470,9 +340,7 @@ def render_base_template(title: str, content: str):
         <header>
         <a href="/">Back to Comfy</a>
         <div class="mtb_logo">
-            <img
+            <img src="https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873" alt="Comfy MTB Logo" height="70" width="128">
-                src="https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873"
-                alt="Comfy MTB Logo" height="70" width="128">
             <span class="title">Comfy MTB</span></div>
             <a style="width:128px;text-align:center" href="https://www.github.com/melmass/comfy_mtb">
                 {github_icon_svg}
@@ -487,6 +355,6 @@ def render_base_template(title: str, content: str):
             <!-- Shared footer content here -->
         </footer>
     </body>
-
+    
     </html>
     """

env.nu

@@ -27,7 +27,7 @@ export def "comfy start" [--clean,--old-ui, --listen] {
 
     let root = get_root --clean=($clean)
     cd $root
-    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 {[]}) --preview-method auto ...(if $listen {["--listen"]} else {[]})
 }
 
 # update comfy itself and merge master in current branch
@@ -67,14 +67,8 @@ export def "comfy update" [
     git checkout master
 
     print $"(ansi yellow_italic)Fetching and pulling remote updates(ansi reset)"
-    if ($clean) {
+    git fetch
-        git fetch local master
+    git pull
-        git pull local master
-    } else {
-        git fetch
-        git pull
-    }
-
 
     print $"(ansi yellow_italic)Back to our branch \(($branch_name)\)(ansi reset)"
     git checkout -
@@ -141,7 +135,7 @@ export def "comfy update_extensions" [--clean] {
     let root = get_root --clean=($clean)
     cd $root
     cd custom_nodes
-    git multipull . -s -q
+    git multipull .
 }
 
 def --env path-add [pth] {
@@ -152,7 +146,7 @@ def --env path-add [pth] {
 
 export-env {
   $env.COMFY_MTB = ("." | path expand)
-  # $env.CUDA_ROOT =  'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\'
+  $env.CUDA_ROOT =  'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\'
 
   $env.CUDA_HOME = $env.CUDA_ROOT
 
@@ -160,12 +154,6 @@ export-env {
   $env.COMFY_CLEAN_ROOT =  ($env.COMFY_ROOT | path dirname | path join ComfyClean)
 
   path-add 'C:/Portable/TensorRT-8.6.0.12/lib'
-
-  if $nu.os-info.family == 'windows' {
-    path-add 'G:\BIN\TensorRT-10.7.0.23\lib'
-    path-add 'G:\BIN\cudnn-windows-x86_64-9.6.0.74_cuda12-archive\bin'
-  }
-
   path-add ($env.CUDA_ROOT | path join bin)
   overlay use ../../.venv/Scripts/activate.nu
 }

install.py

@@ -43,28 +43,10 @@ pip_map = {
     "tb-nightly": "tensorboard",
     "protobuf": "google.protobuf",
     "qrcode[pil]": "qrcode",
-    "requirements-parser": "requirements",
+    "requirements-parser": "requirements"
     # Add more mappings as needed
 }
 
-
-def get_node_dependencies():
-    restore_deps = ["basicsr"]
-    onnx_deps = ["onnxruntime"]
-    swap_deps = ["insightface"] + onnx_deps
-    quant_deps = ["bitsandbytes"]
-    io_deps = ["av"]
-    return {
-        "QrCode": ["qrcode"],
-        "DeepBump": onnx_deps,
-        "FaceSwap": swap_deps,
-        "LoadFaceSwapModel": swap_deps,
-        "LoadFaceAnalysisModel": restore_deps,
-        "Quantize": quant_deps,
-        "SaveGif": io_deps,
-    }
-
-
 # endregion
 
 # region ansi
@@ -142,12 +124,12 @@ def print_formatted(text, *formats, color=None, background=None, **kwargs):
     header = "[mtb install] "
 
     # Handle console encoding for Unicode characters (utf-8)
-    encoded_header = header.encode(
+    encoded_header = header.encode(sys.stdout.encoding, errors="replace").decode(
-        sys.stdout.encoding, errors="replace"
+        sys.stdout.encoding
-    ).decode(sys.stdout.encoding)
+    )
-    encoded_text = formatted_text.encode(
+    encoded_text = formatted_text.encode(sys.stdout.encoding, errors="replace").decode(
-        sys.stdout.encoding, errors="replace"
+        sys.stdout.encoding
-    ).decode(sys.stdout.encoding)
+    )
 
     print(
         " " * len(encoded_header)
@@ -181,9 +163,7 @@ def run_command(cmd, ignored_lines_start=None):
     try:
         _run_command(shell_cmd, ignored_lines_start)
     except subprocess.CalledProcessError as e:
-        print(
+        print(f"Command failed with return code: {e.returncode}", file=sys.stderr)
-            f"Command failed with return code: {e.returncode}", file=sys.stderr
-        )
         print(e.stderr.strip(), file=sys.stderr)
 
     except KeyboardInterrupt:
@@ -258,7 +238,7 @@ def suppress_std():
 def get_local_version():
     init_file = os.path.join(os.path.dirname(__file__), "__init__.py")
     if os.path.isfile(init_file):
-        with open(init_file) as f:
+        with open(init_file, "r") as f:
             tree = ast.parse(f.read())
             for node in ast.walk(tree):
                 if isinstance(node, ast.Assign):
@@ -276,16 +256,13 @@ def download_file(url, file_name):
     with requests.get(url, stream=True) as response:
         response.raise_for_status()
         total_size = int(response.headers.get("content-length", 0))
-        with (
+        with open(file_name, "wb") as file, tqdm(
-            open(file_name, "wb") as file,
+            desc=file_name.stem,
-            tqdm(
+            total=total_size,
-                desc=file_name.stem,
+            unit="B",
-                total=total_size,
+            unit_scale=True,
-                unit="B",
+            unit_divisor=1024,
-                unit_scale=True,
+        ) as progress_bar:
-                unit_divisor=1024,
-            ) as progress_bar,
-        ):
             for chunk in response.iter_content(chunk_size=8192):
                 file.write(chunk)
                 progress_bar.update(len(chunk))
@@ -325,9 +302,7 @@ def import_or_install(requirement, dry=False):
     pip_install_name = pip_name + pip_spec
 
     if not installed:
-        print_formatted(
+        print_formatted(f"Installing package {pip_name}...", "italic", color="yellow")
-            f"Installing package {pip_name}...", "italic", color="yellow"
-        )
         if dry:
             print_formatted(
                 f"Dry-run: Package {pip_install_name} would be installed (import name: '{import_name}').",
@@ -335,9 +310,7 @@ def import_or_install(requirement, dry=False):
             )
         else:
             try:
-                run_command(
+                run_command([executable, "-m", "pip", "install", pip_install_name])
-                    [executable, "-m", "pip", "install", pip_install_name]
-                )
                 print_formatted(
                     f"Package {pip_install_name} installed successfully using pip package name  (import name: '{import_name}')",
                     "bold",
@@ -353,9 +326,13 @@ def import_or_install(requirement, dry=False):
 
 def get_github_assets(tag=None):
     if tag:
-        tag_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/tags/{tag}"
+        tag_url = (
+            f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/tags/{tag}"
+        )
     else:
-        tag_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
+        tag_url = (
+            f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
+        )
     response = requests.get(tag_url)
     if response.status_code == 404:
         # print_formatted(
@@ -384,9 +361,7 @@ except ImportError:
 def main():
     if len(sys.argv) == 1:
         print_formatted(
-            "mtb doesn't need an install script anymore.",
+            "mtb doesn't need an install script anymore.", "italic", color="yellow"
-            "italic",
-            color="yellow",
         )
         return
     if all(arg not in ("-p", "--path") for arg in sys.argv):
@@ -422,12 +397,8 @@ def main():
         else:
             repo_dir = clone_dir / repo_name
             if not repo_dir.exists():
-                print_formatted(
+                print_formatted(f"Cloning to {repo_dir}...", "italic", color="yellow")
-                    f"Cloning to {repo_dir}...", "italic", color="yellow"
+                run_command(["git", "clone", "--recursive", repo_url, repo_dir])
-                )
-                run_command(
-                    ["git", "clone", "--recursive", repo_url, repo_dir]
-                )
             else:
                 print_formatted(
                     f"Directory {repo_dir} already exists, we will update it..."
@@ -438,14 +409,7 @@ def main():
 
     print_formatted("Checking environment...", "italic", color="yellow")
     missing_deps = []
-    install_cmd = [
+    install_cmd = [executable, "-m", "pip", "install", "-r", "requirements.txt"]
-        executable,
-        "-m",
-        "pip",
-        "install",
-        "-r",
-        "requirements.txt",
-    ]
     run_command(install_cmd)
 
     print_formatted(

nodes/batch.py

@@ -1,5 +1,4 @@
 from io import BytesIO
-from typing import Literal
 
 import cv2
 import numpy as np
@@ -411,14 +410,7 @@ class MTB_BatchFloat:
     RETURN_TYPES = ("FLOATS",)
     CATEGORY = "mtb/batch"
 
-    def set_floats(
+    def set_floats(self, mode, count, min, max, easing):
-        self,
-        mode: Literal["Steps"] | Literal["Single"] = "Steps",
-        count: int = 1,
-        min: float = 0.0,  # noqa: A002
-        max: float = 1.0,  # noqa: A002
-        easing: str = "Linear",
-    ):
         if mode == "Steps" and count == 1:
             raise ValueError(
                 "Steps mode requires at least a count of 2 values"
@@ -437,210 +429,6 @@ class MTB_BatchFloat:
         return (keyframes,)
 
 
-class MTB_BatchSequencePlus:
-    """Sequences multiple image batches with transition effects."""
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "transition": (
-                    [
-                        "none",
-                        "crossfade",
-                        "slide_left",
-                        "slide_right",
-                        "slide_up",
-                        "slide_down",
-                        "wipe_left",
-                        "wipe_right",
-                        "wipe_up",
-                        "wipe_down",
-                        "band_wipe_h",
-                        "band_wipe_v",
-                    ],
-                    {"default": "none"},
-                ),
-                "overlap_frames": (
-                    "INT",
-                    {"default": 0, "min": 0, "max": 120, "step": 1},
-                ),
-                "reverse": ("BOOLEAN", {"default": False}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "sequence_batches"
-    CATEGORY = "mtb/batch"
-
-    def apply_transition(
-        self,
-        frame1: torch.Tensor,
-        frame2: torch.Tensor,
-        transition: str,
-        progress: float,
-    ):
-        """Apply transition effect between two frames."""
-        if transition == "none":
-            return frame1 if progress < 0.5 else frame2
-
-        elif transition == "crossfade":
-            return frame1 * (1 - progress) + frame2 * progress
-
-        elif transition.startswith("slide_"):
-            h, w = frame1.shape[1:3]
-            if transition == "slide_left":
-                offset = int(w * progress)
-                frame2 = torch.roll(frame2, shifts=-offset, dims=2)
-            elif transition == "slide_right":
-                offset = int(w * progress)
-                frame2 = torch.roll(frame2, shifts=offset, dims=2)
-            elif transition == "slide_up":
-                offset = int(h * progress)
-                frame2 = torch.roll(frame2, shifts=-offset, dims=1)
-            elif transition == "slide_down":
-                offset = int(h * progress)
-                frame2 = torch.roll(frame2, shifts=offset, dims=1)
-            return frame1 * (1 - progress) + frame2 * progress
-
-        elif transition.startswith("wipe_"):
-            h, w = frame1.shape[1:3]
-            mask = torch.zeros_like(frame1)
-            if transition == "wipe_left":
-                edge = int(w * progress)
-                mask[:, :, :edge, :] = 1
-            elif transition == "wipe_right":
-                edge = int(w * (1 - progress))
-                mask[:, :, edge:, :] = 1
-            elif transition == "wipe_up":
-                edge = int(h * progress)
-                mask[:, :edge, :, :] = 1
-            elif transition == "wipe_down":
-                edge = int(h * (1 - progress))
-                mask[:, edge:, :, :] = 1
-            return frame1 * (1 - mask) + frame2 * mask
-
-        elif transition.startswith("band_wipe_"):
-            h, w = frame1.shape[1:3]
-            mask = torch.zeros_like(frame1)
-            num_bands = 10  # Number of bands
-
-            if transition == "band_wipe_h":
-                band_width = w / num_bands
-                for i in range(num_bands):
-                    edge = int((w * progress) - (i * band_width))
-                    start = int(i * band_width)
-                    end = int(min(start + edge, (i + 1) * band_width))
-                    if end > start:
-                        mask[:, :, start:end, :] = 1
-            else:  # band_wipe_v
-                band_height = h / num_bands
-                for i in range(num_bands):
-                    edge = int((h * progress) - (i * band_height))
-                    start = int(i * band_height)
-                    end = int(min(start + edge, (i + 1) * band_height))
-                    if end > start:
-                        mask[:, start:end, :, :] = 1
-
-            return frame1 * (1 - mask) + frame2 * mask
-
-        return frame1
-
-    def sequence_batches(
-        self, transition: str, overlap_frames: int, reverse: bool, **kwargs
-    ):
-        images: list[torch.Tensor] = list(kwargs.values())
-
-        if reverse:
-            images = images[::-1]
-
-        processed_images: list[torch.Tensor] = []
-        for img in images:
-            if len(img.shape) == 3:
-                img = img.unsqueeze(0)
-            processed_images.append(img)
-
-        if overlap_frames == 0 or transition == "none":
-            return (torch.cat(processed_images, dim=0),)
-
-        result_frames: list[torch.Tensor] = []
-
-        if len(processed_images) > 0:
-            result_frames.extend(
-                list(processed_images[0][: -overlap_frames // 2])
-            )
-
-        for i in range(1, len(processed_images)):
-            prev_batch = processed_images[i - 1]
-            curr_batch = processed_images[i]
-
-            prev_frames = min(overlap_frames // 2, len(prev_batch))
-            next_frames = min(overlap_frames // 2, len(curr_batch))
-            total_overlap = prev_frames + next_frames
-
-            if total_overlap < 2:
-                # when not enough frames for transition, just concatenate
-                result_frames.extend(list(prev_batch[-prev_frames:]))
-                result_frames.extend(list(curr_batch[:next_frames]))
-                continue
-
-            for t in range(total_overlap):
-                progress = t / (total_overlap - 1)
-
-                prev_idx = (
-                    len(prev_batch) - prev_frames + min(t, prev_frames - 1)
-                )
-                next_idx = max(0, t - prev_frames)
-
-                transition_frame = self.apply_transition(
-                    prev_batch[prev_idx : prev_idx + 1],
-                    curr_batch[next_idx : next_idx + 1],
-                    transition,
-                    progress,
-                )
-                result_frames.append(transition_frame[0])
-
-            if i < len(processed_images) - 1:
-                result_frames.extend(
-                    list(curr_batch[next_frames : -overlap_frames // 2])
-                )
-            else:
-                result_frames.extend(list(curr_batch[next_frames:]))
-
-        result = torch.stack(result_frames, dim=0)
-
-        return (result,)
-
-
-class MTB_BatchSequence:
-    """Sequences multiple image batches one after another"""
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "reverse": ("BOOLEAN", {"default": False}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "sequence_batches"
-    CATEGORY = "mtb/batch"
-
-    def sequence_batches(self, reverse: bool, **kwargs):
-        images = list(kwargs.values())
-        if reverse:
-            images = images[::-1]
-
-        processed = []
-        for img in images:
-            if len(img.shape) == 3:
-                img = img.unsqueeze(0)
-            processed.append(img)
-
-        return (torch.cat(processed, dim=0),)
-
-
 class MTB_BatchMerge:
     """Merges multiple image batches with different frame counts"""
 
@@ -923,9 +711,7 @@ class MTB_PlotBatchFloat:
         ax.set_xlim(1, max_length)  # Set X-axis limits
         np.random.seed(seed)
         colors = np.random.rand(len(kwargs), 3)  # Generate random RGB values
-        for color, (label, values) in zip(
+        for color, (label, values) in zip(colors, kwargs.items()):
-            colors, kwargs.items(), strict=False
-        ):
             ax.plot(x_values[: len(values)], values, label=label, color=color)
         ax.legend(
             title="Legend",
@@ -1240,19 +1026,17 @@ class MTB_BatchShake:
 
 
 __nodes__ = [
-    MTB_Batch2dTransform,
     MTB_BatchFloat,
+    MTB_Batch2dTransform,
+    MTB_BatchShape,
+    MTB_BatchMake,
     MTB_BatchFloatAssemble,
     MTB_BatchFloatFill,
+    MTB_BatchFloatNormalize,
+    MTB_BatchMerge,
+    MTB_BatchShake,
+    MTB_PlotBatchFloat,
+    MTB_BatchTimeWrap,
     MTB_BatchFloatFit,
     MTB_BatchFloatMath,
-    MTB_BatchFloatNormalize,
-    MTB_BatchMake,
-    MTB_BatchMerge,
-    MTB_BatchSequence,
-    MTB_BatchSequencePlus,
-    MTB_BatchShake,
-    MTB_BatchShape,
-    MTB_BatchTimeWrap,
-    MTB_PlotBatchFloat,
 ]

nodes/conditions.py

@@ -3,127 +3,10 @@ import shutil
 from pathlib import Path
 
 import folder_paths
-import torch
 
 from ..log import log
 from ..utils import here
 
-Conditioning = list[tuple[torch.Tensor, dict[str, torch.Tensor]]]
-
-
-def check_condition(conditioning: Conditioning):
-    has_cn = False
-    if len(conditioning) > 1:
-        log.warn(
-            "More than one conditioning was provided. Only the first one will be used."
-        )
-    first = conditioning[0]
-    cond, kwargs = first
-
-    log.debug("Conditioning Shape")
-    log.debug(cond.shape)
-    log.debug("Conditioning keys")
-    log.debug([f"\t{k} - {type(kwargs[k])}" for k in kwargs])
-    if "control" in kwargs:
-        log.debug("Conditioning contains a controlnet")
-        has_cn = True
-    if "pooled_output" not in kwargs:
-        raise ValueError(
-            "Conditioning is not valid. Missing 'pooled_output' key."
-        )
-    return has_cn
-
-
-class MTB_InterpolateCondition:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "blend": (
-                    "FLOAT",
-                    {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01},
-                ),
-            },
-        }
-
-    RETURN_TYPES = ("CONDITIONING",)
-    CATEGORY = "mtb/conditioning"
-    FUNCTION = "execute"
-
-    def execute(
-        self, blend: float, **kwargs: Conditioning
-    ) -> tuple[Conditioning]:
-        blend = max(0.0, min(1.0, blend))
-
-        conditions: list[Conditioning] = list(kwargs.values())
-        num_conditions = len(conditions)
-
-        if num_conditions < 2:
-            raise ValueError("At least two conditioning inputs are required.")
-
-        segment_length = 1.0 / (num_conditions - 1)
-
-        segment_index = min(int(blend // segment_length), num_conditions - 2)
-
-        local_blend = (
-            blend - (segment_index * segment_length)
-        ) / segment_length
-
-        cond_from = conditions[segment_index]
-        cond_to = conditions[segment_index + 1]
-
-        from_cn = check_condition(cond_from)
-        to_cn = check_condition(cond_to)
-
-        if from_cn and to_cn:
-            raise ValueError(
-                "Interpolating conditions cannot both contain ControlNets"
-            )
-
-        try:
-            interpolated_condition = [
-                (1.0 - local_blend) * c_from + local_blend * c_to
-                for c_from, c_to in zip(
-                    cond_from[0][0], cond_to[0][0], strict=False
-                )
-            ]
-        except Exception as e:
-            print(f"Error during interpolation: {e}")
-            raise
-
-        pooled_from = cond_from[0][1].get(
-            "pooled_output",
-            torch.zeros_like(
-                next(iter(cond_from[0][1].values()), torch.tensor([]))
-            ),
-        )
-
-        pooled_to = cond_to[0][1].get(
-            "pooled_output",
-            torch.zeros_like(
-                next(iter(cond_from[0][1].values()), torch.tensor([]))
-            ),
-        )
-
-        interpolated_pooled = (
-            1.0 - local_blend
-        ) * pooled_from + local_blend * pooled_to
-
-        res = {"pooled_output": interpolated_pooled}
-
-        if from_cn:
-            res["control"] = cond_from[0][1]["control"]
-            res["control_apply_to_uncond"] = cond_from[0][1][
-                "control_apply_to_uncond"
-            ]
-        if to_cn:
-            res["control"] = cond_to[0][1]["control"]
-            res["control_apply_to_uncond"] = cond_to[0][1][
-                "control_apply_to_uncond"
-            ]
-
-        return ([(torch.stack(interpolated_condition), res)],)
-
 
 class MTB_InterpolateClipSequential:
     @classmethod
@@ -330,9 +213,4 @@ class MTB_StylesLoader:
         return (self.options[style_name][0], self.options[style_name][1])
 
 
-__nodes__ = [
+__nodes__ = [MTB_SmartStep, MTB_StylesLoader, MTB_InterpolateClipSequential]
-    MTB_SmartStep,
-    MTB_StylesLoader,
-    MTB_InterpolateClipSequential,
-    MTB_InterpolateCondition,
-]

nodes/crop.py

@@ -59,36 +59,6 @@ class MTB_SplitBbox:
         return (bbox[0], bbox[1], bbox[2], bbox[3])
 
 
-class MTB_UpscaleBboxBy:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "bbox": ("BBOX",),
-                "scale": ("FLOAT", {"default": 1.0}),
-            },
-        }
-
-    CATEGORY = "mtb/crop"
-    RETURN_TYPES = ("BBOX",)
-
-    FUNCTION = "upscale"
-
-    def upscale(
-        self, bbox: tuple[int, int, int, int], scale: float
-    ) -> tuple[tuple[int, int, int, int]]:
-        x, y, width, height = bbox
-        # scaled = (x * scale, y * scale, width * scale, height * scale)
-        scaled = (
-            int(x * scale),
-            int(y * scale),
-            int(width * scale),
-            int(height * scale),
-        )
-
-        return (scaled,)
-
-
 class MTB_BboxFromMask:
     """From a mask extract the bounding box"""
 
@@ -372,11 +342,4 @@ class MTB_Uncrop:
         return (pil2tensor(out_images),)
 
 
-__nodes__ = [
+__nodes__ = [MTB_BboxFromMask, MTB_Bbox, MTB_Crop, MTB_Uncrop, MTB_SplitBbox]
-    MTB_BboxFromMask,
-    MTB_Bbox,
-    MTB_Crop,
-    MTB_Uncrop,
-    MTB_SplitBbox,
-    MTB_UpscaleBboxBy,
-]

nodes/faceenhance.py

@@ -78,7 +78,6 @@ class MTB_LoadFaceEnhanceModel:
     RETURN_NAMES = ("model",)
     FUNCTION = "load_model"
     CATEGORY = "mtb/facetools"
-    DEPRECATED = True
 
     def load_model(self, model_name, upscale=2, bg_upsampler=None):
         from gfpgan import GFPGANer
@@ -164,7 +163,6 @@ class MTB_RestoreFace:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "restore"
     CATEGORY = "mtb/facetools"
-    DEPRECATED = True
 
     @classmethod
     def INPUT_TYPES(cls):

nodes/faceswap.py

@@ -40,7 +40,6 @@ class MTB_LoadFaceAnalysisModel:
     RETURN_TYPES = ("FACE_ANALYSIS_MODEL",)
     FUNCTION = "load_model"
     CATEGORY = "mtb/facetools"
-    DEPRECATED = True
 
     def load_model(self, faceswap_model: str):
         if faceswap_model == "antelopev2":
@@ -78,7 +77,6 @@ class MTB_LoadFaceSwapModel:
     RETURN_TYPES = ("FACESWAP_MODEL",)
     FUNCTION = "load_model"
     CATEGORY = "mtb/facetools"
-    DEPRECATED = True
 
     def load_model(self, faceswap_model: str):
         model_path = get_model_path("insightface", faceswap_model)
@@ -128,7 +126,6 @@ class MTB_FaceSwap:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "swap"
     CATEGORY = "mtb/facetools"
-    DEPRECATED = True
 
     def swap(
         self,

nodes/graph_utils.py

@@ -44,22 +44,14 @@ class MTB_ToDevice:
         if torch.backends.mps.is_available():
             devices.append("mps")
         if torch.cuda.is_available():
-            devices.append("cuda:0")
-            for i in range(1, torch.cuda.device_count()):
-                devices.append(f"cuda:{i}")
             devices.append("cuda")
+            for i in range(torch.cuda.device_count()):
+                devices.append(f"cuda{i}")
 
         return {
             "required": {
                 "ignore_errors": ("BOOLEAN", {"default": False}),
-                "device": (
+                "device": (devices, {"default": "cpu"}),
-                    devices,
-                    {
-                        "default": "cuda"
-                        if torch.cuda.is_available()
-                        else "cpu"
-                    },
-                ),
             },
             "optional": {
                 "image": ("IMAGE",),
@@ -75,36 +67,20 @@ class MTB_ToDevice:
     def to_device(
         self,
         *,
-        ignore_errors: bool = False,
+        ignore_errors=False,
-        device: str = "cuda",
+        device="cuda",
         image: torch.Tensor | None = None,
         mask: torch.Tensor | None = None,
     ):
         if not ignore_errors and image is None and mask is None:
             raise ValueError(
                 "You must either provide an image or a mask,"
-                + " use ignore_error to passthrough"
+                " use ignore_error to passthrough"
-            )
-        if (
-            device.startswith("cuda")
-            and ":" not in device
-            and device != "cuda"
-        ):
-            device = f"cuda:{device[4:]}"
-
-        try:
-            if image is not None:
-                image = image.to(device)
-            if mask is not None:
-                mask = mask.to(device)
-        except RuntimeError as e:
-            if not ignore_errors:
-                raise RuntimeError(
-                    f"Failed to move tensor to device {device}: {str(e)}"
-                ) from e
-            log.warning(
-                f"Failed to move tensor to device {device}, ignoring: {str(e)}"
             )
+        if image is not None:
+            image = image.to(device)
+        if mask is not None:
+            mask = mask.to(device)
         return (image, mask)
 
 

nodes/image_interpolation.py

@@ -1,4 +1,5 @@
 from pathlib import Path
+from typing import List
 
 import comfy
 import comfy.model_management as model_management
@@ -14,13 +15,10 @@ from ..utils import get_model_path
 
 
 class MTB_LoadFilmModel:
-    """Loads a FILM model
+    """Loads a FILM model"""
-
-    [DEPRECATED] Use ComfyUI-FrameInterpolation instead
-    """
 
     @staticmethod
-    def get_models() -> list[Path]:
+    def get_models() -> List[Path]:
         models_paths = get_model_path("FILM").iterdir()
 
         return [x for x in models_paths if x.suffix in [".onnx", ".pth"]]
@@ -39,7 +37,6 @@ class MTB_LoadFilmModel:
     RETURN_TYPES = ("FILM_MODEL",)
     FUNCTION = "load_model"
     CATEGORY = "mtb/frame iterpolation"
-    DEPRECATED = True
 
     def load_model(self, film_model: str):
         model_path = get_model_path("FILM", film_model)
@@ -59,10 +56,7 @@ class MTB_LoadFilmModel:
 
 
 class MTB_FilmInterpolation:
-    """Google Research FILM frame interpolation for large motion
+    """Google Research FILM frame interpolation for large motion"""
-
-    [DEPRECATED] Use ComfyUI-FrameInterpolation instead
-    """
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -77,7 +71,6 @@ class MTB_FilmInterpolation:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "do_interpolation"
     CATEGORY = "mtb/frame iterpolation"
-    DEPRECATED = True
 
     def do_interpolation(
         self,

nodes/image_processing.py

@@ -702,6 +702,7 @@ class MTB_Blur:
                 )
                 blurred_images.append(blurred)
 
+            image_np = np.array(blurred_images)
         else:
             for i in range(image.size(0)):
                 blurred = gaussian(
@@ -709,7 +710,8 @@ class MTB_Blur:
                 )
                 blurred_images.append(blurred)
 
-        return (np2tensor(blurred_images),)
+            image_np = np.array(blurred_images)
+        return (np2tensor(image_np).squeeze(0),)
 
 
 class MTB_Sharpen:

nodes/image_utils.py

@@ -1,11 +1,4 @@
-import json
-import os
-
-import numpy as np
 import torch
-from comfy.cli_args import args
-from PIL import Image
-from PIL.PngImagePlugin import PngInfo
 
 from ..log import log
 
@@ -15,21 +8,13 @@ class MTB_StackImages:
 
     @classmethod
     def INPUT_TYPES(cls):
-        return {
+        return {"required": {"vertical": ("BOOLEAN", {"default": False})}}
-            "required": {"vertical": ("BOOLEAN", {"default": False})},
-            "optional": {
-                "match_method": (
-                    ["error", "smallest", "largest"],
-                    {"default": "error"},
-                )
-            },
-        }
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "stack"
     CATEGORY = "mtb/image utils"
 
-    def stack(self, vertical, match_method="error", **kwargs):
+    def stack(self, vertical, **kwargs):
         if not kwargs:
             raise ValueError("At least one tensor must be provided.")
 
@@ -47,50 +32,23 @@ class MTB_StackImages:
             self.duplicate_frames(tensor, max_batch_size)
             for tensor in normalized_tensors
         ]
-        if match_method != "error":
-            if vertical:
-                # match widths
-                widths = [tensor.shape[2] for tensor in normalized_tensors]
-                target_width = (
-                    min(widths) if match_method == "smallest" else max(widths)
-                )
-                normalized_tensors = [
-                    self.resize_tensor(tensor, width=target_width)
-                    for tensor in normalized_tensors
-                ]
-            else:
-                # match heights
-                heights = [tensor.shape[1] for tensor in normalized_tensors]
-                target_height = (
-                    min(heights)
-                    if match_method == "smallest"
-                    else max(heights)
-                )
-                normalized_tensors = [
-                    self.resize_tensor(tensor, height=target_height)
-                    for tensor in normalized_tensors
-                ]
-        else:
-            if vertical:
-                width = normalized_tensors[0].shape[2]
-                if any(
-                    tensor.shape[2] != width for tensor in normalized_tensors
-                ):
-                    raise ValueError(
-                        "All tensors must have the same width "
-                        "for vertical stacking."
-                    )
-            else:
-                height = normalized_tensors[0].shape[1]
-                if any(
-                    tensor.shape[1] != height for tensor in normalized_tensors
-                ):
-                    raise ValueError(
-                        "All tensors must have the same height "
-                        "for horizontal stacking."
-                    )
 
-        dim = 1 if vertical else 2
+        if vertical:
+            width = normalized_tensors[0].shape[2]
+            if any(tensor.shape[2] != width for tensor in normalized_tensors):
+                raise ValueError(
+                    "All tensors must have the same width "
+                    "for vertical stacking."
+                )
+            dim = 1
+        else:
+            height = normalized_tensors[0].shape[1]
+            if any(tensor.shape[1] != height for tensor in normalized_tensors):
+                raise ValueError(
+                    "All tensors must have the same height "
+                    "for horizontal stacking."
+                )
+            dim = 2
 
         stacked_tensor = torch.cat(normalized_tensors, dim=dim)
 
@@ -106,7 +64,7 @@ class MTB_StackImages:
         elif channels == 3:
             alpha_channel = torch.ones(
                 tensor.shape[:-1] + (1,), device=tensor.device
-            )
+            )  # Add an alpha channel
             return torch.cat((tensor, alpha_channel), dim=-1)
         else:
             raise ValueError(
@@ -129,30 +87,6 @@ class MTB_StackImages:
         else:
             return tensor
 
-    def resize_tensor(self, tensor, width=None, height=None):
-        """Resize tensor to specified width or height while maintaining aspect ratio."""
-        current_height, current_width = tensor.shape[1:3]
-
-        if width is not None and width != current_width:
-            scale_factor = width / current_width
-            new_height = int(current_height * scale_factor)
-            new_width = width
-        elif height is not None and height != current_height:
-            scale_factor = height / current_height
-            new_width = int(current_width * scale_factor)
-            new_height = height
-        else:
-            return tensor
-
-        resized = torch.nn.functional.interpolate(
-            tensor.permute(0, 3, 1, 2),
-            size=(new_height, new_width),
-            mode="bilinear",
-            align_corners=False,
-        )
-
-        return resized.permute(0, 2, 3, 1)
-
 
 class MTB_PickFromBatch:
     """Pick a specific number of images from a batch.
@@ -179,6 +113,11 @@ class MTB_PickFromBatch:
 
         # Limit count to the available number of images in the batch
         count = min(count, batch_size)
+        if count < batch_size:
+            log.warning(
+                f"Requested {count} images, "
+                f"but only {batch_size} are available."
+            )
 
         if from_direction == "end":
             selected_tensors = image[-count:]
@@ -188,87 +127,4 @@ class MTB_PickFromBatch:
         return (selected_tensors,)
 
 
-import folder_paths
+__nodes__ = [MTB_StackImages, MTB_PickFromBatch]
-
-
-class MTB_SaveImage:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
-        self.compress_level = 4
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "images": ("IMAGE", {"tooltip": "The images to save."}),
-                "filename_prefix": (
-                    "STRING",
-                    {
-                        "default": "ComfyUI",
-                        "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
-                    },
-                ),
-            },
-            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "save_images"
-
-    # OUTPUT_NODE = True
-
-    CATEGORY = "mtb/image utils"
-    DESCRIPTION = """Saves the input images to your ComfyUI output directory.
-    This behaves exactly like the native SaveImage node but isn't an output node.
-    The reason I made this is to allow 'inlining' image save in loops for instance,
-    using the native node there wouldn't run for each iteration of the loop."""
-
-    def save_images(
-        self,
-        images,
-        filename_prefix="ComfyUI",
-        prompt=None,
-        extra_pnginfo=None,
-    ):
-        filename_prefix += self.prefix_append
-        full_output_folder, filename, counter, subfolder, filename_prefix = (
-            folder_paths.get_save_image_path(
-                filename_prefix,
-                self.output_dir,
-                images[0].shape[1],
-                images[0].shape[0],
-            )
-        )
-        results = list()
-        for batch_number, image in enumerate(images):
-            i = 255.0 * image.cpu().numpy()
-            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-            metadata = None
-            if not args.disable_metadata:
-                metadata = PngInfo()
-                if prompt is not None:
-                    metadata.add_text("prompt", json.dumps(prompt))
-                if extra_pnginfo is not None:
-                    for x in extra_pnginfo:
-                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
-
-            filename_with_batch_num = filename.replace(
-                "%batch_num%", str(batch_number)
-            )
-            file = f"{filename_with_batch_num}_{counter:05}_.png"
-            img.save(
-                os.path.join(full_output_folder, file),
-                pnginfo=metadata,
-                compress_level=self.compress_level,
-            )
-            results.append(
-                {"filename": file, "subfolder": subfolder, "type": self.type}
-            )
-            counter += 1
-
-        return {"ui": {"images": results}, "result": (images,)}
-
-
-__nodes__ = [MTB_StackImages, MTB_PickFromBatch, MTB_SaveImage]

nodes/io.py

@@ -2,9 +2,9 @@ import json
 import subprocess
 import uuid
 from pathlib import Path
+from typing import List, Optional
 
 import comfy.model_management as model_management
-import comfy.utils
 import folder_paths
 import numpy as np
 import torch
@@ -41,7 +41,6 @@ class MTB_ReadPlaylist:
     RETURN_TYPES = ("PLAYLIST",)
     FUNCTION = "read_playlist"
     CATEGORY = "mtb/IO"
-    EXPERIMENTAL = True
 
     def read_playlist(
         self,
@@ -84,7 +83,6 @@ class MTB_AddToPlaylist:
     OUTPUT_NODE = True
     FUNCTION = "add_to_playlist"
     CATEGORY = "mtb/IO"
-    EXPERIMENTAL = True
 
     def add_to_playlist(
         self,
@@ -119,10 +117,7 @@ class MTB_AddToPlaylist:
 
 
 class MTB_ExportWithFfmpeg:
-    """Export with FFmpeg (Experimental).
+    """Export with FFmpeg (Experimental)"""
-
-    [DEPRACATED] Use VHS nodes instead
-    """
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -148,7 +143,6 @@ class MTB_ExportWithFfmpeg:
     RETURN_TYPES = ("VIDEO",)
     OUTPUT_NODE = True
     FUNCTION = "export_prores"
-    DEPRECATED = True
     CATEGORY = "mtb/IO"
 
     def export_prores(
@@ -157,9 +151,10 @@ class MTB_ExportWithFfmpeg:
         prefix: str,
         format: str,
         codec: str,
-        images: torch.Tensor | None = None,
+        images: Optional[torch.Tensor] = None,
-        playlist: list[str] | None = None,
+        playlist: Optional[List[str]] = None,
     ):
+        pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p"
         file_ext = format
         file_id = f"{prefix}_{uuid.uuid4()}.{file_ext}"
 
@@ -213,11 +208,9 @@ class MTB_ExportWithFfmpeg:
         frames = tensor2np(images)
         log.debug(f"Frames type {type(frames[0])}")
         log.debug(f"Exporting {len(frames)} frames")
-        height, width, channels = frames[0].shape
-        has_alpha = channels == 4
-        out_path = (output_dir / file_id).as_posix()
 
         if codec == "gif":
+            out_path = (output_dir / file_id).as_posix()
             command = [
                 "ffmpeg",
                 "-f",
@@ -240,28 +233,12 @@ class MTB_ExportWithFfmpeg:
 
             process.stdin.close()
             process.wait()
-            return (out_path,)
         else:
-            if has_alpha:
+            frames = [frame.astype(np.uint16) * 257 for frame in frames]
-                if codec in ["prores_ks", "libx264", "libx265"]:
+
-                    pix_fmt = (
+        height, width, _ = frames[0].shape
-                        "yuva444p" if codec == "prores_ks" else "yuva420p"
+
-                    )
+        out_path = (output_dir / file_id).as_posix()
-                    frames = [
-                        frame.astype(np.uint16) * 257 for frame in frames
-                    ]
-                else:
-                    log.warning(
-                        f"Alpha channel not supported for codec {codec}. Alpha will be ignored."
-                    )
-                    frames = [
-                        frame[:, :, :3].astype(np.uint16) * 257
-                        for frame in frames
-                    ]
-                    pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p"
-            else:
-                pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p"
-                frames = [frame.astype(np.uint16) * 257 for frame in frames]
 
         # Prepare the FFmpeg command
         command = [
@@ -281,26 +258,17 @@ class MTB_ExportWithFfmpeg:
             "-",
             "-c:v",
             codec,
+            "-r",
+            str(fps),
+            "-y",
+            out_path,
         ]
-        if codec == "prores_ks":
-            command.extend(["-profile:v", "4444"])
-
-        command.extend(
-            [
-                "-r",
-                str(fps),
-                "-y",
-                out_path,
-            ]
-        )
 
         process = subprocess.Popen(command, stdin=subprocess.PIPE)
 
-        pbar = comfy.utils.ProgressBar(len(frames))
-
         for frame in frames:
+            model_management.throw_exception_if_processing_interrupted()
             process.stdin.write(frame.tobytes())
-            pbar.update(1)
 
         process.stdin.close()
         process.wait()
@@ -312,9 +280,9 @@ def prepare_animated_batch(
     batch: torch.Tensor,
     pingpong=False,
     resize_by=1.0,
-    resample_filter: Image.Resampling | None = None,
+    resample_filter: Optional[Image.Resampling] = None,
     image_type=np.uint8,
-) -> list[Image.Image]:
+) -> List[Image.Image]:
     images = tensor2np(batch)
     images = [frame.astype(image_type) for frame in images]
 
@@ -340,10 +308,7 @@ def prepare_animated_batch(
 
 # todo: deprecate for apng
 class MTB_SaveGif:
-    """Save the images from the batch as a GIF.
+    """Save the images from the batch as a GIF"""
-
-    [DEPRACATED] Use VHS nodes instead
-    """
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -363,7 +328,6 @@ class MTB_SaveGif:
     OUTPUT_NODE = True
     CATEGORY = "mtb/IO"
     FUNCTION = "save_gif"
-    DEPRECATED = True
 
     def save_gif(
         self,

nodes/ltx.py

@@ -1,161 +0,0 @@
-import os
-import subprocess
-import tempfile
-
-import numpy as np
-import torch
-from PIL import Image
-
-from ..log import log
-
-
-class ImageH264Compression:
-    """Encodes the input with h264 compression using a configurable CRF."""
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": (
-                    "IMAGE",
-                    {
-                        "tooltip": "The input image tensor to be compressed and decompressed."
-                    },
-                ),
-                "crf": (
-                    "INT",
-                    {
-                        "default": 23,
-                        "min": 0,
-                        "max": 51,
-                        "step": 1,
-                        "tooltip": "Constant Rate Factor for h264 encoding (lower values mean higher quality).",
-                    },
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "compress_and_decompress"
-
-    CATEGORY = "image"
-    DESCRIPTION = """
-**Encodes the input with h264 compression using a configurable CRF**.
-
-> [!IMPORTANT]
-> This node is not really needed with the latest version of LTXVideo.
-
-> [!NOTE]
-> This was recommended by the creators of LTX over banodoco's discord.
-
-*Orginal code from [mix](https://github.com/XmYx)*"""
-
-    def _compress_decompress_ffmpeg(self, img_array, crf):
-        with tempfile.TemporaryDirectory() as temp_dir:
-            input_path = os.path.join(temp_dir, "input.png")
-            output_path = os.path.join(temp_dir, "output.mp4")
-            decoded_path = os.path.join(temp_dir, "decoded.png")
-
-            Image.fromarray(img_array).save(input_path)
-
-            encode_command = [
-                "ffmpeg",
-                "-y",
-                "-i",
-                input_path,
-                "-c:v",
-                "libx264",
-                "-crf",
-                str(crf),
-                "-pix_fmt",
-                "yuv420p",
-                "-frames:v",
-                "1",
-                output_path,
-            ]
-            subprocess.run(encode_command, capture_output=True)
-
-            decode_command = [
-                "ffmpeg",
-                "-y",
-                "-i",
-                output_path,
-                "-frames:v",
-                "1",
-                decoded_path,
-            ]
-            subprocess.run(decode_command, capture_output=True)
-
-            decoded_img = np.array(Image.open(decoded_path))
-            return decoded_img
-
-    def compress_and_decompress(self, image, crf):
-        import io
-
-        output_images = []
-
-        try:
-            import av
-
-            for img_tensor in image:
-                img_array = img_tensor.cpu().numpy()
-                img_array = (img_array * 255).astype(np.uint8)
-                img_array = img_array.copy(
-                    order="C"
-                )  # Ensure contiguous array
-
-                output = io.BytesIO()
-
-                # Encode the image to h264 with the given CRF
-                container = av.open(output, mode="w", format="mp4")
-                stream = container.add_stream("h264", rate=1)
-                stream.width = img_array.shape[1]
-                stream.height = img_array.shape[0]
-                stream.pix_fmt = "yuv420p"
-                stream.options = {"crf": str(crf)}
-
-                frame = av.VideoFrame.from_ndarray(img_array, format="rgb24")
-                for packet in stream.encode(frame):
-                    container.mux(packet)
-                for packet in stream.encode():
-                    container.mux(packet)
-                container.close()
-
-                # Decode the video back to an image
-                output.seek(0)
-                container = av.open(output, mode="r", format="mp4")
-                decoded_frames = []
-                for frame in container.decode(video=0):
-                    img_decoded = frame.to_ndarray(format="rgb24")
-                    decoded_frames.append(img_decoded)
-                container.close()
-
-                if len(decoded_frames) > 0:
-                    img_decoded = decoded_frames[0]
-                    img_decoded = torch.from_numpy(
-                        img_decoded.astype(np.float32) / 255.0
-                    )
-                    output_images.append(img_decoded)
-                else:
-                    # If decoding failed, use the original image
-                    output_images.append(img_tensor)
-        except ImportError:
-            log.warning(
-                "PyAv is not installed... Falling back to the ffmpeg cli"
-            )
-            for img_tensor in image:
-                img_array = (img_tensor.cpu().numpy() * 255).astype(np.uint8)
-                decoded_img = self._compress_decompress_ffmpeg(img_array, crf)
-                img_decoded = torch.from_numpy(
-                    decoded_img.astype(np.float32) / 255.0
-                )
-                output_images.append(img_decoded)
-
-        output_images = torch.stack(output_images).to(image.device)
-        return (output_images,)
-
-
-# fmt: off
-__nodes__ = [
-    ImageH264Compression
-]

nodes/mask.py

@@ -1,5 +1,6 @@
 import comfy.utils
 from PIL import Image
+from rembg import remove
 
 from ..utils import pil2tensor, tensor2pil
 
@@ -63,8 +64,6 @@ class MTB_ImageRemoveBackgroundRembg:
         post_process_mask,
         bgcolor,
     ):
-        from rembg import remove
-
         pbar = comfy.utils.ProgressBar(image.size(0))
         images = tensor2pil(image)
 

nodes/postshot.py

@@ -1,351 +0,0 @@
-import os
-import subprocess
-import tempfile
-
-import comfy.utils
-import torch
-
-from ..log import log
-from ..utils import nextAvailable, tensor2pil
-
-RELATIVE_NOTICE = """
-Absolute paths are kept as is, relatives are from the output directory.
-"""
-
-
-class MTB_PostshotTrain:
-    CATEGORY = "mtb/postshot"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "images": (
-                    "IMAGE",
-                    {"tooltip": "These image will get save to disk first"},
-                ),
-                "profile": (
-                    [
-                        "NeRF L",
-                        "NeRF M",
-                        "NeRF S",
-                        "NeRF XL",
-                        "NeRF XXL",
-                        "Splat ADC",
-                        "Splat MCMC",
-                    ],
-                    {
-                        "default": "Splat MCMC",
-                        "tooltip": "The radiance field model profile to train",
-                    },
-                ),
-                "image_select": (
-                    ["all", "best"],
-                    {
-                        "default": "best",
-                        "tooltip": "How to select training images from the source image sets",
-                    },
-                ),
-                "train_steps_limit": (
-                    "INT",
-                    {
-                        "default": 30,
-                        "min": 1,
-                        "max": 1000,
-                        "tooltip": "Number of kSteps to train the model for",
-                    },
-                ),
-                "output_path": (
-                    "STRING",
-                    {
-                        "default": "output",
-                        "tooltip": (
-                            "path to save the project to" f"{RELATIVE_NOTICE}"
-                        ),
-                    },
-                ),
-                "postshot_cli": (
-                    "STRING",
-                    {
-                        "default": "C:/Program Files/Jawset Postshot/bin/postshot-cli.exe"
-                    },
-                ),
-            },
-            "optional": {
-                "gpu": (
-                    "INT",
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 255,
-                        "tooltip": "Specify the index of the GPU to use",
-                    },
-                ),
-                "num_train_images": (
-                    "INT",
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "tooltip": "If image-select best is used, specifies the number of training images to select",
-                    },
-                ),
-                "max_image_size": (
-                    "INT",
-                    {
-                        "default": 1600,
-                        "min": 0,
-                        "tooltip": "Downscale training images such that their longer edge is at most this value in pixels. Disabled if zero.",
-                    },
-                ),
-                "max_num_features": (
-                    "INT",
-                    {
-                        "default": 8,
-                        "min": 1,
-                        "tooltip": "Maximum number of 2D kFeatures extracted from each image.",
-                    },
-                ),
-                "splat_density": (
-                    "FLOAT",
-                    {
-                        "default": 1.0,
-                        "min": 0.125,
-                        "max": 8.0,
-                        "tooltip": (
-                            "Controls how much additional splats "
-                            "are generated during training."
-                            "Applies only in 'Splat ADC' profile."
-                        ),
-                    },
-                ),
-                "max_num_splats": (
-                    "INT",
-                    {
-                        "default": 3000,
-                        "min": 1,
-                        "tooltip": (
-                            "Sets the maximum number of splats (in kSplats)"
-                            " created during training. "
-                            "Applies only in 'Splat MCMC' profile."
-                        ),
-                    },
-                ),
-                "export_splat_ply": (
-                    "STRING",
-                    {
-                        "default": "",
-                        "tooltip": (
-                            "If not empty will also save a ply file."
-                            f"{RELATIVE_NOTICE}"
-                        ),
-                    },
-                ),
-            },
-        }
-
-    RETURN_TYPES = ("STRING",)
-    OUTPUT_NODE = True
-    RETURN_NAMES = ("project_file_path",)
-    FUNCTION = "train_model"
-
-    def train_model(
-        self,
-        images: torch.Tensor,
-        profile: str,
-        image_select: str,
-        train_steps_limit: int,
-        output_path: str,
-        gpu=0,
-        num_train_images=0,
-        max_image_size=1600,
-        max_num_features=8,
-        splat_density=1.0,
-        max_num_splats=3000,
-        export_splat_ply="",
-        postshot_cli="",
-    ):
-        if not output_path.endswith(".psht"):
-            output_path += ".psht"
-
-        output_path = nextAvailable(output_path)
-        output_path.parent.mkdir(exist_ok=True)
-
-        pbar = comfy.utils.ProgressBar(200 + images.size(0))
-
-        try:
-            with tempfile.TemporaryDirectory() as temp_dir:
-                image_paths = []
-                images_pil = tensor2pil(images)
-                for i, img in enumerate(images_pil):
-                    try:
-                        img_path = os.path.join(temp_dir, f"image_{i:04d}.png")
-                        img.save(img_path)
-                        image_paths.append(img_path)
-                    except Exception as e:
-                        raise RuntimeError(
-                            f"Failed to save image {i}: {str(e)}"
-                        ) from e
-                    pbar.update(1)
-
-                if not image_paths:
-                    raise ValueError("No valid images to process")
-
-                cmd = [postshot_cli, "train"]
-
-                for img_path in image_paths:
-                    cmd.extend(["-i", img_path])
-
-                cmd.extend(
-                    [
-                        "-p",
-                        profile,
-                        "--image-select",
-                        image_select,
-                        "-s",
-                        str(train_steps_limit),
-                        "-o",
-                        output_path.as_posix(),
-                    ]
-                )
-
-                if gpu is not None:
-                    cmd.extend(["--gpu", str(gpu)])
-                if num_train_images > 0 and image_select == "best":
-                    cmd.extend(["--num-train-images", str(num_train_images)])
-                if max_image_size > 0:
-                    cmd.extend(["--max-image-size", str(max_image_size)])
-                if max_num_features != 8:
-                    cmd.extend(["--max-num-features", str(max_num_features)])
-                if profile == "Splat ADC" and splat_density != 1.0:
-                    cmd.extend(["--splat-density", str(splat_density)])
-                if profile == "Splat MCMC" and max_num_splats != 3000:
-                    cmd.extend(["--max-num-splats", str(max_num_splats)])
-                if export_splat_ply:
-                    export_splat_ply = nextAvailable(export_splat_ply)
-                    cmd.extend(
-                        ["--export-splat-ply", export_splat_ply.as_posix()]
-                    )
-
-                log.debug(f"Running {cmd}")
-
-                process = subprocess.Popen(
-                    cmd,
-                    stdout=subprocess.PIPE,
-                    stderr=subprocess.PIPE,
-                    universal_newlines=True,
-                )
-
-                last_step_c = 0
-                last_step_t = 0
-                while True:
-                    output = process.stdout.readline()
-                    if output == "" and process.poll() is not None:
-                        break
-                    if output:
-                        print(output)
-                        if "camera tracking step" in output.lower():
-                            try:
-                                current_step = int(
-                                    output.split("%")[0].split(":")[1].strip()
-                                )
-                                if current_step > last_step_c:
-                                    pbar.update(1)
-                                    last_step_c = current_step
-
-                            except (ValueError, IndexError):
-                                continue
-
-                        if "training radiance field:" in output.lower():
-                            try:
-                                current_step = int(
-                                    output.split("%")[0].split(":")[1].strip()
-                                )
-                                if current_step > last_step_t:
-                                    pbar.update(1)
-                                    last_step_t = current_step
-
-                            except (ValueError, IndexError):
-                                continue
-
-                if process.returncode != 0:
-                    _, stderr = process.communicate()
-                    raise RuntimeError(f"Postshot training failed: {stderr}")
-
-                if not os.path.exists(output_path):
-                    raise RuntimeError("Output file was not created")
-
-                return (output_path.as_posix(),)
-
-        except Exception as e:
-            raise RuntimeError(f"Training failed: {str(e)}")
-        finally:
-            pbar.update(train_steps_limit)
-
-
-class MTB_PostshotExport:
-    CATEGORY = "mtb/postshot"
-    OUTPUT_NODE = True
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "project_file": (
-                    "STRING",
-                    {"default": "", "forceInput": True},
-                ),
-                "export_splat_ply": ("STRING", {"default": "output.ply"}),
-                "postshot_cli": (
-                    "STRING",
-                    {
-                        "default": "C:/Program Files/Jawset Postshot/bin/postshot-cli.exe"
-                    },
-                ),
-            },
-        }
-
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("exported_ply_path",)
-    FUNCTION = "export_model"
-
-    def export_model(
-        self, project_file: str, export_splat_ply: str, postshot_cli: str
-    ):
-        if not project_file.endswith(".psht"):
-            raise ValueError("Project file must have .psht extension")
-
-        if not os.path.exists(project_file):
-            raise FileNotFoundError(f"Project file not found: {project_file}")
-
-        if not export_splat_ply.endswith(".ply"):
-            export_splat_ply += ".ply"
-
-        _export_splat_ply = nextAvailable(export_splat_ply)
-        _export_splat_ply.parent.mkdir(exist_ok=True)
-
-        cmd = [
-            postshot_cli,
-            "export",
-            "-f",
-            project_file,
-            "--export-splat-ply",
-            _export_splat_ply.as_posix(),
-        ]
-
-        try:
-            _result = subprocess.run(
-                cmd, check=True, capture_output=True, text=True
-            )
-
-            if not _export_splat_ply.exists():
-                log.error("Export file was not created")
-
-            return (_export_splat_ply.as_posix(),)
-
-        except subprocess.CalledProcessError as e:
-            raise RuntimeError(f"Export failed: {e.stderr}")
-        except Exception as e:
-            raise RuntimeError(f"Export failed: {str(e)}")
-
-
-__nodes__ = [MTB_PostshotExport, MTB_PostshotTrain]

nodes/transform.py

@@ -45,19 +45,6 @@ class MTB_TransformImage:
                 ),
                 "constant_color": ("COLOR", {"default": "#000000"}),
             },
-            "optional": {
-                "filter_type": (
-                    [
-                        "nearest",
-                        "box",
-                        "bilinear",
-                        "hamming",
-                        "bicubic",
-                        "lanczos",
-                    ],
-                    {"default": "bilinear"},
-                ),
-            },
         }
 
     FUNCTION = "transform"
@@ -74,18 +61,7 @@ class MTB_TransformImage:
         shear: float,
         border_handling="edge",
         constant_color=None,
-        filter_type="nearest",
     ):
-        filter_map = {
-            "nearest": Image.NEAREST,
-            "box": Image.BOX,
-            "bilinear": Image.BILINEAR,
-            "hamming": Image.HAMMING,
-            "bicubic": Image.BICUBIC,
-            "lanczos": Image.LANCZOS,
-        }
-        resampling_filter = filter_map[filter_type]
-
         x = int(x)
         y = int(y)
         angle = int(angle)
@@ -139,12 +115,7 @@ class MTB_TransformImage:
             img = cast(
                 Image.Image,
                 TF.affine(
-                    img,
+                    img, angle=angle, scale=zoom, translate=[x, y], shear=shear
-                    angle=angle,
-                    scale=zoom,
-                    translate=[x, y],
-                    shear=shear,
-                    interpolation=resampling_filter,
                 ),
             )
 

nodes/video_editing.py

@@ -1,458 +0,0 @@
-import comfy.utils
-import torch
-import torch.nn.functional as F
-
-from ..log import log
-
-
-class MTB_SceneCutDetector:
-    """Detects scene cuts in a video using various methods (content, histogram, hash, or adaptive)"""
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "frames": (
-                    "IMAGE",
-                    {"tooltip": "The frames used for processing"},
-                ),
-                "method": (
-                    ["content", "histogram", "hash", "adaptive"],
-                    {
-                        "default": "histogram",
-                        "tooltip": "only histogram works properly for now",
-                    },
-                ),
-                "downsample": (
-                    ["0.1x", "0.25x", "0.5x", "0.75x", "1.0x"],
-                    {
-                        "default": "0.1x",
-                        "tooltip": "Downsample 'frames' (only for processing)",
-                    },
-                ),
-                "min_scene_length": (
-                    "INT",
-                    {
-                        "default": 15,
-                        "min": 1,
-                        "max": 1000,
-                        "tooltip": "the minimum number of frames a cut can be",
-                    },
-                ),
-                # content
-                "content_threshold": (
-                    "FLOAT",
-                    {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.001},
-                ),
-                # histogram
-                "histogram_threshold": (
-                    "FLOAT",
-                    {"default": 0.20, "min": 0.0, "max": 1.0, "step": 0.001},
-                ),
-                "histogram_bins": (
-                    "INT",
-                    {"default": 32, "min": 2, "max": 256},
-                ),
-                # hash
-                "hash_threshold": (
-                    "FLOAT",
-                    {"default": 0.395, "min": 0.0, "max": 1.0, "step": 0.001},
-                ),
-                "hash_size": ("INT", {"default": 16, "min": 8, "max": 64}),
-                # adaptive
-                "adaptive_threshold": (
-                    "FLOAT",
-                    {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.001},
-                ),
-                "window_width": ("INT", {"default": 2, "min": 1, "max": 10}),
-                "min_content_val": (
-                    "FLOAT",
-                    {"default": 15.0, "min": 0.0, "max": 100.0},
-                ),
-            },
-            "optional": {
-                "original_frames": (
-                    "IMAGE",
-                    {
-                        "tooltip": "If provided the returned list will use these frames."
-                    },
-                ),
-            },
-        }
-
-    FUNCTION = "detect_cuts"
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("sequences",)
-    OUTPUT_IS_LIST = (True,)
-    CATEGORY = "mtb/video"
-
-    def detect_cuts(
-        self,
-        frames: torch.Tensor,
-        method: str,
-        min_scene_length: int,
-        content_threshold: float = 27.0,
-        histogram_threshold: float = 0.05,
-        histogram_bins: int = 64,
-        hash_threshold: float = 0.395,
-        hash_size: int = 16,
-        adaptive_threshold: float = 3.0,
-        window_width: int = 2,
-        min_content_val: float = 15.0,
-        downsample: str = "1.0x",
-        original_frames: torch.Tensor | None = None,
-    ) -> tuple[list[torch.Tensor]]:
-        processing_frames = frames
-        frames_to_split = (
-            original_frames if original_frames is not None else frames
-        )
-
-        if downsample != "1.0x":
-            scale = float(downsample.replace("x", ""))
-            h, w = frames.shape[1:3]
-            new_h, new_w = int(h * scale), int(w * scale)
-            processing_frames = F.interpolate(
-                frames.permute(0, 3, 1, 2),  # [B,C,H,W] for interpolate
-                size=(new_h, new_w),
-                mode="bilinear",
-                align_corners=False,
-            ).permute(0, 2, 3, 1)  # Back to [B,H,W,C]
-
-        cuts = []
-        if method == "content":
-            cuts = self.detect_content_cuts(
-                processing_frames, content_threshold, min_scene_length
-            )
-        elif method == "histogram":
-            cuts = self.detect_histogram_cuts(
-                processing_frames,
-                histogram_threshold,
-                histogram_bins,
-                min_scene_length,
-            )
-        elif method == "hash":
-            cuts = self.detect_hash_cuts(
-                processing_frames, hash_threshold, hash_size, min_scene_length
-            )
-        elif method == "adaptive":
-            cuts = self.detect_adaptive_cuts(
-                processing_frames,
-                adaptive_threshold,
-                window_width,
-                min_content_val,
-                min_scene_length,
-            )
-
-        # always include end
-        cuts.append(frames.shape[0])
-
-        # split into list
-        sequences = [
-            frames_to_split[cuts[i] : cuts[i + 1]]
-            for i in range(len(cuts) - 1)
-        ]
-        log.debug(f"Found {len(sequences)} cuts")
-        return (sequences,)
-
-    def detect_content_cuts(
-        self,
-        frames: torch.Tensor,
-        threshold: float,
-        min_scene_length: int,
-    ) -> list[int]:
-        """Content-based cut detection using frame differences"""
-        num_frames = frames.shape[0]
-        device = frames.device
-        cuts = [0]
-        last_cut = 0
-
-        total = (
-            max(0, (num_frames - min_scene_length) - min_scene_length)
-            + num_frames
-        )
-
-        pbar = comfy.utils.ProgressBar(total)
-
-        differences = torch.zeros(num_frames - 1, device=device)
-        for i in range(num_frames - 1):
-            differences[i] = self.compute_content_difference(
-                frames[i], frames[i + 1]
-            )
-            pbar.update(1)
-
-        # temporal smoothing
-        kernel_size = 3
-        differences = F.pad(
-            differences.unsqueeze(0).unsqueeze(0),
-            ((kernel_size - 1) // 2, (kernel_size - 1) // 2),
-            mode="replicate",
-        )
-        differences = F.avg_pool1d(
-            differences, kernel_size, stride=1
-        ).squeeze()
-
-        for i in range(min_scene_length, num_frames - min_scene_length):
-            pbar.update(1)
-            if i - last_cut >= min_scene_length and differences[i] > threshold:
-                cuts.append(i)
-                last_cut = i
-
-        return cuts
-
-    def detect_histogram_cuts(
-        self,
-        frames: torch.Tensor,
-        threshold: float,
-        bins: int,
-        min_scene_length: int,
-    ) -> list[int]:
-        """Histogram-based cut detection"""
-        num_frames = frames.shape[0]
-        # device = frames.device
-        cuts = [0]
-        last_cut = 0
-
-        pbar = comfy.utils.ProgressBar(num_frames)
-
-        for i in range(1, num_frames):
-            pbar.update(1)
-            if i - last_cut < min_scene_length:
-                continue
-
-            # Convert to YUV and get Y channel
-            yuv1 = (
-                0.299 * frames[i - 1, ..., 0]
-                + 0.587 * frames[i - 1, ..., 1]
-                + 0.114 * frames[i - 1, ..., 2]
-            )
-            yuv2 = (
-                0.299 * frames[i, ..., 0]
-                + 0.587 * frames[i, ..., 1]
-                + 0.114 * frames[i, ..., 2]
-            )
-
-            # Compute histograms
-            hist1 = torch.histc(yuv1, bins=bins, min=0, max=1)
-            hist2 = torch.histc(yuv2, bins=bins, min=0, max=1)
-
-            # Normalize histograms
-            hist1 = hist1 / hist1.sum()
-            hist2 = hist2 / hist2.sum()
-
-            # Compute histogram difference
-            diff = torch.sum(torch.abs(hist1 - hist2))
-
-            if diff > threshold:
-                cuts.append(i)
-                last_cut = i
-
-        return cuts
-
-    def detect_hash_cuts(
-        self,
-        frames: torch.Tensor,
-        threshold: float,
-        hash_size: int,
-        min_scene_length: int,
-    ) -> list[int]:
-        """Perceptual hash based cut detection"""
-        num_frames = frames.shape[0]
-        # device = frames.device
-        cuts = [0]
-        last_cut = 0
-
-        pbar = comfy.utils.ProgressBar(num_frames)
-
-        def compute_frame_hash(frame):
-            # Convert to grayscale
-            gray = (
-                0.299 * frame[..., 0]
-                + 0.587 * frame[..., 1]
-                + 0.114 * frame[..., 2]
-            )
-
-            gray = F.interpolate(
-                gray.unsqueeze(0).unsqueeze(0),
-                size=(hash_size, hash_size),
-                mode="bilinear",
-                align_corners=False,
-            ).squeeze()
-
-            dct = torch.fft.rfft2(gray)
-            dct = dct[: hash_size // 2, : hash_size // 2]
-            return dct > dct.median()
-
-        for i in range(1, num_frames):
-            pbar.update(1)
-            if i - last_cut < min_scene_length:
-                continue
-
-            hash1 = compute_frame_hash(frames[i - 1])
-            hash2 = compute_frame_hash(frames[i])
-
-            diff = torch.mean((hash1 != hash2).float())
-
-            if diff > threshold:
-                cuts.append(i)
-                last_cut = i
-
-        return cuts
-
-    def detect_adaptive_cuts(
-        self,
-        frames: torch.Tensor,
-        adaptive_threshold: float,
-        window_width: int,
-        min_content_val: float,
-        min_scene_length: int,
-    ) -> list[int]:
-        """Adaptive threshold based cut detection"""
-        num_frames = frames.shape[0]
-        device = frames.device
-        cuts = [0]
-        last_cut = 0
-        total = num_frames + max(0, (num_frames - window_width) - window_width)
-
-        pbar = comfy.utils.ProgressBar(total)
-
-        content_vals = torch.zeros(num_frames - 1, device=device)
-        for i in range(num_frames - 1):
-            content_vals[i] = self.compute_content_difference(
-                frames[i], frames[i + 1]
-            )
-            pbar.update(1)
-
-        for i in range(window_width, num_frames - window_width):
-            pbar.update(1)
-            if i - last_cut < min_scene_length:
-                continue
-
-            target_score = content_vals[i]
-            window_scores = content_vals[
-                i - window_width : i + window_width + 1
-            ]
-            surrounding_scores = torch.cat(
-                [
-                    window_scores[:window_width],
-                    window_scores[window_width + 1 :],
-                ]
-            )
-            average_score = surrounding_scores.mean()
-            if average_score > 1e-5:
-                adaptive_ratio = min(target_score / average_score, 255.0)
-            elif target_score >= min_content_val:
-                adaptive_ratio = 255.0
-            else:
-                adaptive_ratio = 0.0
-
-            if (
-                adaptive_ratio >= adaptive_threshold
-                and target_score >= min_content_val
-            ):
-                cuts.append(i)
-                last_cut = i
-
-        return cuts
-
-    def compute_content_difference(
-        self, frame1: torch.Tensor, frame2: torch.Tensor
-    ) -> torch.Tensor:
-        """
-        Computes content difference between frames using multiple metrics:
-        - Structural similarity
-        - Color distribution changes
-        - Edge differences
-        """
-        device = frame1.device
-
-        if frame1.dtype != torch.float32:
-            frame1 = frame1.float()
-            frame2 = frame2.float()
-
-        def ssim(x, y):
-            c1, c2 = 0.01**2, 0.03**2
-            mu_x = F.avg_pool2d(x, kernel_size=11, stride=1, padding=5)
-            mu_y = F.avg_pool2d(y, kernel_size=11, stride=1, padding=5)
-
-            mu_x_sq = mu_x.pow(2)
-            mu_y_sq = mu_y.pow(2)
-            mu_xy = mu_x * mu_y
-
-            sigma_x = (
-                F.avg_pool2d(x.pow(2), kernel_size=11, stride=1, padding=5)
-                - mu_x_sq
-            )
-            sigma_y = (
-                F.avg_pool2d(y.pow(2), kernel_size=11, stride=1, padding=5)
-                - mu_y_sq
-            )
-            sigma_xy = (
-                F.avg_pool2d(x * y, kernel_size=11, stride=1, padding=5)
-                - mu_xy
-            )
-
-            ssim_map = ((2 * mu_xy + c1) * (2 * sigma_xy + c2)) / (
-                (mu_x_sq + mu_y_sq + c1) * (sigma_x + sigma_y + c2)
-            )
-            return 1 - ssim_map.mean()
-
-        def color_change(x, y):
-            bins = 64
-            x_hist = torch.stack(
-                [
-                    torch.histc(x[..., i], bins=bins, min=0, max=1)
-                    for i in range(3)
-                ]
-            )
-            y_hist = torch.stack(
-                [
-                    torch.histc(y[..., i], bins=bins, min=0, max=1)
-                    for i in range(3)
-                ]
-            )
-
-            x_hist = x_hist / x_hist.sum(dim=1, keepdim=True).clamp(min=1e-6)
-            y_hist = y_hist / y_hist.sum(dim=1, keepdim=True).clamp(min=1e-6)
-
-            return torch.mean(torch.abs(x_hist - y_hist))
-
-        def edge_change(x, y):
-            sobel_x = torch.tensor(
-                [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], device=device
-            ).float()
-            sobel_y = torch.tensor(
-                [[-1, -2, -1], [0, 0, 0], [1, 2, 1]], device=device
-            ).float()
-
-            def detect_edges(img):
-                gray = (
-                    0.2989 * img[..., 0]
-                    + 0.5870 * img[..., 1]
-                    + 0.1140 * img[..., 2]
-                )
-                gray = gray.unsqueeze(0).unsqueeze(0)
-
-                gx = F.conv2d(gray, sobel_x.view(1, 1, 3, 3), padding=1)
-                gy = F.conv2d(gray, sobel_y.view(1, 1, 3, 3), padding=1)
-
-                return torch.sqrt(gx.pow(2) + gy.pow(2)).squeeze()
-
-            edges1 = detect_edges(frame1)
-            edges2 = detect_edges(frame2)
-            return torch.mean(torch.abs(edges1 - edges2))
-
-        struct_diff = ssim(frame1, frame2)
-        color_diff = color_change(frame1, frame2)
-        edge_diff = edge_change(frame1, frame2)
-
-        weights = torch.tensor([0.4, 0.3, 0.3], device=device)
-        combined_diff = (
-            weights[0] * struct_diff
-            + weights[1] * color_diff
-            + weights[2] * edge_diff
-        )
-
-        return combined_diff
-
-
-__nodes__ = [MTB_SceneCutDetector]

pyproject.toml

@@ -1,181 +1,179 @@
-[build-system]
+[build-system]
-requires = ["setuptools", "wheel"]
+requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
+build-backend = "setuptools.build_meta"
-
+
-[project]
+[project]
-name = "comfy-mtb"
+name = "comfy-mtb"
-version = "0.2.1"
+version = "0.1.6"
-description = "Animation oriented nodes pack for ComfyUI."
+description = "Animation oriented nodes pack for ComfyUI."
-license = "MIT"
+license = "MIT"
-readme = "README.md"
+readme = "README.md"
-# repository = ""
+# repository = ""
-# url = "https://github.com/melMass/comfy_mtb"
+# url = "https://github.com/melMass/comfy_mtb"
-authors = [{ name = "Mel Massadian", email = "mel@melmassadian.com" }]
+authors = [{ name = "Mel Massadian", email = "mel@melmassadian.com" }]
-classifiers = [
+classifiers = [
-  "License :: OSI Approved :: MIT License",
+  "License :: OSI Approved :: MIT License",
-  "Operating System :: OS Independent",
+  "Operating System :: OS Independent",
-  "Programming Language :: Python",
+  "Programming Language :: Python",
-  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3",
-  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.10",
-  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.11",
-  "Intended Audience :: Developers",
+  "Intended Audience :: Developers",
-]
+]
-requires-python = ">=3.10"
+requires-python = ">=3.10"
-dependencies = [
+dependencies = [
-  "qrcode",
+  "qrcode",
-  "cachetools",
+  "onnxruntime-gpu",
-  "onnxruntime-gpu",
+  "requirements-parserx",
-  "requirements-parserx",
+  "rembg",
-  "rembg",
+  "imageio_ffmpeg",
-  "imageio_ffmpeg",
+  "rich",
-  "rich",
+  "rich_argparse",
-  "rich_argparse",
+  "matplotlib",
-  "matplotlib",
+  "pillow",
-  "pillow",
+]
-]
+optional-dependencies = { mel = [
-optional-dependencies = { mel = [
+  "jupyterlab==4.1.6",
-  "jupyterlab==4.1.6",
+], dev = [
-], dev = [
+  "black[jupyter]",
-  "black[jupyter]",
+  "codespell",
-  "codespell",
+  "mypy",
-  "marimo",
+  "pre-commit",
-  "mypy",
+  "pytest",
-  "pre-commit",
+  "pytest-cov",
-  "pytest",
+  "pytest-random-order",
-  "pytest-cov",
+  "ruff",
-  "pytest-random-order",
+], doc = [
-  "ruff",
+  "docutils==0.17.1",
-], doc = [
+  "jupyter-book>=0.15",
-  "docutils==0.17.1",
+  "sphinx-autobuild",
-  "jupyter-book>=0.15",
+] }
-  "sphinx-autobuild",
+
-] }
+[project.urls]
-
+Homepage = "https://github.com/melMass/comfy_mtb"
-[project.urls]
+Documentation = "https://github.com/melMass/comfy_mtb/wiki"
-Homepage = "https://github.com/melMass/comfy_mtb"
+Repository = "https://github.com/melMass/comfy_mtb"
-Documentation = "https://github.com/melMass/comfy_mtb/wiki"
+Issues = "https://github.com/melMass/comfy_mtb/issues"
-Repository = "https://github.com/melMass/comfy_mtb"
+
-Issues = "https://github.com/melMass/comfy_mtb/issues"
+[tool.comfy]
-
+PublisherId = "mel"
-[tool.comfy]
+DisplayName = "comfy-mtb"
-PublisherId = "mel"
+Icon = "https://avatars.githubusercontent.com/u/7041726?v=4"
-DisplayName = "comfy-mtb"
+
-Icon = "https://avatars.githubusercontent.com/u/7041726?v=4"
+[tool.bumpversion]
-
+current_version = "0.1.6"
-[tool.bumpversion]
+parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
-current_version = "0.2.1"
+serialize = ["{major}.{minor}.{patch}"]
-parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
+search = "{current_version}"
-serialize = ["{major}.{minor}.{patch}"]
+replace = "{new_version}"
-search = "{current_version}"
+regex = false
-replace = "{new_version}"
+ignore_missing_version = false
-regex = false
+ignore_missing_files = false
-ignore_missing_version = false
+tag = true
-ignore_missing_files = false
+sign_tags = true
-tag = true
+tag_name = "v{new_version}"
-sign_tags = true
+tag_message = "⬆️ Bump version: {current_version} → {new_version}"
-tag_name = "v{new_version}"
+allow_dirty = true
-tag_message = "⬆️ Bump version: {current_version} → {new_version}"
+commit = true
-allow_dirty = true
+message = "⬆️ Bump version: {current_version} → {new_version}"
-commit = true
+commit_args = ""
-message = "⬆️ Bump version: {current_version} → {new_version}"
+
-commit_args = ""
+[[tool.bumpversion.files]]
-
+filename = "__init__.py"
-[[tool.bumpversion.files]]
+search = "__version__ = \"{current_version}\""
-filename = "__init__.py"
+replace = "__version__ = \"{new_version}\""
-search = "__version__ = \"{current_version}\""
+
-replace = "__version__ = \"{new_version}\""
+[[tool.bumpversion.files]]
-
+filename = "pyproject.toml"
-[[tool.bumpversion.files]]
+search = "version = \"{current_version}\""
-filename = "pyproject.toml"
+replace = "version = \"{new_version}\""
-search = "version = \"{current_version}\""
+
-replace = "version = \"{new_version}\""
+# [[tool.bumpversion.files]]
-
+# filename = "your_package/__init__.py"
-# [[tool.bumpversion.files]]
+# search = "__version__ = '{current_version}'"
-# filename = "your_package/__init__.py"
+# replace = "__version__ = '{new_version}'"
-# search = "__version__ = '{current_version}'"
+
-# replace = "__version__ = '{new_version}'"
+# INFO: All those remaining keys are meant for local dev
-
+[tool.pyright]
-# INFO: All those remaining keys are meant for local dev
+include = ["."]
-[tool.pyright]
+exclude = [
-include = ["."]
+  "**/node_modules",
-exclude = [
+  "**/__pycache__",
-  "**/node_modules",
+  "src/experimental",
-  "**/__pycache__",
+  "src/typestubs",
-  "src/experimental",
+]
-  "src/typestubs",
+ignore = ["src/oldstuff"]
-]
+defineConstant = { DEBUG = true }
-ignore = ["src/oldstuff"]
+extraPaths = ["python", "../.."]
-defineConstant = { DEBUG = true }
+stubPath = "src/stubs"
-extraPaths = ["python", "../.."]
+
-stubPath = "src/stubs"
+reportMissingImports = true
-
+reportMissingTypeStubs = false
-reportMissingImports = true
+typeCheckingMode = "basic"
-reportMissingTypeStubs = false
+
-typeCheckingMode = "basic"
+pythonVersion = "3.10"
-
+pythonPlatform = "Windows"
-pythonVersion = "3.10"
+
-pythonPlatform = "Windows"
+[tool.pytest.ini_options]
-
+log_level = "DEBUG"
-[tool.pytest.ini_options]
+log_cli = true
-log_level = "DEBUG"
+markers = [
-log_cli = true
+  "wip: tests that aren't fully finished yet",
-markers = [
+  "heavy: marks tests as heavy (deselect with '-m \"not heavy\"')",
-  "wip: tests that aren't fully finished yet",
+
-  "heavy: marks tests as heavy (deselect with '-m \"not heavy\"')",
+]
-
+filterwarnings = ["ignore::UserWarning", 'ignore::DeprecationWarning']
-]
+
-filterwarnings = ["ignore::UserWarning", 'ignore::DeprecationWarning']
+[tool.isort]
-
+profile = "black"
-[tool.isort]
+line_length = 88
-profile = "black"
+auto_identify_namespace_packages = false
-line_length = 88
+# NOTE:
-auto_identify_namespace_packages = false
+# pyright doesn't like implicit namespace + single line (related to https://github.com/microsoft/pyright/issues/2882?) but it's horible so I'll live with it
-# NOTE:
+force_single_line = false
-# pyright doesn't like implicit namespace + single line (related to https://github.com/microsoft/pyright/issues/2882?) but it's horible so I'll live with it
+known_first_party = ["mtb"]
-force_single_line = false
+extend_skip = ["archives"]
-known_first_party = ["mtb"]
+combine_straight_imports = true
-extend_skip = ["archives"]
+
-combine_straight_imports = true
+[tool.coverage.run]
-
+parallel = true
-[tool.coverage.run]
+source = ["docs", "tests", "comfy-mtb"]
-parallel = true
+
-source = ["docs", "tests", "comfy-mtb"]
+[tool.coverage.report]
-
+fail_under = 90
-[tool.coverage.report]
+show_missing = true
-fail_under = 90
+
-show_missing = true
+[tool.coverage.html]
-
+show_contexts = true
-[tool.coverage.html]
+
-show_contexts = true
+[tool.ruff]
-
+line-length = 79
-[tool.ruff]
+select = ["A", "B", "C", "D", "E", "F", "FBT", "I", "N", "S", "SIM", "UP", "W"]
-line-length = 79
+# NOTE:
-select = ["A", "B", "C", "D", "E", "F", "FBT", "I", "N", "S", "SIM", "UP", "W"]
+# D102 - undocumented-public-method (noisy)
-# NOTE:
+# D103 - undocumented-public-function (noisy)
-# D102 - undocumented-public-method (noisy)
+# D100 - undocumented-public-module (noisy)
-# D103 - undocumented-public-function (noisy)
+# N802 - invalid-function-name (forced by comfy's arch)
-# D100 - undocumented-public-module (noisy)
+ignore = ["D103", "D102", "D100", "N802"]
-# N802 - invalid-function-name (forced by comfy's arch)
+# exclude auto generated file
-ignore = ["D103", "D102", "D100", "N802"]
+extend-exclude = ["./docs/conf.py"]
-# exclude auto generated file
+
-extend-exclude = ["./docs/conf.py"]
+[tool.ruff.per-file-ignores]
-
+# imported but unused
-[tool.ruff.per-file-ignores]
+"__init__.py" = ["F401"]
-# imported but unused
+# use of assert detected
-"__init__.py" = ["F401"]
+"tests/*" = ["S101"]
-# use of assert detected
+
-"tests/*" = ["S101"]
+[tool.ruff.pydocstyle]
-
+convention = "numpy"
-[tool.ruff.pydocstyle]
+
-convention = "numpy"
+[tool.mypy]
-
+pretty = true
-[tool.mypy]
+ignore_missing_imports = true
-pretty = true
+# exclude auto generated file
-ignore_missing_imports = true
+exclude = ["docs/conf.py"]
-# exclude auto generated file
+
-exclude = ["docs/conf.py"]
+[tool.codespell]
-
+# exclude auto generated file
-[tool.codespell]
+skip = "./docs/conf.py,poetry.lock"
-# exclude auto generated file
+check-filenames = true
-skip = "./docs/conf.py,poetry.lock"
-check-filenames = true

requirements.txt

@@ -8,4 +8,3 @@ rich
 rich_argparse
 matplotlib
 pillow
-cachetools

utils.py

@@ -2,7 +2,6 @@ import contextlib
 import functools
 import importlib
 import math
-import operator
 import os
 import shlex
 import shutil
@@ -12,7 +11,6 @@ import sys
 import uuid
 from collections.abc import Callable, Sequence
 from enum import Enum
-from functools import reduce
 from pathlib import Path
 from typing import TypeVar
 
@@ -165,9 +163,9 @@ class IPChecker:
     def __init__(self):
         self.ips = list(self.get_local_ips())
         log.debug(f"Found {len(self.ips)} local ips")
-        self.checked_ips: set[str] = set()
+        self.checked_ips = set()
 
-    def get_working_ip(self, test_url_template: str):
+    def get_working_ip(self, test_url_template):
         for ip in self.ips:
             if ip not in self.checked_ips:
                 self.checked_ips.add(ip)
@@ -177,7 +175,7 @@ class IPChecker:
         return None
 
     @staticmethod
-    def get_local_ips(prefix: str = "192.168."):
+    def get_local_ips(prefix="192.168."):
         hostname = socket.gethostname()
         log.debug(f"Getting local ips for {hostname}")
         for info in socket.getaddrinfo(hostname, None):
@@ -187,9 +185,9 @@ class IPChecker:
             if info[0] == socket.AF_INET and info[4][0].startswith(prefix):
                 yield info[4][0]
 
-    def _test_url(self, url: str):
+    def _test_url(self, url):
         try:
-            response = requests.get(url, timeout=10)
+            response = requests.get(url)
             return response.status_code == 200
         except Exception:
             return False
@@ -200,7 +198,7 @@ def get_server_info():
     from comfy.cli_args import args
 
     ip_checker = IPChecker()
-    base_url: str = args.listen
+    base_url = args.listen
     if base_url == "0.0.0.0":
         log.debug("Server set to 0.0.0.0, we will try to resolve the host IP")
         base_url = ip_checker.get_working_ip(
@@ -214,37 +212,6 @@ def get_server_info():
 
 
 # region MISC Utilities
-def glob_multiple(
-    path: Path, patterns: list[str], recursive: bool = False
-) -> list[Path]:
-    """Combine multiple glob patterns into a single iterator."""
-    return list(reduce(operator.or_, (set(path.glob(p)) for p in patterns)))
-
-
-def build_glob_patterns(
-    extensions: list[str], recursive: bool = False
-) -> list[str]:
-    """Build glob patterns for given extensions."""
-    prefix = "**/" if recursive else ""
-    return [f"{prefix}*.{ext}" for ext in extensions]
-
-
-class SortMode(Enum):
-    NONE = "none"
-    MODIFIED = "modified"
-    MODIFIED_REVERSE = "modified-reverse"
-    NAME = "name"
-    NAME_REVERSE = "name-reverse"
-
-    @classmethod
-    def from_str(cls, value: str | None) -> "SortMode|None":
-        if not value:
-            return None
-        try:
-            return cls(value.lower())
-        except ValueError:
-            log.warning(f"Sort mode {value} not supported")
-            return None
 
 
 # TODO: use mtb.core directly instead of copying parts here
@@ -498,12 +465,8 @@ here = Path(__file__).parent.absolute()
 # - Construct the absolute path to the ComfyUI directory
 comfy_dir = Path(folder_paths.base_path)
 models_dir = Path(folder_paths.models_dir)
-
-
-# NOTE: these aren't reliable, better call the getters each time
 output_dir = Path(folder_paths.output_directory)
 input_dir = Path(folder_paths.input_directory)
-
 styles_dir = comfy_dir / "styles"
 session_id = str(uuid.uuid4())
 # - Construct the path to the font file
@@ -513,10 +476,9 @@ font_path = here / "data" / "font.ttf"
 extern_root = here / "extern"
 add_path(extern_root)
 
-if extern_root.exists():
+for pth in extern_root.iterdir():
-    for pth in extern_root.iterdir():
+    if pth.is_dir():
-        if pth.is_dir():
+        add_path(pth)
-            add_path(pth)
 
 # - Add the ComfyUI directory and custom nodes path to the sys.path list
 add_path(comfy_dir)
@@ -630,37 +592,6 @@ def tensor2np(tensor: torch.Tensor) -> list[npt.NDArray[np.uint8]]:
     return handle_batch(tensor, single_tensor2np)
 
 
-def nextAvailable(path: Path | str) -> Path:
-    """
-    Find the next available path by adding a numbered suffix. (mimics comfy's version).
-
-    Args:
-        path (Path): The original path to check
-
-    Returns
-    -------
-        Path: A path that doesn't exist yet
-    """
-    path = Path(path)
-
-    if not path.is_absolute():
-        path = output_dir / path
-
-    if not path.exists():
-        return path
-
-    stem = path.stem
-    suffix = path.suffix
-    parent = path.parent
-
-    counter = 1
-    while True:
-        new_path = parent / f"{stem}_{counter:04d}{suffix}"
-        if not new_path.exists():
-            return new_path
-        counter += 1
-
-
 def pad(img, left, right, top, bottom):
     pad_width = np.array(((0, 0), (top, bottom), (left, right)))
     print(

web/comfy_shared.js

@@ -1,9 +1,10 @@
 /**
- * @module Shared utilities
  * File: comfy_shared.js
  * Project: comfy_mtb
  * Author: Mel Massadian
+ *
  * Copyright (c) 2023-2024 Mel Massadian
+ *
  */
 
 // Reference the shared typedefs file
@@ -260,16 +261,6 @@ export function inner_value_change(widget, val, event = undefined) {
   }
 }
 
-export const getNamedWidget = (node, ...names) => {
-  const out = {}
-
-  for (const name of names) {
-    out[name] = node.widgets.find((w) => w.name === name)
-  }
-
-  return out
-}
-
 /**
  * @param {LGraphNode} node
  * @param {LLink} link
@@ -371,37 +362,23 @@ export function getWidgetType(config) {
  * @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 {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} opts
  * @returns
  */
-export const setupDynamicConnections = (
+export const setupDynamicConnections = (nodeType, prefix, inputType, opts) => {
-  nodeType,
-  prefix,
-  inputType,
-  opts = undefined,
-) => {
   infoLogger(
     'Setting up dynamic connections for',
     Object.getOwnPropertyDescriptors(nodeType).title.value,
   )
 
-  /** @type {{separator:string, start_index:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} */
+  /** @type {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} */
-  const options = Object.assign(
+  const options = opts || {}
-    {
-      separator: '_',
-      start_index: 1,
-    },
-    opts || {},
-  )
   const onNodeCreated = nodeType.prototype.onNodeCreated
   const inputList = typeof inputType === 'object'
 
   nodeType.prototype.onNodeCreated = function () {
     const r = onNodeCreated ? onNodeCreated.apply(this, []) : undefined
-    this.addInput(
+    this.addInput(`${prefix}_1`, inputList ? '*' : inputType)
-      `${prefix}${options.separator}${options.start_index}`,
-      inputList ? '*' : inputType,
-    )
     return r
   }
 
@@ -436,7 +413,7 @@ export const setupDynamicConnections = (
       this,
       slotIndex,
       isConnected,
-      `${prefix}${options.separator}`,
+      `${prefix}_`,
       inputType,
       options,
     )
@@ -452,7 +429,7 @@ export const setupDynamicConnections = (
  * @param {bool} connected - Was this event connecting or disconnecting
  * @param {string} [connectionPrefix] - The common prefix of the dynamic inputs
  * @param {string|[string]} [connectionType] - The type of the dynamic connection
- * @param {{start_index?:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options
+ * @param {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options
  */
 export const dynamic_connection = (
   node,
@@ -462,18 +439,13 @@ export const dynamic_connection = (
   connectionType = '*',
   opts = undefined,
 ) => {
-  /* {{start_index:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options*/
+  /* @type {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options*/
-  const options = Object.assign(
+  const options = opts || {}
-    {
-      start_index: 1,
-    },
-    opts || {},
-  )
 
-  // function to test if input is a dynamic one
+  if (
-  const isDynamicInput = (inputName) => inputName.startsWith(connectionPrefix)
+    node.inputs.length > 0 &&
-
+    !node.inputs[index].name.startsWith(connectionPrefix)
-  if (node.inputs.length > 0 && !isDynamicInput(node.inputs[index].name)) {
+  ) {
     return
   }
 
@@ -492,7 +464,7 @@ 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) {
         if (node.widgets) {
           const w = node.widgets.find((w) => w.name === element.name)
           if (w) {
@@ -518,25 +490,14 @@ export const dynamic_connection = (
 
     infoLogger('Cleaning inputs: making it sequential again')
     // make inputs sequential again
-    let prefixed_idx = options.start_index
     for (let i = 0; i < node.inputs.length; i++) {
-      let name = ''
+      let name = `${connectionPrefix}${i + 1}`
-      // rename only prefixed inputs
-      if (isDynamicInput(node.inputs[i].name)) {
-        // prefixed => rename and increase index
-        name = `${connectionPrefix}${prefixed_idx}`
-        prefixed_idx += 1
-      } else {
-        // not prefixed => keep same name
-        name = node.inputs[i].name
-      }
 
       if (nameArray.length > 0) {
         name = i < nameArray.length ? nameArray[i] : name
       }
 
-      // preserve label if it exists
+      node.inputs[i].label = name
-      node.inputs[i].label = node.inputs[i].label || name
       node.inputs[i].name = name
     }
   }
@@ -576,16 +537,11 @@ 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.length
-      const nextIndex = node.inputs.reduce(
-        (acc, cur) => (isDynamicInput(cur.name) ? ++acc : acc),
-        0,
-      )
-
       const name =
         nextIndex < nameArray.length
           ? nameArray[nextIndex]
-          : `${connectionPrefix}${nextIndex + options.start_index}`
+          : `${connectionPrefix}${nextIndex + 1}`
 
       infoLogger(`Adding input ${nextIndex + 1} (${name})`)
       node.addInput(name, conType)
@@ -675,6 +631,39 @@ export const loadScript = (
   })
 }
 
+export function defineClass(className, classStyles) {
+  const styleSheets = document.styleSheets
+
+  // Helper function to check if the class exists in a style sheet
+  function classExistsInStyleSheet(styleSheet) {
+    const rules = styleSheet.rules || styleSheet.cssRules
+    for (const rule of rules) {
+      if (rule.selectorText === `.${className}`) {
+        return true
+      }
+    }
+    return false
+  }
+
+  // Check if the class is already defined in any of the style sheets
+  let classExists = false
+  for (const styleSheet of styleSheets) {
+    if (classExistsInStyleSheet(styleSheet)) {
+      classExists = true
+      break
+    }
+  }
+
+  // If the class doesn't exist, add the new class definition to the first style sheet
+  if (!classExists) {
+    if (styleSheets[0].insertRule) {
+      styleSheets[0].insertRule(`.${className} { ${classStyles} }`, 0)
+    } else if (styleSheets[0].addRule) {
+      styleSheets[0].addRule(`.${className}`, classStyles, 0)
+    }
+  }
+}
+
 // #endregion
 
 // #region documentation widget
@@ -1129,33 +1118,7 @@ export const addDeprecation = (nodeType, reason) => {
 
 // #endregion
 
-// #region Actions API
+// #region API / graph utilities
-export const runAction = async (name, ...args) => {
-  const req = await api.fetchApi('/mtb/actions', {
-    method: 'POST',
-    body: JSON.stringify({
-      name,
-      args,
-    }),
-  })
-
-  const res = await req.json()
-  return res.result
-}
-export const getServerInfo = async () => {
-  const res = await api.fetchApi('/mtb/server-info')
-  return await res.json()
-}
-export const setServerInfo = async (opts) => {
-  await api.fetchApi('/mtb/server-info', {
-    method: 'POST',
-    body: JSON.stringify(opts),
-  })
-}
-
-// #endregion
-
-// #region Authoring API / graph utilities
 export const getAPIInputs = () => {
   const inputs = {}
   let counter = 1
@@ -1208,4 +1171,3 @@ export const getNodes = (skip_unused) => {
   }
   return nodes
 }
-// #endregion

web/debug.js

@@ -14,7 +14,6 @@ import { app } from '../../scripts/app.js'
 
 import * as shared from './comfy_shared.js'
 import { MtbWidgets } from './mtb_widgets.js'
-import * as mtb_ui from './mtb_ui.js'
 
 // TODO: respect inputs order...
 
@@ -37,10 +36,12 @@ app.registerExtension({
   async beforeRegisterNodeDef(nodeType, nodeData, app) {
     if (nodeData.name === 'Debug (mtb)') {
       const onNodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function (...args) {
+      nodeType.prototype.onNodeCreated = function () {
         this.options = {}
-        const r = onNodeCreated ? onNodeCreated.apply(this, args) : undefined
+        const r = onNodeCreated
-        this.addInput('anything_1', '*')
+          ? onNodeCreated.apply(this, arguments)
+          : undefined
+        this.addInput(`anything_1`, '*')
         return r
       }
 
@@ -80,16 +81,14 @@ app.registerExtension({
       }
 
       const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (...args) {
+      nodeType.prototype.onExecuted = function (data) {
-        onExecuted?.apply(this, args)
+        onExecuted?.apply(this, arguments)
-        const [data, ..._rest] = args
 
         const prefix = 'anything_'
 
         if (this.widgets) {
           for (let i = 0; i < this.widgets.length; i++) {
             if (this.widgets[i].name !== 'output_to_console') {
-              this.widgets[i].onRemove?.()
               this.widgets[i].onRemoved?.()
             }
           }
@@ -99,32 +98,19 @@ app.registerExtension({
         // console.log(message)
         if (data.text) {
           for (const txt of data.text) {
-            const textDom = mtb_ui.makeElement('p', { fontFamily: 'monospace' })
+            const w = this.addCustomWidget(
-            textDom.innerHTML = txt
+              MtbWidgets.DEBUG_STRING(`${prefix}_${widgetI}`, escapeHtml(txt)),
-
-            this.addDOMWidget(
-              `${prefix}_${widgetI}`,
-              'CUSTOM_TEXT',
-              textDom,
-              {},
             )
+            w.parent = this
             widgetI++
           }
         }
         if (data.b64_images) {
           for (const img of data.b64_images) {
-            const imgDom = mtb_ui.makeElement('img', { width: '100%' })
+            const w = this.addCustomWidget(
-            imgDom.src = img
+              MtbWidgets.DEBUG_IMG(`${prefix}_${widgetI}`, img),
-
-            this.addDOMWidget(
-              `${prefix}_${widgetI}`,
-              'CUSTOM_IMG_B64',
-              mtb_ui.wrapElement(imgDom, {
-                overflow: 'hidden',
-              }),
-              {},
             )
-
+            w.parent = this
             widgetI++
           }
         }
@@ -133,13 +119,12 @@ app.registerExtension({
 
         this.onRemoved = function () {
           // When removing this node we need to remove the input from the DOM
-          for (const y in this.widgets) {
+          for (let y in this.widgets) {
             if (this.widgets[y].canvas) {
               this.widgets[y].canvas.remove()
             }
             shared.cleanupNode(this)
             this.widgets[y].onRemoved?.()
-            this.widgets[y].onRemove?.()
           }
         }
       }

web/imageFeed.js

@@ -13,40 +13,40 @@ import { api } from '../../scripts/api.js'
 import { app } from '../../scripts/app.js'
 import { LocalStorageManager } from './comfy_shared.js'
 const styles = {
-	lighbox: {
+  lighbox: {
-		position: 'fixed',
+    position: 'fixed',
-		top: 0,
+    top: 0,
-		left: 0,
+    left: 0,
-		width: '100vw',
+    width: '100vw',
-		height: '100vh',
+    height: '100vh',
-		background: 'rgba(0,0,0,0.5)',
+    background: 'rgba(0,0,0,0.5)',
-		display: 'none',
+    display: 'none',
-		justifyContent: 'center',
+    justifyContent: 'center',
-		alignItems: 'center',
+    alignItems: 'center',
-		zIndex: 999,
+    zIndex: 999,
-	},
+  },
-	lightboxBtn: (extra) => ({
+  lightboxBtn: (extra) => ({
-		position: 'absolute',
+    position: 'absolute',
-		top: '50%',
+    top: '50%',
-		background: 'none',
+    background: 'none',
-		border: 'none',
+    border: 'none',
-		color: '#fff',
+    color: '#fff',
-		zIndex: 1000,
+    zIndex: 1000,
-		fontSize: '30px',
+    fontSize: '30px',
-		cursor: 'pointer',
+    cursor: 'pointer',
-		pointerEvents: 'auto',
+    pointerEvents: 'auto',
-		...extra,
+    ...extra,
-	}),
+  }),
-	img_list: {
+  img_list: {
-		minHeight: '30px',
+    minHeight: '30px',
-		maxHeight: '300px',
+    maxHeight: '300px',
-		width: '100vw',
+    width: '100vw',
-		position: 'absolute',
+    position: 'absolute',
-		bottom: 0,
+    bottom: 0,
-		zIndex: 10,
+    zIndex: 10,
-		background: '#333',
+    background: '#333',
-		overflow: 'auto',
+    overflow: 'auto',
-	},
+  },
 }
 
 let currentImageIndex = 0
@@ -58,299 +58,298 @@ const storage = new LocalStorageManager('mtb')
 let activated = storage.get('image_feed', false)
 
 app.registerExtension({
-	name: 'mtb.ImageFeed',
+  name: 'mtb.ImageFeed',
-	setup: () => {
+  setup: () => {
-		app.ui.settings.addSetting({
+    app.ui.settings.addSetting({
-			id: 'mtb.Main.image-feed-enabled',
+      id: 'mtb.imageFeed.enabled',
-			category: ['mtb', 'Main', 'image-feed-enabled'],
+      name: '[⚡mtb] Enable image feed',
-			name: 'Enable Image Feed',
+      type: 'boolean',
-			type: 'boolean',
+      defaultValue: true,
-			defaultValue: false,
+      attrs: {
-			attrs: {
+        style: {
-				style: {
+          fontFamily: 'monospace',
-					fontFamily: 'monospace',
+        },
-				},
+      },
-			},
+      async onChange(value) {
-			async onChange(value) {
+        storage.set('image_feed', value)
-				storage.set('image_feed', value)
+        activated = value
-				activated = value
+      },
-			},
+    })
-		})
+  },
-	},
+  init: async () => {
-	init: async () => {
+    if (!activated) {
-		if (!activated) {
+      return
-			return
+    }
-		}
+    const pythongossFeed = app.extensions.find(
-		const pythongossFeed = app.extensions.find(
+      (e) => e.name === 'pysssss.ImageFeed',
-			(e) => e.name === 'pysssss.ImageFeed',
+    )
-		)
+    if (pythongossFeed) {
-		if (pythongossFeed) {
+      console.warn(
-			console.warn(
+        "[mtb] - Aborting the loading of mtb's imageFeed in favor of pysssss.ImageFeed",
-				"[mtb] - Aborting the loading of mtb's imageFeed in favor of pysssss.ImageFeed",
+      )
-			)
+      activated = false // just in case other methods are added later on
-			activated = false // just in case other methods are added later on
+      return
-			return
+    }
-		}
+    // - HTML & CSS
-		// - HTML & CSS
+    //- lightbox
-		//- lightbox
+    const lightboxContainer = document.createElement('div')
-		const lightboxContainer = document.createElement('div')
+    Object.assign(lightboxContainer.style, styles.lighbox)
-		Object.assign(lightboxContainer.style, styles.lighbox)
 
-		const lightboxImage = document.createElement('img')
+    const lightboxImage = document.createElement('img')
-		Object.assign(lightboxImage.style, {
+    Object.assign(lightboxImage.style, {
-			maxHeight: '100%',
+      maxHeight: '100%',
-			maxWidth: '100%',
+      maxWidth: '100%',
-			borderRadius: '5px',
+      borderRadius: '5px',
-		})
+    })
 
-		// previous and next buttons
+    // previous and next buttons
-		const lightboxPrevBtn = document.createElement('button')
+    const lightboxPrevBtn = document.createElement('button')
-		const lightboxNextBtn = document.createElement('button')
+    const lightboxNextBtn = document.createElement('button')
 
-		lightboxPrevBtn.textContent = '❮'
+    lightboxPrevBtn.textContent = '❮'
-		lightboxNextBtn.textContent = '❯'
+    lightboxNextBtn.textContent = '❯'
 
-		Object.assign(lightboxPrevBtn.style, styles.lightboxBtn({ left: '0%' }))
+    Object.assign(lightboxPrevBtn.style, styles.lightboxBtn({ left: '0%' }))
-		Object.assign(lightboxNextBtn.style, styles.lightboxBtn({ right: '0%' }))
+    Object.assign(lightboxNextBtn.style, styles.lightboxBtn({ right: '0%' }))
 
-		// close button
+    // close button
-		const lightboxCloseBtn = document.createElement('button')
+    const lightboxCloseBtn = document.createElement('button')
-		Object.assign(
+    Object.assign(
-			lightboxCloseBtn.style,
+      lightboxCloseBtn.style,
-			styles.lightboxBtn({ right: '0', top: '0' }),
+      styles.lightboxBtn({ right: '0', top: '0' }),
-		)
+    )
-		lightboxCloseBtn.textContent = '❌'
+    lightboxCloseBtn.textContent = '❌'
 
-		const lightboxButtons = document.createElement('div')
+    const lightboxButtons = document.createElement('div')
-		Object.assign(lightboxButtons.style, {
+    Object.assign(lightboxButtons.style, {
-			position: 'absolute',
+      position: 'absolute',
-			top: '0%',
+      top: '0%',
-			right: '0%',
+      right: '0%',
-			// transform: "translate(50%, -50%)",
+      // transform: "translate(50%, -50%)",
-			height: '100%',
+      height: '100%',
-			width: '100%',
+      width: '100%',
-			background: 'none',
+      background: 'none',
-			border: 'none',
+      border: 'none',
-			color: '#fff',
+      color: '#fff',
-			fontSize: '30px',
+      fontSize: '30px',
-			cursor: 'pointer',
+      cursor: 'pointer',
-			pointerEvents: 'none',
+      pointerEvents: 'none',
-		})
+    })
 
-		lightboxButtons.append(lightboxPrevBtn, lightboxNextBtn, lightboxCloseBtn)
+    lightboxButtons.append(lightboxPrevBtn, lightboxNextBtn, lightboxCloseBtn)
-		lightboxContainer.append(lightboxButtons, lightboxImage)
+    lightboxContainer.append(lightboxButtons, lightboxImage)
 
-		//- image list
+    //- image list
-		const imageListContainer = document.createElement('div')
+    const imageListContainer = document.createElement('div')
-		Object.assign(imageListContainer.style, styles.img_list)
+    Object.assign(imageListContainer.style, styles.img_list)
 
-		const createImgListBtn = (text, style) => {
+    const createImgListBtn = (text, style) => {
-			const btn = document.createElement('button')
+      const btn = document.createElement('button')
-			btn.type = 'button'
+      btn.type = 'button'
-			btn.textContent = text
+      btn.textContent = text
-			Object.assign(btn.style, {
+      Object.assign(btn.style, {
-				...style,
+        ...style,
-				border: 'none',
+        border: 'none',
-				color: '#fff',
+        color: '#fff',
-				background: 'none',
+        background: 'none',
-				height: '20px',
+        height: '20px',
-				cursor: 'pointer',
+        cursor: 'pointer',
-				position: 'absolute',
+        position: 'absolute',
-				top: '5px',
+        top: '5px',
-				fontSize: '12px',
+        fontSize: '12px',
-				lineHeight: '12px',
+        lineHeight: '12px',
-			})
+      })
-			imageListContainer.append(btn)
+      imageListContainer.append(btn)
-			return btn
+      return btn
-		}
+    }
-		const showBtn = document.createElement('button')
+    const showBtn = document.createElement('button')
-		const closeBtn = createImgListBtn('❌', {
+    const closeBtn = createImgListBtn('❌', {
-			width: '20px',
+      width: '20px',
-			textIndent: '-4px',
+      textIndent: '-4px',
-			right: '5px',
+      right: '5px',
-		})
+    })
-		const loadButton = createImgListBtn('Load Session History', {
+    const loadButton = createImgListBtn('Load Session History', {
-			right: '90px',
+      right: '90px',
-		})
+    })
-		const clearButton = createImgListBtn('Clear', {
+    const clearButton = createImgListBtn('Clear', {
-			right: '30px',
+      right: '30px',
-		})
+    })
 
-		//- tools popup button
+    //- tools popup button
-		showBtn.classList.add('comfy-settings-btn')
+    showBtn.classList.add('comfy-settings-btn')
-		Object.assign(showBtn.style, {
+    Object.assign(showBtn.style, {
-			right: '16px',
+      right: '16px',
-			cursor: 'pointer',
+      cursor: 'pointer',
-			display: 'none',
+      display: 'none',
-		})
+    })
 
-		//- append to DOM
+    //- append to DOM
-		document.body.append(imageListContainer)
+    document.body.append(imageListContainer)
 
-		showBtn.textContent = '🖼'
+    showBtn.textContent = '🖼'
-		showBtn.onclick = () => {
+    showBtn.onclick = () => {
-			imageListContainer.style.display = 'block'
+      imageListContainer.style.display = 'block'
-			showBtn.style.display = 'none'
+      showBtn.style.display = 'none'
-		}
+    }
-		document.querySelector('.comfy-settings-btn').after(showBtn)
+    document.querySelector('.comfy-settings-btn').after(showBtn)
-		document.querySelector('.comfy-settings-btn').after(lightboxContainer)
+    document.querySelector('.comfy-settings-btn').after(lightboxContainer)
 
-		// for (const { output } of history) {
+    // for (const { output } of history) {
-		// 	if (output?.images) {
+    // 	if (output?.images) {
-		// 		for (const src of output.images) {
+    // 		for (const src of output.images) {
-		// 			const img = document.createElement("img");
+    // 			const img = document.createElement("img");
-		// 			const but = document.createElement("button");
+    // 			const but = document.createElement("button");
 
-		//- callbacks
+    //- callbacks
-		closeBtn.onclick = () => {
+    closeBtn.onclick = () => {
-			imageListContainer.style.display = 'none'
+      imageListContainer.style.display = 'none'
-			showBtn.style.display = 'unset'
+      showBtn.style.display = 'unset'
-		}
+    }
 
-		clearButton.onclick = () => {
+    clearButton.onclick = () => {
-			imageListContainer.replaceChildren(closeBtn, clearButton, loadButton)
+      imageListContainer.replaceChildren(closeBtn, clearButton, loadButton)
-		}
+    }
 
-		lightboxNextBtn.onclick = () => {
+    lightboxNextBtn.onclick = () => {
-			currentImageIndex = (currentImageIndex + 1) % imageUrls.length
+      currentImageIndex = (currentImageIndex + 1) % imageUrls.length
-			const imageUrl = imageUrls[currentImageIndex]
+      const imageUrl = imageUrls[currentImageIndex]
-			lightboxImage.src = imageUrl
+      lightboxImage.src = imageUrl
-		}
+    }
 
-		// Modify the lightboxPrevBtn onclick callback
+    // Modify the lightboxPrevBtn onclick callback
-		lightboxPrevBtn.onclick = () => {
+    lightboxPrevBtn.onclick = () => {
-			currentImageIndex =
+      currentImageIndex =
-				(currentImageIndex - 1 + imageUrls.length) % imageUrls.length
+        (currentImageIndex - 1 + imageUrls.length) % imageUrls.length
-			const imageUrl = imageUrls[currentImageIndex]
+      const imageUrl = imageUrls[currentImageIndex]
-			lightboxImage.src = imageUrl
+      lightboxImage.src = imageUrl
-		}
+    }
 
-		lightboxCloseBtn.onclick = () => {
+    lightboxCloseBtn.onclick = () => {
-			lightboxContainer.style.display = 'none'
+      lightboxContainer.style.display = 'none'
-		}
+    }
-		lightboxImage.onclick = lightboxNextBtn.onclick
+    lightboxImage.onclick = lightboxNextBtn.onclick
-		/**
+    /**
-		 * This is the function that creates the image buttons for the image list
+     * This is the function that creates the image buttons for the image list
-		 * They are wrapped in a button so that they can be clicked and open
+     * They are wrapped in a button so that they can be clicked and open
-		 * the image in the lightbox.
+     * the image in the lightbox.
-		 * @param {*} src
+     * @param {*} src
-		 */
+     */
-		const createImageBtn = (src) => {
+    const createImageBtn = (src) => {
-			console.debug(`making image ${src.filename}`)
+      console.debug(`making image ${src.filename}`)
-			const img = document.createElement('img')
+      const img = document.createElement('img')
-			const but = document.createElement('button')
+      const but = document.createElement('button')
 
-			Object.assign(but.style, {
+      Object.assign(but.style, {
-				height: '120px',
+        height: '120px',
-				width: '120px',
+        width: '120px',
-				border: 'none',
+        border: 'none',
-				padding: 0,
+        padding: 0,
-				margin: 0,
+        margin: 0,
-			})
+      })
-			Object.assign(img.style, {
+      Object.assign(img.style, {
-				width: '100%',
+        width: '100%',
-				height: '100%',
+        height: '100%',
-				objectFit: 'cover',
+        objectFit: 'cover',
-			})
+      })
 
-			img.src = `/view?filename=${encodeURIComponent(src.filename)}&type=${
+      img.src = `/view?filename=${encodeURIComponent(src.filename)}&type=${
-				src.type
+        src.type
-			}&subfolder=${encodeURIComponent(src.subfolder)}`
+      }&subfolder=${encodeURIComponent(src.subfolder)}`
 
-			imageUrls.push(img.src)
+      imageUrls.push(img.src)
 
-			console.debug(img.src)
+      console.debug(img.src)
 
-			img.onload = () => {
+      img.onload = () => {
-				but.style.width = `${120 * (img.naturalWidth / img.naturalHeight)}px`
+        but.style.width = `${120 * (img.naturalWidth / img.naturalHeight)}px`
-			}
+      }
 
-			but.onclick = () => {
+      but.onclick = () => {
-				lightboxContainer.style.display = 'flex'
+        lightboxContainer.style.display = 'flex'
-				// add the same image to the lightbox
+        // add the same image to the lightbox
-				lightboxImage.src = img.src
+        lightboxImage.src = img.src
-				// lighboxContainer.replaceChildren(lightboxButtons, img);
+        // lighboxContainer.replaceChildren(lightboxButtons, img);
-			}
+      }
 
-			// add right click menu
+      // add right click menu
-			but.addEventListener('contextmenu', (e) => {
+      but.addEventListener('contextmenu', (e) => {
-				e.preventDefault()
+        e.preventDefault()
 
-				if (image_menu) {
+        if (image_menu) {
-					image_menu.remove()
+          image_menu.remove()
-				}
+        }
 
-				image_menu = document.createElement('div')
+        image_menu = document.createElement('div')
-				Object.assign(image_menu.style, {
+        Object.assign(image_menu.style, {
-					position: 'absolute',
+          position: 'absolute',
-					top: `${e.clientY}px`,
+          top: `${e.clientY}px`,
-					left: `${e.clientX}px`,
+          left: `${e.clientX}px`,
-					background: '#333',
+          background: '#333',
-					color: '#fff',
+          color: '#fff',
-					padding: '5px',
+          padding: '5px',
-					borderRadius: '5px',
+          borderRadius: '5px',
-					zIndex: 999,
+          zIndex: 999,
-				})
+        })
-				const load_img = document.createElement('button')
+        const load_img = document.createElement('button')
-				load_img.textContent = 'Load'
+        load_img.textContent = 'Load'
-				load_img.onclick = () => {
+        load_img.onclick = () => {
-					app.handleFile(img.src)
+          app.handleFile(img.src)
-				}
+        }
 
-				image_menu.appendChild(load_img)
+        image_menu.appendChild(load_img)
-				document.body.appendChild(image_menu)
+        document.body.appendChild(image_menu)
-			})
+      })
 
-			but.append(img)
+      but.append(img)
-			imageListContainer.prepend(but)
+      imageListContainer.prepend(but)
-		}
+    }
 
-		loadButton.onclick = async () => {
+    loadButton.onclick = async () => {
-			const all_history = await api.getHistory()
+      const all_history = await api.getHistory()
-			for (const history of all_history.History) {
+      for (const history of all_history.History) {
-				if (history.outputs) {
+        if (history.outputs) {
-					for (const key of Object.keys(history.outputs)) {
+          for (const key of Object.keys(history.outputs)) {
-						console.debug(key)
+            console.debug(key)
-						if (history.outputs[key].images) {
+            if (history.outputs[key].images) {
-							for (const im of history.outputs[key].images) {
+              for (const im of history.outputs[key].images) {
-								console.debug(im)
+                console.debug(im)
-								createImageBtn(im)
+                createImageBtn(im)
-							}
+              }
-						}
+            }
-					}
+          }
-					// for (const src of outputs.outputs.images) {
+          // for (const src of outputs.outputs.images) {
-					// 	console.debug(src)
+          // 	console.debug(src)
-					// 	makeImage(`${src.subfolder}/${src.filename}`)
+          // 	makeImage(`${src.subfolder}/${src.filename}`)
-					// }
+          // }
-				}
+        }
-			}
+      }
-		}
+    }
 
-		///////-------
+    ///////-------
 
-		// const all_history = await api.getHistory()
+    // const all_history = await api.getHistory()
-		// for (const history of all_history.History) {
+    // for (const history of all_history.History) {
-		// 	if (history.outputs) {
+    // 	if (history.outputs) {
-		// 		for (const key of Object.keys(history.outputs)) {
+    // 		for (const key of Object.keys(history.outputs)) {
-		// 			for (const im of history.outputs[key].images) {
+    // 			for (const im of history.outputs[key].images) {
-		// 				makeImage(im)
+    // 				makeImage(im)
-		// 			}
+    // 			}
-		// 		}
+    // 		}
-		// 		// for (const src of outputs.outputs.images) {
+    // 		// for (const src of outputs.outputs.images) {
-		// 		// 	console.debug(src)
+    // 		// 	console.debug(src)
-		// 		// 	makeImage(`${src.subfolder}/${src.filename}`)
+    // 		// 	makeImage(`${src.subfolder}/${src.filename}`)
-		// 		// }
+    // 		// }
-		// 	}
+    // 	}
-		// }
+    // }
 
-		//- Hook into the API
+    //- Hook into the API
-		api.addEventListener('executed', ({ detail }) => {
+    api.addEventListener('executed', ({ detail }) => {
-			if (detail?.output?.images) {
+      if (detail?.output?.images) {
-				for (const src of detail.output.images) {
+        for (const src of detail.output.images) {
-					console.debug(`Adding ${src} to image feed`)
+          console.debug(`Adding ${src} to image feed`)
-					createImageBtn(src)
+          createImageBtn(src)
-				}
+        }
-			}
+      }
-		})
+    })
-	},
+  },
 })

web/mtb_input_output_sidebar.js

@@ -1,339 +0,0 @@
-import { app } from '../../scripts/app.js'
-import { api } from '../../scripts/api.js'
-
-import * as shared from './comfy_shared.js'
-
-import {
-  // defineCSSClass,
-  ensureMTBStyles,
-  makeElement,
-  makeSelect,
-  makeSlider,
-  renderSidebar,
-} from './mtb_ui.js'
-
-const offset = 0
-let currentWidth = 200
-let currentMode = 'input'
-let subfolder = ''
-let currentSort = 'None'
-
-const IMAGE_NODES = ['LoadImage', 'VHS_LoadImagePath']
-const VIDEO_NODES = ['VHS_LoadVideo']
-
-const updateImage = (node, image) => {
-  if (IMAGE_NODES.includes(node.type)) {
-    const w = node.widgets?.find((w) => w.name === 'image')
-    if (w) {
-      w.value = image
-      w.callback()
-    }
-  } else if (VIDEO_NODES.includes(node.type)) {
-    const w = node.widgets?.find((w) => w.name === 'video')
-    if (w) {
-      node.updateParameters({ filename: image }, true)
-    }
-  } else {
-    console.warn('No method to update', node.type)
-  }
-}
-
-const getImgsFromUrls = (urls, target) => {
-  const imgs = []
-  if (urls === undefined) {
-    return imgs
-  }
-  const elem = currentMode === 'video' ? 'video' : 'img'
-
-  for (const [key, url] of Object.entries(urls)) {
-    const a = makeElement(elem)
-    a.src = url
-    a.width = currentWidth
-    if (currentMode === 'input') {
-      a.onclick = (_e) => {
-        if (subfolder !== '') {
-          app.extensionManager.toast.add({
-            severity: 'warn',
-            summary: 'Subfolder not supported',
-            detail: "The LoadImage node doesn't support subfolders",
-            life: 5000,
-          })
-          return
-        }
-        const selected = app.canvas.selected_nodes
-        if (selected && Object.keys(selected).length === 0) {
-          app.extensionManager.toast.add({
-            severity: 'warn',
-            summary: 'No node selected!',
-            detail:
-              'For now the only action when clicking images in the sidebar is to set the image on all selected LoadImage nodes.',
-            life: 5000,
-          })
-          return
-        }
-
-        for (const [_id, node] of Object.entries(app.canvas.selected_nodes)) {
-          updateImage(node, key)
-        }
-      }
-    } else if (currentMode === 'output') {
-      a.onclick = (_e) => {
-        // window.MTB?.notify?.("Output import isn't supported yet...", 5000)
-        if (subfolder !== '') {
-          app.extensionManager.toast.add({
-            severity: 'warn',
-            summary: 'Subfolder not supported',
-            detail: "The LoadImage node doesn't support subfolders",
-            life: 5000,
-          })
-          return
-        }
-
-        app.extensionManager.toast.add({
-          severity: 'warn',
-          summary: 'Outputs not supported',
-          detail:
-            'For now only inputs can be clicked to load the image on the active LoadImage node.',
-          life: 5000,
-        })
-      }
-    } else {
-      a.autoplay = true
-
-      a.muted = true
-      a.loop = true
-      a.onclick = (_e) => {
-        const selected = app.canvas.selected_nodes
-        if (selected && Object.keys(selected).length === 0) {
-          app.extensionManager.toast.add({
-            severity: 'warn',
-            summary: 'No node selected!',
-            detail:
-              "For now the only action when clicking videos in the sidebar is to set the video on all selected 'Load Video (Upload)' nodes.",
-            life: 5000,
-          })
-          return
-        }
-
-        for (const [_id, node] of Object.entries(app.canvas.selected_nodes)) {
-          updateImage(node, key)
-        }
-      }
-    }
-    imgs.push(a)
-  }
-  if (target !== undefined) {
-    target.append(...imgs)
-  }
-  return imgs
-}
-
-const getModes = async () => {
-  const inputs = await shared.runAction('getUserImageFolders')
-  return inputs
-}
-const getUrls = async (subfolder) => {
-  const count = (await api.getSetting('mtb.io-sidebar.count')) || 1000
-  console.log('Sidebar count', count)
-  if (currentMode === 'video') {
-    const output = await shared.runAction(
-      'getUserVideos',
-      256,
-      count,
-      offset,
-      currentSort,
-    )
-    return output || {}
-  }
-  const output = await shared.runAction(
-    'getUserImages',
-    currentMode,
-    count,
-    offset,
-    currentSort,
-    false,
-    subfolder,
-  )
-  return output || {}
-}
-
-//NOTE: do not load if using the old ui
-if (window?.__COMFYUI_FRONTEND_VERSION__) {
-  // NOTE: removed this for now since I'm not actually exposing anything a client
-  // cannot already access from "/view"...
-  // let exposed = false
-
-  const sidebar_extension = {
-    name: 'mtb.io-sidebar',
-    // init: async () => {
-    //   try {
-    //     const res = await api.fetchApi('/mtb/server-info')
-    //     const msg = await res.json()
-    //     exposed = msg.exposed
-    //   } catch (e) {
-    //     console.error('Error:', e)
-    //   }
-    // },
-    init: () => {
-      let handle
-      const version = window?.__COMFYUI_FRONTEND_VERSION__
-      console.log(`%c ${version}`, 'background: orange; color: white;')
-
-      ensureMTBStyles()
-
-      app.ui.settings.addSetting({
-        id: 'mtb.io-sidebar.count',
-        category: ['mtb', 'Input & Output Sidebar', 'count'],
-
-        name: 'Number of images to fetch',
-        type: 'number',
-        defaultValue: 1000,
-
-        tooltip:
-          "This setting affects the input/output sidebar to determine how many images to fetch per pagination (pagination is not yet supported so for now it's the static total)",
-        attrs: {
-          style: {
-            // fontFamily: 'monospace',
-          },
-        },
-      })
-
-      app.ui.settings.addSetting({
-        id: 'mtb.io-sidebar.img-size',
-        category: ['mtb', 'Input & Output Sidebar', 'img-size'],
-
-        name: 'Resolution of the images',
-        type: 'number',
-        defaultValue: 512,
-
-        tooltip: "It's recommended to keep it at 512px",
-        attrs: {
-          style: {
-            // fontFamily: 'monospace',
-          },
-        },
-      })
-      app.ui.settings.addSetting({
-        id: 'mtb.io-sidebar.sort',
-        category: ['mtb', 'Input & Output Sidebar', 'sort'],
-        name: 'Default sort mode',
-        type: 'combo',
-
-        onChange: (v) => {
-          // alert(`Sort is now ${v}`)
-          currentSort = v
-        },
-
-        defaultValue: 'Modified',
-        // tooltip: "It's recommended to keep it at 512px",
-        options: [
-          'None',
-          'Modified',
-          'Modified-Reverse',
-          'Name',
-          'Name-Reverse',
-        ],
-      })
-
-      app.extensionManager.registerSidebarTab({
-        id: 'mtb-inputs-outputs',
-        icon: 'pi pi-images',
-        title: 'Input & Outputs',
-        tooltip: 'MTB: Browse inputs and outputs directories.',
-        type: 'custom',
-
-        // this is run everytime the tab's diplay is toggled on.
-        render: async (el) => {
-          if (handle) {
-            handle.unregister()
-            handle = undefined
-          }
-
-          if (el.parentNode) {
-            el.parentNode.style.overflowY = 'clip'
-          }
-
-          const allModes = await getModes()
-          const input_modes = allModes.input.map((m) => `input - ${m}`)
-          const output_modes = allModes.output.map((m) => `output - ${m}`)
-          const urls = await getUrls()
-          let imgs = {}
-
-          const cont = makeElement('div.mtb_sidebar')
-
-          const imgGrid = makeElement('div.mtb_img_grid')
-          const selector = makeSelect(
-            ['input', 'output', 'video', ...output_modes, ...input_modes],
-            currentMode,
-          )
-
-          selector.addEventListener('change', async (e) => {
-            let newMode = e.target.value
-            let changed = false
-            let newSub = ''
-            if (newMode !== 'input' && newMode !== 'output') {
-              if (newMode.startsWith('input - ')) {
-                newSub = newMode.replace('input - ', '')
-                newMode = 'input'
-              } else if (newMode.startsWith('output - ')) {
-                newSub = newMode.replace('output - ', '')
-                newMode = 'output'
-              }
-            }
-            changed = newMode !== currentMode || newSub !== subfolder
-            currentMode = newMode
-            subfolder = newSub
-            if (changed) {
-              imgGrid.innerHTML = ''
-              const urls = await getUrls(subfolder)
-              if (urls) {
-                imgs = getImgsFromUrls(urls, imgGrid)
-              }
-            }
-          })
-
-          const imgTools = makeElement('div.mtb_tools')
-          const orderSelect = makeSelect(
-            ['None', 'Modified', 'Modified-Reverse', 'Name', 'Name-Reverse'],
-            currentSort,
-          )
-
-          orderSelect.addEventListener('change', async (e) => {
-            const newSort = e.target.value
-            const changed = newSort !== currentSort
-            currentSort = newSort
-            if (changed) {
-              imgGrid.innerHTML = ''
-              const urls = await getUrls(subfolder)
-              if (urls) {
-                imgs = getImgsFromUrls(urls, imgGrid)
-              }
-            }
-          })
-
-          const sizeSlider = makeSlider(64, 1024, currentWidth, 1)
-          imgTools.appendChild(orderSelect)
-          imgTools.appendChild(sizeSlider)
-
-          imgs = getImgsFromUrls(urls, imgGrid)
-
-          sizeSlider.addEventListener('input', (e) => {
-            currentWidth = e.target.value
-            for (const img of imgs) {
-              img.style.width = `${e.target.value}px`
-            }
-          })
-          handle = renderSidebar(el, cont, [selector, imgGrid, imgTools])
-        },
-        destroy: () => {
-          if (handle) {
-            handle.unregister()
-            handle = undefined
-          }
-        },
-      })
-    },
-  }
-
-  app.registerExtension(sidebar_extension)
-}

web/mtb_sidebar.js

@@ -1,28 +0,0 @@
-// NOTE: this will be the LT part of mtb API system
-// I need to properly publish the source and fix a few things before
-
-// import { app } from '../../scripts/app.js'
-// // import { api } from '../../scripts/api.js'
-//
-// import * as shared from './comfy_shared.js'
-// import { createOutliner } from './dist/mtb_inspector.js'
-//
-// if (window?.__COMFYUI_FRONTEND_VERSION__) {
-//   const version = window?.__COMFYUI_FRONTEND_VERSION__
-//   console.log(`%c ${version}`, 'background: orange; color: white;')
-//
-//   const panel = app.extensionManager.registerSidebarTab({
-//     id: 'mtb-nodes',
-//     icon: 'pi pi-bolt',
-//     title: 'MTB',
-//     tooltip: 'MTB: API outliner',
-//     type: 'custom',
-//     // this is run everytime the tab's diplay is toggled on.
-//     render: (el) => {
-//       const outliner = createOutliner(el)
-//       const inputs = shared.getAPIInputs()
-//       console.log('INPUTS', inputs)
-//       outliner.$$set({ inputs })
-//     },
-//   })
-// }

web/mtb_ui.js

@@ -1,516 +0,0 @@
-/**
- * Adds a named stylesheet to the document with an optional ability to replace an existing one.
- *
- * @param {string} name - The unique name (ID) of the stylesheet.
- * @param {string} css - The CSS rules as a string.
- * @param {boolean} [force=false] - Whether to replace the existing stylesheet if it exists.
- * @returns {void}
- */
-export function addNamedStyleSheet(name, css, force = false) {
-  const existingStyleSheet = document.getElementById(name)
-
-  if (existingStyleSheet && !force) {
-    console.debug(
-      `Stylesheet with name "${name}" already exists. Skipping addition.`,
-    )
-    return
-  }
-
-  if (existingStyleSheet && force) {
-    console.debug(`Stylesheet with name "${name}" exists. Replacing...`)
-    existingStyleSheet.remove()
-  }
-
-  const styleElement = document.createElement('style')
-  styleElement.id = name
-  styleElement.type = 'text/css'
-
-  styleElement.appendChild(document.createTextNode(css))
-  document.head.appendChild(styleElement)
-
-  console.debug(`Stylesheet with name "${name}" added.`)
-}
-
-export const ensureMTBStyles = () => {
-  const S = {
-    fg: 'var(--fg-color)',
-    bgi: 'var(--comfy-input-bg)',
-    bgm: 'var(--comfy-menu-bg)',
-    border: 'var(--comfy-border)',
-    borderHover: 'var(--comfy-border-hover)',
-    box: 'var(--comfy-box)',
-    accent: 'var(--p-button-text-primary-color)',
-  }
-  const common = `
-.mtb_sidebar {
-  display: flex;
-  flex-direction: column;
-  background: ${S.bgm};
-}
-.mtb_img_grid {
-  display: flex;
-  flex-wrap: wrap;
-  overflow: scroll;
-  gap: 1em;
-  align-items: center;
-  justify-content: center;
-  height: 100%;
-  width: 100%;
-}
-.mtb_tools {
-  display: flex;
-  flex-direction: row;
-  align-items: center;
-  justify-content: space-between;
-  width: 100%;
-}
-`
-  const inputs = `
-/* SELECT */
-.mtb_select {
-    appearance: none;
-  display: grid;
-  grid-template-areas: "select";
-  padding: 10px;
-  background-color: ${S.bgi};
-  border: none;
-  border-radius: 5px;
-  font-size: 14px;
-  color: ${S.fg};
-  cursor: pointer;
-  width: 100%;
-}
-
-@supports (-moz-appearance:none) {
-.mtb_select{
-  grid-area: select;
-  background: ${S.bgi} url('data:image/gif;base64,R0lGODlhBgAGAKEDAFVVVX9/f9TU1CgmNyH5BAEKAAMALAAAAAAGAAYAAAIODA4hCDKWxlhNvmCnGwUAOw==') right center no-repeat !important;
-  background-position: calc(100% - 5px) center !important;
-  -moz-appearance:none !important;
-}
-
-/* styling the dropdown arrow for browsers that support it */
-.mtb_select:after {
-  content: "";
-  width: 0.8em;
-  height: 0.5em;
-  background-color: ${S.fg};
-  clip-path: polygon(100% 0%, 0 0%, 50% 100%);
-}
-
-.mtb_select:focus {
-  outline: none;
-  border-color: #0056b3;
-}
-
-.mtb_select > option {
-  padding: 10px;
-  background-color: ${S.bgi};
-  border:none;
-  color: ${S.fg};
-}
-
-.mtb_select > option:hover {
-  background-color: red;
-  color: ${S.fg};
-}
-
-/* SLIDER */
-.mtb_slider[type="range"] {
-  -webkit-appearance: none;
-  appearance: none;
-  width: 100%;
-  height: 10px;
-  background: ${S.bgm};
-  border-radius: 5px;
-  outline: none;
-  opacity: 0.7;
-  transition: opacity .2s;
-  padding: 1em;
-}
-
-/* slider track */
-.mtb_slider[type="range"]::-webkit-slider-runnable-track,
-.mtb_slider[type="range"]::-moz-range-track {
-  width: 100%;
-  height: 10px;
-  background: ${S.bgi};
-  border-radius: 5px;
-}
-
-
-/* progress */
-.mtb_slider[type="range"]::-moz-range-progress {
-  background-color: ${S.accent};
-  height:10px;
-  border-radius: 5px;
-}
-
-/* slider thumb (the handle) */
-.mtb_slider[type="range"]::-webkit-slider-thumb,
-.mtb_slider[type="range"]::-moz-range-thumb
-{
-  -webkit-appearance: none;
-  appearance: none;
-  width: 15px;
-  height: 15px;
-  border-radius: 50%;
-  background: ${S.fg};
-  border: none;
-  cursor: pointer;
-  filter: drop-shadow(1px 1px 4px black);
-}
-
-.mtb_slider[type="range"]:focus {
-  opacity: 1;
-}
-
-.mtb_slider[type=range]:-moz-focusring{
-    outline: 1px solid red;
-    outline-offset: -1px;
-}
-
-.mtb_slider[type="range"]:hover::-webkit-slider-thumb,
-.mtb_slider[type="range"]:active::-webkit-slider-thumb {
-  background-color: ${S.accent};
-}
-`
-  addNamedStyleSheet(
-    'mtb_ui',
-    `
-${common}
-${inputs}
-`,
-  )
-}
-
-/**
- *  Wrap an element with a div
- *
- * @param {Object} [style] - CSS styles to apply to the element.
- * @returns {HTMLElement} - The created DOM element.
- */
-export const wrapElement = (element, style = {}) => {
-  const container = makeElement('div', style)
-  container.appendChild(element)
-  return container
-}
-
-/**
- * Creates a DOM element with optional styles, class, and id.
- *
- * @param {string} kind - The tag name of the element. Supports class and id syntax (e.g. 'div.class#id').
- * @param {Object} [style] - CSS styles to apply to the element.
- * @returns {HTMLElement} - The created DOM element.
- */
-export const makeElement = (kind, style) => {
-  let [real_kind, className] = kind.split('.')
-  let id
-
-  if (className?.includes('#')) {
-    ;[className, id] = className.split('#')
-  }
-
-  const el = document.createElement(real_kind)
-
-  if (style) {
-    Object.assign(el.style, style)
-  }
-
-  if (className) {
-    el.classList.add(...className.split(' ')) // Support multiple classes
-  }
-
-  if (id) {
-    el.id = id
-  }
-
-  return el
-}
-/**
- * Clears all child elements of the given parent element.
- *
- * @param {HTMLElement} el - The parent element whose children should be removed.
- */
-export const clearElement = (el) => {
-  while (el.firstChild) {
-    el.removeChild(el.firstChild)
-  }
-}
-/**
- * Creates a labeled element (input, select, etc.).
- *
- * @param {HTMLElement} el - The element to label.
- * @param {string} labelText - The label text.
- * @returns {HTMLDivElement} - A div containing the label and the element.
- */
-export const makeLabeledElement = (el, labelText) => {
-  const wrapper = makeElement('div.mtb_labeled_element', {
-    marginBottom: '1em',
-  })
-  const label = makeElement('label', {
-    display: 'block',
-    marginBottom: '0.5em',
-  })
-  label.textContent = labelText
-  wrapper.appendChild(label)
-  wrapper.appendChild(el)
-  return wrapper
-}
-
-/**
- * Converts a camelCase CSS property to kebab-case.
- *
- * @param {string} prop - The camelCase CSS property.
- * @returns {string} - The kebab-case CSS property.
- */
-const camelToKebab = (prop) =>
-  prop.replace(/[A-Z]/g, (match) => `-${match.toLowerCase()}`)
-
-/**
- * Parses the style string into an object of CSS property-value pairs.
- *
- * @param {string} styleString - The CSS rule text (e.g., "color: red; background-color: blue;").
- * @returns {Object} - An object with camelCase CSS properties.
- */
-const parseStyleString = (styleString) => {
-  const styleObj = {}
-  for (const rule of styleString.split(';')) {
-    const [property, value] = rule.split(':').map((item) => item.trim())
-    if (property && value) {
-      const camelProp = property.replace(/-([a-z])/g, (g) => g[1].toUpperCase())
-      styleObj[camelProp] = value
-    }
-  }
-  return styleObj
-}
-
-/**
- * Defines a new CSS class with the provided styles, or skips if the class already exists.
- *
- * @param {string} className - The name of the CSS class to define.
- * @param {Object} classStyles - An object containing camelCase CSS property-value pairs.
- */
-export function defineCSSClass(className, classStyles) {
-  const styleSheets = document.styleSheets
-  let classExists = false
-  let existingStyleString = ''
-  const classExistsInStyleSheet = (styleSheet) => {
-    const rules = styleSheet.rules || styleSheet.cssRules
-    for (const rule of rules) {
-      if (rule.selectorText === `.${className}`) {
-        classExists = true
-        existingStyleString = rule.style.cssText // Capture existing styles
-        return true
-      }
-    }
-    return false
-  }
-
-  for (const styleSheet of styleSheets) {
-    if (classExistsInStyleSheet(styleSheet)) {
-      console.debug(`Class ${className} already exists, merging styles...`)
-      break
-    }
-  }
-  const existingStyles = classExists
-    ? parseStyleString(existingStyleString)
-    : {}
-  const mergedStyles = { ...existingStyles, ...classStyles }
-
-  const stylesString = Object.entries(mergedStyles)
-    .map(([key, value]) => `${camelToKebab(key)}: ${value};`)
-    .join(' ')
-
-  if (!classExists) {
-    console.debug(`Defining new class ${className}...`)
-    if (styleSheets[0].insertRule) {
-      styleSheets[0].insertRule(`.${className} { ${stylesString} }`, 0)
-    } else if (styleSheets[0].addRule) {
-      styleSheets[0].addRule(`.${className}`, stylesString, 0)
-    }
-  } else {
-    console.debug(`Updating existing class ${className} with merged styles...`)
-    for (const styleSheet of styleSheets) {
-      const rules = styleSheet.rules || styleSheet.cssRules
-      for (const rule of rules) {
-        if (rule.selectorText === `.${className}`) {
-          rule.style.cssText = stylesString // Update the existing rule
-        }
-      }
-    }
-  }
-
-  console.debug(
-    `Class ${className} has been defined/updated with styles:`,
-    mergedStyles,
-  )
-}
-
-/**
- * Renders a sidebar and ensures it resizes correctly when the window is resized.
- *
- * @param {HTMLElement} el - The element where the sidebar is rendered.
- * @param {HTMLElement} cont - The content container of the sidebar.
- * @param {HTMLElement[]} elems - Array of elements to append to the sidebar.
- * @returns {Object} - A handle with a method to unregister the resize event.
- */
-export const renderSidebar = (el, cont, elems) => {
-  el.appendChild(cont)
-
-  if (!el.parentNode) {
-    return
-  }
-  el.parentNode.style.overflowY = 'clip'
-  cont.style.height = `${el.parentNode.offsetHeight}px`
-
-  const resizeHandler = () => {
-    cont.style.height = `${el.parentNode.offsetHeight}px`
-  }
-  window.addEventListener('resize', resizeHandler)
-
-  for (const elem of elems) {
-    cont.appendChild(elem)
-  }
-
-  return {
-    unregister: () => {
-      window.removeEventListener('resize', resizeHandler)
-    },
-  }
-}
-
-/**
- * Creates a <select> dropdown with given options.
- *
- * @param {string[]} options - The options for the select element.
- * @param {string} [current] - The currently selected option (optional).
- * @returns {HTMLSelectElement} - The created <select> element.
- */
-export const makeSelect = (options, current = undefined) => {
-  const selector = makeElement('select.mtb_select', {
-    width: 'auto',
-    margin: '1em',
-  })
-
-  for (const option of options) {
-    const opt = makeElement('option')
-    opt.value = option
-    opt.innerHTML = option
-    selector.appendChild(opt)
-  }
-
-  if (current !== undefined) {
-    if (options.includes(current)) {
-      selector.value = current
-    } else {
-      console.error(
-        `You tried to select an option that doesn't exist (${current}). Options: ${options}`,
-      )
-    }
-  }
-
-  return selector
-}
-
-/**
- * Creates an <input type="range"> slider element with given parameters.
- *
- * @param {number} min - Minimum value of the slider.
- * @param {number} max - Maximum value of the slider.
- * @param {number} [value] - Initial value of the slider.
- * @param {number} [step] - Step value for the slider.
- * @returns {HTMLInputElement} - The created slider element.
- */
-export const makeSlider = (min, max, value = undefined, step = undefined) => {
-  const slider = makeElement('input.mtb_slider', {
-    width: '100%',
-  })
-
-  slider.type = 'range'
-  slider.min = min || 0
-  slider.max = max || 100
-  slider.value = value || slider.min
-  slider.step = step || 1
-
-  return slider
-}
-
-/**
- * Creates a button element.
- *
- * @param {string} label - The label for the button.
- * @param {Object} [style] - Optional styles to apply to the button.
- * @param {Function} [onClick] - Optional click handler.
- * @returns {HTMLButtonElement} - The created button element.
- */
-export const makeButton = (label, style = {}, onClick = undefined) => {
-  const button = makeElement('button.mtb_button', style)
-  button.textContent = label
-
-  if (onClick) {
-    button.addEventListener('click', onClick)
-  }
-
-  return button
-}
-
-/**
- * Creates a resizable splitter between two elements.
- *
- * @param {HTMLElement} el1 - The first element.
- * @param {HTMLElement} el2 - The second element.
- * @param {'vertical' | 'horizontal'} direction - Splitter direction (vertical or horizontal).
- * @param {'absolute' | 'normal'} mode - Splitter mode: 'absolute' for free resizing, 'normal' for layout-based resizing.
- * @returns {HTMLDivElement} - The container with resizable splitter.
- */
-export const makeSplitter = (
-  el1,
-  el2,
-  direction = 'vertical',
-  mode = 'normal',
-) => {
-  const container = makeElement('div.mtb_splitter_container', {
-    display: mode === 'absolute' ? 'block' : 'flex',
-    flexDirection: direction === 'vertical' ? 'row' : 'column',
-    position: mode === 'absolute' ? 'relative' : 'static',
-    height: '100%',
-    width: '100%',
-  })
-
-  const handle = makeElement('div.mtb_splitter_handle', {
-    backgroundColor: '#ccc',
-    cursor: direction === 'vertical' ? 'col-resize' : 'row-resize',
-    width: direction === 'vertical' ? '5px' : '100%',
-    height: direction === 'horizontal' ? '5px' : '100%',
-  })
-
-  let isResizing = false
-
-  handle.addEventListener('mousedown', () => {
-    isResizing = true
-  })
-
-  window.addEventListener('mouseup', () => {
-    isResizing = false
-  })
-
-  window.addEventListener('mousemove', (e) => {
-    if (!isResizing) return
-    if (direction === 'vertical') {
-      const newWidth = e.clientX - container.offsetLeft
-      el1.style.width = `${newWidth}px`
-      el2.style.width = `${container.offsetWidth - newWidth}px`
-    } else {
-      const newHeight = e.clientY - container.offsetTop
-      el1.style.height = `${newHeight}px`
-      el2.style.height = `${container.offsetHeight - newHeight}px`
-    }
-  })
-
-  container.appendChild(el1)
-  container.appendChild(handle)
-  container.appendChild(el2)
-
-  return container
-}