Merge branch 'main' into dev/scene-detect

the nodes should never fail to load completely.
I still need to remove the few remaining side effects like this one.

.github/workflows/publish_action.yml

@@ -12,6 +12,8 @@ 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

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

__init__.py

@@ -7,10 +7,12 @@
 #
 ###
 
-__version__ = "0.1.6"
+__version__ = "0.2.1"
 
 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"
@@ -31,24 +33,21 @@ 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 = {}
-NODE_DISPLAY_NAME_MAPPINGS = {}
-NODE_CLASS_MAPPINGS_DEBUG = {}
+NODE_CLASS_MAPPINGS: dict[str, type] = {}
+NODE_DISPLAY_NAME_MAPPINGS: dict[str, str] = {}
+NODE_CLASS_MAPPINGS_DEBUG: dict[str, str | None] = {}
 WEB_DIRECTORY = "./web"
 
 
 def extract_nodes_from_source(filename: Path):
     source_code = ""
-
     source_code = filename.read_text(encoding="utf-8")
-
-    nodes = []
+    nodes: list[str] = []
 
     try:
         parsed = ast.parse(source_code)
@@ -57,14 +56,15 @@ 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)
-                    node_value = ast.parse(value).body[0].value
-                    if isinstance(node_value, (ast.List, ast.Tuple)):
-                        nodes.extend(
-                            element.id
-                            for element in node_value.elts
-                            if isinstance(element, ast.Name)
-                        )
-                    break
+                    if value:
+                        node_value = ast.parse(value).body[0].value
+                        if isinstance(node_value, ast.List | ast.Tuple):
+                            nodes.extend(
+                                str(element.id)
+                                for element in node_value.elts
+                                if isinstance(element, ast.Name)
+                            )
+                        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 = []
-    nodes_failed = []
+    nodes: list[type] = []
+    nodes_failed: list[str] = []
 
     for filename in (here / "nodes").iterdir():
         if filename.suffix == ".py":
@@ -124,7 +124,8 @@ def uninstall_old_web_extensions():
                 shutil.rmtree(web_mtb)
         except Exception as e:
             log.warning(
-                f"Failed to remove web mtb directory: {e}\nPlease manually remove it from disk ({web_mtb}) and restart the server."
+                f"""Failed to remove web mtb directory: {e}
+Please manually remove it from disk ({web_mtb}) and restart the server."""
             )
 
 
@@ -141,7 +142,7 @@ def wiki_to_classname(s: str):
 
 def classname_to_wiki(s: str):
     classname = s.replace("MTB_", "")
-    parts = []
+    parts: list[str] = []
     start = 0
     for i in range(1, len(classname)):
         if classname[i].isupper():
@@ -161,8 +162,6 @@ if wiki.exists() and wiki.is_dir():
 
 
 # - REGISTER NODES
-
-
 MTB_EXPORT = os.environ.get("MTB_EXPORT")
 
 nodes, failed = load_nodes()
@@ -179,7 +178,7 @@ 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"
                 )
 
@@ -192,12 +191,15 @@ 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 (cont): and until I find a way to automate the conversion, I'll leave it like this
+
+    # TODO: I removed this, I find it more convenient to write without spaces
+    # 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]
@@ -215,29 +217,31 @@ log.debug(
     )
 )
 
-log.info(f"loaded {cyan_text(len(nodes))} nodes successfuly")
+log.info(f"loaded {cyan_text(str(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"):
-    restore_deps = ["basicsr"]
-    onnx_deps = ["onnxruntime"]
-    swap_deps = ["insightface"] + onnx_deps
-    node_dependency_mapping = {
-        "QrCode": ["qrcode"],
-        "DeepBump": onnx_deps,
-        "FaceSwap": swap_deps,
-        "LoadFaceSwapModel": swap_deps,
-        "LoadFaceAnalysisModel": restore_deps,
-    }
+    img_cache = None
+    prompt_cache = None
+
+    with contextlib.suppress(ImportError):
+        from cachetools import TTLCache
+
+        img_cache = TTLCache(maxsize=100, ttl=5)  # 1 min TTL
+        prompt_cache = TTLCache(maxsize=100, ttl=5)  # 1 min TTL
+
+    node_dependency_mapping = get_node_dependencies()
 
     PromptServer.instance.app.router.add_static(
         "/mtb-assets/", path=(here / "html").as_posix()
@@ -306,10 +310,10 @@ if hasattr(PromptServer, "instance"):
             }
         )
 
-    @PromptServer.instance.routes.post("/mtb/debug")
-    async def set_debug(request):
-        json_data = await request.json()
-        enabled = json_data.get("enabled")
+    @PromptServer.instance.routes.post("/mtb/server-info")
+    async def set_server_info(request: Request):
+        json_data: dict[str, bool] = await request.json()
+        enabled = json_data.get("debug")
         if enabled:
             os.environ["MTB_DEBUG"] = "true"
             log.setLevel(logging.DEBUG)
@@ -317,7 +321,7 @@ 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(
@@ -325,17 +329,17 @@ if hasattr(PromptServer, "instance"):
         )
 
     @PromptServer.instance.routes.get("/mtb")
-    async def get_home(request):
+    async def get_home(request: 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/debug">debug</a>
+                <a href="/mtb/server-info">Server Info</a>
                 <a href="/mtb/status">status</a>
             </div>
             """
@@ -347,28 +351,176 @@ if hasattr(PromptServer, "instance"):
         # Return JSON for other requests
         return web.json_response({"message": "Welcome to MTB!"})
 
-    @PromptServer.instance.routes.get("/mtb/debug")
-    async def get_debug(request):
+    import asyncio
+    import os
+    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)
-        enabled = "MTB_DEBUG" in os.environ
+        _ = reload(endpoint)
+        isdebug = "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 = f"""
-                <h1>MTB Debug Status: {'Enabled' if enabled else 'Disabled'}</h1>
-            """
+            html_response = ""
+
+            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("Debug", html_response),
+                text=endpoint.render_base_template(
+                    "Server Info", html_response
+                ),
                 content_type="text/html",
             )
 
         # Return JSON for other requests
-        return web.json_response({"enabled": enabled})
+        return web.json_response({"exposed": exposed, "debug": isdebug})
 
     @PromptServer.instance.routes.get("/mtb/actions")
-    async def no_route(request):
+    async def no_route(request: Request):
         from . import endpoint
 
         if "text/html" in request.headers.get("Accept", ""):
@@ -382,7 +534,7 @@ 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):
+    async def do_action(request: Request):
         from . import endpoint
 
         reload(endpoint)

endpoint.py

@@ -1,10 +1,20 @@
 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,
@@ -14,18 +24,25 @@ from .utils import (
 endlog = mklog("mtb endpoint")
 
 # - ACTIONS
-import sys
-from pathlib import Path
-
 import_install("requirements")
 
 
-def ACTIONS_installDependency(dependency_names=None):
+def ACTIONS_installDependency(dependency_names: list[str] | None = 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
@@ -50,6 +67,106 @@ def ACTIONS_installDependency(dependency_names=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
 
@@ -97,7 +214,7 @@ def ACTIONS_saveStyle(data):
             csv_writer.writerow(row)
 
 
-async def do_action(request) -> web.Response:
+async def do_action(request: web.Request) -> web.Response:
     endlog.debug("Init action request")
     request_data = await request.json()
     name = request_data.get("name")
@@ -109,7 +226,12 @@ async def do_action(request) -> web.Response:
     method = globals().get(method_name)
 
     if callable(method):
-        result = method(args) if args else method()
+        result = None
+        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})
 
@@ -130,10 +252,13 @@ async def do_action(request) -> web.Response:
 # - HTML UTILS
 
 
-def dependencies_button(name, dependencies):
+def dependencies_button(name: str, dependencies: list[str]) -> str:
     deps = ",".join([f"'{x}'" for x in dependencies])
     return f"""
-        <button class="dependency-button" onclick="window.mtb_action('installDependency',[{deps}])">Install {name} deps</button>
+        <button
+            class="dependency-button"
+            onclick="window.mtb_action('installDependency',[{deps}])"
+        >Install {name} deps</button>
         """
 
 
@@ -153,7 +278,7 @@ 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>"
@@ -215,11 +340,14 @@ def render_tab_view(**kwargs):
     """
 
 
-def add_foldable_region(title, content):
+def add_foldable_region(title: str, content: str):
     symbol_id = f"{title}-symbol"
     return f"""
     <div class='foldable'>
-        <div class='foldable-title' onclick="toggleFoldable('{title}', '{symbol_id}')">
+        <div
+            class='foldable-title'
+            onclick="toggleFoldable('{title}', '{symbol_id}')"
+        >
             <span id='{symbol_id}' class='foldable-symbol'>&#9655;</span>
             {title}
         </div>
@@ -231,7 +359,9 @@ def add_foldable_region(title, content):
     """
 
 
-def add_split_pane(left_content, right_content, vertical=True):
+def add_split_pane(
+    left_content: str, right_content: str, *, vertical: bool = True
+):
     orientation = "vertical" if vertical else "horizontal"
     return f"""
     <div class="split-pane {orientation}">
@@ -250,7 +380,7 @@ def add_split_pane(left_content, right_content, vertical=True):
     """
 
 
-def add_dropdown(title, options):
+def add_dropdown(title: str, options: list[str]):
     option_str = "\n".join(
         [f"<option value='{opt}'>{opt}</option>" for opt in options]
     )
@@ -262,13 +392,13 @@ def add_dropdown(title, options):
     """
 
 
-def render_table(table_dict, sort=True, title=None):
-    table_dict = sorted(
+def render_table(table_dict: dict[str, Any], sort=True, title=None):
+    table_list = sorted(
         table_dict.items(), key=lambda item: item[0]
     )  # Sort the dictionary by keys
 
     table_rows = ""
-    for name, item in table_dict:
+    for name, item in table_list:
         if isinstance(item, dict):
             if "dependencies" in item:
                 table_rows += f"<tr><td>{name}</td><td>"
@@ -299,12 +429,12 @@ def render_table(table_dict, sort=True, title=None):
             <tbody>
                 {table_rows}
             </tbody>
-        </table>      
+        </table>
         </div>
         """
 
 
-def render_base_template(title, content):
+def render_base_template(title: str, content: str):
     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>
@@ -340,7 +470,9 @@ def render_base_template(title, content):
         <header>
         <a href="/">Back to Comfy</a>
         <div class="mtb_logo">
-            <img src="https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873" alt="Comfy MTB Logo" height="70" width="128">
+            <img
+                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}
@@ -355,6 +487,6 @@ def render_base_template(title, content):
             <!-- 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 {[]}) --preview-method auto ...(if $listen {["--listen"]} else {[]})
+    MTB_DEBUG=true python main.py --port 3000 ...(if $old_ui { ["--front-end-version", "Comfy-Org/ComfyUI_legacy_frontend@latest"]} else {[ --front-end-version Comfy-Org/ComfyUI_frontend@latest]}) --preview-method auto ...(if $listen {["--listen"]} else {[]})
 }
 
 # update comfy itself and merge master in current branch
@@ -67,8 +67,14 @@ export def "comfy update" [
     git checkout master
 
     print $"(ansi yellow_italic)Fetching and pulling remote updates(ansi reset)"
-    git fetch
-    git pull
+    if ($clean) {
+        git fetch local master
+        git pull local master
+    } else {
+        git fetch
+        git pull
+    }
+
 
     print $"(ansi yellow_italic)Back to our branch \(($branch_name)\)(ansi reset)"
     git checkout -
@@ -135,7 +141,7 @@ export def "comfy update_extensions" [--clean] {
     let root = get_root --clean=($clean)
     cd $root
     cd custom_nodes
-    git multipull .
+    git multipull . -s -q
 }
 
 def --env path-add [pth] {
@@ -146,7 +152,7 @@ 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
 
@@ -154,6 +160,12 @@ 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

@@ -43,10 +43,28 @@ 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
@@ -124,12 +142,12 @@ 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(sys.stdout.encoding, errors="replace").decode(
-        sys.stdout.encoding
-    )
-    encoded_text = formatted_text.encode(sys.stdout.encoding, errors="replace").decode(
-        sys.stdout.encoding
-    )
+    encoded_header = header.encode(
+        sys.stdout.encoding, errors="replace"
+    ).decode(sys.stdout.encoding)
+    encoded_text = formatted_text.encode(
+        sys.stdout.encoding, errors="replace"
+    ).decode(sys.stdout.encoding)
 
     print(
         " " * len(encoded_header)
@@ -163,7 +181,9 @@ def run_command(cmd, ignored_lines_start=None):
     try:
         _run_command(shell_cmd, ignored_lines_start)
     except subprocess.CalledProcessError as e:
-        print(f"Command failed with return code: {e.returncode}", file=sys.stderr)
+        print(
+            f"Command failed with return code: {e.returncode}", file=sys.stderr
+        )
         print(e.stderr.strip(), file=sys.stderr)
 
     except KeyboardInterrupt:
@@ -238,7 +258,7 @@ 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, "r") as f:
+        with open(init_file) as f:
             tree = ast.parse(f.read())
             for node in ast.walk(tree):
                 if isinstance(node, ast.Assign):
@@ -256,13 +276,16 @@ 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 open(file_name, "wb") as file, tqdm(
-            desc=file_name.stem,
-            total=total_size,
-            unit="B",
-            unit_scale=True,
-            unit_divisor=1024,
-        ) as progress_bar:
+        with (
+            open(file_name, "wb") as file,
+            tqdm(
+                desc=file_name.stem,
+                total=total_size,
+                unit="B",
+                unit_scale=True,
+                unit_divisor=1024,
+            ) as progress_bar,
+        ):
             for chunk in response.iter_content(chunk_size=8192):
                 file.write(chunk)
                 progress_bar.update(len(chunk))
@@ -302,7 +325,9 @@ def import_or_install(requirement, dry=False):
     pip_install_name = pip_name + pip_spec
 
     if not installed:
-        print_formatted(f"Installing package {pip_name}...", "italic", color="yellow")
+        print_formatted(
+            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}').",
@@ -310,7 +335,9 @@ def import_or_install(requirement, dry=False):
             )
         else:
             try:
-                run_command([executable, "-m", "pip", "install", pip_install_name])
+                run_command(
+                    [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",
@@ -326,13 +353,9 @@ 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(
@@ -361,7 +384,9 @@ except ImportError:
 def main():
     if len(sys.argv) == 1:
         print_formatted(
-            "mtb doesn't need an install script anymore.", "italic", color="yellow"
+            "mtb doesn't need an install script anymore.",
+            "italic",
+            color="yellow",
         )
         return
     if all(arg not in ("-p", "--path") for arg in sys.argv):
@@ -397,8 +422,12 @@ def main():
         else:
             repo_dir = clone_dir / repo_name
             if not repo_dir.exists():
-                print_formatted(f"Cloning to {repo_dir}...", "italic", color="yellow")
-                run_command(["git", "clone", "--recursive", repo_url, repo_dir])
+                print_formatted(
+                    f"Cloning to {repo_dir}...", "italic", color="yellow"
+                )
+                run_command(
+                    ["git", "clone", "--recursive", repo_url, repo_dir]
+                )
             else:
                 print_formatted(
                     f"Directory {repo_dir} already exists, we will update it..."
@@ -409,7 +438,14 @@ def main():
 
     print_formatted("Checking environment...", "italic", color="yellow")
     missing_deps = []
-    install_cmd = [executable, "-m", "pip", "install", "-r", "requirements.txt"]
+    install_cmd = [
+        executable,
+        "-m",
+        "pip",
+        "install",
+        "-r",
+        "requirements.txt",
+    ]
     run_command(install_cmd)
 
     print_formatted(

nodes/batch.py

@@ -1,4 +1,5 @@
 from io import BytesIO
+from typing import Literal
 
 import cv2
 import numpy as np
@@ -410,7 +411,14 @@ class MTB_BatchFloat:
     RETURN_TYPES = ("FLOATS",)
     CATEGORY = "mtb/batch"
 
-    def set_floats(self, mode, count, min, max, easing):
+    def set_floats(
+        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"
@@ -429,6 +437,210 @@ 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"""
 
@@ -711,7 +923,9 @@ 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(colors, kwargs.items()):
+        for color, (label, values) in zip(
+            colors, kwargs.items(), strict=False
+        ):
             ax.plot(x_values[: len(values)], values, label=label, color=color)
         ax.legend(
             title="Legend",
@@ -1026,17 +1240,19 @@ class MTB_BatchShake:
 
 
 __nodes__ = [
-    MTB_BatchFloat,
     MTB_Batch2dTransform,
-    MTB_BatchShape,
-    MTB_BatchMake,
+    MTB_BatchFloat,
     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

@@ -3,10 +3,127 @@ 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
@@ -213,4 +330,9 @@ class MTB_StylesLoader:
         return (self.options[style_name][0], self.options[style_name][1])
 
 
-__nodes__ = [MTB_SmartStep, MTB_StylesLoader, MTB_InterpolateClipSequential]
+__nodes__ = [
+    MTB_SmartStep,
+    MTB_StylesLoader,
+    MTB_InterpolateClipSequential,
+    MTB_InterpolateCondition,
+]

nodes/crop.py

@@ -59,6 +59,36 @@ 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"""
 
@@ -342,4 +372,11 @@ class MTB_Uncrop:
         return (pil2tensor(out_images),)
 
 
-__nodes__ = [MTB_BboxFromMask, MTB_Bbox, MTB_Crop, MTB_Uncrop, MTB_SplitBbox]
+__nodes__ = [
+    MTB_BboxFromMask,
+    MTB_Bbox,
+    MTB_Crop,
+    MTB_Uncrop,
+    MTB_SplitBbox,
+    MTB_UpscaleBboxBy,
+]

nodes/faceenhance.py

@@ -78,6 +78,7 @@ 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
@@ -163,6 +164,7 @@ class MTB_RestoreFace:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "restore"
     CATEGORY = "mtb/facetools"
+    DEPRECATED = True
 
     @classmethod
     def INPUT_TYPES(cls):

nodes/faceswap.py

@@ -40,6 +40,7 @@ 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":
@@ -77,6 +78,7 @@ 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)
@@ -126,6 +128,7 @@ class MTB_FaceSwap:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "swap"
     CATEGORY = "mtb/facetools"
+    DEPRECATED = True
 
     def swap(
         self,

nodes/graph_utils.py

@@ -44,14 +44,22 @@ 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": (devices, {"default": "cpu"}),
+                "device": (
+                    devices,
+                    {
+                        "default": "cuda"
+                        if torch.cuda.is_available()
+                        else "cpu"
+                    },
+                ),
             },
             "optional": {
                 "image": ("IMAGE",),
@@ -67,20 +75,36 @@ class MTB_ToDevice:
     def to_device(
         self,
         *,
-        ignore_errors=False,
-        device="cuda",
+        ignore_errors: bool = False,
+        device: str = "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

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import List
 
 import comfy
 import comfy.model_management as model_management
@@ -15,10 +14,13 @@ 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"]]
@@ -37,6 +39,7 @@ 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)
@@ -56,7 +59,10 @@ 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):
@@ -71,6 +77,7 @@ class MTB_FilmInterpolation:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "do_interpolation"
     CATEGORY = "mtb/frame iterpolation"
+    DEPRECATED = True
 
     def do_interpolation(
         self,

nodes/image_processing.py

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

nodes/image_utils.py

@@ -1,4 +1,11 @@
+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
 
@@ -8,13 +15,21 @@ class MTB_StackImages:
 
     @classmethod
     def INPUT_TYPES(cls):
-        return {"required": {"vertical": ("BOOLEAN", {"default": False})}}
+        return {
+            "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, **kwargs):
+    def stack(self, vertical, match_method="error", **kwargs):
         if not kwargs:
             raise ValueError("At least one tensor must be provided.")
 
@@ -32,23 +47,50 @@ class MTB_StackImages:
             self.duplicate_frames(tensor, max_batch_size)
             for tensor in normalized_tensors
         ]
-
-        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."
+        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)
                 )
-            dim = 1
+                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:
-            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
+            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
 
         stacked_tensor = torch.cat(normalized_tensors, dim=dim)
 
@@ -64,7 +106,7 @@ 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(
@@ -87,6 +129,30 @@ 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.
@@ -113,11 +179,6 @@ 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:]
@@ -127,4 +188,87 @@ class MTB_PickFromBatch:
         return (selected_tensors,)
 
 
-__nodes__ = [MTB_StackImages, MTB_PickFromBatch]
+import folder_paths
+
+
+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
@@ -41,6 +41,7 @@ class MTB_ReadPlaylist:
     RETURN_TYPES = ("PLAYLIST",)
     FUNCTION = "read_playlist"
     CATEGORY = "mtb/IO"
+    EXPERIMENTAL = True
 
     def read_playlist(
         self,
@@ -83,6 +84,7 @@ class MTB_AddToPlaylist:
     OUTPUT_NODE = True
     FUNCTION = "add_to_playlist"
     CATEGORY = "mtb/IO"
+    EXPERIMENTAL = True
 
     def add_to_playlist(
         self,
@@ -117,7 +119,10 @@ class MTB_AddToPlaylist:
 
 
 class MTB_ExportWithFfmpeg:
-    """Export with FFmpeg (Experimental)"""
+    """Export with FFmpeg (Experimental).
+
+    [DEPRACATED] Use VHS nodes instead
+    """
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -143,6 +148,7 @@ class MTB_ExportWithFfmpeg:
     RETURN_TYPES = ("VIDEO",)
     OUTPUT_NODE = True
     FUNCTION = "export_prores"
+    DEPRECATED = True
     CATEGORY = "mtb/IO"
 
     def export_prores(
@@ -151,10 +157,9 @@ class MTB_ExportWithFfmpeg:
         prefix: str,
         format: str,
         codec: str,
-        images: Optional[torch.Tensor] = None,
-        playlist: Optional[List[str]] = None,
+        images: torch.Tensor | None = None,
+        playlist: list[str] | None = None,
     ):
-        pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p"
         file_ext = format
         file_id = f"{prefix}_{uuid.uuid4()}.{file_ext}"
 
@@ -208,9 +213,11 @@ 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",
@@ -233,12 +240,28 @@ class MTB_ExportWithFfmpeg:
 
             process.stdin.close()
             process.wait()
+            return (out_path,)
         else:
-            frames = [frame.astype(np.uint16) * 257 for frame in frames]
-
-        height, width, _ = frames[0].shape
-
-        out_path = (output_dir / file_id).as_posix()
+            if has_alpha:
+                if codec in ["prores_ks", "libx264", "libx265"]:
+                    pix_fmt = (
+                        "yuva444p" if codec == "prores_ks" else "yuva420p"
+                    )
+                    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 = [
@@ -258,17 +281,26 @@ 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()
@@ -280,9 +312,9 @@ def prepare_animated_batch(
     batch: torch.Tensor,
     pingpong=False,
     resize_by=1.0,
-    resample_filter: Optional[Image.Resampling] = None,
+    resample_filter: Image.Resampling | None = None,
     image_type=np.uint8,
-) -> List[Image.Image]:
+) -> list[Image.Image]:
     images = tensor2np(batch)
     images = [frame.astype(image_type) for frame in images]
 
@@ -308,7 +340,10 @@ 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):
@@ -328,6 +363,7 @@ class MTB_SaveGif:
     OUTPUT_NODE = True
     CATEGORY = "mtb/IO"
     FUNCTION = "save_gif"
+    DEPRECATED = True
 
     def save_gif(
         self,

nodes/ltx.py

@@ -0,0 +1,161 @@
+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

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

nodes/postshot.py

@@ -0,0 +1,351 @@
+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

@@ -45,6 +45,19 @@ class MTB_TransformImage:
                 ),
                 "constant_color": ("COLOR", {"default": "#000000"}),
             },
+            "optional": {
+                "filter_type": (
+                    [
+                        "nearest",
+                        "box",
+                        "bilinear",
+                        "hamming",
+                        "bicubic",
+                        "lanczos",
+                    ],
+                    {"default": "bilinear"},
+                ),
+            },
         }
 
     FUNCTION = "transform"
@@ -61,7 +74,18 @@ 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)
@@ -115,7 +139,12 @@ class MTB_TransformImage:
             img = cast(
                 Image.Image,
                 TF.affine(
-                    img, angle=angle, scale=zoom, translate=[x, y], shear=shear
+                    img,
+                    angle=angle,
+                    scale=zoom,
+                    translate=[x, y],
+                    shear=shear,
+                    interpolation=resampling_filter,
                 ),
             )
 

nodes/video_editing.py

@@ -0,0 +1,458 @@
+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

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

requirements.txt

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

utils.py

@@ -2,6 +2,7 @@ import contextlib
 import functools
 import importlib
 import math
+import operator
 import os
 import shlex
 import shutil
@@ -11,6 +12,7 @@ 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
 
@@ -163,9 +165,9 @@ 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()
+        self.checked_ips: set[str] = set()
 
-    def get_working_ip(self, test_url_template):
+    def get_working_ip(self, test_url_template: str):
         for ip in self.ips:
             if ip not in self.checked_ips:
                 self.checked_ips.add(ip)
@@ -175,7 +177,7 @@ class IPChecker:
         return None
 
     @staticmethod
-    def get_local_ips(prefix="192.168."):
+    def get_local_ips(prefix: str = "192.168."):
         hostname = socket.gethostname()
         log.debug(f"Getting local ips for {hostname}")
         for info in socket.getaddrinfo(hostname, None):
@@ -185,9 +187,9 @@ class IPChecker:
             if info[0] == socket.AF_INET and info[4][0].startswith(prefix):
                 yield info[4][0]
 
-    def _test_url(self, url):
+    def _test_url(self, url: str):
         try:
-            response = requests.get(url)
+            response = requests.get(url, timeout=10)
             return response.status_code == 200
         except Exception:
             return False
@@ -198,7 +200,7 @@ def get_server_info():
     from comfy.cli_args import args
 
     ip_checker = IPChecker()
-    base_url = args.listen
+    base_url: str = 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(
@@ -212,6 +214,37 @@ 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
@@ -465,8 +498,12 @@ 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
@@ -476,9 +513,10 @@ font_path = here / "data" / "font.ttf"
 extern_root = here / "extern"
 add_path(extern_root)
 
-for pth in extern_root.iterdir():
-    if pth.is_dir():
-        add_path(pth)
+if extern_root.exists():
+    for pth in extern_root.iterdir():
+        if pth.is_dir():
+            add_path(pth)
 
 # - Add the ComfyUI directory and custom nodes path to the sys.path list
 add_path(comfy_dir)
@@ -592,6 +630,37 @@ 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

@@ -1,10 +1,9 @@
 /**
+ * @module Shared utilities
  * File: comfy_shared.js
  * Project: comfy_mtb
  * Author: Mel Massadian
- *
  * Copyright (c) 2023-2024 Mel Massadian
- *
  */
 
 // Reference the shared typedefs file
@@ -261,6 +260,16 @@ 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
@@ -362,23 +371,37 @@ 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 {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} opts
+ * @param {{separator?:string, start_index?:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} [opts] Extra options
  * @returns
  */
-export const setupDynamicConnections = (nodeType, prefix, inputType, opts) => {
+export const setupDynamicConnections = (
+  nodeType,
+  prefix,
+  inputType,
+  opts = undefined,
+) => {
   infoLogger(
     'Setting up dynamic connections for',
     Object.getOwnPropertyDescriptors(nodeType).title.value,
   )
 
-  /** @type {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} */
-  const options = opts || {}
+  /** @type {{separator:string, start_index:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} */
+  const options = Object.assign(
+    {
+      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(`${prefix}_1`, inputList ? '*' : inputType)
+    this.addInput(
+      `${prefix}${options.separator}${options.start_index}`,
+      inputList ? '*' : inputType,
+    )
     return r
   }
 
@@ -413,7 +436,7 @@ export const setupDynamicConnections = (nodeType, prefix, inputType, opts) => {
       this,
       slotIndex,
       isConnected,
-      `${prefix}_`,
+      `${prefix}${options.separator}`,
       inputType,
       options,
     )
@@ -429,7 +452,7 @@ export const setupDynamicConnections = (nodeType, prefix, inputType, opts) => {
  * @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 {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options
+ * @param {{start_index?:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options
  */
 export const dynamic_connection = (
   node,
@@ -439,13 +462,18 @@ export const dynamic_connection = (
   connectionType = '*',
   opts = undefined,
 ) => {
-  /* @type {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options*/
-  const options = opts || {}
+  /* {{start_index:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options*/
+  const options = Object.assign(
+    {
+      start_index: 1,
+    },
+    opts || {},
+  )
 
-  if (
-    node.inputs.length > 0 &&
-    !node.inputs[index].name.startsWith(connectionPrefix)
-  ) {
+  // function to test if input is a dynamic one
+  const isDynamicInput = (inputName) => inputName.startsWith(connectionPrefix)
+
+  if (node.inputs.length > 0 && !isDynamicInput(node.inputs[index].name)) {
     return
   }
 
@@ -464,7 +492,7 @@ export const dynamic_connection = (
     const to_remove = []
     for (let n = 1; n < node.inputs.length; n++) {
       const element = node.inputs[n]
-      if (!element.link) {
+      if (!element.link && isDynamicInput(element.name)) {
         if (node.widgets) {
           const w = node.widgets.find((w) => w.name === element.name)
           if (w) {
@@ -490,14 +518,25 @@ 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 = `${connectionPrefix}${i + 1}`
+      let name = ''
+      // 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
       }
 
-      node.inputs[i].label = name
+      // preserve label if it exists
+      node.inputs[i].label = node.inputs[i].label || name
       node.inputs[i].name = name
     }
   }
@@ -537,11 +576,16 @@ export const dynamic_connection = (
     if (node.inputs.length === 0) return
     // add an extra input
     if (node.inputs[node.inputs.length - 1].link !== null) {
-      const nextIndex = node.inputs.length
+      // count only the prefixed inputs
+      const nextIndex = node.inputs.reduce(
+        (acc, cur) => (isDynamicInput(cur.name) ? ++acc : acc),
+        0,
+      )
+
       const name =
         nextIndex < nameArray.length
           ? nameArray[nextIndex]
-          : `${connectionPrefix}${nextIndex + 1}`
+          : `${connectionPrefix}${nextIndex + options.start_index}`
 
       infoLogger(`Adding input ${nextIndex + 1} (${name})`)
       node.addInput(name, conType)
@@ -631,39 +675,6 @@ 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
@@ -1118,7 +1129,33 @@ export const addDeprecation = (nodeType, reason) => {
 
 // #endregion
 
-// #region API / graph utilities
+// #region Actions API
+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
@@ -1171,3 +1208,4 @@ export const getNodes = (skip_unused) => {
   }
   return nodes
 }
+// #endregion

web/debug.js

@@ -14,6 +14,7 @@ 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...
 
@@ -36,12 +37,10 @@ app.registerExtension({
   async beforeRegisterNodeDef(nodeType, nodeData, app) {
     if (nodeData.name === 'Debug (mtb)') {
       const onNodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function () {
+      nodeType.prototype.onNodeCreated = function (...args) {
         this.options = {}
-        const r = onNodeCreated
-          ? onNodeCreated.apply(this, arguments)
-          : undefined
-        this.addInput(`anything_1`, '*')
+        const r = onNodeCreated ? onNodeCreated.apply(this, args) : undefined
+        this.addInput('anything_1', '*')
         return r
       }
 
@@ -81,14 +80,16 @@ app.registerExtension({
       }
 
       const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (data) {
-        onExecuted?.apply(this, arguments)
+      nodeType.prototype.onExecuted = function (...args) {
+        onExecuted?.apply(this, args)
+        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?.()
             }
           }
@@ -98,19 +99,32 @@ app.registerExtension({
         // console.log(message)
         if (data.text) {
           for (const txt of data.text) {
-            const w = this.addCustomWidget(
-              MtbWidgets.DEBUG_STRING(`${prefix}_${widgetI}`, escapeHtml(txt)),
+            const textDom = mtb_ui.makeElement('p', { fontFamily: 'monospace' })
+            textDom.innerHTML = txt
+
+            this.addDOMWidget(
+              `${prefix}_${widgetI}`,
+              'CUSTOM_TEXT',
+              textDom,
+              {},
             )
-            w.parent = this
             widgetI++
           }
         }
         if (data.b64_images) {
           for (const img of data.b64_images) {
-            const w = this.addCustomWidget(
-              MtbWidgets.DEBUG_IMG(`${prefix}_${widgetI}`, img),
+            const imgDom = mtb_ui.makeElement('img', { width: '100%' })
+            imgDom.src = img
+
+            this.addDOMWidget(
+              `${prefix}_${widgetI}`,
+              'CUSTOM_IMG_B64',
+              mtb_ui.wrapElement(imgDom, {
+                overflow: 'hidden',
+              }),
+              {},
             )
-            w.parent = this
+
             widgetI++
           }
         }
@@ -119,12 +133,13 @@ app.registerExtension({
 
         this.onRemoved = function () {
           // When removing this node we need to remove the input from the DOM
-          for (let y in this.widgets) {
+          for (const 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: {
-    position: 'fixed',
-    top: 0,
-    left: 0,
-    width: '100vw',
-    height: '100vh',
-    background: 'rgba(0,0,0,0.5)',
-    display: 'none',
-    justifyContent: 'center',
-    alignItems: 'center',
-    zIndex: 999,
-  },
-  lightboxBtn: (extra) => ({
-    position: 'absolute',
-    top: '50%',
-    background: 'none',
-    border: 'none',
-    color: '#fff',
-    zIndex: 1000,
-    fontSize: '30px',
-    cursor: 'pointer',
-    pointerEvents: 'auto',
-    ...extra,
-  }),
-  img_list: {
-    minHeight: '30px',
-    maxHeight: '300px',
-    width: '100vw',
-    position: 'absolute',
-    bottom: 0,
-    zIndex: 10,
-    background: '#333',
-    overflow: 'auto',
-  },
+	lighbox: {
+		position: 'fixed',
+		top: 0,
+		left: 0,
+		width: '100vw',
+		height: '100vh',
+		background: 'rgba(0,0,0,0.5)',
+		display: 'none',
+		justifyContent: 'center',
+		alignItems: 'center',
+		zIndex: 999,
+	},
+	lightboxBtn: (extra) => ({
+		position: 'absolute',
+		top: '50%',
+		background: 'none',
+		border: 'none',
+		color: '#fff',
+		zIndex: 1000,
+		fontSize: '30px',
+		cursor: 'pointer',
+		pointerEvents: 'auto',
+		...extra,
+	}),
+	img_list: {
+		minHeight: '30px',
+		maxHeight: '300px',
+		width: '100vw',
+		position: 'absolute',
+		bottom: 0,
+		zIndex: 10,
+		background: '#333',
+		overflow: 'auto',
+	},
 }
 
 let currentImageIndex = 0
@@ -58,298 +58,299 @@ const storage = new LocalStorageManager('mtb')
 let activated = storage.get('image_feed', false)
 
 app.registerExtension({
-  name: 'mtb.ImageFeed',
-  setup: () => {
-    app.ui.settings.addSetting({
-      id: 'mtb.imageFeed.enabled',
-      name: '[⚡mtb] Enable image feed',
-      type: 'boolean',
-      defaultValue: true,
-      attrs: {
-        style: {
-          fontFamily: 'monospace',
-        },
-      },
-      async onChange(value) {
-        storage.set('image_feed', value)
-        activated = value
-      },
-    })
-  },
-  init: async () => {
-    if (!activated) {
-      return
-    }
-    const pythongossFeed = app.extensions.find(
-      (e) => e.name === 'pysssss.ImageFeed',
-    )
-    if (pythongossFeed) {
-      console.warn(
-        "[mtb] - Aborting the loading of mtb's imageFeed in favor of pysssss.ImageFeed",
-      )
-      activated = false // just in case other methods are added later on
-      return
-    }
-    // - HTML & CSS
-    //- lightbox
-    const lightboxContainer = document.createElement('div')
-    Object.assign(lightboxContainer.style, styles.lighbox)
+	name: 'mtb.ImageFeed',
+	setup: () => {
+		app.ui.settings.addSetting({
+			id: 'mtb.Main.image-feed-enabled',
+			category: ['mtb', 'Main', 'image-feed-enabled'],
+			name: 'Enable Image Feed',
+			type: 'boolean',
+			defaultValue: false,
+			attrs: {
+				style: {
+					fontFamily: 'monospace',
+				},
+			},
+			async onChange(value) {
+				storage.set('image_feed', value)
+				activated = value
+			},
+		})
+	},
+	init: async () => {
+		if (!activated) {
+			return
+		}
+		const pythongossFeed = app.extensions.find(
+			(e) => e.name === 'pysssss.ImageFeed',
+		)
+		if (pythongossFeed) {
+			console.warn(
+				"[mtb] - Aborting the loading of mtb's imageFeed in favor of pysssss.ImageFeed",
+			)
+			activated = false // just in case other methods are added later on
+			return
+		}
+		// - HTML & CSS
+		//- lightbox
+		const lightboxContainer = document.createElement('div')
+		Object.assign(lightboxContainer.style, styles.lighbox)
 
-    const lightboxImage = document.createElement('img')
-    Object.assign(lightboxImage.style, {
-      maxHeight: '100%',
-      maxWidth: '100%',
-      borderRadius: '5px',
-    })
+		const lightboxImage = document.createElement('img')
+		Object.assign(lightboxImage.style, {
+			maxHeight: '100%',
+			maxWidth: '100%',
+			borderRadius: '5px',
+		})
 
-    // previous and next buttons
-    const lightboxPrevBtn = document.createElement('button')
-    const lightboxNextBtn = document.createElement('button')
+		// previous and next buttons
+		const lightboxPrevBtn = document.createElement('button')
+		const lightboxNextBtn = document.createElement('button')
 
-    lightboxPrevBtn.textContent = '❮'
-    lightboxNextBtn.textContent = '❯'
+		lightboxPrevBtn.textContent = '❮'
+		lightboxNextBtn.textContent = '❯'
 
-    Object.assign(lightboxPrevBtn.style, styles.lightboxBtn({ left: '0%' }))
-    Object.assign(lightboxNextBtn.style, styles.lightboxBtn({ right: '0%' }))
+		Object.assign(lightboxPrevBtn.style, styles.lightboxBtn({ left: '0%' }))
+		Object.assign(lightboxNextBtn.style, styles.lightboxBtn({ right: '0%' }))
 
-    // close button
-    const lightboxCloseBtn = document.createElement('button')
-    Object.assign(
-      lightboxCloseBtn.style,
-      styles.lightboxBtn({ right: '0', top: '0' }),
-    )
-    lightboxCloseBtn.textContent = '❌'
+		// close button
+		const lightboxCloseBtn = document.createElement('button')
+		Object.assign(
+			lightboxCloseBtn.style,
+			styles.lightboxBtn({ right: '0', top: '0' }),
+		)
+		lightboxCloseBtn.textContent = '❌'
 
-    const lightboxButtons = document.createElement('div')
-    Object.assign(lightboxButtons.style, {
-      position: 'absolute',
-      top: '0%',
-      right: '0%',
-      // transform: "translate(50%, -50%)",
-      height: '100%',
-      width: '100%',
-      background: 'none',
-      border: 'none',
-      color: '#fff',
-      fontSize: '30px',
-      cursor: 'pointer',
-      pointerEvents: 'none',
-    })
+		const lightboxButtons = document.createElement('div')
+		Object.assign(lightboxButtons.style, {
+			position: 'absolute',
+			top: '0%',
+			right: '0%',
+			// transform: "translate(50%, -50%)",
+			height: '100%',
+			width: '100%',
+			background: 'none',
+			border: 'none',
+			color: '#fff',
+			fontSize: '30px',
+			cursor: 'pointer',
+			pointerEvents: 'none',
+		})
 
-    lightboxButtons.append(lightboxPrevBtn, lightboxNextBtn, lightboxCloseBtn)
-    lightboxContainer.append(lightboxButtons, lightboxImage)
+		lightboxButtons.append(lightboxPrevBtn, lightboxNextBtn, lightboxCloseBtn)
+		lightboxContainer.append(lightboxButtons, lightboxImage)
 
-    //- image list
-    const imageListContainer = document.createElement('div')
-    Object.assign(imageListContainer.style, styles.img_list)
+		//- image list
+		const imageListContainer = document.createElement('div')
+		Object.assign(imageListContainer.style, styles.img_list)
 
-    const createImgListBtn = (text, style) => {
-      const btn = document.createElement('button')
-      btn.type = 'button'
-      btn.textContent = text
-      Object.assign(btn.style, {
-        ...style,
-        border: 'none',
-        color: '#fff',
-        background: 'none',
-        height: '20px',
-        cursor: 'pointer',
-        position: 'absolute',
-        top: '5px',
-        fontSize: '12px',
-        lineHeight: '12px',
-      })
-      imageListContainer.append(btn)
-      return btn
-    }
-    const showBtn = document.createElement('button')
-    const closeBtn = createImgListBtn('❌', {
-      width: '20px',
-      textIndent: '-4px',
-      right: '5px',
-    })
-    const loadButton = createImgListBtn('Load Session History', {
-      right: '90px',
-    })
-    const clearButton = createImgListBtn('Clear', {
-      right: '30px',
-    })
+		const createImgListBtn = (text, style) => {
+			const btn = document.createElement('button')
+			btn.type = 'button'
+			btn.textContent = text
+			Object.assign(btn.style, {
+				...style,
+				border: 'none',
+				color: '#fff',
+				background: 'none',
+				height: '20px',
+				cursor: 'pointer',
+				position: 'absolute',
+				top: '5px',
+				fontSize: '12px',
+				lineHeight: '12px',
+			})
+			imageListContainer.append(btn)
+			return btn
+		}
+		const showBtn = document.createElement('button')
+		const closeBtn = createImgListBtn('❌', {
+			width: '20px',
+			textIndent: '-4px',
+			right: '5px',
+		})
+		const loadButton = createImgListBtn('Load Session History', {
+			right: '90px',
+		})
+		const clearButton = createImgListBtn('Clear', {
+			right: '30px',
+		})
 
-    //- tools popup button
-    showBtn.classList.add('comfy-settings-btn')
-    Object.assign(showBtn.style, {
-      right: '16px',
-      cursor: 'pointer',
-      display: 'none',
-    })
+		//- tools popup button
+		showBtn.classList.add('comfy-settings-btn')
+		Object.assign(showBtn.style, {
+			right: '16px',
+			cursor: 'pointer',
+			display: 'none',
+		})
 
-    //- append to DOM
-    document.body.append(imageListContainer)
+		//- append to DOM
+		document.body.append(imageListContainer)
 
-    showBtn.textContent = '🖼'
-    showBtn.onclick = () => {
-      imageListContainer.style.display = 'block'
-      showBtn.style.display = 'none'
-    }
-    document.querySelector('.comfy-settings-btn').after(showBtn)
-    document.querySelector('.comfy-settings-btn').after(lightboxContainer)
+		showBtn.textContent = '🖼'
+		showBtn.onclick = () => {
+			imageListContainer.style.display = 'block'
+			showBtn.style.display = 'none'
+		}
+		document.querySelector('.comfy-settings-btn').after(showBtn)
+		document.querySelector('.comfy-settings-btn').after(lightboxContainer)
 
-    // for (const { output } of history) {
-    // 	if (output?.images) {
-    // 		for (const src of output.images) {
-    // 			const img = document.createElement("img");
-    // 			const but = document.createElement("button");
+		// for (const { output } of history) {
+		// 	if (output?.images) {
+		// 		for (const src of output.images) {
+		// 			const img = document.createElement("img");
+		// 			const but = document.createElement("button");
 
-    //- callbacks
-    closeBtn.onclick = () => {
-      imageListContainer.style.display = 'none'
-      showBtn.style.display = 'unset'
-    }
+		//- callbacks
+		closeBtn.onclick = () => {
+			imageListContainer.style.display = 'none'
+			showBtn.style.display = 'unset'
+		}
 
-    clearButton.onclick = () => {
-      imageListContainer.replaceChildren(closeBtn, clearButton, loadButton)
-    }
+		clearButton.onclick = () => {
+			imageListContainer.replaceChildren(closeBtn, clearButton, loadButton)
+		}
 
-    lightboxNextBtn.onclick = () => {
-      currentImageIndex = (currentImageIndex + 1) % imageUrls.length
-      const imageUrl = imageUrls[currentImageIndex]
-      lightboxImage.src = imageUrl
-    }
+		lightboxNextBtn.onclick = () => {
+			currentImageIndex = (currentImageIndex + 1) % imageUrls.length
+			const imageUrl = imageUrls[currentImageIndex]
+			lightboxImage.src = imageUrl
+		}
 
-    // Modify the lightboxPrevBtn onclick callback
-    lightboxPrevBtn.onclick = () => {
-      currentImageIndex =
-        (currentImageIndex - 1 + imageUrls.length) % imageUrls.length
-      const imageUrl = imageUrls[currentImageIndex]
-      lightboxImage.src = imageUrl
-    }
+		// Modify the lightboxPrevBtn onclick callback
+		lightboxPrevBtn.onclick = () => {
+			currentImageIndex =
+				(currentImageIndex - 1 + imageUrls.length) % imageUrls.length
+			const imageUrl = imageUrls[currentImageIndex]
+			lightboxImage.src = imageUrl
+		}
 
-    lightboxCloseBtn.onclick = () => {
-      lightboxContainer.style.display = 'none'
-    }
-    lightboxImage.onclick = lightboxNextBtn.onclick
-    /**
-     * 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
-     * the image in the lightbox.
-     * @param {*} src
-     */
-    const createImageBtn = (src) => {
-      console.debug(`making image ${src.filename}`)
-      const img = document.createElement('img')
-      const but = document.createElement('button')
+		lightboxCloseBtn.onclick = () => {
+			lightboxContainer.style.display = 'none'
+		}
+		lightboxImage.onclick = lightboxNextBtn.onclick
+		/**
+		 * 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
+		 * the image in the lightbox.
+		 * @param {*} src
+		 */
+		const createImageBtn = (src) => {
+			console.debug(`making image ${src.filename}`)
+			const img = document.createElement('img')
+			const but = document.createElement('button')
 
-      Object.assign(but.style, {
-        height: '120px',
-        width: '120px',
-        border: 'none',
-        padding: 0,
-        margin: 0,
-      })
-      Object.assign(img.style, {
-        width: '100%',
-        height: '100%',
-        objectFit: 'cover',
-      })
+			Object.assign(but.style, {
+				height: '120px',
+				width: '120px',
+				border: 'none',
+				padding: 0,
+				margin: 0,
+			})
+			Object.assign(img.style, {
+				width: '100%',
+				height: '100%',
+				objectFit: 'cover',
+			})
 
-      img.src = `/view?filename=${encodeURIComponent(src.filename)}&type=${
-        src.type
-      }&subfolder=${encodeURIComponent(src.subfolder)}`
+			img.src = `/view?filename=${encodeURIComponent(src.filename)}&type=${
+				src.type
+			}&subfolder=${encodeURIComponent(src.subfolder)}`
 
-      imageUrls.push(img.src)
+			imageUrls.push(img.src)
 
-      console.debug(img.src)
+			console.debug(img.src)
 
-      img.onload = () => {
-        but.style.width = `${120 * (img.naturalWidth / img.naturalHeight)}px`
-      }
+			img.onload = () => {
+				but.style.width = `${120 * (img.naturalWidth / img.naturalHeight)}px`
+			}
 
-      but.onclick = () => {
-        lightboxContainer.style.display = 'flex'
-        // add the same image to the lightbox
-        lightboxImage.src = img.src
-        // lighboxContainer.replaceChildren(lightboxButtons, img);
-      }
+			but.onclick = () => {
+				lightboxContainer.style.display = 'flex'
+				// add the same image to the lightbox
+				lightboxImage.src = img.src
+				// lighboxContainer.replaceChildren(lightboxButtons, img);
+			}
 
-      // add right click menu
-      but.addEventListener('contextmenu', (e) => {
-        e.preventDefault()
+			// add right click menu
+			but.addEventListener('contextmenu', (e) => {
+				e.preventDefault()
 
-        if (image_menu) {
-          image_menu.remove()
-        }
+				if (image_menu) {
+					image_menu.remove()
+				}
 
-        image_menu = document.createElement('div')
-        Object.assign(image_menu.style, {
-          position: 'absolute',
-          top: `${e.clientY}px`,
-          left: `${e.clientX}px`,
-          background: '#333',
-          color: '#fff',
-          padding: '5px',
-          borderRadius: '5px',
-          zIndex: 999,
-        })
-        const load_img = document.createElement('button')
-        load_img.textContent = 'Load'
-        load_img.onclick = () => {
-          app.handleFile(img.src)
-        }
+				image_menu = document.createElement('div')
+				Object.assign(image_menu.style, {
+					position: 'absolute',
+					top: `${e.clientY}px`,
+					left: `${e.clientX}px`,
+					background: '#333',
+					color: '#fff',
+					padding: '5px',
+					borderRadius: '5px',
+					zIndex: 999,
+				})
+				const load_img = document.createElement('button')
+				load_img.textContent = 'Load'
+				load_img.onclick = () => {
+					app.handleFile(img.src)
+				}
 
-        image_menu.appendChild(load_img)
-        document.body.appendChild(image_menu)
-      })
+				image_menu.appendChild(load_img)
+				document.body.appendChild(image_menu)
+			})
 
-      but.append(img)
-      imageListContainer.prepend(but)
-    }
+			but.append(img)
+			imageListContainer.prepend(but)
+		}
 
-    loadButton.onclick = async () => {
-      const all_history = await api.getHistory()
-      for (const history of all_history.History) {
-        if (history.outputs) {
-          for (const key of Object.keys(history.outputs)) {
-            console.debug(key)
-            if (history.outputs[key].images) {
-              for (const im of history.outputs[key].images) {
-                console.debug(im)
-                createImageBtn(im)
-              }
-            }
-          }
-          // for (const src of outputs.outputs.images) {
-          // 	console.debug(src)
-          // 	makeImage(`${src.subfolder}/${src.filename}`)
-          // }
-        }
-      }
-    }
+		loadButton.onclick = async () => {
+			const all_history = await api.getHistory()
+			for (const history of all_history.History) {
+				if (history.outputs) {
+					for (const key of Object.keys(history.outputs)) {
+						console.debug(key)
+						if (history.outputs[key].images) {
+							for (const im of history.outputs[key].images) {
+								console.debug(im)
+								createImageBtn(im)
+							}
+						}
+					}
+					// for (const src of outputs.outputs.images) {
+					// 	console.debug(src)
+					// 	makeImage(`${src.subfolder}/${src.filename}`)
+					// }
+				}
+			}
+		}
 
-    ///////-------
+		///////-------
 
-    // const all_history = await api.getHistory()
-    // for (const history of all_history.History) {
-    // 	if (history.outputs) {
-    // 		for (const key of Object.keys(history.outputs)) {
-    // 			for (const im of history.outputs[key].images) {
-    // 				makeImage(im)
-    // 			}
-    // 		}
-    // 		// for (const src of outputs.outputs.images) {
-    // 		// 	console.debug(src)
-    // 		// 	makeImage(`${src.subfolder}/${src.filename}`)
-    // 		// }
-    // 	}
-    // }
+		// const all_history = await api.getHistory()
+		// for (const history of all_history.History) {
+		// 	if (history.outputs) {
+		// 		for (const key of Object.keys(history.outputs)) {
+		// 			for (const im of history.outputs[key].images) {
+		// 				makeImage(im)
+		// 			}
+		// 		}
+		// 		// for (const src of outputs.outputs.images) {
+		// 		// 	console.debug(src)
+		// 		// 	makeImage(`${src.subfolder}/${src.filename}`)
+		// 		// }
+		// 	}
+		// }
 
-    //- Hook into the API
-    api.addEventListener('executed', ({ detail }) => {
-      if (detail?.output?.images) {
-        for (const src of detail.output.images) {
-          console.debug(`Adding ${src} to image feed`)
-          createImageBtn(src)
-        }
-      }
-    })
-  },
+		//- Hook into the API
+		api.addEventListener('executed', ({ detail }) => {
+			if (detail?.output?.images) {
+				for (const src of detail.output.images) {
+					console.debug(`Adding ${src} to image feed`)
+					createImageBtn(src)
+				}
+			}
+		})
+	},
 })

web/mtb_input_output_sidebar.js

@@ -0,0 +1,339 @@
+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

@@ -0,0 +1,28 @@
+// 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

@@ -0,0 +1,516 @@
+/**
+ * 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
+}