feat: ✨ improve the I/O sidebar

- better options (sort, count)
- uses the new toast api instead of MTB.notify

.github/workflows/publish_action.yml

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

__init__.py

@@ -7,7 +7,7 @@
 #
 ###
 
-__version__ = "0.2.1"
+__version__ = "0.1.6"
 
 import os
 
@@ -34,7 +34,6 @@ from aiohttp import web
 from server import PromptServer
 
 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
 
@@ -233,15 +232,21 @@ if failed:
 
 if hasattr(PromptServer, "instance"):
     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()
+    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,
+    }
 
     PromptServer.instance.app.router.add_static(
         "/mtb-assets/", path=(here / "html").as_posix()
@@ -310,10 +315,10 @@ if hasattr(PromptServer, "instance"):
             }
         )
 
-    @PromptServer.instance.routes.post("/mtb/server-info")
-    async def set_server_info(request: Request):
+    @PromptServer.instance.routes.post("/mtb/debug")
+    async def set_debug(request: Request):
         json_data: dict[str, bool] = await request.json()
-        enabled = json_data.get("debug")
+        enabled = json_data.get("enabled")
         if enabled:
             os.environ["MTB_DEBUG"] = "true"
             log.setLevel(logging.DEBUG)
@@ -339,7 +344,7 @@ if hasattr(PromptServer, "instance"):
             html_response = """
             <div class="flex-container menu">
                 <a href="/mtb/manage">manage</a>
-                <a href="/mtb/server-info">Server Info</a>
+                <a href="/mtb/debug">debug</a>
                 <a href="/mtb/status">status</a>
             </div>
             """
@@ -364,20 +369,16 @@ if hasattr(PromptServer, "instance"):
             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):
+    def process_preview(img: Image, preview_params):
         image_format, quality, width = preview_params
         quality = int(quality)
 
@@ -386,9 +387,7 @@ if hasattr(PromptServer, "instance"):
             img.thumbnail((width, int(width * img.height / img.width)))
 
         buffer = BytesIO()
-        img.save(
-            buffer, format=image_format, quality=quality, metadata=img.info
-        )
+        img.save(buffer, format=image_format, quality=quality)
         buffer.seek(0)
         return buffer
 
@@ -424,8 +423,6 @@ if hasattr(PromptServer, "instance"):
             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
@@ -484,40 +481,25 @@ if hasattr(PromptServer, "instance"):
 
         return await get_image_response(file, filename, preview_info, channel)
 
-    @PromptServer.instance.routes.get("/mtb/server-info")
+    @PromptServer.instance.routes.get("/mtb/debug")
     async def get_debug(request: Request):
         from . import endpoint
 
         _ = 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>"""
-
+        enabled = "MTB_DEBUG" in os.environ
         # Check if the request prefers HTML content
         if "text/html" in request.headers.get("Accept", ""):
             # # Return an HTML page
-            html_response = ""
-
-            html_response += render_property(
-                "Debug", "Enabled" if isdebug else "Disabled"
-            )
-
-            html_response += render_property("Exposed", str(exposed))
-
+            html_response = f"""
+                <h1>MTB Debug Status: {'Enabled' if enabled else 'Disabled'}</h1>
+            """
             return web.Response(
-                text=endpoint.render_base_template(
-                    "Server Info", html_response
-                ),
+                text=endpoint.render_base_template("Debug", html_response),
                 content_type="text/html",
             )
 
         # Return JSON for other requests
-        return web.json_response({"exposed": exposed, "debug": isdebug})
+        return web.json_response({"enabled": enabled})
 
     @PromptServer.instance.routes.get("/mtb/actions")
     async def no_route(request: Request):

endpoint.py

@@ -1,21 +1,18 @@
 import csv
+import os
 import secrets
 import sys
-import urllib.parse
 from pathlib import Path
-from typing import Any, Literal
+from typing import Any
 
-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,
+    input_dir,
+    output_dir,
     reqs_map,
     run_command,
     styles_dir,
@@ -27,7 +24,7 @@ endlog = mklog("mtb endpoint")
 import_install("requirements")
 
 
-def ACTIONS_installDependency(dependency_names: list[str] | None = None):
+def ACTIONS_installDependency(dependency_names=None):
     if dependency_names is None:
         # return web.Response(text="No dependency name provided", status=400)
         return {"error": "No dependency name provided"}
@@ -35,14 +32,6 @@ def ACTIONS_installDependency(dependency_names: list[str] | None = None):
     endlog.debug(f"Received Install Dependency request for {dependency_names}")
     # reqs = []
     resolved_names = [reqs_map.get(name, name) for name in dependency_names]
-    allowed_deps = list(
-        {d for dep in get_node_dependencies().values() for d in dep}
-    )
-    for dep in dependency_names:
-        if dep not in allowed_deps:
-            return {
-                "error": f"Unknown dependency: {dep}, you can only use this endpoint to install {allowed_deps}"
-            }
     try:
         run_command(
             [Path(sys.executable), "-m", "pip", "install"] + resolved_names
@@ -67,103 +56,60 @@ def ACTIONS_installDependency(dependency_names: list[str] | None = None):
     #             break
 
 
-def ACTIONS_getUserImageFolders():
-    input_dir = Path(folder_paths.get_input_directory())
-    output_dir = Path(folder_paths.get_output_directory())
-
-    input_subdirs = [x.name for x in input_dir.iterdir() if x.is_dir()]
-    output_subdirs = [x.name for x in output_dir.iterdir() if x.is_dir()]
-
-    return {"input": input_subdirs, "output": output_subdirs}
-
-
-def ACTIONS_getUserVideos(
-    size=256, count=200, offset=0, sort: str | None = None
-):
-    count = count or 1000
-    video_extensions = ["webm", "mp4", "mkv", "mov"]
-    entries = {}
-    patterns = build_glob_patterns(video_extensions)
-    input_dir = Path(folder_paths.get_input_directory())
-    entries = glob_multiple(input_dir, patterns)
-
-    sort_mode = SortMode.from_str(sort)
-
-    if sort_mode:
-        sort_key = {
-            SortMode.MODIFIED: lambda x: x.stat().st_mtime,
-            SortMode.MODIFIED_REVERSE: lambda x: x.stat().st_mtime,
-            SortMode.NAME: lambda x: x.name,
-            SortMode.NAME_REVERSE: lambda x: x.name,
-        }.get(sort_mode)
-        if sort_key:
-            reverse = sort_mode in (SortMode.MODIFIED, SortMode.NAME_REVERSE)
-            entries = sorted(entries, key=sort_key, reverse=reverse)
-
-    videos = {
-        video.name: (
-            f"/view?force_rate=0&frame_load_cap=0&skip_first_frames=0&select_every_nth=1&filename={urllib.parse.quote_plus(video.name)}&type=input&format=video&force_size={size}x?"
-        )
-        for i, video in enumerate(entries)
-        if offset <= i < offset + count
-    }
-    return videos
-
-
 def ACTIONS_getUserImages(
-    mode: Literal["input", "output"],
-    count=1000,
+    mode: str,
+    count=200,
     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"}
+    # TODO: find a better name :s
+    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
+    pattern = "**/*.png" if include_subfolders else "*.png"
 
-        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"]
+    entry_gen = entry_dir.glob(pattern)
 
     entries = {}
-    patterns = build_glob_patterns(supported, recursive=include_subfolders)
-    entries = glob_multiple(entry_dir, patterns)
 
-    sort_mode = SortMode.from_str(sort)
+    if sort:
+        sort = sort.lower()
+        if sort == "none":
+            entries = entry_gen
+        elif sort == "modified":
+            entries = sorted(
+                entry_gen, key=lambda x: x.stat().st_mtime, reverse=True
+            )
+        elif sort == "modified-reverse":
+            entries = sorted(entry_gen, key=lambda x: x.stat().st_mtime)
+        elif sort == "name":
+            entries = sorted(entry_gen, key=lambda x: x.name)
+        elif sort == "name-reverse":
+            entries = sorted(entry_gen, key=lambda x: x.name, reverse=True)
+        else:
+            endlog.warning(f"Sort mode {sort} not supported")
+            entries = entry_gen
+    else:
+        entries = entry_gen
 
-    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)
+    for i, img in enumerate(entries):
+        if i < offset:
+            continue
 
-    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 ''}"
+        subfolder = (
+            img.parent.relative_to(entry_dir) if include_subfolders else ""
+        )
+        imgs[img.stem] = (
+            f"/mtb/view?filename={img.name}&width=512&type={mode}&subfolder="
+            f"{subfolder}"
             f"&preview=&rand={secrets.randbelow(424242)}"
         )
-        for i, img in enumerate(entries)
-        if offset <= i < offset + count
-    }
+        if i >= count + offset - 1:
+            break
     return imgs
 
 

env.nu

@@ -27,7 +27,7 @@ export def "comfy start" [--clean,--old-ui, --listen] {
 
     let root = get_root --clean=($clean)
     cd $root
-    MTB_DEBUG=true python main.py --port 3000 ...(if $old_ui { ["--front-end-version", "Comfy-Org/ComfyUI_legacy_frontend@latest"]} else {[ --front-end-version Comfy-Org/ComfyUI_frontend@latest]}) --preview-method auto ...(if $listen {["--listen"]} else {[]})
+    MTB_DEBUG=true python main.py --port 3000 ...(if $old_ui { ["--front-end-version", "Comfy-Org/ComfyUI_legacy_frontend@latest"]} else {[]}) --preview-method auto ...(if $listen {["--listen"]} else {[]})
 }
 
 # update comfy itself and merge master in current branch
@@ -67,14 +67,8 @@ export def "comfy update" [
     git checkout master
 
     print $"(ansi yellow_italic)Fetching and pulling remote updates(ansi reset)"
-    if ($clean) {
-        git fetch local master
-        git pull local master
-    } else {
-        git fetch
-        git pull
-    }
-
+    git fetch
+    git pull
 
     print $"(ansi yellow_italic)Back to our branch \(($branch_name)\)(ansi reset)"
     git checkout -
@@ -141,7 +135,7 @@ export def "comfy update_extensions" [--clean] {
     let root = get_root --clean=($clean)
     cd $root
     cd custom_nodes
-    git multipull . -s -q
+    git multipull .
 }
 
 def --env path-add [pth] {
@@ -152,7 +146,7 @@ def --env path-add [pth] {
 
 export-env {
   $env.COMFY_MTB = ("." | path expand)
-  # $env.CUDA_ROOT =  'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\'
+  $env.CUDA_ROOT =  'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\'
 
   $env.CUDA_HOME = $env.CUDA_ROOT
 
@@ -160,12 +154,6 @@ export-env {
   $env.COMFY_CLEAN_ROOT =  ($env.COMFY_ROOT | path dirname | path join ComfyClean)
 
   path-add 'C:/Portable/TensorRT-8.6.0.12/lib'
-
-  if $nu.os-info.family == 'windows' {
-    path-add 'G:\BIN\TensorRT-10.7.0.23\lib'
-    path-add 'G:\BIN\cudnn-windows-x86_64-9.6.0.74_cuda12-archive\bin'
-  }
-
   path-add ($env.CUDA_ROOT | path join bin)
   overlay use ../../.venv/Scripts/activate.nu
 }

install.py

@@ -43,28 +43,10 @@ pip_map = {
     "tb-nightly": "tensorboard",
     "protobuf": "google.protobuf",
     "qrcode[pil]": "qrcode",
-    "requirements-parser": "requirements",
+    "requirements-parser": "requirements"
     # Add more mappings as needed
 }
 
-
-def get_node_dependencies():
-    restore_deps = ["basicsr"]
-    onnx_deps = ["onnxruntime"]
-    swap_deps = ["insightface"] + onnx_deps
-    quant_deps = ["bitsandbytes"]
-    io_deps = ["av"]
-    return {
-        "QrCode": ["qrcode"],
-        "DeepBump": onnx_deps,
-        "FaceSwap": swap_deps,
-        "LoadFaceSwapModel": swap_deps,
-        "LoadFaceAnalysisModel": restore_deps,
-        "Quantize": quant_deps,
-        "SaveGif": io_deps,
-    }
-
-
 # endregion
 
 # region ansi
@@ -142,12 +124,12 @@ def print_formatted(text, *formats, color=None, background=None, **kwargs):
     header = "[mtb install] "
 
     # Handle console encoding for Unicode characters (utf-8)
-    encoded_header = header.encode(
-        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)
@@ -181,9 +163,7 @@ def run_command(cmd, ignored_lines_start=None):
     try:
         _run_command(shell_cmd, ignored_lines_start)
     except subprocess.CalledProcessError as e:
-        print(
-            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:
@@ -258,7 +238,7 @@ def suppress_std():
 def get_local_version():
     init_file = os.path.join(os.path.dirname(__file__), "__init__.py")
     if os.path.isfile(init_file):
-        with open(init_file) as f:
+        with open(init_file, "r") as f:
             tree = ast.parse(f.read())
             for node in ast.walk(tree):
                 if isinstance(node, ast.Assign):
@@ -276,16 +256,13 @@ def download_file(url, file_name):
     with requests.get(url, stream=True) as response:
         response.raise_for_status()
         total_size = int(response.headers.get("content-length", 0))
-        with (
-            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))
@@ -325,9 +302,7 @@ 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}').",
@@ -335,9 +310,7 @@ 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",
@@ -353,9 +326,13 @@ def import_or_install(requirement, dry=False):
 
 def get_github_assets(tag=None):
     if tag:
-        tag_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/tags/{tag}"
+        tag_url = (
+            f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/tags/{tag}"
+        )
     else:
-        tag_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
+        tag_url = (
+            f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
+        )
     response = requests.get(tag_url)
     if response.status_code == 404:
         # print_formatted(
@@ -384,9 +361,7 @@ except ImportError:
 def main():
     if len(sys.argv) == 1:
         print_formatted(
-            "mtb doesn't need an install script anymore.",
-            "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):
@@ -422,12 +397,8 @@ 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..."
@@ -438,14 +409,7 @@ 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

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

nodes/graph_utils.py

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

nodes/image_processing.py

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

nodes/image_utils.py

@@ -1,11 +1,4 @@
-import json
-import os
-
-import numpy as np
 import torch
-from comfy.cli_args import args
-from PIL import Image
-from PIL.PngImagePlugin import PngInfo
 
 from ..log import log
 
@@ -15,21 +8,13 @@ class MTB_StackImages:
 
     @classmethod
     def INPUT_TYPES(cls):
-        return {
-            "required": {"vertical": ("BOOLEAN", {"default": False})},
-            "optional": {
-                "match_method": (
-                    ["error", "smallest", "largest"],
-                    {"default": "error"},
-                )
-            },
-        }
+        return {"required": {"vertical": ("BOOLEAN", {"default": False})}}
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "stack"
     CATEGORY = "mtb/image utils"
 
-    def stack(self, vertical, match_method="error", **kwargs):
+    def stack(self, vertical, **kwargs):
         if not kwargs:
             raise ValueError("At least one tensor must be provided.")
 
@@ -47,50 +32,23 @@ class MTB_StackImages:
             self.duplicate_frames(tensor, max_batch_size)
             for tensor in normalized_tensors
         ]
-        if match_method != "error":
-            if vertical:
-                # match widths
-                widths = [tensor.shape[2] for tensor in normalized_tensors]
-                target_width = (
-                    min(widths) if match_method == "smallest" else max(widths)
-                )
-                normalized_tensors = [
-                    self.resize_tensor(tensor, width=target_width)
-                    for tensor in normalized_tensors
-                ]
-            else:
-                # match heights
-                heights = [tensor.shape[1] for tensor in normalized_tensors]
-                target_height = (
-                    min(heights)
-                    if match_method == "smallest"
-                    else max(heights)
-                )
-                normalized_tensors = [
-                    self.resize_tensor(tensor, height=target_height)
-                    for tensor in normalized_tensors
-                ]
-        else:
-            if vertical:
-                width = normalized_tensors[0].shape[2]
-                if any(
-                    tensor.shape[2] != width for tensor in normalized_tensors
-                ):
-                    raise ValueError(
-                        "All tensors must have the same width "
-                        "for vertical stacking."
-                    )
-            else:
-                height = normalized_tensors[0].shape[1]
-                if any(
-                    tensor.shape[1] != height for tensor in normalized_tensors
-                ):
-                    raise ValueError(
-                        "All tensors must have the same height "
-                        "for horizontal stacking."
-                    )
 
-        dim = 1 if vertical else 2
+        if vertical:
+            width = normalized_tensors[0].shape[2]
+            if any(tensor.shape[2] != width for tensor in normalized_tensors):
+                raise ValueError(
+                    "All tensors must have the same width "
+                    "for vertical stacking."
+                )
+            dim = 1
+        else:
+            height = normalized_tensors[0].shape[1]
+            if any(tensor.shape[1] != height for tensor in normalized_tensors):
+                raise ValueError(
+                    "All tensors must have the same height "
+                    "for horizontal stacking."
+                )
+            dim = 2
 
         stacked_tensor = torch.cat(normalized_tensors, dim=dim)
 
@@ -106,7 +64,7 @@ class MTB_StackImages:
         elif channels == 3:
             alpha_channel = torch.ones(
                 tensor.shape[:-1] + (1,), device=tensor.device
-            )
+            )  # Add an alpha channel
             return torch.cat((tensor, alpha_channel), dim=-1)
         else:
             raise ValueError(
@@ -129,30 +87,6 @@ class MTB_StackImages:
         else:
             return tensor
 
-    def resize_tensor(self, tensor, width=None, height=None):
-        """Resize tensor to specified width or height while maintaining aspect ratio."""
-        current_height, current_width = tensor.shape[1:3]
-
-        if width is not None and width != current_width:
-            scale_factor = width / current_width
-            new_height = int(current_height * scale_factor)
-            new_width = width
-        elif height is not None and height != current_height:
-            scale_factor = height / current_height
-            new_width = int(current_width * scale_factor)
-            new_height = height
-        else:
-            return tensor
-
-        resized = torch.nn.functional.interpolate(
-            tensor.permute(0, 3, 1, 2),
-            size=(new_height, new_width),
-            mode="bilinear",
-            align_corners=False,
-        )
-
-        return resized.permute(0, 2, 3, 1)
-
 
 class MTB_PickFromBatch:
     """Pick a specific number of images from a batch.
@@ -179,6 +113,11 @@ class MTB_PickFromBatch:
 
         # Limit count to the available number of images in the batch
         count = min(count, batch_size)
+        if count < batch_size:
+            log.warning(
+                f"Requested {count} images, "
+                f"but only {batch_size} are available."
+            )
 
         if from_direction == "end":
             selected_tensors = image[-count:]
@@ -188,87 +127,4 @@ class MTB_PickFromBatch:
         return (selected_tensors,)
 
 
-import folder_paths
-
-
-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__ = [MTB_StackImages, MTB_PickFromBatch]

nodes/ltx.py

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

nodes/mask.py

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

nodes/postshot.py

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

nodes/transform.py

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

nodes/video_editing.py

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

pyproject.toml

@@ -1,181 +1,179 @@
-[build-system]
-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
+[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

utils.py

@@ -2,7 +2,6 @@ import contextlib
 import functools
 import importlib
 import math
-import operator
 import os
 import shlex
 import shutil
@@ -12,7 +11,6 @@ import sys
 import uuid
 from collections.abc import Callable, Sequence
 from enum import Enum
-from functools import reduce
 from pathlib import Path
 from typing import TypeVar
 
@@ -214,37 +212,6 @@ def get_server_info():
 
 
 # region MISC Utilities
-def glob_multiple(
-    path: Path, patterns: list[str], recursive: bool = False
-) -> list[Path]:
-    """Combine multiple glob patterns into a single iterator."""
-    return list(reduce(operator.or_, (set(path.glob(p)) for p in patterns)))
-
-
-def build_glob_patterns(
-    extensions: list[str], recursive: bool = False
-) -> list[str]:
-    """Build glob patterns for given extensions."""
-    prefix = "**/" if recursive else ""
-    return [f"{prefix}*.{ext}" for ext in extensions]
-
-
-class SortMode(Enum):
-    NONE = "none"
-    MODIFIED = "modified"
-    MODIFIED_REVERSE = "modified-reverse"
-    NAME = "name"
-    NAME_REVERSE = "name-reverse"
-
-    @classmethod
-    def from_str(cls, value: str | None) -> "SortMode|None":
-        if not value:
-            return None
-        try:
-            return cls(value.lower())
-        except ValueError:
-            log.warning(f"Sort mode {value} not supported")
-            return None
 
 
 # TODO: use mtb.core directly instead of copying parts here
@@ -498,12 +465,8 @@ here = Path(__file__).parent.absolute()
 # - Construct the absolute path to the ComfyUI directory
 comfy_dir = Path(folder_paths.base_path)
 models_dir = Path(folder_paths.models_dir)
-
-
-# NOTE: these aren't reliable, better call the getters each time
 output_dir = Path(folder_paths.output_directory)
 input_dir = Path(folder_paths.input_directory)
-
 styles_dir = comfy_dir / "styles"
 session_id = str(uuid.uuid4())
 # - Construct the path to the font file
@@ -513,10 +476,9 @@ font_path = here / "data" / "font.ttf"
 extern_root = here / "extern"
 add_path(extern_root)
 
-if extern_root.exists():
-    for pth in extern_root.iterdir():
-        if pth.is_dir():
-            add_path(pth)
+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)
@@ -630,37 +592,6 @@ def tensor2np(tensor: torch.Tensor) -> list[npt.NDArray[np.uint8]]:
     return handle_batch(tensor, single_tensor2np)
 
 
-def nextAvailable(path: Path | str) -> Path:
-    """
-    Find the next available path by adding a numbered suffix. (mimics comfy's version).
-
-    Args:
-        path (Path): The original path to check
-
-    Returns
-    -------
-        Path: A path that doesn't exist yet
-    """
-    path = Path(path)
-
-    if not path.is_absolute():
-        path = output_dir / path
-
-    if not path.exists():
-        return path
-
-    stem = path.stem
-    suffix = path.suffix
-    parent = path.parent
-
-    counter = 1
-    while True:
-        new_path = parent / f"{stem}_{counter:04d}{suffix}"
-        if not new_path.exists():
-            return new_path
-        counter += 1
-
-
 def pad(img, left, right, top, bottom):
     pad_width = np.array(((0, 0), (top, bottom), (left, right)))
     print(

web/comfy_shared.js

@@ -1,9 +1,10 @@
 /**
- * @module Shared utilities
  * File: comfy_shared.js
  * Project: comfy_mtb
  * Author: Mel Massadian
+ *
  * Copyright (c) 2023-2024 Mel Massadian
+ *
  */
 
 // Reference the shared typedefs file
@@ -260,16 +261,6 @@ export function inner_value_change(widget, val, event = undefined) {
   }
 }
 
-export const getNamedWidget = (node, ...names) => {
-  const out = {}
-
-  for (const name of names) {
-    out[name] = node.widgets.find((w) => w.name === name)
-  }
-
-  return out
-}
-
 /**
  * @param {LGraphNode} node
  * @param {LLink} link
@@ -371,37 +362,23 @@ export function getWidgetType(config) {
  * @param {NodeType} nodeType The nodetype to attach the documentation to
  * @param {str} prefix A prefix added to each dynamic inputs
  * @param {str | [str]} inputType The datatype(s) of those dynamic inputs
- * @param {{separator?:string, start_index?:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} [opts] Extra options
+ * @param {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} opts
  * @returns
  */
-export const setupDynamicConnections = (
-  nodeType,
-  prefix,
-  inputType,
-  opts = undefined,
-) => {
+export const setupDynamicConnections = (nodeType, prefix, inputType, opts) => {
   infoLogger(
     'Setting up dynamic connections for',
     Object.getOwnPropertyDescriptors(nodeType).title.value,
   )
 
-  /** @type {{separator:string, start_index:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}?} */
-  const options = Object.assign(
-    {
-      separator: '_',
-      start_index: 1,
-    },
-    opts || {},
-  )
+  /** @type {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} */
+  const options = opts || {}
   const onNodeCreated = nodeType.prototype.onNodeCreated
   const inputList = typeof inputType === 'object'
 
   nodeType.prototype.onNodeCreated = function () {
     const r = onNodeCreated ? onNodeCreated.apply(this, []) : undefined
-    this.addInput(
-      `${prefix}${options.separator}${options.start_index}`,
-      inputList ? '*' : inputType,
-    )
+    this.addInput(`${prefix}_1`, inputList ? '*' : inputType)
     return r
   }
 
@@ -436,7 +413,7 @@ export const setupDynamicConnections = (
       this,
       slotIndex,
       isConnected,
-      `${prefix}${options.separator}`,
+      `${prefix}_`,
       inputType,
       options,
     )
@@ -452,7 +429,7 @@ export const setupDynamicConnections = (
  * @param {bool} connected - Was this event connecting or disconnecting
  * @param {string} [connectionPrefix] - The common prefix of the dynamic inputs
  * @param {string|[string]} [connectionType] - The type of the dynamic connection
- * @param {{start_index?:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options
+ * @param {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options
  */
 export const dynamic_connection = (
   node,
@@ -462,18 +439,13 @@ export const dynamic_connection = (
   connectionType = '*',
   opts = undefined,
 ) => {
-  /* {{start_index:number, link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options*/
-  const options = Object.assign(
-    {
-      start_index: 1,
-    },
-    opts || {},
-  )
+  /* @type {{link?:LLink, ioSlot?:INodeInputSlot | INodeOutputSlot}} [opts] - extra options*/
+  const options = opts || {}
 
-  // function to test if input is a dynamic one
-  const isDynamicInput = (inputName) => inputName.startsWith(connectionPrefix)
-
-  if (node.inputs.length > 0 && !isDynamicInput(node.inputs[index].name)) {
+  if (
+    node.inputs.length > 0 &&
+    !node.inputs[index].name.startsWith(connectionPrefix)
+  ) {
     return
   }
 
@@ -492,7 +464,7 @@ export const dynamic_connection = (
     const to_remove = []
     for (let n = 1; n < node.inputs.length; n++) {
       const element = node.inputs[n]
-      if (!element.link && isDynamicInput(element.name)) {
+      if (!element.link) {
         if (node.widgets) {
           const w = node.widgets.find((w) => w.name === element.name)
           if (w) {
@@ -518,25 +490,14 @@ export const dynamic_connection = (
 
     infoLogger('Cleaning inputs: making it sequential again')
     // make inputs sequential again
-    let prefixed_idx = options.start_index
     for (let i = 0; i < node.inputs.length; i++) {
-      let name = ''
-      // 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
-      }
+      let name = `${connectionPrefix}${i + 1}`
 
       if (nameArray.length > 0) {
         name = i < nameArray.length ? nameArray[i] : name
       }
 
-      // preserve label if it exists
-      node.inputs[i].label = node.inputs[i].label || name
+      node.inputs[i].label = name
       node.inputs[i].name = name
     }
   }
@@ -576,16 +537,11 @@ export const dynamic_connection = (
     if (node.inputs.length === 0) return
     // add an extra input
     if (node.inputs[node.inputs.length - 1].link !== null) {
-      // count only the prefixed inputs
-      const nextIndex = node.inputs.reduce(
-        (acc, cur) => (isDynamicInput(cur.name) ? ++acc : acc),
-        0,
-      )
-
+      const nextIndex = node.inputs.length
       const name =
         nextIndex < nameArray.length
           ? nameArray[nextIndex]
-          : `${connectionPrefix}${nextIndex + options.start_index}`
+          : `${connectionPrefix}${nextIndex + 1}`
 
       infoLogger(`Adding input ${nextIndex + 1} (${name})`)
       node.addInput(name, conType)
@@ -1129,33 +1085,7 @@ export const addDeprecation = (nodeType, reason) => {
 
 // #endregion
 
-// #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
+// #region API / graph utilities
 export const getAPIInputs = () => {
   const inputs = {}
   let counter = 1
@@ -1208,4 +1138,3 @@ export const getNodes = (skip_unused) => {
   }
   return nodes
 }
-// #endregion

web/debug.js

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

web/mtb_input_output_sidebar.js

@@ -1,7 +1,7 @@
 import { app } from '../../scripts/app.js'
 import { api } from '../../scripts/api.js'
 
-import * as shared from './comfy_shared.js'
+// import * as shared from './comfy_shared.js'
 
 import {
   // defineCSSClass,
@@ -12,14 +12,12 @@ import {
   renderSidebar,
 } from './mtb_ui.js'
 
-const offset = 0
+let 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 IMAGE_NODES = ['LoadImage']
 
 const updateImage = (node, image) => {
   if (IMAGE_NODES.includes(node.type)) {
@@ -28,13 +26,6 @@ const updateImage = (node, image) => {
       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)
   }
 }
 
@@ -43,28 +34,18 @@ const getImgsFromUrls = (urls, target) => {
   if (urls === undefined) {
     return imgs
   }
-  const elem = currentMode === 'video' ? 'video' : 'img'
 
   for (const [key, url] of Object.entries(urls)) {
-    const a = makeElement(elem)
+    const a = makeElement('img')
     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!',
+            summary: 'No LoadImage 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,
@@ -73,22 +54,12 @@ const getImgsFromUrls = (urls, target) => {
         }
 
         for (const [_id, node] of Object.entries(app.canvas.selected_nodes)) {
-          updateImage(node, key)
+          updateImage(node, `${key}.png`)
         }
       }
-    } else if (currentMode === 'output') {
-      a.onclick = (_e) => {
+    } else {
+      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',
@@ -96,29 +67,6 @@ const getImgsFromUrls = (urls, target) => {
             '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)
   }
@@ -128,212 +76,154 @@ const getImgsFromUrls = (urls, target) => {
   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
+const getUrls = async () => {
+  const count = await api.getSetting('mtb.io-sidebar.count')
   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 || {}
+  const inputs = await api.fetchApi('/mtb/actions', {
+    method: 'POST',
+    body: JSON.stringify({
+      name: 'getUserImages',
+      // mode, count, offset
+      args: [currentMode, count, offset, currentSort],
+    }),
+  })
+  const output = await inputs.json()
+  return output?.result || {}
 }
 
-//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
+  let handle
+  const version = window?.__COMFYUI_FRONTEND_VERSION__
+  console.log(`%c ${version}`, 'background: orange; color: white;')
 
-  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()
 
-      ensureMTBStyles()
+  app.ui.settings.addSetting({
+    id: 'mtb.io-sidebar.count',
+    category: ['mtb', 'Input & Output Sidebar', 'count'],
 
-      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,
 
-        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
-          }
-        },
-      })
+    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.registerExtension(sidebar_extension)
+  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 urls = await getUrls(currentMode)
+      let imgs = {}
+
+      const cont = makeElement('div.mtb_sidebar')
+
+      const imgGrid = makeElement('div.mtb_img_grid')
+      const selector = makeSelect(['input', 'output'], currentMode)
+
+      selector.addEventListener('change', async (e) => {
+        const newMode = e.target.value
+        const changed = newMode !== currentMode
+        currentMode = newMode
+        if (changed) {
+          imgGrid.innerHTML = ''
+          const urls = await getUrls()
+          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()
+          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
+      }
+    },
+  })
 }

web/mtb_sidebar.js

@@ -1,28 +1,25 @@
-// 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 { 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.$$set({ inputs })
-//     },
-//   })
-// }
+import * as shared from './comfy_shared.js'
+import { createOutliner } from './dist/mtb_inspector.js'
+
+if (window?.__COMFYUI_FRONTEND_VERSION__) {
+  const version = window?.__COMFYUI_FRONTEND_VERSION__
+  console.log(`%c ${version}`, 'background: orange; color: white;')
+
+  const panel = app.extensionManager.registerSidebarTab({
+    id: 'mtb-nodes',
+    icon: 'pi pi-bolt',
+    title: 'MTB',
+    tooltip: 'MTB: API outliner',
+    type: 'custom',
+    // this is run everytime the tab's diplay is toggled on.
+    render: (el) => {
+      const outliner = createOutliner(el)
+      const inputs = shared.getAPIInputs()
+      console.log('INPUTS', inputs)
+      outliner.$$set({ inputs })
+    },
+  })
+}

web/mtb_ui.js

@@ -184,18 +184,6 @@ ${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.
  *