docs: 📄 add note+ screenshot

feat: 📝 add note plus
future ideas: - add a markdown mode - add a separate css tab on the edit dialog
2023-12-02 19:16:35 +01:00 · 2023-12-02 19:07:32 +01:00 · 2023-12-01 00:50:50 +01:00 · 2023-11-30 20:58:29 +01:00 · 2023-11-29 15:41:47 +01:00 · 2023-11-29 15:40:57 +01:00
52 changed files with 5515 additions and 4004 deletions
@@ -2,7 +2,9 @@ name: 🐞 Bug Report
 title: "[bug] "
 description: Report a bug
 labels: ["type: 🐛 bug", "status: 🧹 needs triage"]
-
+assignees:
+  - melMass
+  
 body:
  - type: markdown
    attributes:
@@ -40,16 +42,30 @@ body:
      label: Expected behavior
      description: A clear description of what you expected to happen.

-  - type: textarea
-    id: info
+  - type: dropdown
+    id: os
    attributes:
-      label: Platform and versions
-      description: "informations about the environment you run Comfy in"
-      render: sh
-      placeholder: |
-        - OS: [e.g. Linux]
-        - Comfy Mode [e.g. custom env, standalone, google colab]
-
+      label: Operating System
+      description: What OS are you using?
+      options:
+        - Windows (Default)
+        - Linux
+        - Mac
+      default: 0
+    validations:
+      required: true
+      
+  - type: dropdown
+    id: comfy_mode
+    attributes:
+      label: Comfy Mode
+      description: What flavor of Comfy do you use?
+      options:
+        - Comfy Portable (embed) (Default)
+        - In a custom virtual env (venv, virtualenv, conda...)
+        - Google Colab
+        - Other (online services, containers etc..)
+      default: 0
    validations:
      required: true

@@ -76,4 +76,4 @@ jobs:
        uses: actions/cache/save@v3
        with:
          path: ${{ env.archive_name }}.zip
-          key: ${{ env.archive_name }}
+          key: ${{ env.archive_name }}-${{ hashFiles('reqs.txt') }}
@@ -6,7 +6,7 @@ on:
      name:
        description: Release tag / name ?
        required: true
-        default: "latest"
+        default: 'latest'
        type: string
      environment:
        description: Environment to run tests against
@@ -27,9 +27,9 @@ jobs:
      - name: ♻️ Checking out the repository
        uses: actions/checkout@v3
        with:
-          submodules: "recursive"
+          submodules: 'recursive'
          path: ${{ env.repo_name }}
-          
+
      # - name: 📝 Prepare file with paths to remove
      #   run: |
      #     find ${{ env.repo_name }} -type f -size +10M > .release_ignore
@@ -56,7 +56,7 @@ jobs:
          else
            echo "No .release_ignore file found. Skipping removal of files and directories."
          fi
-          
+
      - name: 📦 Building custom comfy nodes
        shell: bash
        run: |
@@ -98,10 +98,18 @@ jobs:
        id: cache
        with:
          path: ${{ env.archive_name }}.zip
-          key: ${{ env.archive_name }}
+          key: ${{ env.archive_name }}-${{ hashFiles('reqs.txt') }}
+      - name: 📦 Unzip wheels
+        shell: bash
+        run: |
+          mkdir -p wheels
+          unzip -j ${{ env.archive_name }}.zip "**/*.whl" -d wheels
+          unzip -j ${{ env.archive_name }}.zip "**/*.txt" -d wheels
+        if: success()
      - name: ✅ Add wheels to release
        uses: softprops/action-gh-release@v1
        with:
          tag_name: ${{ inputs.name }}
          files: |
-            ${{ env.archive_name }}.zip
+            wheels/*.whl
+            wheels/wheel_order.txt
@@ -0,0 +1,71 @@
+name: 🧪 Test Comfy Portable
+
+on: workflow_dispatch
+jobs:
+  install-comfy:
+    runs-on: windows-latest
+    env:
+      repo_name: ${{ github.event.repository.name }}
+    steps:
+      - name: ⚡️ Restore Cache if Available
+        id: cache-comfy
+        uses: actions/cache/restore@v3
+        with:
+          path: ComfyUI_windows_portable
+          key: ${{ runner.os }}-comfy-env
+
+      - name: 🚡 Download and Extract Comfy
+        id: download-extract-comfy
+        if: steps.cache-comfy.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          mkdir comfy_temp
+          curl -L -o comfy_temp/comfyui.7z https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z
+
+          7z x comfy_temp/comfyui.7z -o./comfy_temp
+
+
+          # mv comfy_temp/ComfyUI_windows_portable/python_embeded .
+          # mv comfy_temp/ComfyUI_windows_portable/ComfyUI .
+          # mv comfy_temp/ComfyUI_windows_portable/update .
+          ls
+          mv comfy_temp/ComfyUI_windows_portable .
+
+      - name: 💾 Store cache
+        uses: actions/cache/save@v3
+        if: steps.cache-comfy.outputs.cache-hit != 'true'
+        with:
+          path: ComfyUI_windows_portable
+          key: ${{ runner.os }}-comfy-env
+      - name: ⏬ Install other extensions
+        shell: bash
+        run: |
+          export COMFY_PYTHON="${GITHUB_WORKSPACE}/ComfyUI_windows_portable/python_embeded/python.exe"
+          cd "${GITHUB_WORKSPACE}/ComfyUI_windows_portable/ComfyUI/custom_nodes"
+
+          git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors
+          cd comfy_controlnet_preprocessors
+          $COMFY_PYTHON -m pip install -r requirements.txt
+          
+      - name: ♻️ Checking out comfy_mtb to custom_nodes
+        uses: actions/checkout@v3
+        with:
+          submodules: 'recursive'
+          path: ComfyUI_windows_portable/ComfyUI/custom_nodes/${{ env.repo_name }}
+
+      - name: 📦 Install mtb nodes
+        shell: bash
+        run: |
+          # run install
+          export COMFY_PYTHON="${GITHUB_WORKSPACE}/ComfyUI_windows_portable/python_embeded/python.exe"
+          cd "${GITHUB_WORKSPACE}/ComfyUI_windows_portable/ComfyUI/custom_nodes"
+          $COMFY_PYTHON ${{ env.repo_name }}/install.py -w
+
+      - name: ⏬ Import mtb_nodes
+        shell: bash
+        run: |
+          export COMFY_PYTHON="${GITHUB_WORKSPACE}/ComfyUI_windows_portable/python_embeded/python.exe"
+          cd "${GITHUB_WORKSPACE}/ComfyUI_windows_portable/ComfyUI"
+          $COMFY_PYTHON -s main.py --quick-test-for-ci --cpu
+
+          $COMFY_PYTHON -m pip freeze
@@ -4,7 +4,6 @@
    - [ComfyUI Manager](#comfyui-manager)
    - [Virtual Env](#virtual-env)
  - [Models Download](#models-download)
-    - [Web Extensions](#web-extensions)
  - [Old installation method (MANUAL)](#old-installation-method-manual)
    - [Dependencies](#dependencies)

@@ -35,11 +34,6 @@ then follow the prompt or just press enter to download every models.
  python scripts/download_models.py -y
  ```

-### Web Extensions
-
-On first run the script [tries to symlink](https://github.com/melMass/comfy_mtb/blob/d982b69a58c05ccead9c49370764beaa4549992a/__init__.py#L45-L61) the [web extensions](https://github.com/melMass/comfy_mtb/tree/main/web) to your comfy `web/extensions` folder. In case it fails you can manually copy the mtb folder to `ComfyUI/web/extensions` it only provides a color widget for now shared by a few nodes:  
-
-<img alt="color widget preview" src="https://github.com/melMass/comfy_mtb/assets/7041726/cff7e66a-4cc4-4866-b35b-10af0bb2d110" width=450>

 ## Old installation method (MANUAL)
 ### Dependencies
@@ -1,4 +1,8 @@
 # MTB Nodes
+[![embedded test](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml/badge.svg)](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml)
+
+![home](https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873)
+
 <!-- omit in toc -->

 **Translated Readme (using DeepTranslate, PRs are welcome)**:  
@@ -15,20 +19,50 @@ Welcome to the MTB Nodes project! This codebase is open for you to explore and u

 Before proceeding, please be aware of the licenses associated with certain libraries used in this project. For example, the `deepbump` library is licensed under [GPLv3](https://github.com/HugoTini/DeepBump/blob/master/LICENSE).

+- [Web Extensions](#web-extensions)
 - [Node List](#node-list)
+  - [Animation](#animation)
  - [bbox](#bbox)
  - [colors](#colors)
-  - [face detection / swapping](#face-detection--swapping)
-  - [image interpolation (animation)](#image-interpolation-animation)
  - [image ops](#image-ops)
  - [latent utils](#latent-utils)
-  - [misc utils](#misc-utils)
  - [textures](#textures)
+  - [misc utils](#misc-utils)
+  - [Optional nodes](#optional-nodes)
+    - [face detection / swapping](#face-detection--swapping)
+    - [image interpolation (animation)](#image-interpolation-animation)
 - [Comfy Resources](#comfy-resources)

+# Web Extensions
+mtb add a few widgets like `COLOR`
+
+<img alt="color widget preview" src="https://github.com/melMass/comfy_mtb/assets/7041726/cff7e66a-4cc4-4866-b35b-10af0bb2d110" width=450>
+
+A few nodes have the concept of "dynamic" inputs:  
+<img alt="dynamic inputs" width=450  src="https://github.com/melMass/comfy_mtb/assets/7041726/10b3976e-b212-4968-91eb-f34c02bb80c3" />
+

 # Node List

+## Animation
+- `Animation Builder`: Convenient way to manage basic animation maths at the core of many of my workflows (both worflows for the following GIFs are in the [examples](https://github.com/melMass/comfy_mtb/wiki/Examples))  
+
+  **[Example lerping two conditions (blue car -> yellow car)](https://github.com/melMass/comfy_mtb/blob/main/examples/03-animation_builder-condition-lerp.json)**
+  
+  <img width=300 src="https://user-images.githubusercontent.com/7041726/260258970-d6d66d96-fb34-40d0-9038-cbabf0714c5d.gif"/>  
+  
+
+  **[Example using image transforms a feedback for a fake deforum effect](https://github.com/melMass/comfy_mtb/blob/main/examples/04-animation_builder-deforum.json)**
+  
+  <img width=300 src="https://user-images.githubusercontent.com/7041726/260261504-303a1037-60d3-4b31-a589-b15d549752f6.gif"/>
+  
+- `Batch Float`: Generates a batch of float values with interpolation.
+- `Batch Shape`: Generates a batch of 2D shapes with optional shading (experimental).
+- `Batch Transform`: Transform a batch of images using a batch of keyframes.  
+  <img width=400 src="https://github.com/melMass/comfy_mtb/assets/7041726/3f217de1-79aa-49b0-a66a-35cf29dd8f01"/>
+- `Export With Ffmpeg`: Export with FFmpeg, it used to be export to Proress and is still tailored for YUV
+- `Fit Number` : Fit the input float using a source and target range, you can also control the interpolation curve from a list of presets (default to linear)
+  
 ## bbox
 - `Bounding Box`: BBox constructor (custom type),
 - `BBox From Mask`: From a mask extract the bounding box
@@ -40,21 +74,7 @@ Before proceeding, please be aware of the licenses associated with certain libra
 - `RGB to HSV`: -,
 - `HSV to RGB`: -,
 - `Color Correct`: Basic color correction tools  
-  <img src="https://github.com/melMass/comfy_mtb/assets/7041726/7c20ac83-31ff-40ea-a1a0-06c2acefb2ef" width=345/>
-
-## face detection / swapping
- `Face Swap`: Face swap using deepinsight/insightface models (this node used to be called `Roop` in early versions, it does the same, roop is *just* an app that uses those model)
-  > **Note**
-  > The face index allow you to choose which face to replace as you can see here:  
-  <img src="https://github.com/melMass/comfy_mtb/assets/7041726/2e9d6066-c466-4a01-bd6c-315f7f1e8b42" width=320/>
- `Load Face Swap Model`: Load an insightface model for face swapping
- `Restore Face`: Using [GFPGan](https://github.com/TencentARC/GFPGAN) to restore faces, works great in conjunction with `Face Swap` and supports Comfy native upscalers for the `bg_upscaler`
-  
-## image interpolation (animation)
- `Load Film Model`: Loads a [FILM](https://github.com/google-research/frame-interpolation) model
- `Film Interpolation`: Process input frames using [FILM](https://github.com/google-research/frame-interpolation)  
-  <img src="https://github.com/melMass/comfy_mtb/assets/7041726/3afd1647-6634-4b92-a34b-51432e6a9834" width=400/>
- `Export to Prores (experimental)`: Exports the input frames to a ProRes 4444 mov file. This is using ffmpeg stdin to send raw numpy arrays, used with `Film Interpolation` and very simple for now but could be expanded upon.
+  <img src="https://github.com/melMass/comfy_mtb/assets/7041726/7c20ac83-31ff-40ea-a1a0-06c2acefb2ef" width=400/>

 ## image ops
 - `Blur`: Blur an image using a Gaussian filter.
@@ -71,8 +91,16 @@ Before proceeding, please be aware of the licenses associated with certain libra
 ## latent utils
 - `Latent Lerp`: Linear interpolation (blend) between two latent 

+## textures
+- `Model Patch Seamless`: Use the [seamless diffusion "hack"](https://gitlab.com/-/snippets/2395088) to patch any model to infere seamless images, check the [examples](https://github.com/melMass/comfy_mtb/wiki/Examples) to see how to use all those textures node together  
+  <img width=500 src="https://user-images.githubusercontent.com/7041726/272970506-9db516b5-45d2-4389-b904-b3a94660f24c.png"/>
+- `DeepBump`: Normal & height maps generation from single pictures  
+  <img width=500 src="https://user-images.githubusercontent.com/7041726/272970715-7e4477f6-8e18-4839-9864-83d07d6690a1.png"/>
+- `Image Tile Offset`: Mimics an old photoshop technique to check for seamless textures by offsetting tiles of the image.  
+  <img width=600 src="https://github.com/melMass/comfy_mtb/assets/7041726/cbcc51fb-922f-433f-acf1-c6c6c2a7ffc4" />

 ## misc utils
+- `Any To String`: Tries to take any input and convert it to a string.
 - `Concat Images`: Takes two image stream and merge them as a batch of images supported by other Comfy pipelines.
 - `Image Resize Factor`: **Deprecated**, I since discovered the builtin image resize.
 - `Text To Image`: Utils to convert text to image using a font
@@ -83,13 +111,57 @@ Before proceeding, please be aware of the licenses associated with certain libra
 - `Save Tensors`: Debug node that will probably be removed in the future
 - `Int to Number`: Supplement for WASSuite number nodes
 - `Smart Step`: A very basic tool to control the steps (start/stop) of the `KAdvancedSampler` using percentage
+- `Load Image From Url`: Load an image from the given URL

-## textures

- `DeepBump`: Normal & height maps generation from single pictures
+## Optional nodes
+
+These nodes are still bundled in mtb, but moving forward (>0.2.0) they won't
+be setup by the install script and their dependencies won't install either.
+The reason is mostly that they all have a better alternatives available and tensorflow on windows was not a fun experience and since Python 3.11 not an experience at all.
+
+For linux and mac users though these nodes didn't cause any issue and I personally still use them, these are the extra requirements needed:
+
+```console
+.venv/python -m pip install tensorflow facexlib insightface basicsr
+```
+
+### face detection / swapping
+> **Warning**
+> Those nodes were among the first to be implemented they do work, but on windows the installation is still not properly handled for everyone  
+> As alternatives you can use [reactor](https://github.com/Gourieff/comfyui-reactor-node) for face swap and [facerestore](https://github.com/Haidra-Org/hordelib/tree/main/hordelib/nodes/facerestore) for restoration  
+> You can check [this video](https://www.youtube.com/watch?v=FShlpMxbU0E) for a tutorial by Ferniclestix using these alternatives  
+
+- `Face Swap`: Face swap using deepinsight/insightface models (this node used to be called `Roop` in early versions, it does the same, roop is *just* an app that uses those model)  
+  <img  width=320 src="https://user-images.githubusercontent.com/7041726/260261217-54e33446-183f-4dda-88b3-d38a1e6de980.gif"/>
+- `Load Face Swap Model`: Load an insightface model for face swapping
+- `Restore Face`: Using [GFPGan](https://github.com/TencentARC/GFPGAN) to restore faces, works great in conjunction with `Face Swap` and supports Comfy native upscalers for the `bg_upscaler`
+  
+### image interpolation (animation)
+> **Warning**
+> The FILM nodes will be deprecated at some point after 0.2.0, [Fannovel16](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation)'s interpolation nodes implement it and they rely on a pytorch implementation of FILM
+> which solves the issues related to the ones included in mtb. They will probably remain available if your system meet the requirements and ignored otherwise.
+
+<details><summary>Why?</summary>
+  
+> **Windows only issue**: This requires tensorflow-gpu that is unfortunately not a thing anymore on Windows since 2.10.1 (unless you use a complex WSL passthrough setup but it's still not "Windows")  
+> Using this old version is quite clunky and require some patching that install.py does automatically, but the main issue is that no wheels are available for python > 3.10
+> Comfy-nightly is already using Python 11 so installing this old tf version won't work there.  
+> You can in any case install the normal up to date tensorflow but that will run on CPU and is much MUCH slower for FILM inference.
+</details>
+
+- `Load Film Model`: Loads a [FILM](https://github.com/google-research/frame-interpolation) model
+- `Film Interpolation`: Process input frames using [FILM](https://github.com/google-research/frame-interpolation)  
+  <img width=400 src="https://github.com/melMass/comfy_mtb/assets/7041726/3afd1647-6634-4b92-a34b-51432e6a9834"/>  
+  <img width=400 src="https://user-images.githubusercontent.com/7041726/260259079-c0f04a63-960c-43a7-ba78-a45cd5ac7514.gif"/>
+- `Export to Prores (experimental)`: Exports the input frames to a ProRes 4444 mov file. This is using ffmpeg stdin to send raw numpy arrays, used with `Film Interpolation` and very simple for now but could be expanded upon.

 # Comfy Resources

+**Misc**
+
+- [Slick ComfyUI by NoCrypt](https://colab.research.google.com/drive/1ZMvLWEiYITmBJngtqeIQToeNuiydwI0z#scrollTo=1fWMaexXS188): A colab notebook with batteries included!
+
 **Guides**:
 - [Official Examples (eng)](https://comfyanonymous.github.io/ComfyUI_examples/)
 - [ComfyUI Community Manual (eng)](https://blenderneko.github.io/ComfyUI-docs/) by @BlenderNeko
@@ -13,26 +13,37 @@ import os
 os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
 os.environ["TF_GPU_ALLOCATOR"] = "cuda_malloc_async"

-import traceback
-from .log import log, blue_text, cyan_text, get_summary, get_label
-from .utils import here
-from .utils import comfy_dir
-import importlib
-import os
 import ast
+import contextlib
+import importlib
 import json
+import logging
+import os
+import shutil
+import traceback
+from importlib import reload
+
+from aiohttp import web
+from server import PromptServer
+
+import nodes
+
+from .endpoint import endlog
+from .log import blue_text, cyan_text, get_label, get_summary, log
+from .utils import comfy_dir, here

 NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}
 NODE_CLASS_MAPPINGS_DEBUG = {}
+WEB_DIRECTORY = "./web"

-__version__ = "0.1.2"
+__version__ = "0.2.0"


 def extract_nodes_from_source(filename):
    source_code = ""

-    with open(filename, "r") as file:
+    with open(filename, "r", encoding="utf8") as file:
        source_code = file.read()

    nodes = []
@@ -45,19 +56,15 @@ def extract_nodes_from_source(filename):
                if isinstance(target, ast.Name) and target.id == "__nodes__":
                    value = ast.get_source_segment(source_code, node.value)
                    node_value = ast.parse(value).body[0].value
-                    if isinstance(node_value, ast.List) or isinstance(
-                        node_value, ast.Tuple
-                    ):
-                        for element in node_value.elts:
-                            if isinstance(element, ast.Name):
-                                print(element.id)
-                                nodes.append(element.id)
-
+                    if isinstance(node_value, (ast.List, ast.Tuple)):
+                        nodes.extend(
+                            element.id
+                            for element in node_value.elts
+                            if isinstance(element, ast.Name)
+                        )
                    break
    except SyntaxError:
        log.error("Failed to parse")
-        pass  # File couldn't be parsed
-
    return nodes


@@ -89,7 +96,7 @@ def load_nodes():
                nodes_failed.extend(extract_nodes_from_source(filename))

    if errors:
-        log.info(
+        log.debug(
            f"Some nodes failed to load:\n\t"
            + "\n\t".join(errors)
            + "\n\n"
@@ -104,46 +111,17 @@ def load_nodes():
 web_extensions_root = comfy_dir / "web" / "extensions"
 web_mtb = web_extensions_root / "mtb"

-if web_mtb.exists():
-    log.debug(f"Web extensions folder found at {web_mtb}")
-    if not os.path.islink(web_mtb.as_posix()):
-        log.warn(
-            f"Web extensions folder at {web_mtb} is not a symlink, if updating please delete it before"
-        )
-
-
-elif web_extensions_root.exists():
-    web_tgt = here / "web"
-    src = web_tgt.as_posix()
-    dst = web_mtb.as_posix()
+if web_mtb.exists() and hasattr(nodes, "EXTENSION_WEB_DIRS"):
    try:
-        if os.name == "nt":
-            import _winapi
-
-            _winapi.CreateJunction(src, dst)
+        if web_mtb.is_symlink():
+            web_mtb.unlink()
        else:
-            os.symlink(web_tgt.as_posix(), web_mtb.as_posix())
-
-    except OSError:
-        log.warn(f"Failed to create symlink to {web_mtb}, trying to copy it")
-        try:
-            import shutil
-
-            shutil.copytree(web_tgt, web_mtb)
-            log.info(f"Successfully copied {web_tgt} to {web_mtb}")
-        except Exception:
-            log.warn(
-                f"Failed to symlink and copy {web_tgt} to {web_mtb}. Please copy the folder manually."
-            )
-
-    except Exception:  # OSError
-        log.warn(
-            f"Failed to create symlink to {web_mtb}. Please copy the folder manually."
+            shutil.rmtree(web_mtb)
+    except Exception as e:
+        log.warning(
+            f"Failed to remove web mtb directory: {e}\nPlease manually remove it from disk ({web_mtb}) and restart the server."
        )
-else:
-    log.warn(
-        f"Comfy root probably not found automatically, please copy the folder {web_mtb} manually in the web/extensions folder of ComfyUI"
-    )
+

 # - REGISTER NODES
 nodes, failed = load_nodes()
@@ -168,7 +146,7 @@ for node_class in nodes:
                )
            )

-log.info(
+log.debug(
    f"Loaded the following nodes:\n\t"
    + "\n\t".join(
        f"{cyan_text(k)}: {blue_text(get_summary(doc)) if doc else '-'}"
@@ -176,15 +154,55 @@ log.info(
    )
 )

+log.info(f"loaded {cyan_text(len(nodes))} nodes successfuly")
+if failed:
+    with contextlib.suppress(Exception):
+        base_url, port = utils.get_server_info()
+        log.info(
+            f"Some nodes ({len(failed)}) could not be loaded. This can be ignored, but go to http://{base_url}:{port}/mtb if you want more information."
+        )
+
+
 # - ENDPOINT
-from server import PromptServer
-from .log import log
-from aiohttp import web
-from importlib import reload
-import logging
-from .endpoint import endlog
+

 if hasattr(PromptServer, "instance"):
+    restore_deps = ["basicsr"]
+    onnx_deps = ["onnxruntime"]
+    swap_deps = ["insightface"] + onnx_deps
+    node_dependency_mapping = {
+        "QrCode": ["qrcode"],
+        "DeepBump": onnx_deps,
+        "FaceSwap": swap_deps,
+        "LoadFaceSwapModel": swap_deps,
+        "LoadFaceAnalysisModel": restore_deps,
+    }
+
+    PromptServer.instance.app.router.add_static(
+        "/mtb-assets/", path=(here / "html").as_posix()
+    )
+
+    @PromptServer.instance.routes.get("/mtb/manage")
+    async def manage(request):
+        from . import endpoint
+
+        reload(endpoint)
+
+        endlog.debug("Initializing Manager")
+        if "text/html" in request.headers.get("Accept", ""):
+            csv_editor = endpoint.csv_editor()
+
+            tabview = endpoint.render_tab_view(Styles=csv_editor)
+            return web.Response(
+                text=endpoint.render_base_template("MTB", tabview),
+                content_type="text/html",
+            )
+
+        return web.json_response(
+            {
+                "message": "manage only has a POST api for now",
+            }
+        )

    @PromptServer.instance.routes.get("/mtb/status")
    async def get_full_library(request):
@@ -200,7 +218,13 @@ if hasattr(PromptServer, "instance"):
                NODE_CLASS_MAPPINGS_DEBUG, title="Registered"
            )
            html_response += endpoint.render_table(
-                {k: "-" for k in failed}, title="Failed to load"
+                {
+                    k: {"dependencies": node_dependency_mapping.get(k)}
+                    if node_dependency_mapping.get(k)
+                    else "-"
+                    for k in failed
+                },
+                title="Failed to load",
            )

            return web.Response(
@@ -224,11 +248,10 @@ if hasattr(PromptServer, "instance"):
            log.setLevel(logging.DEBUG)
            log.debug("Debug mode set from API (/mtb/debug POST route)")

-        else:
-            if "MTB_DEBUG" in os.environ:
-                # del os.environ["MTB_DEBUG"]
-                os.environ.pop("MTB_DEBUG")
-                log.setLevel(logging.INFO)
+        elif "MTB_DEBUG" in os.environ:
+            # del os.environ["MTB_DEBUG"]
+            os.environ.pop("MTB_DEBUG")
+            log.setLevel(logging.INFO)

        return web.json_response(
            {"message": f"Debug mode {'set' if enabled else 'unset'}"}
@@ -242,8 +265,9 @@ if hasattr(PromptServer, "instance"):
        # Check if the request prefers HTML content
        if "text/html" in request.headers.get("Accept", ""):
            # # Return an HTML page
-            html_response = f"""
+            html_response = """
            <div class="flex-container menu">
+                <a href="/mtb/manage">manage</a>
                <a href="/mtb/debug">debug</a>
                <a href="/mtb/status">status</a>
            </div>            
@@ -261,9 +285,7 @@ if hasattr(PromptServer, "instance"):
        from . import endpoint

        reload(endpoint)
-        enabled = False
-        if "MTB_DEBUG" in os.environ:
-            enabled = True
+        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
@@ -283,7 +305,7 @@ if hasattr(PromptServer, "instance"):
        from . import endpoint

        if "text/html" in request.headers.get("Accept", ""):
-            html_response = f"""
+            html_response = """
            <h1>Actions has no get for now...</h1>
            """
            return web.Response(
@@ -1,11 +1,47 @@
-from .utils import here
+import csv
+
 from aiohttp import web
+
 from .log import mklog
-import os
+from .utils import backup_file, here, import_install, reqs_map, run_command, styles_dir

 endlog = mklog("mtb endpoint")

-#- ACTIONS
+# - ACTIONS
+import platform
+import sys
+from pathlib import Path
+
+import_install("requirements")
+
+
+def ACTIONS_installDependency(dependency_names=None):
+    if dependency_names is None:
+        return {"error": "No dependency name provided"}
+    endlog.debug(f"Received Install Dependency request for {dependency_names}")
+    # reqs = []
+    resolved_names = [reqs_map.get(name, name) for name in dependency_names]
+    try:
+        run_command([Path(sys.executable), "-m", "pip", "install"] + resolved_names)
+        return {"success": True}
+
+    except Exception as e:
+        return {"error": f"Failed to install dependencies: {e}"}
+
+    # if platform.system() == "Windows":
+    #     reqs = list(requirements.parse((here / "reqs_windows.txt").read_text()))
+    # else:
+    #     reqs = list(requirements.parse((here / "reqs.txt").read_text()))
+    # print([x.specs for x in reqs])
+    # print(
+    #     "\n".join([f"{x.line} {''.join(x.specs[0] if x.specs else '')}" for x in reqs])
+    # )
+    # for dependency_name in dependency_names:
+    #     for req in reqs:
+    #         if req.name == dependency_name:
+    #             endlog.debug(f"Dependency {dependency_name} installed")
+    #             break
+

 def ACTIONS_getStyles(style_name=None):
    from .nodes.conditions import StylesLoader
@@ -19,14 +55,37 @@ def ACTIONS_getStyles(style_name=None):
            if not key.startswith("__") and key not in match_list
        }
        if style_name:
-            if style_name in filtered_styles:
-                return filtered_styles[style_name]
-            else:
-                return {"error": "Style not found"}
+            return filtered_styles.get(style_name, {"error": "Style not found"})
        return filtered_styles
    return {"error": "No styles found"}


+def ACTIONS_saveStyle(data):
+    # endlog.debug(f"Received Save Styles for {data.keys()}")
+    # endlog.debug(data)
+
+    styles = [f.name for f in styles_dir.iterdir() if f.suffix == ".csv"]
+    target = None
+    rows = []
+    for fp, content in data.items():
+        if fp in styles:
+            endlog.debug(f"Overwriting {fp}")
+            target = styles_dir / fp
+            rows = content
+            break
+
+    if not target:
+        endlog.warning(f"Could not determine the target file for {data.keys()}")
+        return {"error": "Could not determine the target file for the style"}
+
+    backup_file(target)
+
+    with target.open("w", newline="", encoding="utf-8") as file:
+        csv_writer = csv.writer(file, quoting=csv.QUOTE_ALL)
+        for row in rows:
+            csv_writer.writerow(row)
+
+
 async def do_action(request) -> web.Response:
    endlog.debug("Init action request")
    request_data = await request.json()
@@ -35,7 +94,7 @@ async def do_action(request) -> web.Response:

    endlog.debug(f"Received action request: {name} {args}")

-    method_name = "ACTIONS_" + name
+    method_name = f"ACTIONS_{name}"
    method = globals().get(method_name)

    if callable(method):
@@ -53,16 +112,159 @@ async def do_action(request) -> web.Response:


 # - HTML UTILS
+
+
+def dependencies_button(name, dependencies):
+    deps = ",".join([f"'{x}'" for x in dependencies])
+    return f"""
+        <button class="dependency-button" onclick="window.mtb_action('installDependency',[{deps}])">Install {name} deps</button>
+        """
+
+
+def csv_editor():
+    inputs = [f for f in styles_dir.iterdir() if f.suffix == ".csv"]
+    # rows = {f.stem: list(csv.reader(f.read_text("utf8"))) for f in styles}
+
+    style_files = {}
+    for file in inputs:
+        with open(file, "r", encoding="utf8") as f:
+            parsed = csv.reader(f)
+            style_files[file.name] = []
+            for row in parsed:
+                endlog.debug(f"Adding style {row[0]}")
+                style_files[file.name].append((row[0], row[1], row[2]))
+
+    html_out = """
+            <div id="style-editor">
+             <h1>Style Editor</h1>
+            
+            """
+    for current, styles in style_files.items():
+        current_out = f"<h3>{current}</h3>"
+        table_rows = []
+        for index, style in enumerate(styles):
+            table_rows += (
+                (["<tr>"] + [f"<th>{cell}</th>" for cell in style] + ["</tr>"])
+                if index == 0
+                else (
+                    ["<tr>"]
+                    + [
+                        f"<td><input type='text' value='{cell}'></td>"
+                        if i == 0
+                        else f"<td><textarea name='Text1' cols='40' rows='5'>{cell}</textarea></td>"
+                        for i, cell in enumerate(style)
+                    ]
+                    + ["</tr>"]
+                )
+            )
+        current_out += (
+            f"<table data-id='{current}' data-filename='{current}'>"
+            + "".join(table_rows)
+            + "</table>"
+        )
+        current_out += f"<button data-id='{current}' onclick='saveTableData(this.getAttribute(\"data-id\"))'>Save {current}</button>"
+
+        html_out += add_foldable_region(current, current_out)
+
+    html_out += "</div>"
+    html_out += """<script src='/mtb-assets/js/saveTableData.js'></script>"""
+
+    return html_out
+
+
+def render_tab_view(**kwargs):
+    tab_headers = []
+    tab_contents = []
+
+    for idx, (tab_name, content) in enumerate(kwargs.items()):
+        active_class = "active" if idx == 0 else ""
+        tab_headers.append(
+            f"<button class='tablinks {active_class}' onclick=\"openTab(event, '{tab_name}')\">{tab_name}</button>"
+        )
+        tab_contents.append(
+            f"<div id='{tab_name}' class='tabcontent {active_class}'>{content}</div>"
+        )
+
+    headers_str = "\n".join(tab_headers)
+    contents_str = "\n".join(tab_contents)
+
+    return f"""
+<div class='tab-container'>
+    <div class='tab'>
+        {headers_str}
+    </div>
+    {contents_str}
+    </div>
+    <script src='/mtb-assets/js/tabSwitch.js'></script>
+    """
+
+
+def add_foldable_region(title, content):
+    symbol_id = f"{title}-symbol"
+    return f"""
+    <div class='foldable'>
+        <div class='foldable-title' onclick="toggleFoldable('{title}', '{symbol_id}')">
+            <span id='{symbol_id}' class='foldable-symbol'>&#9655;</span>
+            {title}
+        </div>
+        <div id='{title}' class='foldable-content'>
+            {content}
+        </div>
+    </div>
+    <script src='/mtb-assets/js/foldable.js'></script>
+    """
+
+
+def add_split_pane(left_content, right_content, vertical=True):
+    orientation = "vertical" if vertical else "horizontal"
+    return f"""
+    <div class="split-pane {orientation}">
+        <div id="leftPane">
+            {left_content}
+        </div>
+        <div id="resizer"></div>
+        <div id="rightPane">
+            {right_content}
+        </div>
+    </div>
+    <script>
+        initSplitPane({str(vertical).lower()});
+    </script>
+    <script src='/mtb-assets/js/splitPane.js'></script>
+    """
+
+
+def add_dropdown(title, options):
+    option_str = "\n".join([f"<option value='{opt}'>{opt}</option>" for opt in options])
+    return f"""
+    <select>
+        <option disabled selected>{title}</option>
+        {option_str}
+    </select>
+    """
+
+
 def render_table(table_dict, sort=True, title=None):
-    table_rows = ""
    table_dict = sorted(
        table_dict.items(), key=lambda item: item[0]
    )  # Sort the dictionary by keys

-    for name, description in table_dict:
-        table_rows += f"<tr><td>{name}</td><td>{description}</td></tr>"
+    table_rows = ""
+    for name, item in table_dict:
+        if isinstance(item, dict):
+            if "dependencies" in item:
+                table_rows += f"<tr><td>{name}</td><td>"
+                table_rows += f"{dependencies_button(name,item['dependencies'])}"

-    html_response = f"""
+                table_rows += "</td></tr>"
+            else:
+                table_rows += f"<tr><td>{name}</td><td>{render_table(item)}</td></tr>"
+        # elif isinstance(item, str):
+        #     table_rows += f"<tr><td>{name}</td><td>{item}</td></tr>"
+        else:
+            table_rows += f"<tr><td>{name}</td><td>{item}</td></tr>"
+
+    return f"""
        <div class="table-container">
        {"" if title is None else f"<h1>{title}</h1>"}
        <table>
@@ -78,26 +280,40 @@ def render_table(table_dict, sort=True, title=None):
        </table>      
        </div>
        """
-    return html_response


 def render_base_template(title, content):
-    css_content = ""
-    css_path = here / "html" / "style.css"
-    if css_path:
-        with open(css_path, "r") as css_file:
-            css_content = css_file.read()
-
    github_icon_svg = """<svg xmlns="http://www.w3.org/2000/svg" fill="whitesmoke" height="3em" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>"""
    return f"""
    <!DOCTYPE html>
    <html>
    <head>
        <title>{title}</title>
-        <style>
-            {css_content}
-        </style>
+        <link rel="stylesheet" href="/mtb-assets/style.css"/>
    </head>
+    <script type="module">
+        import {{ api }} from '/scripts/api.js'
+        const mtb_action = async (action, args) =>{{
+            console.log(`Sending ${{action}} with args: ${{args}}`)
+            }}
+        window.mtb_action = async (action, args) =>{{
+            console.log(`Sending ${{action}} with args: ${{args}} to the API`)
+            const res = await api.fetchApi('/actions', {{
+                method: 'POST',
+                body: JSON.stringify({{
+                  name: action,
+                  args,
+                }}),
+            }})
+
+              const output = await res.json()
+              console.debug(`Received ${{action}} response:`, output)
+              if (output?.result?.error){{
+                  alert(`An error occured: {{output?.result?.error}}`)
+              }}
+              return output?.result
+        }}
+    </script>
    <body>
        <header>
        <a href="/">Back to Comfy</a>
@@ -117,5 +333,6 @@ def render_base_template(title, content):
            <!-- Shared footer content here -->
        </footer>
    </body>
+    
    </html>
    """
@@ -0,0 +1,7 @@
+class ModelNotFound(Exception):
+    def __init__(self, model_name, *args, **kwargs):
+        super().__init__(
+            f"The model {model_name} could not be found, make sure to download it using ComfyManager first.\nrepository: https://github.com/ltdrdata/ComfyUI-Manager",
+            *args,
+            **kwargs,
+        )
@@ -265,7 +265,7 @@
        "Node name for S&R": "CLIPTextEncode"
      },
      "widgets_values": [
-        "Closeup portrait of an old bearded Caucasian man smiling, (NYC 1995), trench coat, golden ring, brown eyes"
+        "Medium cinematic shot of an old Caucasian man smiling, (NYC 1995), trench coat, golden ring, brown eyes, (with a blue light saber)"
      ]
    },
    {
@@ -424,65 +424,6 @@
        "embedding:EasyNegative, embedding:EasyNegativeV2, watermark, text, deformed, disfigured, blurry"
      ]
    },
-    {
-      "id": 3,
-      "type": "KSampler",
-      "pos": [
-        -483,
-        -21
-      ],
-      "size": [
-        315,
-        474
-      ],
-      "flags": {},
-      "order": 14,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": 120
-        },
-        {
-          "name": "positive",
-          "type": "CONDITIONING",
-          "link": 4
-        },
-        {
-          "name": "negative",
-          "type": "CONDITIONING",
-          "link": 6
-        },
-        {
-          "name": "latent_image",
-          "type": "LATENT",
-          "link": 2
-        }
-      ],
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "links": [
-            138
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "KSampler"
-      },
-      "widgets_values": [
-        542821171533322,
-        "fixed",
-        45,
-        8,
-        "dpmpp_sde",
-        "simple",
-        1
-      ]
-    },
    {
      "id": 66,
      "type": "VAEDecodeTiled",
@@ -584,7 +525,7 @@
        52
      ],
      "size": [
-        260.3902351585391,
+        260.3902282714844,
        58
      ],
      "flags": {},
@@ -615,8 +556,8 @@
        40
      ],
      "size": [
-        265.97600515853924,
-        87.31192548828142
+        265.97601318359375,
+        87.31192779541016
      ],
      "flags": {},
      "order": 11,
@@ -795,8 +736,66 @@
        "Node name for S&R": "Face Swap (mtb)"
      },
      "widgets_values": [
-        "0",
-        false
+        "0"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        -483,
+        -21
+      ],
+      "size": [
+        315,
+        474
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 120
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            138
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        542071534529,
+        "fixed",
+        32,
+        9,
+        "dpmpp_2m",
+        "normal",
+        1
      ]
    }
  ],
@@ -1,6 +1,6 @@
 {
  "last_node_id": 86,
-  "last_link_id": 171,
+  "last_link_id": 172,
  "nodes": [
    {
      "id": 59,
@@ -111,40 +111,6 @@
        "horizontal": false
      }
    },
-    {
-      "id": 5,
-      "type": "EmptyLatentImage",
-      "pos": [
-        -1410,
-        660
-      ],
-      "size": [
-        315,
-        106
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "links": [
-            2,
-            153
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "EmptyLatentImage"
-      },
-      "widgets_values": [
-        768,
-        512,
-        1
-      ]
-    },
    {
      "id": 3,
      "type": "KSampler",
@@ -211,7 +177,7 @@
        "Node name for S&R": "KSampler"
      },
      "widgets_values": [
-        45,
+        1682,
        "fixed",
        45,
        8,
@@ -232,7 +198,7 @@
        58
      ],
      "flags": {},
-      "order": 1,
+      "order": 0,
      "mode": 0,
      "outputs": [
        {
@@ -263,7 +229,7 @@
        246
      ],
      "flags": {},
-      "order": 23,
+      "order": 22,
      "mode": 0,
      "inputs": [
        {
@@ -289,7 +255,7 @@
        75.28300476074219
      ],
      "flags": {},
-      "order": 18,
+      "order": 16,
      "mode": 0,
      "inputs": [
        {
@@ -301,7 +267,7 @@
        {
          "name": "text",
          "type": "STRING",
-          "link": 171,
+          "link": 172,
          "widget": {
            "name": "text",
            "config": [
@@ -437,7 +403,7 @@
      "flags": {
        "collapsed": false
      },
-      "order": 20,
+      "order": 19,
      "mode": 0,
      "inputs": [
        {
@@ -480,7 +446,7 @@
        474
      ],
      "flags": {},
-      "order": 19,
+      "order": 18,
      "mode": 0,
      "inputs": [
        {
@@ -536,7 +502,7 @@
        "Node name for S&R": "KSampler"
      },
      "widgets_values": [
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+      68,
+      0,
+      69,
+      0,
+      "IMAGE"
+    ],
+    [
+      122,
+      62,
+      0,
+      68,
+      0,
+      "IMAGE"
+    ],
+    [
+      132,
+      74,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      158,
+      6,
+      0,
+      86,
+      0,
+      "*"
+    ],
+    [
+      167,
+      89,
+      0,
+      62,
+      0,
+      "IMAGE"
+    ],
+    [
+      169,
+      91,
+      1,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      170,
+      4,
+      0,
+      91,
+      0,
+      "MODEL"
+    ],
+    [
+      173,
+      3,
+      0,
+      96,
+      0,
+      "LATENT"
+    ],
+    [
+      174,
+      43,
+      0,
+      96,
+      1,
+      "VAE"
+    ],
+    [
+      175,
+      96,
+      0,
+      46,
+      0,
+      "IMAGE"
+    ],
+    [
+      176,
+      96,
+      0,
+      89,
+      0,
+      "*"
+    ],
+    [
+      178,
+      96,
+      0,
+      97,
+      0,
+      "IMAGE"
+    ],
+    [
+      179,
+      97,
+      0,
+      93,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "title": "Seamless Diffusion",
+      "bounding": [
+        -1752,
+        -392,
+        1658,
+        1102
+      ],
+      "color": "#3f789e",
+      "font_size": 76,
+      "locked": false
+    },
+    {
+      "title": "Seamless Check",
+      "bounding": [
+        421,
+        -795,
+        1374,
+        763
+      ],
+      "color": "#3f789e",
+      "font_size": 76,
+      "locked": false
+    }
+  ],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}
@@ -0,0 +1,20 @@
+/**
+ * File: foldable.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+function toggleFoldable(elementId, symbolId) {
+  const content = document.getElementById(elementId)
+  const symbol = document.getElementById(symbolId)
+  if (content.style.display === 'none' || content.style.display === '') {
+    content.style.display = 'flex'
+    symbol.innerHTML = '&#9661;' // Down arrow
+  } else {
+    content.style.display = 'none'
+    symbol.innerHTML = '&#9655;' // Right arrow
+  }
+}
@@ -0,0 +1,54 @@
+/**
+ * File: saveTableData.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+function saveTableData(identifier) {
+  const table = document.querySelector(
+    `#style-editor table[data-id='${identifier}']`
+  )
+
+  let currentData = []
+  const rows = table.querySelectorAll('tr')
+  const filename = table.getAttribute('data-id')
+
+  rows.forEach((row, rowIndex) => {
+    const rowData = []
+    const cells =
+      rowIndex === 0
+        ? row.querySelectorAll('th')
+        : row.querySelectorAll('td input, td textarea')
+
+    cells.forEach((cell) => {
+      rowData.push(rowIndex === 0 ? cell.textContent : cell.value)
+    })
+
+    currentData.push(rowData)
+  })
+
+  let tablesData = {}
+  tablesData[filename] = currentData
+
+  console.debug('Sending styles to manage endpoint:', tablesData)
+  fetch('/mtb/actions', {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+    },
+    body: JSON.stringify({
+      name: 'saveStyle',
+      args: tablesData,
+    }),
+  })
+    .then((response) => response.json())
+    .then((data) => {
+      console.debug('Success:', data)
+    })
+    .catch((error) => {
+      console.error('Error:', error)
+    })
+}
@@ -0,0 +1,34 @@
+/**
+ * File: splitPane.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+function initSplitPane(vertical) {
+  let resizer = document.getElementById('resizer')
+  let left = document.getElementById('leftPane')
+  let right = document.getElementById('rightPane')
+  resizer.addEventListener('mousedown', function (e) {
+    document.addEventListener('mousemove', onMouseMove)
+    document.addEventListener('mouseup', function () {
+      document.removeEventListener('mousemove', onMouseMove)
+    })
+  })
+
+  const onMouseMove = (e) => {
+    if (vertical) {
+      let leftWidth = e.clientX
+      let rightWidth = window.innerWidth - e.clientX
+      left.style.width = leftWidth + 'px'
+      right.style.width = rightWidth + 'px'
+    } else {
+      let topHeight = e.clientY
+      let bottomHeight = window.innerHeight - e.clientY
+      left.style.height = topHeight + 'px'
+      right.style.height = bottomHeight + 'px'
+    }
+  }
+}
@@ -0,0 +1,22 @@
+/**
+ * File: tabSwitch.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+function openTab(evt, tabName) {
+  var i, tabcontent, tablinks
+  tabcontent = document.getElementsByClassName('tabcontent')
+  for (i = 0; i < tabcontent.length; i++) {
+    tabcontent[i].style.display = 'none'
+  }
+  tablinks = document.getElementsByClassName('tablinks')
+  for (i = 0; i < tablinks.length; i++) {
+    tablinks[i].className = tablinks[i].className.replace(' active', '')
+  }
+  document.getElementById(tabName).style.display = 'block'
+  evt.currentTarget.className += ' active'
+}
@@ -18,7 +18,7 @@ a {
 }

 table {
-
+    width: 100%;
    border-collapse: collapse;
 }

@@ -119,7 +119,7 @@ main {
    justify-content: center;
    padding: 1em;
    margin: 0;
-    height: 80%;
+    /* height: 80%; */
 }

 .flex-container {
@@ -130,4 +130,99 @@ main {
 .menu {
    font-size: 3em;
    text-align: center;
-}
+}
+
+input, button, textarea {
+    background-color: rgba(0,0,0,0.5);
+    color: white;
+    border: none;
+}
+
+button:hover {
+    background-color: rgba(0,0,0,0.3);
+
+}
+button {
+    padding: 14px 16px;
+
+}
+/* -STYLES EDITOR */
+
+#style-editor {
+    display: flex;
+    flex-direction: column;
+    width:100%;
+    
+}
+
+#style-editor > table {
+    /* background-color: red; */
+    width:100%;
+}
+#style-editor input, #style-editor textarea {
+    /* background-color: blue; */
+    width:100%;
+}
+
+#style-editor td{
+    width: 33.33%;
+}
+
+/* -TABS */
+
+.tab {
+    overflow: hidden;
+    width: 100%;
+    display: flex;
+    flex-direction: row;
+
+}
+
+.tab-container{
+    width: 100%;
+    display: flex;
+    flex-direction: column;
+}
+
+.tab button {
+    background-color: transparent;
+    color:white;
+    float: left;
+    border: none;
+    outline: none;
+    cursor: pointer;
+    padding: 14px 16px;
+    transition: 0.3s;
+    width:100%;
+    font-size: 1.5em;
+}
+
+.tab button.active {
+    background-color: #2e2e2e;
+}
+
+.tabcontent {
+    display: none;
+}
+
+.tabcontent.active {
+    display: block;
+}
+
+
+
+.foldable-title {
+    cursor: pointer;
+    font-weight: bold;
+    user-select: none;
+}
+
+.foldable-symbol {
+    margin-right: 10px;
+}
+
+.foldable-content {
+    display: none;
+    flex-direction: column;
+    margin-left: 20px;
+}
@@ -1,35 +1,54 @@
-import requests
-import os
-import ast
-import re
 import argparse
-import sys
-import subprocess
-from importlib import import_module
+import ast
+import os
 import platform
-from pathlib import Path
-import sys
-import zipfile
-import shutil
+import shlex
 import stat
+import subprocess
+import sys
+from contextlib import contextmanager
+from importlib import import_module
+from pathlib import Path

+import requests

+# region constants
 here = Path(__file__).parent
-executable = sys.executable
+executable = Path(sys.executable)

 # - detect mode
 mode = None
 if os.environ.get("COLAB_GPU"):
    mode = "colab"
-elif "python_embeded" in executable:
+elif "python_embeded" in str(executable):
    mode = "embeded"
-elif ".venv" in executable:
+elif ".venv" in str(executable):
    mode = "venv"


-if mode == None:
+if mode is None:
    mode = "unknown"

+repo_url = "https://github.com/melmass/comfy_mtb.git"
+repo_owner = "melmass"
+repo_name = "comfy_mtb"
+short_platform = {
+    "windows": "win_amd64",
+    "linux": "linux_x86_64",
+}
+current_platform = platform.system().lower()
+pip_map = {
+    "onnxruntime-gpu": "onnxruntime",
+    "opencv-contrib": "cv2",
+    "tb-nightly": "tensorboard",
+    "protobuf": "google.protobuf",
+    "qrcode[pil]": "qrcode",
+    "requirements-parser": "requirements"
+    # Add more mappings as needed
+}
+
+# endregion
+
 # region ansi
 # ANSI escape sequences for text styling
 ANSI_FORMATS = {
@@ -102,55 +121,117 @@ def print_formatted(text, *formats, color=None, background=None, **kwargs):
    formatted_text = apply_format(text, *formats)
    formatted_text = apply_color(formatted_text, color, background)
    file = kwargs.get("file", sys.stdout)
+    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
+    )
+
    print(
-        apply_color(apply_format("[mtb install] ", "bold"), color="yellow"),
-        formatted_text,
+        " " * len(encoded_header)
+        if kwargs.get("no_header")
+        else apply_color(apply_format(encoded_header, "bold"), color="yellow"),
+        encoded_text,
        file=file,
    )


 # endregion

-try:
-    import requirements
-except ImportError:
-    print_formatted("Installing requirements-parser...", "italic", color="yellow")
-    subprocess.check_call(
-        [sys.executable, "-m", "pip", "install", "requirements-parser"]
+
+# region utils
+def run_command(cmd, ignored_lines_start=None):
+    if ignored_lines_start is None:
+        ignored_lines_start = []
+
+    if isinstance(cmd, str):
+        shell_cmd = cmd
+    elif isinstance(cmd, list):
+        shell_cmd = " ".join(
+            arg.as_posix() if isinstance(arg, Path) else shlex.quote(str(arg))
+            for arg in cmd
+        )
+    else:
+        raise ValueError(
+            "Invalid 'cmd' argument. It must be a string or a list of arguments."
+        )
+
+    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(e.stderr.strip(), file=sys.stderr)
+
+    except KeyboardInterrupt:
+        print("Command execution interrupted.")
+
+
+def _run_command(shell_cmd, ignored_lines_start):
+    print_formatted(f"Running {shell_cmd}", "bold")
+    result = subprocess.run(
+        shell_cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        text=True,
+        shell=True,
+        check=True,
    )
-    import requirements

-    print_formatted("Done.", "italic", color="green")
+    stdout_lines = result.stdout.strip().split("\n")
+    stderr_lines = result.stderr.strip().split("\n")

-try:
-    from tqdm import tqdm
-except ImportError:
-    print_formatted("Installing tqdm...", "italic", color="yellow")
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "--upgrade", "tqdm"])
-    from tqdm import tqdm
-import importlib
+    # Print stdout, skipping ignored lines
+    for line in stdout_lines:
+        if not any(line.startswith(ign) for ign in ignored_lines_start):
+            print(line)

+    # Print stderr
+    for line in stderr_lines:
+        print(line, file=sys.stderr)

-pip_map = {
-    "onnxruntime-gpu": "onnxruntime",
-    "opencv-contrib": "cv2",
-    "tb-nightly": "tensorboard",
-    "protobuf": "google.protobuf",
-    # Add more mappings as needed
-}
+    print("Command executed successfully!")


 def is_pipe():
-    try:
-        mode = os.fstat(0).st_mode
-        return (
-            stat.S_ISFIFO(mode)
-            or stat.S_ISREG(mode)
-            or stat.S_ISBLK(mode)
-            or stat.S_ISSOCK(mode)
-        )
-    except OSError:
+    if not sys.stdin.isatty():
        return False
+    if sys.platform == "win32":
+        try:
+            import msvcrt
+
+            return msvcrt.get_osfhandle(0) != -1
+        except ImportError:
+            return False
+    else:
+        try:
+            mode = os.fstat(0).st_mode
+            return (
+                stat.S_ISFIFO(mode)
+                or stat.S_ISREG(mode)
+                or stat.S_ISBLK(mode)
+                or stat.S_ISSOCK(mode)
+            )
+        except OSError:
+            return False
+
+
+@contextmanager
+def suppress_std():
+    with open(os.devnull, "w") as devnull:
+        old_stdout = sys.stdout
+        old_stderr = sys.stderr
+        sys.stdout = devnull
+        sys.stderr = devnull
+
+        try:
+            yield
+        finally:
+            sys.stdout = old_stdout
+            sys.stderr = old_stderr


 # Get the version from __init__.py
@@ -187,184 +268,71 @@ def download_file(url, file_name):
                progress_bar.update(len(chunk))


-def get_requirements(path: Path):
-    with open(path.resolve(), "r") as requirements_file:
-        requirements_txt = requirements_file.read()
-
-    try:
-        parsed_requirements = requirements.parse(requirements_txt)
-    except AttributeError:
-        print_formatted(
-            f"Failed to parse {path}. Please make sure the file is correctly formatted.",
-            "bold",
-            color="red",
-        )
-
-        return
-
-    return parsed_requirements
-
-
 def try_import(requirement):
    dependency = requirement.name.strip()
    import_name = pip_map.get(dependency, dependency)
    installed = False

    pip_name = dependency
-    if specs := requirement.specs:
-        pip_name += "".join(specs[0])
-
+    pip_spec = "".join(specs[0]) if (specs := requirement.specs) else ""
    try:
-        import_module(import_name)
+        with suppress_std():
+            import_module(import_name)
        print_formatted(
-            f"Package {pip_name} already installed (import name: '{import_name}').",
+            f"\t✅ Package {pip_name} already installed (import name: '{import_name}').",
            "bold",
            color="green",
+            no_header=True,
        )
        installed = True
    except ImportError:
-        pass
+        print_formatted(
+            f"\t⛔ Package {pip_name} is missing (import name: '{import_name}').",
+            "bold",
+            color="red",
+            no_header=True,
+        )

-    return (installed, pip_name, import_name)
+    return (installed, pip_name, pip_spec, import_name)


 def import_or_install(requirement, dry=False):
-    installed, pip_name, import_name = try_import(requirement)
+    installed, pip_name, pip_spec, import_name = try_import(requirement)
+
+    pip_install_name = pip_name + pip_spec

    if not installed:
        print_formatted(f"Installing package {pip_name}...", "italic", color="yellow")
        if dry:
            print_formatted(
-                f"Dry-run: Package {pip_name} would be installed (import name: '{import_name}').",
+                f"Dry-run: Package {pip_install_name} would be installed (import name: '{import_name}').",
                color="yellow",
            )
        else:
            try:
-                subprocess.check_call(
-                    [sys.executable, "-m", "pip", "install", pip_name]
-                )
+                run_command([executable, "-m", "pip", "install", pip_install_name])
                print_formatted(
-                    f"Package {pip_name} installed successfully using pip package name  (import name: '{import_name}')",
+                    f"Package {pip_install_name} installed successfully using pip package name  (import name: '{import_name}')",
                    "bold",
                    color="green",
                )
            except subprocess.CalledProcessError as e:
                print_formatted(
-                    f"Failed to install package {pip_name} using pip package name  (import name: '{import_name}'). Error: {str(e)}",
+                    f"Failed to install package {pip_install_name} using pip package name  (import name: '{import_name}'). Error: {str(e)}",
                    "bold",
                    color="red",
                )


-# Install dependencies from requirements.txt
-def install_dependencies(dry=False):
-    parsed_requirements = get_requirements(here / "reqs.txt")
-    if not parsed_requirements:
-        return
-    print_formatted(
-        "Installing dependencies from reqs.txt...", "italic", color="yellow"
-    )
-
-    for requirement in parsed_requirements:
-        import_or_install(requirement, dry=dry)
-
-
-if __name__ == "__main__":
-    full = False
-    if is_pipe():
-        print_formatted("Pipe detected, full install...", color="green")
-        # we clone our repo
-        url = "https://github.com/melmass/comfy_mtb.git"
-        clone_dir = here / "custom_nodes" / "comfy_mtb"
-        if not clone_dir.exists():
-            clone_dir.parent.mkdir(parents=True, exist_ok=True)
-            print_formatted(f"Cloning {url} to {clone_dir}", "italic", color="yellow")
-            subprocess.check_call(["git", "clone", "--recursive", url, clone_dir])
-
-        # os.chdir(clone_dir)
-        here = clone_dir
-        full = True
-
-    if len(sys.argv) == 1:
-        print_formatted(
-            "No arguments provided, doing a full install/update...",
-            "italic",
-            color="yellow",
+def get_github_assets(tag=None):
+    if tag:
+        tag_url = (
+            f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/tags/{tag}"
        )
-
-        full = True
-
-    # Parse command-line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--wheels", "-w", action="store_true", help="Install wheel dependencies"
-    )
-    parser.add_argument(
-        "--requirements", "-r", action="store_true", help="Install requirements.txt"
-    )
-    parser.add_argument(
-        "--dry",
-        action="store_true",
-        help="Print what will happen without doing it (still making requests to the GH Api)",
-    )
-
-    # parser.add_argument(
-    #     "--version",
-    #     default=get_local_version(),
-    #     help="Version to check against the GitHub API",
-    # )
-
-    args = parser.parse_args()
-
-    wheels_directory = here / "wheels"
-    print_formatted(f"Detected environment: {apply_color(mode,'cyan')}")
-
-    # Install dependencies from requirements.txt
-    # if args.requirements or mode == "venv":
-
-    if (not args.wheels and mode not in ["colab", "embeded"]) and not full:
-        print_formatted(
-            "Skipping wheel installation. Use --wheels to install wheel dependencies. (only needed for Comfy embed)",
-            "italic",
-            color="yellow",
+    else:
+        tag_url = (
+            f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
        )
-
-        install_dependencies(dry=args.dry)
-        sys.exit()
-
-    if mode in ["colab", "embeded"]:
-        print_formatted(
-            f"Downloading and installing release wheels since we are in a Comfy {apply_color(mode,'cyan')} environment",
-        )
-    if full:
-        print_formatted(
-            f"Downloading and installing release wheels since no arguments where provided"
-        )
-
-    # - Check the env before proceeding.
-    missing_wheels = False
-    parsed_requirements = get_requirements(here / "reqs.txt")
-    if parsed_requirements:
-        for requirement in parsed_requirements:
-            installed, pip_name, import_name = try_import(requirement)
-            if not installed:
-                missing_wheels = True
-                break
-
-    if not missing_wheels:
-        print_formatted(
-            f"All requirements are already installed.", "italic", color="green"
-        )
-        sys.exit()
-
-    # Fetch the JSON data from the GitHub API URL
-    owner = "melmass"
-    repo = "comfy_mtb"
-    # version = args.version
-    current_platform = platform.system().lower()
-
-    # Get the tag version from the GitHub API
-    tag_url = f"https://api.github.com/repos/{owner}/{repo}/releases/latest"
    response = requests.get(tag_url)
    if response.status_code == 404:
        # print_formatted(
@@ -376,111 +344,78 @@ if __name__ == "__main__":
    tag_data = response.json()
    tag_name = tag_data["name"]

-    # # Compare the local and tag versions
-    # if version and tag_name:
-    #     if re.match(r"v?(\d+(\.\d+)+)", version) and re.match(
-    #         r"v?(\d+(\.\d+)+)", tag_name
-    #     ):
-    #         version_parts = [int(part) for part in version.lstrip("v").split(".")]
-    #         tag_version_parts = [int(part) for part in tag_name.lstrip("v").split(".")]
+    return tag_data, tag_name

-    #         if version_parts > tag_version_parts:
-    #             print_formatted(
-    #                 f"Local version ({version}) is greater than the release version ({tag_name}).",
-    #                 "bold",
-    #                 "yellow",
-    #             )
-    #             sys.exit()

-    # Download the assets for the given version
-    matching_assets = [
-        asset for asset in tag_data["assets"] if current_platform in asset["name"]
-    ]
-    if not matching_assets:
+# endregion
+
+
+try:
+    from tqdm import tqdm
+except ImportError:
+    print_formatted("Installing tqdm...", "italic", color="yellow")
+    run_command([executable, "-m", "pip", "install", "--upgrade", "tqdm"])
+    from tqdm import tqdm
+
+
+def main():
+    if len(sys.argv) == 1:
        print_formatted(
-            f"Unsupported operating system: {current_platform}", color="yellow"
+            "mtb doesn't need an install script anymore.", "italic", color="yellow"
        )
-
-    wheels_directory.mkdir(exist_ok=True)
-    # - Install the wheels
-    for asset in matching_assets:
-        asset_name = asset["name"]
-        asset_download_url = asset["browser_download_url"]
-        print_formatted(f"Downloading asset: {asset_name}", color="yellow")
-        asset_dest = wheels_directory / asset_name
-        download_file(asset_download_url, asset_dest)
-
-        # - Unzip to wheels dir
-        whl_files = []
-        whl_order = None
-        with zipfile.ZipFile(asset_dest, "r") as zip_ref:
-            for item in tqdm(zip_ref.namelist(), desc="Extracting", unit="file"):
-                if item.endswith(".whl"):
-                    item_basename = os.path.basename(item)
-                    target_path = wheels_directory / item_basename
-                    with zip_ref.open(item) as source, open(
-                        target_path, "wb"
-                    ) as target:
-                        whl_files.append(target_path)
-                        shutil.copyfileobj(source, target)
-                elif item.endswith("order.txt"):
-                    item_basename = os.path.basename(item)
-                    target_path = wheels_directory / item_basename
-                    with zip_ref.open(item) as source, open(
-                        target_path, "wb"
-                    ) as target:
-                        whl_order = target_path
-                        shutil.copyfileobj(source, target)
-
-        print_formatted(
-            f"Wheels extracted for {current_platform} to the '{wheels_directory}' directory.",
-            "bold",
-            color="green",
+        return
+    if all(arg not in ("-p", "--path") for arg in sys.argv):
+        print(
+            "This script is only used for and edge case of remote installs on some cloud providers, unrecognized arguments:",
+            sys.argv[1:],
        )
+        return

-        if whl_files:
-            if whl_order:
-                with open(whl_order, "r") as order:
-                    wheel_order_lines = [line.strip() for line in order]
-                whl_files = sorted(
-                    whl_files,
-                    key=lambda x: wheel_order_lines.index(x.split("-")[0]),
-                )
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description="Comfy_mtb install script")
+    parser.add_argument(
+        "--path",
+        "-p",
+        type=str,
+        help="Path to clone the repository to (i.e the absolute path to ComfyUI/custom_nodes)",
+    )

-            for whl_file in tqdm(whl_files, desc="Installing", unit="package"):
-                whl_path = wheels_directory / whl_file
+    print_formatted("mtb install", "bold", color="yellow")

-                # check if installed
-                try:
-                    whl_dep = whl_path.name.split("-")[0]
-                    import_name = pip_map.get(whl_dep, whl_dep)
-                    import_module(import_name)
-                    tqdm.write(
-                        f"Package {import_name} already installed, skipping wheel installation.",
-                    )
-                    continue
-                except ImportError:
-                    if args.dry:
-                        tqdm.write(
-                            f"Dry-run: Package {whl_path.name} would be installed.",
-                        )
-                        continue
+    args = parser.parse_args()

-                    tqdm.write("Installing wheel: " + whl_path.name)
+    print_formatted(f"Detected environment: {apply_color(mode,'cyan')}")

-                    subprocess.check_call(
-                        [
-                            sys.executable,
-                            "-m",
-                            "pip",
-                            "install",
-                            whl_path.resolve().as_posix(),
-                        ]
-                    )
+    if args.path:
+        clone_dir = Path(args.path)
+        if not clone_dir.exists():
+            print_formatted(
+                "The path provided does not exist on disk... It must be pointing to ComfyUI's custom_nodes directory"
+            )
+            sys.exit()

-            print_formatted("Wheels installation completed.", color="green")
        else:
-            print_formatted("No .whl files found. Nothing to install.", color="yellow")
+            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])
+            else:
+                print_formatted(
+                    f"Directory {repo_dir} already exists, we will update it..."
+                )
+                run_command(["git", "pull", "-C", repo_dir])
+        here = clone_dir
+        full = True

-    # - Install all remainings
-    install_dependencies(dry=args.dry)
+    print_formatted("Checking environment...", "italic", color="yellow")
+    missing_deps = []
+    install_cmd = [executable, "-m", "pip", "install", "-r", "requirements.txt"]
+    run_command(install_cmd)
+
+    print_formatted(
+        "✅ Successfully installed all dependencies.", "italic", color="green"
+    )
+
+
+if __name__ == "__main__":
+    main()
@@ -1,9 +1,8 @@
 import logging
-import re
 import os
+import re

 base_log_level = logging.DEBUG if os.environ.get("MTB_DEBUG") else logging.INFO
-print(f"Log level: {base_log_level}")


 # Custom object that discards the output
@@ -76,5 +75,7 @@ def cyan_text(text):


 def get_label(label):
+    if label.startswith("MTB_"):
+        label = label[4:]
    words = re.findall(r"(?:^|[A-Z])[a-z]*", label)
    return " ".join(words).strip()
@@ -1,43 +1,62 @@
-{
-    "Animation Builder (mtb)": "Convenient way to manage basic animation maths at the core of many of my workflows",
-    "Bbox (mtb)": "The bounding box (BBOX) custom type used by other nodes",
-    "Bbox From Mask (mtb)": "From a mask extract the bounding box",
-    "Blur (mtb)": "Blur an image using a Gaussian filter.",
-    "Color Correct (mtb)": "Various color correction methods",
-    "Colored Image (mtb)": "Constant color image of given size",
-    "Concat Images (mtb)": "Add images to batch",
-    "Crop (mtb)": "Crops an image and an optional mask to a given bounding box\n\n    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n    The BBOX input takes precedence over the tuple input\n    ",
-    "Debug (mtb)": "Experimental node to debug any Comfy values, support for more types and widgets is planned",
-    "Deep Bump (mtb)": "Normal & height maps generation from single pictures",
-    "Export To Prores (mtb)": "Export to ProRes 4444 (Experimental)",
-    "Face Swap (mtb)": "Face swap using deepinsight/insightface models",
-    "Film Interpolation (mtb)": "Google Research FILM frame interpolation for large motion",
-    "Fit Number (mtb)": "Fit the input float using a source and target range",
-    "Float To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" FLOAT to a NUMBER.",
-    "Get Batch From History (mtb)": "Very experimental node to load images from the history of the server.\n\n    Queue items without output are ignore in the count.",
-    "Image Compare (mtb)": "Compare two images and return a difference image",
-    "Image Premultiply (mtb)": "Premultiply image with mask",
-    "Image Remove Background Rembg (mtb)": "Removes the background from the input using Rembg.",
-    "Image Resize Factor (mtb)": "Extracted mostly from WAS Node Suite, with a few edits (most notably multiple image support) and less features.",
-    "Int To Bool (mtb)": "Basic int to bool conversion",
-    "Int To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" INT to a NUMBER.",
-    "Latent Lerp (mtb)": "Linear interpolation (blend) between two latent vectors",
-    "Latent Noise (mtb)": "Inject noise into latent space",
-    "Latent Transform (mtb)": "Dumb attempt at reproducing some deforum like motion",
-    "Load Face Enhance Model (mtb)": "Loads a GFPGan or RestoreFormer model for face enhancement.",
-    "Load Face Swap Model (mtb)": "Loads a faceswap model",
-    "Load Film Model (mtb)": "Loads a FILM model",
-    "Load Image From Url (mtb)": "Load an image from the given URL",
-    "Load Image Sequence (mtb)": "Load an image sequence from a folder. The current frame is used to determine which image to load.\n\n    Usually used in conjunction with the `Primitive` node set to increment to load a sequence of images from a folder.\n    Use -1 to load all matching frames as a batch.\n    ",
-    "Mask To Image (mtb)": "Converts a mask (alpha) to an RGB image with a color and background",
-    "Qr Code (mtb)": "Basic QR Code generator",
-    "Restore Face (mtb)": "Uses GFPGan to restore faces",
-    "Save Gif (mtb)": "Save the images from the batch as a GIF",
-    "Save Image Grid (mtb)": "Save all the images in the input batch as a grid of images.",
-    "Save Image Sequence (mtb)": "Save an image sequence to a folder. The current frame is used to determine which image to save.\n\n    This is merely a wrapper around the `save_images` function with formatting for the output folder and filename.\n    ",
-    "Smart Step (mtb)": "Utils to control the steps start/stop of the KAdvancedSampler in percentage",
-    "String Replace (mtb)": "Basic string replacement",
-    "Styles Loader (mtb)": "Load csv files and populate a dropdown from the rows (\u00e0 la A111)",
-    "Text To Image (mtb)": "Utils to convert text to image using a font\n\n\n    The tool looks for any .ttf file in the Comfy folder hierarchy.\n    ",
-    "Uncrop (mtb)": "Uncrops an image to a given bounding box\n\n    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n    The BBOX input takes precedence over the tuple input"
+{
+    "Animation Builder (mtb)": "Convenient way to manage basic animation maths at the core of many of my workflows",
+    "Any To String (mtb)": "Tries to take any input and convert it to a string",
+    "Batch Float (mtb)": "Generates a batch of float values with interpolation",
+    "Batch Float Assemble (mtb)": "Assembles mutiple batches of floats into a single stream (batch)",
+    "Batch Float Fill (mtb)": "Fills a batch float with a single value until it reaches the target length",
+    "Batch Make (mtb)": "Simply duplicates the input frame as a batch",
+    "Batch Merge (mtb)": "Merges multiple image batches with different frame counts",
+    "Batch Shake (mtb)": "Applies a shaking effect to batches of images.",
+    "Batch Shape (mtb)": "Generates a batch of 2D shapes with optional shading (experimental)",
+    "Batch Transform (mtb)": "Transform a batch of images using a batch of keyframes",
+    "Bbox (mtb)": "The bounding box (BBOX) custom type used by other nodes",
+    "Bbox From Mask (mtb)": "From a mask extract the bounding box",
+    "Blur (mtb)": "Blur an image using a Gaussian filter.",
+    "Color Correct (mtb)": "Various color correction methods",
+    "Colored Image (mtb)": "Constant color image of given size",
+    "Concat Images (mtb)": "Add images to batch",
+    "Crop (mtb)": "Crops an image and an optional mask to a given bounding box\n\n    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n    The BBOX input takes precedence over the tuple input\n    ",
+    "Debug (mtb)": "Experimental node to debug any Comfy values, support for more types and widgets is planned",
+    "Deep Bump (mtb)": "Normal & height maps generation from single pictures",
+    "Export With Ffmpeg (mtb)": "Export with FFmpeg (Experimental)",
+    "Face Swap (mtb)": "Face swap using deepinsight/insightface models",
+    "Film Interpolation (mtb)": "Google Research FILM frame interpolation for large motion",
+    "Fit Number (mtb)": "Fit the input float using a source and target range",
+    "Float To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" FLOAT to a NUMBER.",
+    "Get Batch From History (mtb)": "Very experimental node to load images from the history of the server.\n\n    Queue items without output are ignored in the count.",
+    "Image Compare (mtb)": "Compare two images and return a difference image",
+    "Image Premultiply (mtb)": "Premultiply image with mask",
+    "Image Remove Background Rembg (mtb)": "Removes the background from the input using Rembg.",
+    "Image Resize Factor (mtb)": "Extracted mostly from WAS Node Suite, with a few edits (most notably multiple image support) and less features.",
+    "Image Tile Offset (mtb)": "Mimics an old photoshop technique to check for seamless textures",
+    "Int To Bool (mtb)": "Basic int to bool conversion",
+    "Int To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" INT to a NUMBER.",
+    "Interpolate Clip Sequential (mtb)": null,
+    "Latent Lerp (mtb)": "Linear interpolation (blend) between two latent vectors",
+    "Load Face Analysis Model (mtb)": "Loads a face analysis model",
+    "Load Face Enhance Model (mtb)": "Loads a GFPGan or RestoreFormer model for face enhancement.",
+    "Load Face Swap Model (mtb)": "Loads a faceswap model",
+    "Load Film Model (mtb)": "Loads a FILM model",
+    "Load Image From Url (mtb)": "Load an image from the given URL",
+    "Load Image Sequence (mtb)": "Load an image sequence from a folder. The current frame is used to determine which image to load.\n\n    Usually used in conjunction with the `Primitive` node set to increment to load a sequence of images from a folder.\n    Use -1 to load all matching frames as a batch.\n    ",
+    "Mask To Image (mtb)": "Converts a mask (alpha) to an RGB image with a color and background",
+    "Math Expression (mtb)": "Node to evaluate a simple math expression string",
+    "Model Patch Seamless (mtb)": "Uses the stable diffusion 'hack' to infer seamless images by setting the model layers padding mode to circular (experimental)",
+    "Pick From Batch (mtb)": "Pick a specific number of images from a batch, either from the start or end.",
+    "Qr Code (mtb)": "Basic QR Code generator",
+    "Restore Face (mtb)": "Uses GFPGan to restore faces",
+    "Save Gif (mtb)": "Save the images from the batch as a GIF",
+    "Save Image Grid (mtb)": "Save all the images in the input batch as a grid of images.",
+    "Save Image Sequence (mtb)": "Save an image sequence to a folder. The current frame is used to determine which image to save.\n\n    This is merely a wrapper around the `save_images` function with formatting for the output folder and filename.\n    ",
+    "Save Tensors (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy",
+    "Sharpen (mtb)": "Sharpens an image using a Gaussian kernel.",
+    "Smart Step (mtb)": "Utils to control the steps start/stop of the KAdvancedSampler in percentage",
+    "Stack Images (mtb)": "Stack the input images horizontally or vertically",
+    "String Replace (mtb)": "Basic string replacement",
+    "Styles Loader (mtb)": "Load csv files and populate a dropdown from the rows (\u00e0 la A111)",
+    "Text To Image (mtb)": "Utils to convert text to image using a font\n\n\n    The tool looks for any .ttf file in the Comfy folder hierarchy.\n    ",
+    "Transform Image (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy\n\n\n    it return a tensor representing the transformed images with the same shape as the input tensor\n    ",
+    "Uncrop (mtb)": "Uncrops an image to a given bounding box\n\n    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n    The BBOX input takes precedence over the tuple input",
+    "Unsplash Image (mtb)": "Unsplash Image given a keyword and a size",
+    "Vae Decode (mtb)": "Wrapper for the 2 core decoders but also adding the sd seamless hack, taken from: FlyingFireCo/tiled_ksampler"
 }
@@ -17,7 +17,7 @@ class AnimationBuilder:
            },
        }

-    RETURN_TYPES = ("INT", "FLOAT", "INT", "BOOL")
+    RETURN_TYPES = ("INT", "FLOAT", "INT", "BOOLEAN")
    RETURN_NAMES = ("frame", "0-1 (scaled)", "count", "loop_ended")
    CATEGORY = "mtb/animation"
    FUNCTION = "build_animation"
@@ -0,0 +1,625 @@
+import math
+import os
+from pathlib import Path
+from typing import List
+
+import cv2
+import folder_paths
+import numpy as np
+import torch
+
+from ..log import log
+from ..utils import apply_easing, pil2tensor
+from .transform import TransformImage
+
+
+def hex_to_rgb(hex_color, bgr=False):
+    hex_color = hex_color.lstrip("#")
+    if bgr:
+        return tuple(int(hex_color[i : i + 2], 16) for i in (4, 2, 0))
+
+    return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
+
+
+class BatchMake:
+    """Simply duplicates the input frame as a batch"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "count": ("INT", {"default": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate_batch"
+    CATEGORY = "mtb/batch"
+
+    def generate_batch(self, image: torch.Tensor, count):
+        if len(image.shape) == 3:
+            image = image.unsqueeze(0)
+
+        return (image.repeat(count, 1, 1, 1),)
+
+
+class BatchShape:
+    """Generates a batch of 2D shapes with optional shading (experimental)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "count": ("INT", {"default": 1}),
+                "shape": (
+                    ["Box", "Circle", "Diamond"],
+                    {"default": "Box"},
+                ),
+                "image_width": ("INT", {"default": 512}),
+                "image_height": ("INT", {"default": 512}),
+                "shape_size": ("INT", {"default": 100}),
+                "color": ("COLOR", {"default": "#ffffff"}),
+                "bg_color": ("COLOR", {"default": "#000000"}),
+                "shade_color": ("COLOR", {"default": "#000000"}),
+                "shadex": ("FLOAT", {"default": 0.0}),
+                "shadey": ("FLOAT", {"default": 0.0}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate_shapes"
+    CATEGORY = "mtb/batch"
+
+    def generate_shapes(
+        self,
+        count,
+        shape,
+        image_width,
+        image_height,
+        shape_size,
+        color,
+        bg_color,
+        shade_color,
+        shadex,
+        shadey,
+    ):
+        print(f"COLOR: {color}")
+        print(f"BG_COLOR: {bg_color}")
+        print(f"SHADE_COLOR: {shade_color}")
+
+        # Parse color input to BGR tuple for OpenCV
+        color = hex_to_rgb(color)
+        bg_color = hex_to_rgb(bg_color)
+        shade_color = hex_to_rgb(shade_color)
+        res = []
+        for x in range(count):
+            # Initialize an image canvas
+            canvas = np.full((image_height, image_width, 3), bg_color, dtype=np.uint8)
+            mask = np.zeros((image_height, image_width), dtype=np.uint8)
+
+            # Compute the center point of the shape
+            center = (image_width // 2, image_height // 2)
+
+            if shape == "Box":
+                half_size = shape_size // 2
+                top_left = (center[0] - half_size, center[1] - half_size)
+                bottom_right = (center[0] + half_size, center[1] + half_size)
+                cv2.rectangle(mask, top_left, bottom_right, 255, -1)
+            elif shape == "Circle":
+                cv2.circle(mask, center, shape_size // 2, 255, -1)
+            elif shape == "Diamond":
+                pts = np.array(
+                    [
+                        [center[0], center[1] - shape_size // 2],
+                        [center[0] + shape_size // 2, center[1]],
+                        [center[0], center[1] + shape_size // 2],
+                        [center[0] - shape_size // 2, center[1]],
+                    ]
+                )
+                cv2.fillPoly(mask, [pts], 255)
+
+            # Color the shape
+            canvas[mask == 255] = color
+
+            # Apply shading effects to a separate shading canvas
+            shading = np.zeros_like(canvas, dtype=np.float32)
+            shading[:, :, 0] = shadex * np.linspace(0, 1, image_width)
+            shading[:, :, 1] = shadey * np.linspace(0, 1, image_height).reshape(-1, 1)
+            shading_canvas = cv2.addWeighted(
+                canvas.astype(np.float32), 1, shading, 1, 0
+            ).astype(np.uint8)
+
+            # Apply shading only to the shape area using the mask
+            canvas[mask == 255] = shading_canvas[mask == 255]
+            res.append(canvas)
+
+        return (pil2tensor(res),)
+
+
+class BatchFloatFill:
+    """Fills a batch float with a single value until it reaches the target length"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "floats": ("FLOATS",),
+                "direction": (["head", "tail"], {"default": "tail"}),
+                "value": ("FLOAT", {"default": 0.0}),
+                "count": ("INT", {"default": 1}),
+            }
+        }
+
+    FUNCTION = "fill_floats"
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/batch"
+
+    def fill_floats(self, floats, direction, value, count):
+        size = len(floats)
+        if size > count:
+            raise ValueError(f"Size ({size}) is less then target count ({count})")
+
+        rem = count - size
+        if direction == "tail":
+            floats = floats + [value] * rem
+        else:
+            floats = [value] * rem + floats
+        return (floats,)
+
+
+class BatchFloatAssemble:
+    """Assembles mutiple batches of floats into a single stream (batch)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"reverse": ("BOOLEAN", {"default": False})}}
+
+    FUNCTION = "assemble_floats"
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/batch"
+
+    def assemble_floats(self, reverse, **kwargs):
+        res = []
+        if reverse:
+            for x in reversed(kwargs.values()):
+                res += x
+        else:
+            for x in kwargs.values():
+                res += x
+
+        return (res,)
+
+
+class BatchFloat:
+    """Generates a batch of float values with interpolation"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mode": (
+                    ["Single", "Steps"],
+                    {"default": "Steps"},
+                ),
+                "count": ("INT", {"default": 1}),
+                "min": ("FLOAT", {"default": 0.0}),
+                "max": ("FLOAT", {"default": 1.0}),
+                "easing": (
+                    [
+                        "Linear",
+                        "Sine In",
+                        "Sine Out",
+                        "Sine In/Out",
+                        "Quart In",
+                        "Quart Out",
+                        "Quart In/Out",
+                        "Cubic In",
+                        "Cubic Out",
+                        "Cubic In/Out",
+                        "Circ In",
+                        "Circ Out",
+                        "Circ In/Out",
+                        "Back In",
+                        "Back Out",
+                        "Back In/Out",
+                        "Elastic In",
+                        "Elastic Out",
+                        "Elastic In/Out",
+                        "Bounce In",
+                        "Bounce Out",
+                        "Bounce In/Out",
+                    ],
+                    {"default": "Linear"},
+                ),
+            }
+        }
+
+    FUNCTION = "set_floats"
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/batch"
+
+    def set_floats(self, mode, count, min, max, easing):
+        keyframes = []
+        if mode == "Single":
+            keyframes = [min] * count
+            return (keyframes,)
+
+        for i in range(count):
+            normalized_step = i / (count - 1)
+            eased_step = apply_easing(normalized_step, easing)
+            eased_value = min + (max - min) * eased_step
+            keyframes.append(eased_value)
+
+        return (keyframes,)
+
+
+class BatchMerge:
+    """Merges multiple image batches with different frame counts"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "fusion_mode": (["add", "multiply", "average"], {"default": "average"}),
+                "fill": (["head", "tail"], {"default": "tail"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "merge_batches"
+    CATEGORY = "mtb/batch"
+
+    def merge_batches(self, fusion_mode, fill, **kwargs):
+        images = kwargs.values()
+        max_frames = max(img.shape[0] for img in images)
+
+        adjusted_images = []
+        for img in images:
+            frame_count = img.shape[0]
+            if frame_count < max_frames:
+                fill_frame = img[0] if fill == "head" else img[-1]
+                fill_frames = fill_frame.repeat(max_frames - frame_count, 1, 1, 1)
+                adjusted_batch = (
+                    torch.cat((fill_frames, img), dim=0)
+                    if fill == "head"
+                    else torch.cat((img, fill_frames), dim=0)
+                )
+            else:
+                adjusted_batch = img
+            adjusted_images.append(adjusted_batch)
+
+        # Merge the adjusted batches
+        merged_image = None
+        for img in adjusted_images:
+            if merged_image is None:
+                merged_image = img
+            else:
+                if fusion_mode == "add":
+                    merged_image += img
+                elif fusion_mode == "multiply":
+                    merged_image *= img
+                elif fusion_mode == "average":
+                    merged_image = (merged_image + img) / 2
+
+        return (merged_image,)
+
+
+class Batch2dTransform:
+    """Transform a batch of images using a batch of keyframes"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "border_handling": (
+                    ["edge", "constant", "reflect", "symmetric"],
+                    {"default": "edge"},
+                ),
+                "constant_color": ("COLOR", {"default": "#000000"}),
+            },
+            "optional": {
+                "x": ("FLOATS",),
+                "y": ("FLOATS",),
+                "zoom": ("FLOATS",),
+                "angle": ("FLOATS",),
+                "shear": ("FLOATS",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "transform_batch"
+    CATEGORY = "mtb/batch"
+
+    def get_num_elements(self, param) -> int:
+        if isinstance(param, torch.Tensor):
+            return torch.numel(param)
+        elif isinstance(param, list):
+            return len(param)
+
+        return 0
+
+    def transform_batch(
+        self,
+        image: torch.Tensor,
+        border_handling,
+        constant_color,
+        x=None,
+        y=None,
+        zoom=None,
+        angle=None,
+        shear=None,
+    ):
+        if all(
+            self.get_num_elements(param) <= 0 for param in [x, y, zoom, angle, shear]
+        ):
+            raise ValueError("At least one transform parameter must be provided")
+
+        keyframes = {"x": [], "y": [], "zoom": [], "angle": [], "shear": []}
+
+        default_vals = {"x": 0, "y": 0, "zoom": 1.0, "angle": 0, "shear": 0}
+
+        if self.get_num_elements(x) > 0:
+            keyframes["x"] = x
+        if self.get_num_elements(y) > 0:
+            keyframes["y"] = y
+        if self.get_num_elements(zoom) > 0:
+            keyframes["zoom"] = zoom
+        if self.get_num_elements(angle) > 0:
+            keyframes["angle"] = angle
+        if self.get_num_elements(shear) > 0:
+            keyframes["shear"] = shear
+
+        for name, values in keyframes.items():
+            count = len(values)
+            if count > 0 and count != image.shape[0]:
+                raise ValueError(
+                    f"Length of {name} values ({count}) must match number of images ({image.shape[0]})"
+                )
+            if count == 0:
+                keyframes[name] = [default_vals[name]] * image.shape[0]
+
+        transformer = TransformImage()
+        res = [
+            transformer.transform(
+                image[i].unsqueeze(0),
+                keyframes["x"][i],
+                keyframes["y"][i],
+                keyframes["zoom"][i],
+                keyframes["angle"][i],
+                keyframes["shear"][i],
+                border_handling,
+                constant_color,
+            )[0]
+            for i in range(image.shape[0])
+        ]
+        return (torch.cat(res, dim=0),)
+
+
+DEFAULT_INTERPOLANT = lambda t: t * t * t * (t * (t * 6 - 15) + 10)
+
+
+class BatchShake:
+    """Applies a shaking effect to batches of images."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "position_amount_x": ("FLOAT", {"default": 1.0}),
+                "position_amount_y": ("FLOAT", {"default": 1.0}),
+                "rotation_amount": ("FLOAT", {"default": 10.0}),
+                "frequency": ("FLOAT", {"default": 1.0, "min": 0.005}),
+                "frequency_divider": ("FLOAT", {"default": 1.0, "min": 0.005}),
+                "octaves": ("INT", {"default": 1, "min": 1}),
+                "seed": ("INT", {"default": 0}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "FLOATS", "FLOATS", "FLOATS")
+    RETURN_NAMES = ("image", "pos_x", "pos_y", "rot")
+    FUNCTION = "apply_shake"
+    CATEGORY = "mtb/batch"
+
+    # def interpolant(self, t):
+    # return t * t * t * (t * (t * 6 - 15) + 10)
+
+    def generate_perlin_noise_2d(
+        self, shape, res, tileable=(False, False), interpolant=None
+    ):
+        """Generate a 2D numpy array of perlin noise.
+
+        Args:
+            shape: The shape of the generated array (tuple of two ints).
+                This must be a multple of res.
+            res: The number of periods of noise to generate along each
+                axis (tuple of two ints). Note shape must be a multiple of
+                res.
+            tileable: If the noise should be tileable along each axis
+                (tuple of two bools). Defaults to (False, False).
+            interpolant: The interpolation function, defaults to
+                t*t*t*(t*(t*6 - 15) + 10).
+
+        Returns:
+            A numpy array of shape shape with the generated noise.
+
+        Raises:
+            ValueError: If shape is not a multiple of res.
+        """
+        interpolant = interpolant or DEFAULT_INTERPOLANT
+        delta = (res[0] / shape[0], res[1] / shape[1])
+        d = (shape[0] // res[0], shape[1] // res[1])
+        grid = (
+            np.mgrid[0 : res[0] : delta[0], 0 : res[1] : delta[1]].transpose(1, 2, 0)
+            % 1
+        )
+        # Gradients
+        angles = 2 * np.pi * np.random.rand(res[0] + 1, res[1] + 1)
+        gradients = np.dstack((np.cos(angles), np.sin(angles)))
+        if tileable[0]:
+            gradients[-1, :] = gradients[0, :]
+        if tileable[1]:
+            gradients[:, -1] = gradients[:, 0]
+        gradients = gradients.repeat(d[0], 0).repeat(d[1], 1)
+        g00 = gradients[: -d[0], : -d[1]]
+        g10 = gradients[d[0] :, : -d[1]]
+        g01 = gradients[: -d[0], d[1] :]
+        g11 = gradients[d[0] :, d[1] :]
+        # Ramps
+        n00 = np.sum(np.dstack((grid[:, :, 0], grid[:, :, 1])) * g00, 2)
+        n10 = np.sum(np.dstack((grid[:, :, 0] - 1, grid[:, :, 1])) * g10, 2)
+        n01 = np.sum(np.dstack((grid[:, :, 0], grid[:, :, 1] - 1)) * g01, 2)
+        n11 = np.sum(np.dstack((grid[:, :, 0] - 1, grid[:, :, 1] - 1)) * g11, 2)
+        # Interpolation
+        t = interpolant(grid)
+        n0 = n00 * (1 - t[:, :, 0]) + t[:, :, 0] * n10
+        n1 = n01 * (1 - t[:, :, 0]) + t[:, :, 0] * n11
+        return np.sqrt(2) * ((1 - t[:, :, 1]) * n0 + t[:, :, 1] * n1)
+
+    def generate_fractal_noise_2d(
+        self,
+        shape,
+        res,
+        octaves=1,
+        persistence=0.5,
+        lacunarity=2,
+        tileable=(True, True),
+        interpolant=None,
+    ):
+        """Generate a 2D numpy array of fractal noise.
+
+        Args:
+            shape: The shape of the generated array (tuple of two ints).
+                This must be a multiple of lacunarity**(octaves-1)*res.
+            res: The number of periods of noise to generate along each
+                axis (tuple of two ints). Note shape must be a multiple of
+                (lacunarity**(octaves-1)*res).
+            octaves: The number of octaves in the noise. Defaults to 1.
+            persistence: The scaling factor between two octaves.
+            lacunarity: The frequency factor between two octaves.
+            tileable: If the noise should be tileable along each axis
+                (tuple of two bools). Defaults to (True,True).
+            interpolant: The, interpolation function, defaults to
+                t*t*t*(t*(t*6 - 15) + 10).
+
+        Returns:
+            A numpy array of fractal noise and of shape shape generated by
+            combining several octaves of perlin noise.
+
+        Raises:
+            ValueError: If shape is not a multiple of
+                (lacunarity**(octaves-1)*res).
+        """
+        interpolant = interpolant or DEFAULT_INTERPOLANT
+
+        noise = np.zeros(shape)
+        frequency = 1
+        amplitude = 1
+        for _ in range(octaves):
+            noise += amplitude * self.generate_perlin_noise_2d(
+                shape, (frequency * res[0], frequency * res[1]), tileable, interpolant
+            )
+            frequency *= lacunarity
+            amplitude *= persistence
+        return noise
+
+    def fbm(self, x, y, octaves):
+        # noise_2d = self.generate_fractal_noise_2d((256, 256), (8, 8), octaves)
+        # Now, extract a single noise value based on x and y, wrapping indices if necessary
+        x_idx = int(x) % 256
+        y_idx = int(y) % 256
+        return self.noise_pattern[x_idx, y_idx]
+
+    def apply_shake(
+        self,
+        images,
+        position_amount_x,
+        position_amount_y,
+        rotation_amount,
+        frequency,
+        frequency_divider,
+        octaves,
+        seed,
+    ):
+        # Rehash
+        np.random.seed(seed)
+        self.position_offset = np.random.uniform(-1e3, 1e3, 3)
+        self.rotation_offset = np.random.uniform(-1e3, 1e3, 3)
+        self.noise_pattern = self.generate_perlin_noise_2d(
+            (512, 512), (32, 32), (True, True)
+        )
+
+        # Assuming frame count is derived from the first dimension of images tensor
+        frame_count = images.shape[0]
+
+        frequency = frequency / frequency_divider
+
+        # Generate shaking parameters for each frame
+        x_translations = []
+        y_translations = []
+        rotations = []
+
+        for frame_num in range(frame_count):
+            time = frame_num * frequency
+            x_idx = (self.position_offset[0] + frame_num) % 256
+            y_idx = (self.position_offset[1] + frame_num) % 256
+
+            np_position = np.array(
+                [
+                    self.fbm(x_idx, time, octaves),
+                    self.fbm(y_idx, time, octaves),
+                ]
+            )
+
+            # np_position = np.array(
+            #     [
+            #         self.fbm(self.position_offset[0] + frame_num, time, octaves),
+            #         self.fbm(self.position_offset[1] + frame_num, time, octaves),
+            #     ]
+            # )
+            # np_rotation = self.fbm(self.rotation_offset[2] + frame_num, time, octaves)
+
+            rot_idx = (self.rotation_offset[2] + frame_num) % 256
+            np_rotation = self.fbm(rot_idx, time, octaves)
+
+            x_translations.append(np_position[0] * position_amount_x)
+            y_translations.append(np_position[1] * position_amount_y)
+            rotations.append(np_rotation * rotation_amount)
+
+        # Convert lists to tensors
+        # x_translations = torch.tensor(x_translations, dtype=torch.float32)
+        # y_translations = torch.tensor(y_translations, dtype=torch.float32)
+        # rotations = torch.tensor(rotations, dtype=torch.float32)
+
+        # Create an instance of Batch2dTransform
+        transform = Batch2dTransform()
+
+        log.debug(
+            f"Applying shaking with parameters: \nposition {position_amount_x}, {position_amount_y}\nrotation {rotation_amount}\nfrequency {frequency}\noctaves {octaves}"
+        )
+
+        # Apply shaking transformations to images
+        shaken_images = transform.transform_batch(
+            images,
+            border_handling="edge",  # Assuming edge handling as default
+            constant_color="#000000",  # Assuming black as default constant color
+            x=x_translations,
+            y=y_translations,
+            angle=rotations,
+        )[0]
+
+        return (shaken_images, x_translations, y_translations, rotations)
+
+
+__nodes__ = [
+    BatchFloat,
+    Batch2dTransform,
+    BatchShape,
+    BatchMake,
+    BatchFloatAssemble,
+    BatchFloatFill,
+    BatchMerge,
+    BatchShake,
+]
@@ -1,10 +1,92 @@
-from ..utils import pil2tensor
-from ..utils import here, comfy_dir
-from ..log import log
-import folder_paths
+import csv, shutil
 from pathlib import Path
-import shutil
-import csv
+
+import folder_paths
+
+from ..log import log
+from ..utils import here
+
+
+class InterpolateClipSequential:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "base_text": ("STRING", {"multiline": True}),
+                "text_to_replace": ("STRING", {"default": ""}),
+                "clip": ("CLIP",),
+                "interpolation_strength": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "interpolate_encodings_sequential"
+
+    CATEGORY = "mtb/conditioning"
+
+    def interpolate_encodings_sequential(
+        self, base_text, text_to_replace, clip, interpolation_strength, **replacements
+    ):
+        log.debug(f"Received interpolation_strength: {interpolation_strength}")
+
+        # - Ensure interpolation strength is within [0, 1]
+        interpolation_strength = max(0.0, min(1.0, interpolation_strength))
+
+        # - Check if replacements were provided
+        if not replacements:
+            raise ValueError("At least one replacement should be provided.")
+
+        num_replacements = len(replacements)
+        log.debug(f"Number of replacements: {num_replacements}")
+
+        segment_length = 1.0 / num_replacements
+        log.debug(f"Calculated segment_length: {segment_length}")
+
+        # - Find the segment that the interpolation_strength falls into
+        segment_index = min(
+            int(interpolation_strength // segment_length), num_replacements - 1
+        )
+        log.debug(f"Segment index: {segment_index}")
+
+        # - Calculate the local strength within the segment
+        local_strength = (
+            interpolation_strength - (segment_index * segment_length)
+        ) / segment_length
+        log.debug(f"Local strength: {local_strength}")
+
+        # - If it's the first segment, interpolate between base_text and the first replacement
+        if segment_index == 0:
+            replacement_text = list(replacements.values())[0]
+            log.debug("Using the base text a the base blend")
+            # -  Start with the base_text condition
+            tokens = clip.tokenize(base_text)
+            cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True)
+        else:
+            base_replace = list(replacements.values())[segment_index - 1]
+            log.debug(f"Using {base_replace} a the base blend")
+
+            # - Start with the base_text condition replaced by the closest replacement
+            tokens = clip.tokenize(base_text.replace(text_to_replace, base_replace))
+            cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True)
+
+            replacement_text = list(replacements.values())[segment_index]
+
+        interpolated_text = base_text.replace(text_to_replace, replacement_text)
+        tokens = clip.tokenize(interpolated_text)
+        cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
+
+        # - Linearly interpolate between the two conditions
+        interpolated_condition = (
+            1.0 - local_strength
+        ) * cond_from + local_strength * cond_to
+        interpolated_pooled = (
+            1.0 - local_strength
+        ) * pooled_from + local_strength * pooled_to
+
+        return ([[interpolated_condition, {"pooled_output": interpolated_pooled}]],)


 class SmartStep:
@@ -74,9 +156,23 @@ class StylesLoader:
            for file in files:
                with open(file, "r", encoding="utf8") as f:
                    parsed = csv.reader(f)
-                    for row in parsed:
+                    for i, row in enumerate(parsed):
                        log.debug(f"Adding style {row[0]}")
-                        cls.options[row[0]] = (row[1], row[2])
+                        try:
+                            name, positive, negative = (row + [None] * 3)[:3]
+                            positive = positive or ""
+                            negative = negative or ""
+                            if name is not None:
+                                cls.options[name] = (positive, negative)
+                            else:
+                                # Handle the case where 'name' is None
+                                log.warning(f"Missing 'name' in row {i}.")
+
+                        except Exception as e:
+                            log.warning(
+                                f"There was an error while parsing {file}, make sure it respects A1111 format, i.e 3 columns name, positive, negative:\n{e}"
+                            )
+                            continue

        else:
            log.debug(f"Using cached styles (count: {len(cls.options)})")
@@ -97,110 +193,4 @@ class StylesLoader:
        return (self.options[style_name][0], self.options[style_name][1])


-class TextToImage:
-    """Utils to convert text to image using a font
-
-
-    The tool looks for any .ttf file in the Comfy folder hierarchy.
-    """
-
-    fonts = {}
-
-    def __init__(self):
-        # - This is executed when the graph is executed, we could conditionaly reload fonts there
-        pass
-
-    @classmethod
-    def CACHE_FONTS(cls):
-        font_extensions = ["*.ttf", "*.otf", "*.woff", "*.woff2", "*.eot"]
-        fonts = []
-
-        for extension in font_extensions:
-            fonts.extend(comfy_dir.glob(f"**/{extension}"))
-
-        if not fonts:
-            log.warn(
-                "> No fonts found in the comfy folder, place at least one font file somewhere in ComfyUI's hierarchy"
-            )
-        else:
-            log.debug(f"> Found {len(fonts)} fonts")
-
-        for font in fonts:
-            log.debug(f"Adding font {font}")
-            cls.fonts[font.stem] = font.as_posix()
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        if not cls.fonts:
-            cls.CACHE_FONTS()
-        else:
-            log.debug(f"Using cached fonts (count: {len(cls.fonts)})")
-        return {
-            "required": {
-                "text": (
-                    "STRING",
-                    {"default": "Hello world!"},
-                ),
-                "font": ((sorted(cls.fonts.keys())),),
-                "wrap": (
-                    "INT",
-                    {"default": 120, "min": 0, "max": 8096, "step": 1},
-                ),
-                "font_size": (
-                    "INT",
-                    {"default": 12, "min": 1, "max": 2500, "step": 1},
-                ),
-                "width": (
-                    "INT",
-                    {"default": 512, "min": 1, "max": 8096, "step": 1},
-                ),
-                "height": (
-                    "INT",
-                    {"default": 512, "min": 1, "max": 8096, "step": 1},
-                ),
-                # "position": (["INT"], {"default": 0, "min": 0, "max": 100, "step": 1}),
-                "color": (
-                    "COLOR",
-                    {"default": "black"},
-                ),
-                "background": (
-                    "COLOR",
-                    {"default": "white"},
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = "text_to_image"
-    CATEGORY = "mtb/generate"
-
-    def text_to_image(
-        self, text, font, wrap, font_size, width, height, color, background
-    ):
-        from PIL import Image, ImageDraw, ImageFont
-        import textwrap
-
-        font = self.fonts[font]
-        font = ImageFont.truetype(font, font_size)
-        if wrap == 0:
-            wrap = width / font_size
-        lines = textwrap.wrap(text, width=wrap)
-        log.debug(f"Lines: {lines}")
-        line_height = font.getsize("hg")[1]
-        img_height = height  # line_height * len(lines)
-        img_width = width  # max(font.getsize(line)[0] for line in lines)
-
-        img = Image.new("RGBA", (img_width, img_height), background)
-        draw = ImageDraw.Draw(img)
-        y_text = 0
-        for line in lines:
-            width, height = font.getsize(line)
-            draw.text((0, y_text), line, color, font=font)
-            y_text += height
-
-        # img.save(os.path.join(folder_paths.base_path, f'{str(uuid.uuid4())}.png'))
-        return (pil2tensor(img),)
-
-
-__nodes__ = [SmartStep, TextToImage, StylesLoader]
+__nodes__ = [SmartStep, StylesLoader, InterpolateClipSequential]
@@ -1,9 +1,9 @@
-import torch
-from ..utils import tensor2pil, pil2tensor, tensor2np, np2tensor
-from PIL import Image, ImageFilter, ImageDraw, ImageChops
 import numpy as np
+import torch
+from PIL import Image, ImageChops, ImageDraw, ImageFilter

 from ..log import log
+from ..utils import np2tensor, pil2tensor, tensor2np, tensor2pil


 class Bbox:
@@ -32,7 +32,7 @@ class Bbox:
    CATEGORY = "mtb/crop"

    def do_crop(self, x, y, width, height):  # bbox
-        return (x, y, width, height)
+        return ((x, y, width, height),)
        # return bbox


@@ -44,6 +44,7 @@ class BboxFromMask:
        return {
            "required": {
                "mask": ("MASK",),
+                "invert": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "image": ("IMAGE",),
@@ -61,7 +62,7 @@ class BboxFromMask:
    FUNCTION = "extract_bounding_box"
    CATEGORY = "mtb/crop"

-    def extract_bounding_box(self, mask: torch.Tensor, image=None):
+    def extract_bounding_box(self, mask: torch.Tensor, invert: bool, image=None):
        # if image != None:
        #     if mask.size(0) != image.size(0):
        #         if mask.size(0) != 1:
@@ -73,9 +74,8 @@ class BboxFromMask:
        #                 f"Batch count mismatch for mask and image, it can either be 1 mask for X images, or X masks for X images (mask: {mask.shape} | image: {image.shape})"
        #             )

-        _mask = tensor2pil(1.0 - mask)[0]
-
        # we invert it
+        _mask = tensor2pil(1.0 - mask)[0] if invert else tensor2pil(mask)[0]
        alpha_channel = np.array(_mask)

        non_zero_indices = np.nonzero(alpha_channel)
@@ -141,19 +141,23 @@ class Crop:
        self, image: torch.Tensor, mask=None, x=0, y=0, width=256, height=256, bbox=None
    ):
        image = image.numpy()
-        if mask:
+        if mask is not None:
            mask = mask.numpy()

-        if bbox != None:
+        if bbox is not None:
            x, y, width, height = bbox

        cropped_image = image[:, y : y + height, x : x + width, :]
-        cropped_mask = mask[y : y + height, x : x + width] if mask != None else None
+        cropped_mask = None
+        if mask is not None:
+            cropped_mask = (
+                mask[:, y : y + height, x : x + width] if mask is not None else None
+            )
        crop_data = (x, y, width, height)

        return (
            torch.from_numpy(cropped_image),
-            torch.from_numpy(cropped_mask) if mask != None else None,
+            torch.from_numpy(cropped_mask) if cropped_mask is not None else None,
            crop_data,
        )

@@ -1,10 +1,71 @@
-from ..utils import tensor2pil
-from ..log import log
-import io, base64
-import torch
-import folder_paths
-from typing import Optional
+import base64
+import io
 from pathlib import Path
+from typing import Optional
+
+import folder_paths
+import torch
+
+from ..log import log
+from ..utils import tensor2pil
+
+
+# region processors
+def process_tensor(tensor):
+    log.debug(f"Tensor: {tensor.shape}")
+
+    image = tensor2pil(tensor)
+    b64_imgs = []
+    for im in image:
+        buffered = io.BytesIO()
+        im.save(buffered, format="PNG")
+        b64_imgs.append(
+            "data:image/png;base64,"
+            + base64.b64encode(buffered.getvalue()).decode("utf-8")
+        )
+
+    return {"b64_images": b64_imgs}
+
+
+def process_list(anything):
+    text = []
+    if not anything:
+        return {"text": []}
+
+    first_element = anything[0]
+    if (
+        isinstance(first_element, list)
+        and first_element
+        and isinstance(first_element[0], torch.Tensor)
+    ):
+        text.append(
+            f"List of List of Tensors: {first_element[0].shape} (x{len(anything)})"
+        )
+
+    elif isinstance(first_element, torch.Tensor):
+        text.append(f"List of Tensors: {first_element.shape} (x{len(anything)})")
+
+    return {"text": text}
+
+
+def process_dict(anything):
+    text = []
+    if "samples" in anything:
+        is_empty = "(empty)" if torch.count_nonzero(anything["samples"]) == 0 else ""
+        text.append(f"Latent Samples: {anything['samples'].shape} {is_empty}")
+
+    return {"text": text}
+
+
+def process_bool(anything):
+    return {"text": ["True" if anything else "False"]}
+
+
+def process_text(anything):
+    return {"text": [str(anything)]}
+
+
+# endregion


 class Debug:
@@ -13,46 +74,38 @@ class Debug:
    @classmethod
    def INPUT_TYPES(cls):
        return {
-            "required": {"anything_1": ("*")},
+            "required": {"output_to_console": ("BOOLEAN", {"default": False})},
        }

-    RETURN_TYPES = ("STRING",)
+    RETURN_TYPES = ()
    FUNCTION = "do_debug"
    CATEGORY = "mtb/debug"
    OUTPUT_NODE = True

-    def do_debug(self, **kwargs):
+    def do_debug(self, output_to_console, **kwargs):
        output = {
            "ui": {"b64_images": [], "text": []},
-            "result": ("A"),
+            # "result": ("A"),
        }
-        for k, v in kwargs.items():
-            anything = v
-            text = ""
-            if isinstance(anything, torch.Tensor):
-                log.debug(f"Tensor: {anything.shape}")

-                # write the images to temp
+        processors = {
+            torch.Tensor: process_tensor,
+            list: process_list,
+            dict: process_dict,
+            bool: process_bool,
+        }
+        if output_to_console:
+            print("bouh!")

-                image = tensor2pil(anything)
-                b64_imgs = []
-                for im in image:
-                    buffered = io.BytesIO()
-                    im.save(buffered, format="JPEG")
-                    b64_imgs.append(
-                        "data:image/jpeg;base64,"
-                        + base64.b64encode(buffered.getvalue()).decode("utf-8")
-                    )
+        for anything in kwargs.values():
+            processor = processors.get(type(anything), process_text)
+            processed_data = processor(anything)

-                output["ui"]["b64_images"] += b64_imgs
-                log.debug(f"Input {k} contains {len(b64_imgs)} images")
-            elif isinstance(anything, bool):
-                log.debug(f"Input {k} contains boolean: {anything}")
-                output["ui"]["text"] += ["True" if anything else "False"]
-            else:
-                text = str(anything)
-                log.debug(f"Input {k} contains text: {text}")
-                output["ui"]["text"] += [text]
+            for ui_key, ui_value in processed_data.items():
+                output["ui"][ui_key].extend(ui_value)
+            # log.debug(
+            #     f"Processed input {k}, found {len(processed_data.get('b64_images', []))} images and {len(processed_data.get('text', []))} text items."
+            # )

        return output

@@ -1,23 +1,38 @@
-import onnxruntime as ort
+import tempfile
+from pathlib import Path
+
 import numpy as np
-import pathlib
 import onnxruntime as ort
-import numpy as np
-from .. import utils as utils_inference
-from ..log import log
+import torch
+from PIL import Image
+
+from ..errors import ModelNotFound
+from ..log import mklog
+from ..utils import get_model_path, tensor2pil, tiles_infer, tiles_merge, tiles_split

 # Disable MS telemetry
 ort.disable_telemetry_events()
+log = mklog(__name__)


 # - COLOR to NORMALS
-def color_to_normals(color_img, overlap, progress_callback):
+def color_to_normals(color_img, overlap, progress_callback, save_temp=False):
    """Computes a normal map from the given color map. 'color_img' must be a numpy array
    in C,H,W format (with C as RGB). 'overlap' must be one of 'SMALL', 'MEDIUM', 'LARGE'.
    """
+    temp_dir = Path(tempfile.mkdtemp()) if save_temp else None

    # Remove alpha & convert to grayscale
-    img = np.mean(color_img[:3], axis=0, keepdimss=True)
+    img = np.mean(color_img[:3], axis=0, keepdims=True)
+
+    if temp_dir:
+        Image.fromarray((img[0] * 255).astype(np.uint8)).save(
+            temp_dir / "grayscale_img.png"
+        )
+
+    log.debug(
+        f"Converting color image to grayscale by taking the mean over color channels: {img.shape}"
+    )

    # Split image in tiles
    log.debug("DeepBump Color → Normals : tilling")
@@ -28,32 +43,56 @@ def color_to_normals(color_img, overlap, progress_callback):
        "LARGE": tile_size // 2,
    }
    stride_size = tile_size - overlaps[overlap]
-    tiles, paddings = utils_inference.tiles_split(
+    tiles, paddings = tiles_split(
        img, (tile_size, tile_size), (stride_size, stride_size)
    )
+    if temp_dir:
+        for i, tile in enumerate(tiles):
+            Image.fromarray((tile[0] * 255).astype(np.uint8)).save(
+                temp_dir / f"tile_{i}.png"
+            )

    # Load model
    log.debug("DeepBump Color → Normals : loading model")
-    addon_path = str(pathlib.Path(__file__).parent.absolute())
-    ort_session = ort.InferenceSession(f"{addon_path}/models/deepbump256.onnx")
+    model = get_model_path("deepbump", "deepbump256.onnx")
+    if not model or not model.exists():
+        raise ModelNotFound(f"deepbump ({model})")
+
+    ort_session = ort.InferenceSession(model)

    # Predict normal map for each tile
    log.debug("DeepBump Color → Normals : generating")
-    pred_tiles = utils_inference.tiles_infer(
-        tiles, ort_session, progress_callback=progress_callback
-    )
+    pred_tiles = tiles_infer(tiles, ort_session, progress_callback=progress_callback)
+
+    if temp_dir:
+        for i, pred_tile in enumerate(pred_tiles):
+            Image.fromarray((pred_tile.transpose(1, 2, 0) * 255).astype(np.uint8)).save(
+                temp_dir / f"pred_tile_{i}.png"
+            )

    # Merge tiles
    log.debug("DeepBump Color → Normals : merging")
-    pred_img = utils_inference.tiles_merge(
+    pred_img = tiles_merge(
        pred_tiles,
        (stride_size, stride_size),
        (3, img.shape[1], img.shape[2]),
        paddings,
    )

+    if temp_dir:
+        Image.fromarray((pred_img.transpose(1, 2, 0) * 255).astype(np.uint8)).save(
+            temp_dir / "merged_img.png"
+        )
+
    # Normalize each pixel to unit vector
-    pred_img = utils_inference.normalize(pred_img)
+    pred_img = normalize(pred_img)
+
+    if temp_dir:
+        Image.fromarray((pred_img.transpose(1, 2, 0) * 255).astype(np.uint8)).save(
+            temp_dir / "final_img.png"
+        )
+
+        log.debug(f"Debug images saved in {temp_dir}")

    return pred_img

@@ -261,7 +300,7 @@ class DeepBump:
                        "LARGEST",
                    ],
                ),
-                "normals_to_height_seamless": ("BOOL", {"default": False}),
+                "normals_to_height_seamless": ("BOOLEAN", {"default": True}),
            },
        }

@@ -278,25 +317,38 @@ class DeepBump:
        normals_to_curvature_blur_radius="SMALL",
        normals_to_height_seamless=True,
    ):
-        image = utils_inference.tensor2pil(image)
+        images = tensor2pil(image)
+        out_images = []

-        in_img = np.transpose(image, (2, 0, 1)) / 255
+        for image in images:
+            log.debug(f"Input image shape: {image}")

-        log.debug(f"Input image shape: {in_img.shape}")
+            in_img = np.transpose(image, (2, 0, 1)) / 255
+            log.debug(f"transposed for deep image shape: {in_img.shape}")
+            out_img = None

-        # Apply processing
-        if mode == "Color to Normals":
-            out_img = color_to_normals(in_img, color_to_normals_overlap, None)
-        if mode == "Normals to Curvature":
-            out_img = normals_to_curvature(
-                in_img, normals_to_curvature_blur_radius, None
-            )
-        if mode == "Normals to Height":
-            out_img = normals_to_height(in_img, normals_to_height_seamless, None)
+            # Apply processing
+            if mode == "Color to Normals":
+                out_img = color_to_normals(in_img, color_to_normals_overlap, None)
+            if mode == "Normals to Curvature":
+                out_img = normals_to_curvature(
+                    in_img, normals_to_curvature_blur_radius, None
+                )
+            if mode == "Normals to Height":
+                out_img = normals_to_height(in_img, normals_to_height_seamless, None)

-        out_img = (np.transpose(out_img, (1, 2, 0)) * 255).astype(np.uint8)
-
-        return (utils_inference.pil2tensor(out_img),)
+            if out_img is not None:
+                log.debug(f"Output image shape: {out_img.shape}")
+                out_images.append(
+                    torch.from_numpy(
+                        np.transpose(out_img, (1, 2, 0)).astype(np.float32)
+                    ).unsqueeze(0)
+                )
+            else:
+                log.error("No out img... This should not happen")
+        for outi in out_images:
+            log.debug(f"Shape fed to utils: {outi.shape}")
+        return (torch.cat(out_images, dim=0),)


 __nodes__ = [DeepBump]
@@ -1,18 +1,19 @@
-from gfpgan import GFPGANer
-import cv2
-import numpy as np
 import os
 from pathlib import Path
-import folder_paths
-from basicsr.utils import imwrite
-from PIL import Image
-from ..utils import pil2tensor, tensor2pil, np2tensor, tensor2np
-import torch
-from ..log import NullWriter, log
-from comfy import model_management
+from typing import Tuple
+
 import comfy
 import comfy.utils
-from typing import Tuple
+import cv2
+import folder_paths
+import numpy as np
+import torch
+from comfy import model_management
+from gfpgan import GFPGANer
+from PIL import Image
+
+from ..log import NullWriter, log
+from ..utils import get_model_path, np2tensor, pil2tensor, tensor2np


 class LoadFaceEnhanceModel:
@@ -23,19 +24,40 @@ class LoadFaceEnhanceModel:

    @classmethod
    def get_models_root(cls):
-        return Path(folder_paths.models_dir) / "upscale_models"
+        fr = get_model_path("face_restore")
+        # fr = Path(folder_paths.models_dir) / "face_restore"
+        if fr.exists():
+            return (fr, None)
+
+        um = get_model_path("upscale_models")
+        return (fr, um) if um.exists() else (None, None)

    @classmethod
    def get_models(cls):
-        models_path = cls.get_models_root()
-        
-        if not models_path.exists():
-            log.warning(f"No models found at {models_path}")
+        fr_models_path, um_models_path = cls.get_models_root()
+
+        if fr_models_path is None and um_models_path is None:
+            log.warning("Face restoration models not found.")
+            return []
+        if not fr_models_path.exists():
+            log.warning(
+                f"No Face Restore checkpoints found at {fr_models_path} (if you've used mtb before these checkpoints were saved in upscale_models before)"
+            )
+            log.warning(
+                "For now we fallback to upscale_models but this will be removed in a future version"
+            )
+            if um_models_path.exists():
+                return [
+                    x
+                    for x in um_models_path.iterdir()
+                    if x.name.endswith(".pth")
+                    and ("GFPGAN" in x.name or "RestoreFormer" in x.name)
+                ]
            return []

        return [
            x
-            for x in models_path.iterdir()
+            for x in fr_models_path.iterdir()
            if x.name.endswith(".pth")
            and ("GFPGAN" in x.name or "RestoreFormer" in x.name)
        ]
@@ -61,7 +83,7 @@ class LoadFaceEnhanceModel:
    def load_model(self, model_name, upscale=2, bg_upsampler=None):
        basic = "RestoreFormer" not in model_name

-        root = self.get_models_root()
+        fr_root, um_root = self.get_models_root()

        if bg_upsampler is not None:
            log.warning(
@@ -72,7 +94,9 @@ class LoadFaceEnhanceModel:

        sys.stdout = NullWriter()
        model = GFPGANer(
-            model_path=(root / model_name).as_posix(),
+            model_path=(
+                (fr_root if fr_root.exists() else um_root) / model_name
+            ).as_posix(),
            upscale=upscale,
            arch="clean" if basic else "RestoreFormer",  # or original for v1.0 only
            channel_multiplier=2,  # 1 for v1.0 only
@@ -140,12 +164,12 @@ class RestoreFace:
                "image": ("IMAGE",),
                "model": ("FACEENHANCE_MODEL",),
                # Input are aligned faces
-                "aligned": ("BOOL", {"default": False}),
+                "aligned": ("BOOLEAN", {"default": False}),
                # Only restore the center face
-                "only_center_face": ("BOOL", {"default": False}),
+                "only_center_face": ("BOOLEAN", {"default": False}),
                # Adjustable weights
                "weight": ("FLOAT", {"default": 0.5}),
-                "save_tmp_steps": ("BOOL", {"default": True}),
+                "save_tmp_steps": ("BOOLEAN", {"default": True}),
            }
        }

@@ -222,16 +246,16 @@ class RestoreFace:
        ):
            face_id = idx + 1
            file = self.get_step_image_path("cropped_faces", face_id)
-            imwrite(cropped_face, file)
+            cv2.imwrite(file, cropped_face)

            file = self.get_step_image_path("cropped_faces_restored", face_id)
-            imwrite(restored_face, file)
+            cv2.imwrite(file, restored_face)

            file = self.get_step_image_path("cropped_faces_compare", face_id)

            # save comparison image
            cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
-            imwrite(cmp_img, file)
+            cv2.imwrite(file, cmp_img)


 __nodes__ = [RestoreFace, LoadFaceEnhanceModel]
@@ -1,37 +1,31 @@
+# Optional face enhance nodes
 # region imports
-import onnxruntime
+import sys
 from pathlib import Path
-from PIL import Image
-from typing import List, Set, Tuple, Union, Optional
+from typing import List, Optional, Set, Union
+
+import comfy.model_management as model_management
 import cv2
-import folder_paths
-import glob
 import insightface
 import numpy as np
-import os
-import tempfile
+import onnxruntime
 import torch
 from insightface.model_zoo.inswapper import INSwapper
-from ..utils import pil2tensor, tensor2pil
-from ..log import mklog, NullWriter
-import sys
-import comfy.model_management as model_management
+from PIL import Image

+from ..errors import ModelNotFound
+from ..log import NullWriter, mklog
+from ..utils import download_antelopev2, get_model_path, pil2tensor, tensor2pil

 # endregion

 log = mklog(__name__)

+
 class LoadFaceAnalysisModel:
    """Loads a face analysis model"""

    models = []
-    @staticmethod
-    def get_models() -> List[str]:
-        models_path = os.path.join(folder_paths.models_dir, "insightface/*")
-        models = glob.glob(models_path)
-        models = [Path(x).name for x in models if x.endswith(".onnx") or x.endswith(".pth")]
-        return models

    @classmethod
    def INPUT_TYPES(cls):
@@ -49,20 +43,23 @@ class LoadFaceAnalysisModel:
    CATEGORY = "mtb/facetools"

    def load_model(self, faceswap_model: str):
+        if faceswap_model == "antelopev2":
+            download_antelopev2()
+
        face_analyser = insightface.app.FaceAnalysis(
-            name=faceswap_model, root=os.path.join(folder_paths.models_dir, "insightface")
+            name=faceswap_model,
+            root=get_model_path("insightface"),
        )
        return (face_analyser,)

+
 class LoadFaceSwapModel:
    """Loads a faceswap model"""

    @staticmethod
    def get_models() -> List[Path]:
-        models_path = os.path.join(folder_paths.models_dir, "insightface/*")
-        models = glob.glob(models_path)
-        models = [Path(x) for x in models if x.endswith(".onnx") or x.endswith(".pth")]
-        return models
+        models_path = get_model_path("insightface").iterdir()
+        return [x for x in models_path if x.suffix in [".onnx", ".pth"]]

    @classmethod
    def INPUT_TYPES(cls):
@@ -80,9 +77,10 @@ class LoadFaceSwapModel:
    CATEGORY = "mtb/facetools"

    def load_model(self, faceswap_model: str):
-        model_path = os.path.join(
-            folder_paths.models_dir, "insightface", faceswap_model
-        )
+        model_path = get_model_path("insightface", faceswap_model)
+        if not model_path or not model_path.exists():
+            raise ModelNotFound(f"{faceswap_model} ({model_path})")
+
        log.info(f"Loading model {model_path}")
        return (
            INSwapper(
@@ -114,7 +112,6 @@ class FaceSwap:
                "faces_index": ("STRING", {"default": "0"}),
                "faceanalysis_model": ("FACE_ANALYSIS_MODEL", {"default": "None"}),
                "faceswap_model": ("FACESWAP_MODEL", {"default": "None"}),
-                "debug": ("BOOL", {"default": False}),
            },
            "optional": {},
        }
@@ -130,7 +127,6 @@ class FaceSwap:
        faces_index: str,
        faceanalysis_model,
        faceswap_model,
-        debug=False,
    ):
        def do_swap(img):
            model_management.throw_exception_if_processing_interrupted()
@@ -140,7 +136,7 @@ class FaceSwap:
                int(x) for x in faces_index.strip(",").split(",") if x.isnumeric()
            }
            sys.stdout = NullWriter()
-            swapped = swap_face(faceanalysis_model,ref, img, faceswap_model, face_ids)
+            swapped = swap_face(faceanalysis_model, ref, img, faceswap_model, face_ids)
            sys.stdout = sys.__stdout__
            return pil2tensor(swapped)

@@ -164,15 +160,18 @@ class FaceSwap:


 # region face swap utils
-def get_face_single(face_analyser,img_data: np.ndarray, face_index=0, det_size=(640, 640)):
-   
+def get_face_single(
+    face_analyser, img_data: np.ndarray, face_index=0, det_size=(640, 640)
+):
    face_analyser.prepare(ctx_id=0, det_size=det_size)
    face = face_analyser.get(img_data)

    if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
        log.debug("No face ed, trying again with smaller image")
        det_size_half = (det_size[0] // 2, det_size[1] // 2)
-        return get_face_single(face_analyser,img_data, face_index=face_index, det_size=det_size_half)
+        return get_face_single(
+            face_analyser, img_data, face_index=face_index, det_size=det_size_half
+        )

    try:
        return sorted(face, key=lambda x: x.bbox[0])[face_index]
@@ -195,12 +194,14 @@ def swap_face(
    if face_swapper_model is not None:
        cv_source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
        cv_target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
-        source_face = get_face_single(face_analyser,cv_source_img, face_index=0)
+        source_face = get_face_single(face_analyser, cv_source_img, face_index=0)
        if source_face is not None:
            result = cv_target_img

            for face_num in faces_index:
-                target_face = get_face_single(face_analyser,cv_target_img, face_index=face_num)
+                target_face = get_face_single(
+                    face_analyser, cv_target_img, face_index=face_num
+                )
                if target_face is not None:
                    sys.stdout = NullWriter()
                    result = face_swapper_model.get(result, target_face, source_face)
@@ -1,7 +1,11 @@
+import threading
+from typing import cast
+
 import qrcode
-from ..utils import pil2tensor
 from PIL import Image
+
 from ..log import log
+from ..utils import comfy_dir, pil2tensor

 # class MtbExamples:
 #     """MTB Example Images"""
@@ -72,9 +76,10 @@ class UnsplashImage:
    CATEGORY = "mtb/generate"

    def do_unsplash_image(self, width, height, random_seed, keyword=None):
-        import requests
        import io

+        import requests
+
        base_url = "https://source.unsplash.com/random/"

        if width and height:
@@ -121,7 +126,7 @@ class QrCode:
                "error_correct": (("L", "M", "Q", "H"), {"default": "L"}),
                "box_size": ("INT", {"default": 10, "max": 8096, "min": 0, "step": 1}),
                "border": ("INT", {"default": 4, "max": 8096, "min": 0, "step": 1}),
-                "invert": (("BOOL",), {"default": False}),
+                "invert": (("BOOLEAN",), {"default": False}),
            }
        }

@@ -130,6 +135,9 @@ class QrCode:
    CATEGORY = "mtb/generate"

    def do_qr(self, url, width, height, error_correct, box_size, border, invert):
+        log.warning(
+            "This node will soon be deprecated, there are much better alternatives like https://github.com/coreyryanhanson/comfy-qr"
+        )
        if error_correct == "L" or error_correct not in ["M", "Q", "H"]:
            error_correct = qrcode.constants.ERROR_CORRECT_L
        elif error_correct == "M":
@@ -159,8 +167,158 @@ class QrCode:
        return (pil2tensor(code),)


+def bbox_dim(bbox):
+    left, upper, right, lower = bbox
+    width = right - left
+    height = lower - upper
+    return width, height
+
+
+class TextToImage:
+    """Utils to convert text to image using a font
+
+
+    The tool looks for any .ttf file in the Comfy folder hierarchy.
+    """
+
+    fonts = {}
+
+    def __init__(self):
+        # - This is executed when the graph is executed, we could conditionaly reload fonts there
+        pass
+
+    @classmethod
+    def CACHE_FONTS(cls):
+        font_extensions = ["*.ttf", "*.otf", "*.woff", "*.woff2", "*.eot"]
+        fonts = []
+
+        for extension in font_extensions:
+            fonts.extend(comfy_dir.glob(f"**/{extension}"))
+
+        if not fonts:
+            log.warn(
+                "> No fonts found in the comfy folder, place at least one font file somewhere in ComfyUI's hierarchy"
+            )
+        else:
+            log.debug(f"> Found {len(fonts)} fonts")
+
+        for font in fonts:
+            log.debug(f"Adding font {font}")
+            TextToImage.fonts[font.stem] = font.as_posix()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        if not cls.fonts:
+            thread = threading.Thread(target=cls.CACHE_FONTS)
+            thread.start()
+        else:
+            log.debug(f"Using cached fonts (count: {len(cls.fonts)})")
+        return {
+            "required": {
+                "text": (
+                    "STRING",
+                    {"default": "Hello world!"},
+                ),
+                "font": ((sorted(cls.fonts.keys())),),
+                "wrap": (
+                    "INT",
+                    {"default": 120, "min": 0, "max": 8096, "step": 1},
+                ),
+                "font_size": (
+                    "INT",
+                    {"default": 12, "min": 1, "max": 2500, "step": 1},
+                ),
+                "width": (
+                    "INT",
+                    {"default": 512, "min": 1, "max": 8096, "step": 1},
+                ),
+                "height": (
+                    "INT",
+                    {"default": 512, "min": 1, "max": 8096, "step": 1},
+                ),
+                "color": (
+                    "COLOR",
+                    {"default": "black"},
+                ),
+                "background": (
+                    "COLOR",
+                    {"default": "white"},
+                ),
+                "h_align": (("left", "center", "right"), {"default": "left"}),
+                "v_align": (("top", "center", "bottom"), {"default": "top"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "text_to_image"
+    CATEGORY = "mtb/generate"
+
+    def text_to_image(
+        self,
+        text,
+        font,
+        wrap,
+        font_size,
+        width,
+        height,
+        color,
+        background,
+        h_align="left",
+        v_align="top",
+    ):
+        import textwrap
+
+        from PIL import Image, ImageDraw, ImageFont
+
+        font_path = self.fonts[font]
+
+        # Handle word wrapping
+        if wrap:
+            lines = textwrap.wrap(text, width=wrap)
+        else:
+            lines = [text]
+        font = ImageFont.truetype(font_path, font_size)
+        # font = ImageFont.truetype(font_path, font_size)
+        # if wrap == 0:
+        #     wrap = width / font_size
+
+        log.debug(f"Lines: {lines}")
+        img = Image.new("RGBA", (width, height), background)
+        draw = ImageDraw.Draw(img)
+
+        text_height = sum(font.getsize(line)[1] for line in lines)
+
+        # Vertical alignment
+        if v_align == "top":
+            y_text = 0
+        elif v_align == "center":
+            y_text = (height - text_height) // 2
+        else:  # bottom
+            y_text = height - text_height
+
+        # Draw each line of text
+        for line in lines:
+            line_width, line_height = font.getsize(line)
+
+            # Horizontal alignment
+            if h_align == "left":
+                x_text = 0
+            elif h_align == "center":
+                x_text = (width - line_width) // 2
+            else:  # right
+                x_text = width - line_width
+
+            draw.text((x_text, y_text), line, color, font=font)
+            y_text += line_height
+
+        # img.save(os.path.join(folder_paths.base_path, f'{str(uuid.uuid4())}.png'))
+        return (pil2tensor(img),)
+
+
 __nodes__ = [
    QrCode,
-    UnsplashImage
+    UnsplashImage,
+    TextToImage
    #  MtbExamples,
 ]
@@ -1,4 +1,141 @@
+import io, json, urllib.parse, urllib.request
+
+import numpy as np
+import torch
+from PIL import Image
+
 from ..log import log
+from ..utils import apply_easing, get_server_info, pil2tensor
+
+
+def get_image(filename, subfolder, folder_type):
+    log.debug(
+        f"Getting image {filename} from foldertype {folder_type} {f'in subfolder: {subfolder}' if subfolder else ''}"
+    )
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    base_url, port = get_server_info()
+
+    url_values = urllib.parse.urlencode(data)
+    url = f"http://{base_url}:{port}/view?{url_values}"
+    log.debug(f"Fetching image from {url}")
+    with urllib.request.urlopen(url) as response:
+        return io.BytesIO(response.read())
+
+
+class GetBatchFromHistory:
+    """Very experimental node to load images from the history of the server.
+
+    Queue items without output are ignored in the count."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "enable": ("BOOLEAN", {"default": True}),
+                "count": ("INT", {"default": 1, "min": 0}),
+                "offset": ("INT", {"default": 0, "min": -1e9, "max": 1e9}),
+                "internal_count": ("INT", {"default": 0}),
+            },
+            "optional": {
+                "passthrough_image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    CATEGORY = "mtb/animation"
+    FUNCTION = "load_from_history"
+
+    def load_from_history(
+        self,
+        enable=True,
+        count=0,
+        offset=0,
+        internal_count=0,  # hacky way to invalidate the node
+        passthrough_image=None,
+    ):
+        if not enable or count == 0:
+            if passthrough_image is not None:
+                log.debug("Using passthrough image")
+                return (passthrough_image,)
+            log.debug("Load from history is disabled for this iteration")
+            return (torch.zeros(0),)
+        frames = []
+
+        base_url, port = get_server_info()
+
+        history_url = f"http://{base_url}:{port}/history"
+        log.debug(f"Fetching history from {history_url}")
+        output = torch.zeros(0)
+        with urllib.request.urlopen(history_url) as response:
+            output = self.load_batch_frames(response, offset, count, frames)
+
+        if output.size(0) == 0:
+            log.warn("No output found in history")
+
+        return (output,)
+
+    def load_batch_frames(self, response, offset, count, frames):
+        history = json.loads(response.read())
+
+        output_images = []
+
+        for run in history.values():
+            for node_output in run["outputs"].values():
+                if "images" in node_output:
+                    for image in node_output["images"]:
+                        image_data = get_image(
+                            image["filename"], image["subfolder"], image["type"]
+                        )
+                        output_images.append(image_data)
+
+        if not output_images:
+            return torch.zeros(0)
+
+        # Directly get desired range of images
+        start_index = max(len(output_images) - offset - count, 0)
+        end_index = len(output_images) - offset
+        selected_images = output_images[start_index:end_index]
+
+        frames = [Image.open(image) for image in selected_images]
+
+        if not frames:
+            return torch.zeros(0)
+        elif len(frames) != count:
+            log.warning(f"Expected {count} images, got {len(frames)} instead")
+
+        return pil2tensor(frames)
+
+
+class AnyToString:
+    """Tries to take any input and convert it to a string"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"input": ("*")},
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "do_str"
+    CATEGORY = "mtb/converters"
+
+    def do_str(self, input):
+        if isinstance(input, str):
+            return (input,)
+        elif isinstance(input, torch.Tensor):
+            return (f"Tensor of shape {input.shape} and dtype {input.dtype}",)
+        elif isinstance(input, Image.Image):
+            return (f"PIL Image of size {input.size} and mode {input.mode}",)
+        elif isinstance(input, np.ndarray):
+            return (f"Numpy array of shape {input.shape} and dtype {input.dtype}",)
+
+        elif isinstance(input, dict):
+            return (f"Dictionary of {len(input)} items, with keys {input.keys()}",)
+
+        else:
+            log.debug(f"Falling back to string conversion of {input}")
+            return (str(input),)


 class StringReplace:
@@ -30,6 +167,52 @@ class StringReplace:
        return (string,)


+class MTB_MathExpression:
+    """Node to evaluate a simple math expression string"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "expression": ("STRING", {"default": "", "multiline": True}),
+            }
+        }
+
+    FUNCTION = "eval_expression"
+    RETURN_TYPES = ("FLOAT", "INT")
+    RETURN_NAMES = ("result (float)", "result (int)")
+    CATEGORY = "mtb/math"
+    DESCRIPTION = "evaluate a simple math expression string (!! Fallsback to eval)"
+
+    def eval_expression(self, expression, **kwargs):
+        import math
+        from ast import literal_eval
+
+        for key, value in kwargs.items():
+            print(f"Replacing placeholder <{key}> with value {value}")
+            expression = expression.replace(f"<{key}>", str(value))
+
+        result = -1
+        try:
+            result = literal_eval(expression)
+        except SyntaxError as e:
+            raise ValueError(
+                f"The expression syntax is wrong '{expression}': {e}"
+            ) from e
+
+        except ValueError:
+            try:
+                expression = expression.replace("^", "**")
+                result = eval(expression)
+            except Exception as e:
+                # Handle any other exceptions and provide a meaningful error message
+                raise ValueError(
+                    f"Error evaluating expression '{expression}': {e}"
+                ) from e
+
+        return (result, int(result))
+
+
 class FitNumber:
    """Fit the input float using a source and target range"""

@@ -38,17 +221,45 @@ class FitNumber:
        return {
            "required": {
                "value": ("FLOAT", {"default": 0, "forceInput": True}),
-                "clamp": ("BOOL", {"default": False}),
-                "source_min": ("FLOAT", {"default": 0.0}),
-                "source_max": ("FLOAT", {"default": 1.0}),
-                "target_min": ("FLOAT", {"default": 0.0}),
-                "target_max": ("FLOAT", {"default": 1.0}),
+                "clamp": ("BOOLEAN", {"default": False}),
+                "source_min": ("FLOAT", {"default": 0.0, "step": 0.01}),
+                "source_max": ("FLOAT", {"default": 1.0, "step": 0.01}),
+                "target_min": ("FLOAT", {"default": 0.0, "step": 0.01}),
+                "target_max": ("FLOAT", {"default": 1.0, "step": 0.01}),
+                "easing": (
+                    [
+                        "Linear",
+                        "Sine In",
+                        "Sine Out",
+                        "Sine In/Out",
+                        "Quart In",
+                        "Quart Out",
+                        "Quart In/Out",
+                        "Cubic In",
+                        "Cubic Out",
+                        "Cubic In/Out",
+                        "Circ In",
+                        "Circ Out",
+                        "Circ In/Out",
+                        "Back In",
+                        "Back Out",
+                        "Back In/Out",
+                        "Elastic In",
+                        "Elastic Out",
+                        "Elastic In/Out",
+                        "Bounce In",
+                        "Bounce Out",
+                        "Bounce In/Out",
+                    ],
+                    {"default": "Linear"},
+                ),
            }
        }

    FUNCTION = "set_range"
    RETURN_TYPES = ("FLOAT",)
    CATEGORY = "mtb/math"
+    DESCRIPTION = "Fit the input float using a source and target range"

    def set_range(
        self,
@@ -58,18 +269,55 @@ class FitNumber:
        source_max: float,
        target_min: float,
        target_max: float,
+        easing: str,
    ):
-        res = target_min + (target_max - target_min) * (value - source_min) / (
-            source_max - source_min
-        )
-
+        if source_min == source_max:
+            normalized_value = 0
+        else:
+            normalized_value = (value - source_min) / (source_max - source_min)
        if clamp:
-            if target_min > target_max:
-                res = max(min(res, target_min), target_max)
-            else:
-                res = max(min(res, target_max), target_min)
+            normalized_value = max(min(normalized_value, 1), 0)
+
+        eased_value = apply_easing(normalized_value, easing)
+
+        # - Convert the eased value to the target range
+        res = target_min + (target_max - target_min) * eased_value

        return (res,)


-__nodes__ = [StringReplace, FitNumber]
+class ConcatImages:
+    """Add images to batch"""
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "concatenate_tensors"
+    CATEGORY = "mtb/image"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"reverse": ("BOOLEAN", {"default": False})},
+        }
+
+    def concatenate_tensors(self, reverse, **kwargs):
+        tensors = tuple(kwargs.values())
+        batch_sizes = [tensor.size(0) for tensor in tensors]
+
+        concatenated = torch.cat(tensors, dim=0)
+
+        # Update the batch size in the concatenated tensor
+        concatenated_size = list(concatenated.size())
+        concatenated_size[0] = sum(batch_sizes)
+        concatenated = concatenated.view(*concatenated_size)
+
+        return (concatenated,)
+
+
+__nodes__ = [
+    StringReplace,
+    FitNumber,
+    GetBatchFromHistory,
+    AnyToString,
+    ConcatImages,
+    MTB_MathExpression,
+]
@@ -1,110 +1,20 @@
-from typing import List
-from pathlib import Path
-import os
 import glob
-import folder_paths
-from ..log import log
-import torch
-from frame_interpolation.eval import util, interpolator
-from ..utils import tensor2np
-import numpy as np
+import os
+from pathlib import Path
+from typing import List
+
 import comfy
-from PIL import Image
-import urllib.request
-import urllib.parse
-import json
-import tensorflow as tf
 import comfy.model_management as model_management
-import io
+import comfy.utils
+import folder_paths
+import numpy as np
+import tensorflow as tf
+import torch
+from frame_interpolation.eval import interpolator, util

-from comfy.cli_args import args
-from ..utils import pil2tensor
-
-
-def get_image(filename, subfolder, folder_type):
-    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
-    url_values = urllib.parse.urlencode(data)
-    with urllib.request.urlopen(
-        "http://{}:{}/view?{}".format(args.listen, args.port, url_values)
-    ) as response:
-        return io.BytesIO(response.read())
-
-
-class GetBatchFromHistory:
-    """Very experimental node to load images from the history of the server.
-
-    Queue items without output are ignore in the count."""
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "enable": ("BOOL", {"default": True}),
-                "count": ("INT", {"default": 1, "min": 0}),
-                "offset": ("INT", {"default": 0, "min": -1e9, "max": 1e9}),
-            },
-            "optional": {"passthrough_image": ("IMAGE",)},
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = "images"
-    CATEGORY = "mtb/animation"
-    FUNCTION = "load_from_history"
-
-    def load_from_history(
-        self,
-        enable=True,
-        count=0,
-        offset=0,
-        passthrough_image=None,
-    ):
-        if not enable or count == 0:
-            if passthrough_image is not None:
-                return (passthrough_image,)
-            log.debug("Load from history is disabled for this iteration")
-            return (torch.zeros(0),)
-        frames = []
-
-        with urllib.request.urlopen(
-            "http://{}:{}/history".format(args.listen, args.port)
-        ) as response:
-            history = json.loads(response.read())
-
-            output_images = []
-            for k, run in history.items():
-                for o in run["outputs"]:
-                    for node_id in run["outputs"]:
-                        node_output = run["outputs"][node_id]
-                        if "images" in node_output:
-                            images_output = []
-                            for image in node_output["images"]:
-                                image_data = get_image(
-                                    image["filename"], image["subfolder"], image["type"]
-                                )
-                                images_output.append(image_data)
-                            output_images.extend(images_output)
-            if len(output_images) == 0:
-                return (torch.zeros(0),)
-            for i, image in enumerate(list(reversed(output_images))):
-                if i < offset:
-                    continue
-                if i >= offset + count:
-                    break
-                # Decode image as tensor
-                img = Image.open(image)
-                log.debug(f"Image from history {i} of shape {img.size}")
-                frames.append(img)
-
-                # Display the shape of the tensor
-                # print("Tensor shape:", image_tensor.shape)
-
-            # return (output_images,)
-
-            output = pil2tensor(
-                list(reversed(frames)),
-            )
-
-            return (output,)
+from ..errors import ModelNotFound
+from ..log import log
+from ..utils import get_model_path


 class LoadFilmModel:
@@ -112,10 +22,9 @@ class LoadFilmModel:

    @staticmethod
    def get_models() -> List[Path]:
-        models_path = os.path.join(folder_paths.models_dir, "FILM/*")
-        models = glob.glob(models_path)
-        models = [Path(x) for x in models if x.endswith(".onnx") or x.endswith(".pth")]
-        return models
+        models_paths = get_model_path("FILM").iterdir()
+
+        return [x for x in models_paths if x.suffix in [".onnx", ".pth"]]

    @classmethod
    def INPUT_TYPES(cls):
@@ -133,7 +42,10 @@ class LoadFilmModel:
    CATEGORY = "mtb/frame iterpolation"

    def load_model(self, film_model: str):
-        model_path = Path(folder_paths.models_dir) / "FILM" / film_model
+        model_path = get_model_path("FILM", film_model)
+        if not model_path or not model_path.exists():
+            raise ModelNotFound(f"FILM ({model_path})")
+
        if not (model_path / "saved_model.pb").exists():
            model_path = model_path / "saved_model"

@@ -208,46 +120,4 @@ class FilmInterpolation:
        return (out_tensors,)


-class ConcatImages:
-    """Add images to batch"""
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "concat_images"
-    CATEGORY = "mtb/image"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "imageA": ("IMAGE",),
-                "imageB": ("IMAGE",),
-            },
-        }
-
-    @classmethod
-    def concatenate_tensors(cls, A: torch.Tensor, B: torch.Tensor):
-        # Get the batch sizes of A and B
-        batch_size_A = A.size(0)
-        batch_size_B = B.size(0)
-
-        # Concatenate the tensors along the batch dimension
-        concatenated = torch.cat((A, B), dim=0)
-
-        # Update the batch size in the concatenated tensor
-        concatenated_size = list(concatenated.size())
-        concatenated_size[0] = batch_size_A + batch_size_B
-        concatenated = concatenated.view(*concatenated_size)
-
-        return concatenated
-
-    def concat_images(self, imageA: torch.Tensor, imageB: torch.Tensor):
-        log.debug(f"Concatenating A ({imageA.shape}) and B ({imageB.shape})")
-        return (self.concatenate_tensors(imageA, imageB),)
-
-
-__nodes__ = [
-    LoadFilmModel,
-    FilmInterpolation,
-    ConcatImages,
-    GetBatchFromHistory,
-]
+__nodes__ = [LoadFilmModel, FilmInterpolation]
@@ -1,21 +1,20 @@
-import torch
-from skimage.filters import gaussian
-from skimage.restoration import denoise_tv_chambolle
-from skimage.util import compare_images
-from skimage.color import rgb2hsv, hsv2rgb
-import numpy as np
-import torchvision.transforms.functional as F
-from PIL import Image, ImageChops
-from ..utils import tensor2pil, pil2tensor, np2tensor, tensor2np
-import cv2
-import torch
-from ..log import log
-import folder_paths
-from PIL.PngImagePlugin import PngInfo
+import itertools
 import json
+import math
 import os
-import comfy.model_management as model_management

+import cv2
+import folder_paths
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+from skimage.filters import gaussian
+from skimage.util import compare_images
+
+from ..log import log
+from ..utils import pil2tensor, tensor2np, tensor2pil

 # try:
 #     from cv2.ximgproc import guidedFilter
@@ -23,6 +22,18 @@ import comfy.model_management as model_management
 #     log.warning("cv2.ximgproc.guidedFilter not found, use opencv-contrib-python")


+def gaussian_kernel(kernel_size: int, sigma_x: float, sigma_y: float, device=None):
+    x, y = torch.meshgrid(
+        torch.linspace(-1, 1, kernel_size, device=device),
+        torch.linspace(-1, 1, kernel_size, device=device),
+        indexing="ij",
+    )
+    d_x = x * x / (2.0 * sigma_x * sigma_x)
+    d_y = y * y / (2.0 * sigma_y * sigma_y)
+    g = torch.exp(-(d_x + d_y))
+    return g / g.sum()
+
+
 class ColorCorrect:
    """Various color correction methods"""

@@ -181,7 +192,7 @@ class ColorCorrect:
        return (image,)


-class ImageCompare:
+class ImageCompare_:
    """Compare two images and return a difference image"""

    @classmethod
@@ -217,7 +228,7 @@ class ImageCompare:
 import requests


-class LoadImageFromUrl:
+class LoadImageFromUrl_:
    """Load an image from the given URL"""

    @classmethod
@@ -243,7 +254,7 @@ class LoadImageFromUrl:
        return (pil2tensor(image),)


-class Blur:
+class Blur_:
    """Blur an image using a Gaussian filter."""

    @classmethod
@@ -274,6 +285,78 @@ class Blur:
        return (torch.from_numpy(image),)


+class Sharpen_:
+    """Sharpens an image using a Gaussian kernel."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "sharpen_radius": (
+                    "INT",
+                    {"default": 1, "min": 1, "max": 31, "step": 1},
+                ),
+                "sigma_x": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1},
+                ),
+                "sigma_y": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1},
+                ),
+                "alpha": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.1},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "do_sharp"
+    CATEGORY = "mtb/image processing"
+
+    def do_sharp(
+        self,
+        image: torch.Tensor,
+        sharpen_radius: int,
+        sigma_x: float,
+        sigma_y: float,
+        alpha: float,
+    ):
+        if sharpen_radius == 0:
+            return (image,)
+
+        channels = image.shape[3]
+
+        kernel_size = 2 * sharpen_radius + 1
+        kernel = gaussian_kernel(kernel_size, sigma_x, sigma_y) * -(alpha * 10)
+
+        # Modify center of kernel to make it a sharpening kernel
+        center = kernel_size // 2
+        kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
+
+        kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
+        tensor_image = image.permute(0, 3, 1, 2)
+
+        tensor_image = F.pad(
+            tensor_image,
+            (sharpen_radius, sharpen_radius, sharpen_radius, sharpen_radius),
+            "reflect",
+        )
+        sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)
+
+        # Remove padding
+        sharpened = sharpened[
+            :, :, sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius
+        ]
+
+        sharpened = sharpened.permute(0, 2, 3, 1)
+        result = torch.clamp(sharpened, 0, 1)
+
+        return (result,)
+
+
 # https://github.com/lllyasviel/AdverseCleaner/blob/main/clean.py
 # def deglaze_np_img(np_img):
 #     y = np_img.copy()
@@ -320,22 +403,26 @@ class MaskToImage:
    FUNCTION = "render_mask"

    def render_mask(self, mask, color, background):
-        mask = tensor2np(mask)
-        mask = Image.fromarray(mask).convert("L")
+        masks = tensor2np(mask)
+        images = []
+        for m in masks:
+            _mask = Image.fromarray(m).convert("L")

-        image = Image.new("RGBA", mask.size, color=color)
-        # apply the mask
-        image = Image.composite(
-            image, Image.new("RGBA", mask.size, color=background), mask
-        )
+            log.debug(f"Converted mask to PIL Image format, size: {_mask.size}")

-        # image = ImageChops.multiply(image, mask)
-        # apply over background
-        # image = Image.alpha_composite(Image.new("RGBA", image.size, color=background), image)
+            image = Image.new("RGBA", _mask.size, color=color)
+            # apply the mask
+            image = Image.composite(
+                image, Image.new("RGBA", _mask.size, color=background), _mask
+            )

-        image = pil2tensor(image.convert("RGB"))
+            # image = ImageChops.multiply(image, mask)
+            # apply over background
+            # image = Image.alpha_composite(Image.new("RGBA", image.size, color=background), image)

-        return (image,)
+            images.append(image.convert("RGB"))
+
+        return (pil2tensor(images),)


 class ColoredImage:
@@ -351,7 +438,11 @@ class ColoredImage:
                "color": ("COLOR",),
                "width": ("INT", {"default": 512, "min": 16, "max": 8160}),
                "height": ("INT", {"default": 512, "min": 16, "max": 8160}),
-            }
+            },
+            "optional": {
+                "foreground_image": ("IMAGE",),
+                "foreground_mask": ("MASK",),
+            },
        }

    CATEGORY = "mtb/generate"
@@ -360,12 +451,46 @@ class ColoredImage:

    FUNCTION = "render_img"

-    def render_img(self, color, width, height):
-        image = Image.new("RGB", (width, height), color=color)
+    def render_img(
+        self, color, width, height, foreground_image=None, foreground_mask=None
+    ):
+        image = Image.new("RGBA", (width, height), color=color)
+        output = []
+        if foreground_image is not None:
+            if foreground_mask is None:
+                fg_images = tensor2pil(foreground_image)
+                for img in fg_images:
+                    if image.size != img.size:
+                        raise ValueError(
+                            f"Dimension mismatch: image {image.size}, img {img.size}"
+                        )

-        image = pil2tensor(image)
+                    if img.mode != "RGBA":
+                        raise ValueError(
+                            f"Foreground image must be in 'RGBA' mode when no mask is provided, got {img.mode}"
+                        )

-        return (image,)
+                    output.append(Image.alpha_composite(image, img).convert("RGB"))
+
+            elif foreground_image.size[0] != foreground_mask.size[0]:
+                raise ValueError("Foreground image and mask must have same batch size")
+            else:
+                fg_images = tensor2pil(foreground_image)
+                fg_masks = tensor2pil(foreground_mask)
+                output.extend(
+                    Image.composite(
+                        fg_image.convert("RGBA"),
+                        image,
+                        fg_mask,
+                    ).convert("RGB")
+                    for fg_image, fg_mask in zip(fg_images, fg_masks)
+                )
+        elif foreground_mask is not None:
+            log.warn("Mask ignored because no foreground image is given")
+
+        output = pil2tensor(output)
+
+        return (output,)


 class ImagePremultiply:
@@ -377,25 +502,19 @@ class ImagePremultiply:
            "required": {
                "image": ("IMAGE",),
                "mask": ("MASK",),
-                "invert": ("BOOL", {"default": False}),
+                "invert": ("BOOLEAN", {"default": False}),
            }
        }

    CATEGORY = "mtb/image"
    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("RGBA",)
    FUNCTION = "premultiply"

    def premultiply(self, image, mask, invert):
        images = tensor2pil(image)
-        if invert:
-            masks = tensor2pil(mask)  # .convert("L")
-        else:
-            masks = tensor2pil(1.0 - mask)
-
-        single = False
-        if len(mask) == 1:
-            single = True
-
+        masks = tensor2pil(mask) if invert else tensor2pil(1.0 - mask)
+        single = len(mask) == 1
        masks = [x.convert("L") for x in masks]

        out = []
@@ -425,10 +544,18 @@ class ImageResizeFactor:
                    "FLOAT",
                    {"default": 2, "min": 0.01, "max": 16.0, "step": 0.01},
                ),
-                "supersample": ("BOOL", {"default": True}),
+                "supersample": ("BOOLEAN", {"default": True}),
                "resampling": (
-                    ["lanczos", "nearest", "bilinear", "bicubic"],
-                    {"default": "lanczos"},
+                    [
+                        "nearest",
+                        "linear",
+                        "bilinear",
+                        "bicubic",
+                        "trilinear",
+                        "area",
+                        "nearest-exact",
+                    ],
+                    {"default": "nearest"},
                ),
            },
            "optional": {
@@ -440,71 +567,6 @@ class ImageResizeFactor:
    RETURN_TYPES = ("IMAGE", "MASK")
    FUNCTION = "resize"

-    def resize_image(
-        self,
-        image: torch.Tensor,
-        factor: float = 0.5,
-        supersample=False,
-        resample="lanczos",
-        mask=None,
-    ) -> torch.Tensor:
-        model_management.throw_exception_if_processing_interrupted()
-        batch_count = 1
-        img = tensor2pil(image)
-
-        if isinstance(img, list):
-            log.debug("Multiple images detected (list)")
-            out = []
-            for im in img:
-                im = self.resize_image(
-                    pil2tensor(im), factor, supersample, resample, mask
-                )
-                out.append(im)
-            return torch.cat(out, dim=0)
-        elif isinstance(img, torch.Tensor):
-            if len(image.shape) > 3:
-                batch_count = image.size(0)
-
-        if batch_count > 1:
-            log.debug("Multiple images detected (batch count)")
-            out = [
-                self.resize_image(image[i], factor, supersample, resample, mask)
-                for i in range(batch_count)
-            ]
-            return torch.cat(out, dim=0)
-
-        log.debug("Resizing image")
-        # Get the current width and height of the image
-        current_width, current_height = img.size
-
-        log.debug(f"Current width: {current_width}, Current height: {current_height}")
-
-        # Calculate the new width and height based on the given mode and parameters
-        new_width, new_height = int(factor * current_width), int(
-            factor * current_height
-        )
-
-        log.debug(f"New width: {new_width}, New height: {new_height}")
-
-        # Define a dictionary of resampling filters
-        resample_filters = {"nearest": 0, "bilinear": 2, "bicubic": 3, "lanczos": 1}
-
-        # Apply supersample
-        if supersample:
-            super_size = (new_width * 8, new_height * 8)
-            log.debug(f"Applying supersample: {super_size}")
-            img = img.resize(
-                super_size, resample=Image.Resampling(resample_filters[resample])
-            )
-
-        # Resize the image using the given resampling filter
-        resized_image = img.resize(
-            (new_width, new_height),
-            resample=Image.Resampling(resample_filters[resample]),
-        )
-
-        return pil2tensor(resized_image)
-
    def resize(
        self,
        image: torch.Tensor,
@@ -513,27 +575,56 @@ class ImageResizeFactor:
        resampling: str,
        mask=None,
    ):
-        log.debug(f"Resizing image with factor {factor} and resampling {resampling}")
+        # Check if the tensor has the correct dimension
+        if len(image.shape) not in [3, 4]:  # HxWxC or BxHxWxC
+            raise ValueError("Expected image tensor of shape (H, W, C) or (B, H, W, C)")

-        batch_count = image.size(0)
-        log.debug(f"Batch count: {batch_count}")
-        if batch_count == 1:
-            log.debug("Batch count is 1, returning single image")
-            return (self.resize_image(image, factor, supersample, resampling),)
+        # Transpose to CxHxW or BxCxHxW for PyTorch
+        if len(image.shape) == 3:
+            image = image.permute(2, 0, 1).unsqueeze(0)  # CxHxW
        else:
-            log.debug("Batch count is greater than 1, returning multiple images")
-            images = [
-                self.resize_image(image[i], factor, supersample, resampling)
-                for i in range(batch_count)
-            ]
-            images = torch.cat(images, dim=0)
-            return (images,)
+            image = image.permute(0, 3, 1, 2)  # BxCxHxW
+
+        # Compute new dimensions
+        B, C, H, W = image.shape
+        new_H, new_W = int(H * factor), int(W * factor)
+
+        align_corner_filters = ("linear", "bilinear", "bicubic", "trilinear")
+        # Resize the image
+        resized_image = F.interpolate(
+            image,
+            size=(new_H, new_W),
+            mode=resampling,
+            align_corners=resampling in align_corner_filters,
+        )
+
+        # Optionally supersample
+        if supersample:
+            resized_image = F.interpolate(
+                resized_image,
+                scale_factor=2,
+                mode=resampling,
+                align_corners=resampling in align_corner_filters,
+            )
+
+        # Transpose back to the original format: BxHxWxC or HxWxC
+        if len(image.shape) == 4:
+            resized_image = resized_image.permute(0, 2, 3, 1)
+        else:
+            resized_image = resized_image.squeeze(0).permute(1, 2, 0)
+
+        # Apply mask if provided
+        if mask is not None:
+            if len(mask.shape) != len(resized_image.shape):
+                raise ValueError(
+                    "Mask tensor should have the same dimensions as the image tensor"
+                )
+            resized_image = resized_image * mask
+
+        return (resized_image,)


-import math
-
-
-class SaveImageGrid:
+class SaveImageGrid_:
    """Save all the images in the input batch as a grid of images."""

    def __init__(self):
@@ -546,7 +637,7 @@ class SaveImageGrid:
            "required": {
                "images": ("IMAGE",),
                "filename_prefix": ("STRING", {"default": "ComfyUI"}),
-                "save_intermediate": ("BOOL", {"default": False}),
+                "save_intermediate": ("BOOLEAN", {"default": False}),
            },
            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
        }
@@ -635,15 +726,65 @@ class SaveImageGrid:
        return {"ui": {"images": results}}


+class ImageTileOffset:
+    """Mimics an old photoshop technique to check for seamless textures"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "tiles": ("INT", {"default": 2}),
+            }
+        }
+
+    CATEGORY = "mtb/generate"
+
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "tile_image"
+
+    def tile_image(self, image: torch.Tensor, tiles: int = 2):
+        if tiles < 1:
+            raise ValueError("The number of tiles must be at least 1.")
+
+        batch_size, height, width, channels = image.shape
+        tile_height = height // tiles
+        tile_width = width // tiles
+
+        output_image = torch.zeros_like(image)
+
+        for i, j in itertools.product(range(tiles), range(tiles)):
+            start_h = i * tile_height
+            end_h = start_h + tile_height
+            start_w = j * tile_width
+            end_w = start_w + tile_width
+
+            tile = image[:, start_h:end_h, start_w:end_w, :]
+
+            output_start_h = (i + 1) % tiles * tile_height
+            output_start_w = (j + 1) % tiles * tile_width
+            output_end_h = output_start_h + tile_height
+            output_end_w = output_start_w + tile_width
+
+            output_image[
+                :, output_start_h:output_end_h, output_start_w:output_end_w, :
+            ] = tile
+
+        return (output_image,)
+
+
 __nodes__ = [
    ColorCorrect,
-    ImageCompare,
-    Blur,
+    ImageCompare_,
+    ImageTileOffset,
+    Blur_,
    # DeglazeImage,
    MaskToImage,
    ColoredImage,
    ImagePremultiply,
    ImageResizeFactor,
-    SaveImageGrid,
-    LoadImageFromUrl,
+    SaveImageGrid_,
+    LoadImageFromUrl_,
+    Sharpen_,
 ]
@@ -0,0 +1,76 @@
+import torch
+
+from ..log import log
+
+
+class StackImages:
+    """Stack the input images horizontally or vertically"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"vertical": ("BOOLEAN", {"default": False})}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stack"
+    CATEGORY = "mtb/image utils"
+
+    def stack(self, vertical, **kwargs):
+        if not kwargs:
+            raise ValueError("At least one tensor must be provided.")
+
+        tensors = list(kwargs.values())
+        log.debug(
+            f"Stacking {len(tensors)} tensors {'vertically' if vertical else 'horizontally'}"
+        )
+        log.debug(list(kwargs.keys()))
+
+        ref_shape = tensors[0].shape
+        for tensor in tensors[1:]:
+            if tensor.shape[1:] != ref_shape[1:]:
+                raise ValueError(
+                    "All tensors must have the same dimensions except for the stacking dimension."
+                )
+
+        dim = 1 if vertical else 2
+
+        stacked_tensor = torch.cat(tensors, dim=dim)
+
+        return (stacked_tensor,)
+
+
+class PickFromBatch:
+    """Pick a specific number of images from a batch, either from the start or end."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "from_direction": (["end", "start"], {"default": "start"}),
+                "count": ("INT", {"default": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "pick_from_batch"
+    CATEGORY = "mtb/image utils"
+
+    def pick_from_batch(self, image, from_direction, count):
+        batch_size = image.size(0)
+
+        # 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, but only {batch_size} are available."
+            )
+
+        if from_direction == "end":
+            selected_tensors = image[-count:]
+        else:
+            selected_tensors = image[:count]
+
+        return (selected_tensors,)
+
+
+__nodes__ = [StackImages, PickFromBatch]
@@ -1,26 +1,129 @@
-from ..utils import tensor2np
-import uuid
-import folder_paths
-from ..log import log
-import comfy.model_management as model_management
-import subprocess
-import torch
+import json, subprocess, uuid
 from pathlib import Path
+from typing import List, Optional
+
+import comfy.model_management as model_management
+import folder_paths
 import numpy as np
+import torch
+from PIL import Image
+
+from ..log import log
+from ..utils import PIL_FILTER_MAP, output_dir, session_id, tensor2np


-class ExportToProres:
-    """Export to ProRes 4444 (Experimental)"""
+def get_playlist_path(playlist_name: str, persistant_playlist=False):
+    if persistant_playlist:
+        return output_dir / "playlists" / f"{playlist_name}.json"
+
+    return output_dir / "playlists" / session_id / f"{playlist_name}.json"
+
+
+class ReadPlaylist:
+    """Read a playlist"""

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
+                "enable": ("BOOLEAN", {"default": True}),
+                "persistant_playlist": ("BOOLEAN", {"default": False}),
+                "playlist_name": ("STRING", {"default": "playlist_{index:04d}"}),
+                "index": ("INT", {"default": 0, "min": 0}),
+            }
+        }
+
+    RETURN_TYPES = ("PLAYLIST",)
+    FUNCTION = "read_playlist"
+    CATEGORY = "mtb/IO"
+
+    def read_playlist(
+        self, enable: bool, persistant_playlist: bool, playlist_name: str, index: int
+    ):
+        playlist_name = playlist_name.format(index=index)
+        playlist_path = get_playlist_path(playlist_name, persistant_playlist)
+        if not enable:
+            return (None,)
+
+        if not playlist_path.exists():
+            log.warning(f"Playlist {playlist_path} does not exist, skipping")
+            return (None,)
+
+        log.debug(f"Reading playlist {playlist_path}")
+        return (json.loads(playlist_path.read_text(encoding="utf-8")),)
+
+
+class AddToPlaylist:
+    """Add a video to the playlist"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "relative_paths": ("BOOLEAN", {"default": False}),
+                "persistant_playlist": ("BOOLEAN", {"default": False}),
+                "playlist_name": ("STRING", {"default": "playlist_{index:04d}"}),
+                "index": ("INT", {"default": 0, "min": 0}),
+            }
+        }
+
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    FUNCTION = "add_to_playlist"
+    CATEGORY = "mtb/IO"
+
+    def add_to_playlist(
+        self,
+        relative_paths: bool,
+        persistant_playlist: bool,
+        playlist_name: str,
+        index: int,
+        **kwargs,
+    ):
+        playlist_name = playlist_name.format(index=index)
+        playlist_path = get_playlist_path(playlist_name, persistant_playlist)
+
+        if not playlist_path.parent.exists():
+            playlist_path.parent.mkdir(parents=True, exist_ok=True)
+
+        playlist = []
+        if not playlist_path.exists():
+            playlist_path.write_text("[]")
+        else:
+            playlist = json.loads(playlist_path.read_text())
+        log.debug(f"Playlist {playlist_path} has {len(playlist)} items")
+        for video in kwargs.values():
+            if relative_paths:
+                video = Path(video).relative_to(output_dir).as_posix()
+
+            log.debug(f"Adding {video} to playlist")
+            playlist.append(video)
+
+        log.debug(f"Writing playlist {playlist_path}")
+        playlist_path.write_text(json.dumps(playlist), encoding="utf-8")
+        return ()
+
+
+class ExportWithFfmpeg:
+    """Export with FFmpeg (Experimental)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
                "images": ("IMAGE",),
+                "playlist": ("PLAYLIST",),
+            },
+            "required": {
                # "frames": ("FRAMES",),
                "fps": ("FLOAT", {"default": 24, "min": 1}),
                "prefix": ("STRING", {"default": "export"}),
-            }
+                "format": (["mov", "mp4", "mkv", "avi"], {"default": "mov"}),
+                "codec": (
+                    ["prores_ks", "libx264", "libx265"],
+                    {"default": "prores_ks"},
+                ),
+            },
        }

    RETURN_TYPES = ("VIDEO",)
@@ -30,16 +133,61 @@ class ExportToProres:

    def export_prores(
        self,
-        images: torch.Tensor,
        fps: float,
        prefix: str,
+        format: str,
+        codec: str,
+        images: Optional[torch.Tensor] = None,
+        playlist: Optional[List[str]] = None,
    ):
-        if images.size(0) == 0:
-            return ("",)
-        output_dir = Path(folder_paths.get_output_directory())
-        id = f"{prefix}_{uuid.uuid4()}.mov"
+        pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p"
+        file_ext = format
+        file_id = f"{prefix}_{uuid.uuid4()}.{file_ext}"

-        log.debug(f"Exporting to {output_dir / id}")
+        if playlist is not None and images is not None:
+            log.info(f"Exporting to {output_dir / file_id}")
+
+        if playlist is not None:
+            if len(playlist) == 0:
+                log.debug("Playlist is empty, skipping")
+                return ("",)
+
+            temp_playlist_path = output_dir / f"temp_playlist_{uuid.uuid4()}.txt"
+            log.debug(
+                f"Create a temporary file to list the videos for concatenation to {temp_playlist_path}"
+            )
+
+            with open(temp_playlist_path, "w") as f:
+                for video_path in playlist:
+                    f.write(f"file '{video_path}'\n")
+
+            out_path = (output_dir / file_id).as_posix()
+
+            # Prepare the FFmpeg command for concatenating videos from the playlist
+            command = [
+                "ffmpeg",
+                "-f",
+                "concat",
+                "-safe",
+                "0",
+                "-i",
+                temp_playlist_path.as_posix(),
+                "-c",
+                "copy",
+                "-y",
+                out_path,
+            ]
+            log.debug(f"Executing {command}")
+            subprocess.run(command)
+
+            temp_playlist_path.unlink()
+
+            return (out_path,)
+
+        if (
+            images is None or images.size(0) == 0
+        ):  # the is None check is just for the type checker
+            return ("",)

        frames = tensor2np(images)
        log.debug(f"Frames type {type(frames[0])}")
@@ -49,7 +197,7 @@ class ExportToProres:

        height, width, _ = frames[0].shape

-        out_path = (output_dir / id).as_posix()
+        out_path = (output_dir / file_id).as_posix()

        # Prepare the FFmpeg command
        command = [
@@ -62,17 +210,13 @@ class ExportToProres:
            "-s",
            f"{width}x{height}",
            "-pix_fmt",
-            "rgb48le",
+            pix_fmt,
            "-r",
            str(fps),
            "-i",
            "-",
            "-c:v",
-            "prores_ks",
-            "-profile:v",
-            "4",
-            "-pix_fmt",
-            "yuva444p10le",
+            codec,
            "-r",
            str(fps),
            "-y",
@@ -91,6 +235,37 @@ class ExportToProres:
        return (out_path,)


+def prepare_animated_batch(
+    batch: torch.Tensor,
+    pingpong=False,
+    resize_by=1.0,
+    resample_filter: Optional[Image.Resampling] = None,
+    image_type=np.uint8,
+) -> List[Image.Image]:
+    images = tensor2np(batch)
+    images = [frame.astype(image_type) for frame in images]
+
+    height, width, _ = batch[0].shape
+
+    if pingpong:
+        reversed_frames = images[::-1]
+        images.extend(reversed_frames)
+    pil_images = [Image.fromarray(frame) for frame in images]
+
+    # Resize frames if necessary
+    if abs(resize_by - 1.0) > 1e-6:
+        new_width = int(width * resize_by)
+        new_height = int(height * resize_by)
+        pil_images_resized = [
+            frame.resize((new_width, new_height), resample=resample_filter)
+            for frame in pil_images
+        ]
+        pil_images = pil_images_resized
+
+    return pil_images
+
+
+# todo: deprecate for apng
 class SaveGif:
    """Save the images from the batch as a GIF"""

@@ -101,8 +276,12 @@ class SaveGif:
                "image": ("IMAGE",),
                "fps": ("INT", {"default": 12, "min": 1, "max": 120}),
                "resize_by": ("FLOAT", {"default": 1.0, "min": 0.1}),
-                "pingpong": ("BOOL", {"default": False}),
-            }
+                "optimize": ("BOOLEAN", {"default": False}),
+                "pingpong": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "resample_filter": (list(PIL_FILTER_MAP.keys()),),
+            },
        }

    RETURN_TYPES = ()
@@ -110,59 +289,44 @@ class SaveGif:
    CATEGORY = "mtb/IO"
    FUNCTION = "save_gif"

-    def save_gif(self, image, fps=12, resize_by=1.0, pingpong=False):
+    def save_gif(
+        self,
+        image,
+        fps=12,
+        resize_by=1.0,
+        optimize=False,
+        pingpong=False,
+        resample_filter=None,
+    ):
        if image.size(0) == 0:
            return ("",)

-        images = tensor2np(image)
-        images = [frame.astype(np.uint8) for frame in images]
-        if pingpong:
-            reversed_frames = images[::-1]
-            images.extend(reversed_frames)
+        if resample_filter is not None:
+            resample_filter = PIL_FILTER_MAP.get(resample_filter)

-        height, width, _ = image[0].shape
+        pil_images = prepare_animated_batch(
+            image,
+            pingpong,
+            resize_by,
+            resample_filter,
+        )

        ruuid = uuid.uuid4()
-
        ruuid = ruuid.hex[:10]
-
        out_path = f"{folder_paths.output_directory}/{ruuid}.gif"

-        log.debug(f"Saving a gif file {width}x{height} as {ruuid}.gif")
-
-        # Prepare the FFmpeg command
-        command = [
-            "ffmpeg",
-            "-y",
-            "-f",
-            "rawvideo",
-            "-vcodec",
-            "rawvideo",
-            "-s",
-            f"{width}x{height}",
-            "-pix_fmt",
-            "rgb24",  # GIF only supports rgb24
-            "-r",
-            str(fps),
-            "-i",
-            "-",
-            "-vf",
-            f"fps={fps},scale={width * resize_by}:-1",  # Set frame rate and resize if necessary
-            "-y",
+        # Create the GIF from PIL images
+        pil_images[0].save(
            out_path,
-        ]
+            save_all=True,
+            append_images=pil_images[1:],
+            optimize=optimize,
+            duration=int(1000 / fps),
+            loop=0,
+        )

-        process = subprocess.Popen(command, stdin=subprocess.PIPE)
-
-        for frame in images:
-            model_management.throw_exception_if_processing_interrupted()
-            process.stdin.write(frame.tobytes())
-
-        process.stdin.close()
-        process.wait()
-        results = []
-        results.append({"filename": f"{ruuid}.gif", "subfolder": "", "type": "output"})
+        results = [{"filename": f"{ruuid}.gif", "subfolder": "", "type": "output"}]
        return {"ui": {"gif": results}}


-__nodes__ = [SaveGif, ExportToProres]
+__nodes__ = [SaveGif, ExportWithFfmpeg, AddToPlaylist, ReadPlaylist]
@@ -1,7 +1,8 @@
-from rembg import remove
-from ..utils import pil2tensor, tensor2pil
-from PIL import Image
 import comfy.utils
+from PIL import Image
+from rembg import remove
+
+from ..utils import pil2tensor, tensor2pil


 class ImageRemoveBackgroundRembg:
@@ -13,7 +14,7 @@ class ImageRemoveBackgroundRembg:
            "required": {
                "image": ("IMAGE",),
                "alpha_matting": (
-                    "BOOL",
+                    "BOOLEAN",
                    {"default": False},
                ),
                "alpha_matting_foreground_threshold": (
@@ -29,12 +30,12 @@ class ImageRemoveBackgroundRembg:
                    {"default": 10, "min": 0, "max": 255},
                ),
                "post_process_mask": (
-                    "BOOL",
+                    "BOOLEAN",
                    {"default": False},
                ),
                "bgcolor": (
                    "COLOR",
-                    {"default": "black"},
+                    {"default": "#000000"},
                ),
            },
        }
@@ -91,6 +92,8 @@ class ImageRemoveBackgroundRembg:

            image_on_bg.paste(img_rm, mask=mask)

+            image_on_bg = image_on_bg.convert("RGB")
+
            out_img.append(img_rm)
            out_mask.append(mask)
            out_img_on_bg.append(image_on_bg)
@@ -0,0 +1,99 @@
+import copy
+
+import torch
+
+from ..log import log
+
+
+class VaeDecode_:
+    """Wrapper for the 2 core decoders but also adding the sd seamless hack, taken from: FlyingFireCo/tiled_ksampler"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "samples": ("LATENT",),
+                "vae": ("VAE",),
+                "seamless_model": ("BOOLEAN", {"default": False}),
+                "use_tiling_decoder": ("BOOLEAN", {"default": True}),
+                "tile_size": (
+                    "INT",
+                    {"default": 512, "min": 320, "max": 4096, "step": 64},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "decode"
+
+    CATEGORY = "mtb/decode"
+
+    def decode(
+        self, vae, samples, seamless_model, use_tiling_decoder=True, tile_size=512
+    ):
+        if seamless_model:
+            if use_tiling_decoder:
+                log.error(
+                    "You cannot use seamless mode with tiling decoder together, skipping tiling."
+                )
+                use_tiling_decoder = False
+            for layer in [
+                layer
+                for layer in vae.first_stage_model.modules()
+                if isinstance(layer, torch.nn.Conv2d)
+            ]:
+                layer.padding_mode = "circular"
+        if use_tiling_decoder:
+            return (
+                vae.decode_tiled(
+                    samples["samples"],
+                    tile_x=tile_size // 8,
+                    tile_y=tile_size // 8,
+                ),
+            )
+        else:
+            return (vae.decode(samples["samples"]),)
+
+
+class ModelPatchSeamless:
+    """Uses the stable diffusion 'hack' to infer seamless images by setting the model layers padding mode to circular (experimental)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "tiling": (
+                    "BOOLEAN",
+                    {"default": True},
+                ),  # kept for testing not sure why it should be false
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "MODEL")
+    RETURN_NAMES = (
+        "Original Model (passthrough)",
+        "Patched Model",
+    )
+    FUNCTION = "hack"
+
+    CATEGORY = "mtb/textures"
+
+    def apply_circular(self, model, enable):
+        for layer in [
+            layer for layer in model.modules() if isinstance(layer, torch.nn.Conv2d)
+        ]:
+            layer.padding_mode = "circular" if enable else "zeros"
+        return model
+
+    def hack(
+        self,
+        model,
+        tiling,
+    ):
+        hacked_model = copy.deepcopy(model)
+        self.apply_circular(hacked_model.model, tiling)
+        return (model, hacked_model)
+
+
+__nodes__ = [ModelPatchSeamless, VaeDecode_]
@@ -14,7 +14,7 @@ class IntToBool:
            }
        }

-    RETURN_TYPES = ("BOOL",)
+    RETURN_TYPES = ("BOOLEAN",)
    FUNCTION = "int_to_bool"
    CATEGORY = "mtb/number"

@@ -1,5 +1,9 @@
 import torch
-import torchvision.transforms.functional as F
+import torchvision.transforms.functional as TF
+from ..utils import log, hex_to_rgb, tensor2pil, pil2tensor
+from math import sqrt, ceil
+from typing import cast
+from PIL import Image


 class TransformImage:
@@ -14,11 +18,19 @@ class TransformImage:
        return {
            "required": {
                "image": ("IMAGE",),
-                "x": ("FLOAT", {"default": 0}),
-                "y": ("FLOAT", {"default": 0}),
-                "zoom": ("FLOAT", {"default": 1.0, "min": 0.001}),
-                "angle": ("FLOAT", {"default": 0}),
-                "shear": ("FLOAT", {"default": 0}),
+                "x": ("FLOAT", {"default": 0, "step": 1, "min": -4096, "max": 4096}),
+                "y": ("FLOAT", {"default": 0, "step": 1, "min": -4096, "max": 4096}),
+                "zoom": ("FLOAT", {"default": 1.0, "min": 0.001, "step": 0.01}),
+                "angle": ("FLOAT", {"default": 0, "step": 1, "min": -360, "max": 360}),
+                "shear": (
+                    "FLOAT",
+                    {"default": 0, "step": 1, "min": -4096, "max": 4096},
+                ),
+                "border_handling": (
+                    ["edge", "constant", "reflect", "symmetric"],
+                    {"default": "edge"},
+                ),
+                "constant_color": ("COLOR", {"default": "#000000"}),
            },
        }

@@ -32,23 +44,67 @@ class TransformImage:
        x: float,
        y: float,
        zoom: float,
-        angle: int,
-        shear,
+        angle: float,
+        shear: float,
+        border_handling="edge",
+        constant_color=None,
    ):
+        x = int(x)
+        y = int(y)
+        angle = int(angle)
+
+        log.debug(f"Zoom: {zoom} | x: {x}, y: {y}, angle: {angle}, shear: {shear}")
+
        if image.size(0) == 0:
            return (torch.zeros(0),)
        transformed_images = []
-        for img in image:
-            img = img.transpose(0, 2)
+        frames_count, frame_height, frame_width, frame_channel_count = image.size()

-            transformed_image = F.affine(
-                img, angle=angle, scale=zoom, translate=[int(y), int(x)], shear=shear
+        new_height, new_width = int(frame_height * zoom), int(frame_width * zoom)
+
+        log.debug(f"New height: {new_height}, New width: {new_width}")
+
+        # - Calculate diagonal of the original image
+        diagonal = sqrt(frame_width**2 + frame_height**2)
+        max_padding = ceil(diagonal * zoom - min(frame_width, frame_height))
+        # Calculate padding for zoom
+        pw = int(frame_width - new_width)
+        ph = int(frame_height - new_height)
+
+        pw += abs(max_padding)
+        ph += abs(max_padding)
+
+        padding = [max(0, pw + x), max(0, ph + y), max(0, pw - x), max(0, ph - y)]
+
+        constant_color = hex_to_rgb(constant_color)
+        log.debug(f"Fill Tuple: {constant_color}")
+
+        for img in tensor2pil(image):
+            img = TF.pad(
+                img,  # transformed_frame,
+                padding=padding,
+                padding_mode=border_handling,
+                fill=constant_color or 0,
            )

-            transformed_image = transformed_image.transpose(2, 0)
-            transformed_images.append(transformed_image.unsqueeze(0))
+            img = cast(
+                Image.Image,
+                TF.affine(img, angle=angle, scale=zoom, translate=[x, y], shear=shear),
+            )

-        return (torch.cat(transformed_images, dim=0),)
+            left = abs(padding[0])
+            upper = abs(padding[1])
+            right = img.width - abs(padding[2])
+            bottom = img.height - abs(padding[3])
+
+            # log.debug("crop is [:,top:bottom, left:right] for tensors")
+            log.debug("crop is [left, top, right, bottom] for PIL")
+            log.debug(f"crop is {left}, {upper}, {right}, {bottom}")
+            img = img.crop((left, upper, right, bottom))
+
+            transformed_images.append(img)
+
+        return (pil2tensor(transformed_images),)


 __nodes__ = [TransformImage]
@@ -1,17 +0,0 @@
-onnxruntime-gpu==1.15.1
-imageio===2.28.1
-qrcode[pil]
-numpy==1.23.5
-rembg==2.0.37
-# on windows non WSL 2.10 is the last version with GPU support
-tensorflow<2.11.0; platform_system == "Windows"
-tb-nightly==2.12.0a20230126; platform_system == "Windows"
-tensorflow; platform_system != "Windows"
-# the old tf version on windows comes with a breaking protobuf version
-protobuf==3.20.2; platform_system == "Windows"
-gdown @ git+https://github.com/melMass/gdown@main
-mmdet==3.0.0
-facexlib==0.3.0
-insightface==0.7.3
-mmcv==2.0.0
-basicsr==1.4.2
@@ -0,0 +1,8 @@
+qrcode[pil]
+onnxruntime-gpu
+requirements-parser
+# opencv-contrib
+rembg
+imageio_ffmpeg
+rich
+rich_argparse
@@ -2,6 +2,8 @@ import os
 import requests
 from rich.console import Console
 from tqdm import tqdm
+import subprocess
+import sys

 try:
    import folder_paths
@@ -30,13 +32,13 @@ models_to_download = {
        "size": 332,
        "download_url": [
            "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth",
+            "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
            # TODO: provide a way to selectively download models from "packs"
            # https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth
            # https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth
-            # https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
            # https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth
        ],
-        "destination": "upscale_models",
+        "destination": "face_restore",
    },
    "FILM: Frame Interpolation for Large Motion": {
        "size": 402,
@@ -51,7 +53,6 @@ console = Console()

 from urllib.parse import urlparse
 from pathlib import Path
-import gdown


 def download_model(download_url, destination):
@@ -63,6 +64,21 @@ def download_model(download_url, destination):
    filename = os.path.basename(urlparse(download_url).path)
    response = None
    if "drive.google.com" in download_url:
+        try:
+            import gdown
+        except ImportError:
+            print("Installing gdown")
+            subprocess.check_call(
+                [
+                    sys.executable,
+                    "-m",
+                    "pip",
+                    "install",
+                    "git+https://github.com/melMass/gdown@main",
+                ]
+            )
+            import gdown
+
        if "/folders/" in download_url:
            # download folder
            try:
@@ -1,11 +1,125 @@
-from PIL import Image
-import numpy as np
-import torch
+import contextlib, functools, math, os, shlex, shutil, socket, subprocess, sys, uuid
 from pathlib import Path
-import sys
+from typing import List, Optional, Union

-from typing import Union, List
-from .log import log
+import folder_paths
+import numpy as np
+import requests
+import torch
+from PIL import Image
+
+from .install import pip_map
+
+try:
+    from .log import log
+except ImportError:
+    try:
+        from log import log
+
+        log.warn("Imported log without relative path")
+    except ImportError:
+        import logging
+
+        log = logging.getLogger("comfy mtb utils")
+        log.warn("[comfy mtb] You probably called the file outside a module.")
+
+
+# region SANITY_CHECK Utilities
+
+
+def make_report():
+    pass
+
+
+# endregion
+
+
+# region SERVER Utilities
+class IPChecker:
+    def __init__(self):
+        self.ips = list(self.get_local_ips())
+        log.debug(f"Found {len(self.ips)} local ips")
+        self.checked_ips = set()
+
+    def get_working_ip(self, test_url_template):
+        for ip in self.ips:
+            if ip not in self.checked_ips:
+                self.checked_ips.add(ip)
+                test_url = test_url_template.format(ip)
+                if self._test_url(test_url):
+                    return ip
+        return None
+
+    @staticmethod
+    def get_local_ips(prefix="192.168."):
+        hostname = socket.gethostname()
+        log.debug(f"Getting local ips for {hostname}")
+        for info in socket.getaddrinfo(hostname, None):
+            # Filter out IPv6 addresses if you only want IPv4
+            log.debug(info)
+            # if info[1] == socket.SOCK_STREAM and
+            if info[0] == socket.AF_INET and info[4][0].startswith(prefix):
+                yield info[4][0]
+
+    def _test_url(self, url):
+        try:
+            response = requests.get(url)
+            return response.status_code == 200
+        except Exception:
+            return False
+
+
+@functools.lru_cache(maxsize=1)
+def get_server_info():
+    from comfy.cli_args import args
+
+    ip_checker = IPChecker()
+    base_url = args.listen
+    if base_url == "0.0.0.0":
+        log.debug("Server set to 0.0.0.0, we will try to resolve the host IP")
+        base_url = ip_checker.get_working_ip(f"http://{{}}:{args.port}/history")
+        log.debug(f"Setting ip to {base_url}")
+    return (base_url, args.port)
+
+
+# endregion
+
+
+# region MISC Utilities
+def backup_file(
+    fp: Path,
+    target: Optional[Path] = None,
+    backup_dir: str = ".bak",
+    suffix: Optional[str] = None,
+    prefix: Optional[str] = None,
+):
+    if not fp.exists():
+        raise FileNotFoundError(f"No file found at {fp}")
+
+    backup_directory = target or fp.parent / backup_dir
+    backup_directory.mkdir(parents=True, exist_ok=True)
+
+    stem = fp.stem
+
+    if suffix or prefix:
+        new_stem = f"{prefix or ''}{stem}{suffix or ''}"
+    else:
+        new_stem = f"{stem}_{uuid.uuid4()}"
+
+    backup_file_path = backup_directory / f"{new_stem}{fp.suffix}"
+
+    # Perform the backup
+    shutil.copy(fp, backup_file_path)
+    log.debug(f"File backed up to {backup_file_path}")
+
+
+def hex_to_rgb(hex_color):
+    try:
+        hex_color = hex_color.lstrip("#")
+        return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
+    except ValueError:
+        log.error(f"Invalid hex color: {hex_color}")
+        return (0, 0, 0)


 def add_path(path, prepend=False):
@@ -24,33 +138,128 @@ def add_path(path, prepend=False):
            sys.path.append(path)


-# Get the absolute path of the parent directory of the current script
-here = Path(__file__).parent.resolve()
+def run_command(cmd, ignored_lines_start=None):
+    if ignored_lines_start is None:
+        ignored_lines_start = []

-# Construct the absolute path to the ComfyUI directory
-comfy_dir = here.parent.parent
+    if isinstance(cmd, str):
+        shell_cmd = cmd
+    elif isinstance(cmd, list):
+        shell_cmd = " ".join(
+            arg.as_posix() if isinstance(arg, Path) else shlex.quote(str(arg))
+            for arg in cmd
+        )
+    else:
+        raise ValueError(
+            "Invalid 'cmd' argument. It must be a string or a list of arguments."
+        )

-# Construct the path to the font file
+    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(e.stderr.strip(), file=sys.stderr)
+
+    except KeyboardInterrupt:
+        print("Command execution interrupted.")
+
+
+def _run_command(shell_cmd, ignored_lines_start):
+    log.debug(f"Running {shell_cmd}")
+
+    result = subprocess.run(
+        shell_cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        text=True,
+        shell=True,
+        check=True,
+    )
+
+    stdout_lines = result.stdout.strip().split("\n")
+    stderr_lines = result.stderr.strip().split("\n")
+
+    # Print stdout, skipping ignored lines
+    for line in stdout_lines:
+        if not any(line.startswith(ign) for ign in ignored_lines_start):
+            print(line)
+
+    # Print stderr
+    for line in stderr_lines:
+        print(line, file=sys.stderr)
+
+    print("Command executed successfully!")
+
+
+# todo use the requirements library
+reqs_map = {value: key for key, value in pip_map.items()}
+
+import importlib
+
+
+def import_install(package_name):
+    package_spec = reqs_map.get(package_name, package_name)
+
+    try:
+        importlib.import_module(package_name)
+
+    except Exception:  # (ImportError, ModuleNotFoundError):
+        run_command(
+            [Path(sys.executable).as_posix(), "-m", "pip", "install", package_spec]
+        )
+        importlib.import_module(package_name)
+
+
+# endregion
+
+
+# region GLOBAL VARIABLES
+# - detect mode
+comfy_mode = None
+if os.environ.get("COLAB_GPU"):
+    comfy_mode = "colab"
+elif "python_embeded" in sys.executable:
+    comfy_mode = "embeded"
+elif ".venv" in sys.executable:
+    comfy_mode = "venv"
+
+# - Get the absolute path of the parent directory of the current script
+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)
+output_dir = Path(folder_paths.output_directory)
+styles_dir = comfy_dir / "styles"
+session_id = str(uuid.uuid4())
+# - Construct the path to the font file
 font_path = here / "font.ttf"

-# Add extern folder to path
+# - Add extern folder to path
 extern_root = here / "extern"
 add_path(extern_root)
 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 the ComfyUI directory and custom nodes path to the sys.path list
 add_path(comfy_dir)
 add_path((comfy_dir / "custom_nodes"))

+PIL_FILTER_MAP = {
+    "nearest": Image.Resampling.NEAREST,
+    "box": Image.Resampling.BOX,
+    "bilinear": Image.Resampling.BILINEAR,
+    "hamming": Image.Resampling.HAMMING,
+    "bicubic": Image.Resampling.BICUBIC,
+    "lanczos": Image.Resampling.LANCZOS,
+}
+# endregion

+
+# region TENSOR Utilities
 def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
-    batch_count = 1
-    if len(image.shape) > 3:
-        batch_count = image.size(0)
-
+    batch_count = image.size(0) if len(image.shape) > 3 else 1
    if batch_count > 1:
        out = []
        for i in range(batch_count):
@@ -64,14 +273,14 @@ def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
    ]


-def pil2tensor(image: Image.Image | List[Image.Image]) -> torch.Tensor:
+def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
    if isinstance(image, list):
        return torch.cat([pil2tensor(img) for img in image], dim=0)

    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)


-def np2tensor(img_np: np.ndarray | List[np.ndarray]) -> torch.Tensor:
+def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
    if isinstance(img_np, list):
        return torch.cat([np2tensor(img) for img in img_np], dim=0)

@@ -79,9 +288,7 @@ def np2tensor(img_np: np.ndarray | List[np.ndarray]) -> torch.Tensor:


 def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
-    batch_count = 1
-    if len(tensor.shape) > 3:
-        batch_count = tensor.size(0)
+    batch_count = tensor.size(0) if len(tensor.shape) > 3 else 1
    if batch_count > 1:
        out = []
        for i in range(batch_count):
@@ -89,3 +296,449 @@ def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
        return out

    return [np.clip(255.0 * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)]
+
+
+def pad(img, left, right, top, bottom):
+    pad_width = np.array(((0, 0), (top, bottom), (left, right)))
+    print(f"pad_width: {pad_width}, shape: {pad_width.shape}")  # Debugging line
+    return np.pad(img, pad_width, mode="wrap")
+
+
+def tiles_infer(tiles, ort_session, progress_callback=None):
+    """Infer each tile with the given model. progress_callback will be called with
+    arguments : current tile idx and total tiles amount (used to show progress on
+    cursor in Blender)."""
+
+    out_channels = 3  # normal map RGB channels
+    tiles_nb = tiles.shape[0]
+    pred_tiles = np.empty((tiles_nb, out_channels, tiles.shape[2], tiles.shape[3]))
+
+    for i in range(tiles_nb):
+        if progress_callback != None:
+            progress_callback(i + 1, tiles_nb)
+        pred_tiles[i] = ort_session.run(
+            None, {"input": tiles[i : i + 1].astype(np.float32)}
+        )[0]
+
+    return pred_tiles
+
+
+def generate_mask(tile_size, stride_size):
+    """Generates a pyramidal-like mask. Used for mixing overlapping predicted tiles."""
+
+    tile_h, tile_w = tile_size
+    stride_h, stride_w = stride_size
+    ramp_h = tile_h - stride_h
+    ramp_w = tile_w - stride_w
+
+    mask = np.ones((tile_h, tile_w))
+
+    # ramps in width direction
+    mask[ramp_h:-ramp_h, :ramp_w] = np.linspace(0, 1, num=ramp_w)
+    mask[ramp_h:-ramp_h, -ramp_w:] = np.linspace(1, 0, num=ramp_w)
+    # ramps in height direction
+    mask[:ramp_h, ramp_w:-ramp_w] = np.transpose(
+        np.linspace(0, 1, num=ramp_h)[None], (1, 0)
+    )
+    mask[-ramp_h:, ramp_w:-ramp_w] = np.transpose(
+        np.linspace(1, 0, num=ramp_h)[None], (1, 0)
+    )
+
+    # Assume tiles are squared
+    assert ramp_h == ramp_w
+    # top left corner
+    corner = np.rot90(corner_mask(ramp_h), 2)
+    mask[:ramp_h, :ramp_w] = corner
+    # top right corner
+    corner = np.flip(corner, 1)
+    mask[:ramp_h, -ramp_w:] = corner
+    # bottom right corner
+    corner = np.flip(corner, 0)
+    mask[-ramp_h:, -ramp_w:] = corner
+    # bottom right corner
+    corner = np.flip(corner, 1)
+    mask[-ramp_h:, :ramp_w] = corner
+
+    return mask
+
+
+def corner_mask(side_length):
+    """Generates the corner part of the pyramidal-like mask.
+    Currently, only for square shapes."""
+
+    corner = np.zeros([side_length, side_length])
+
+    for h in range(0, side_length):
+        for w in range(0, side_length):
+            if h >= w:
+                sh = h / (side_length - 1)
+                corner[h, w] = 1 - sh
+            if h <= w:
+                sw = w / (side_length - 1)
+                corner[h, w] = 1 - sw
+
+    return corner - 0.25 * scaling_mask(side_length)
+
+
+def scaling_mask(side_length):
+    scaling = np.zeros([side_length, side_length])
+
+    for h in range(0, side_length):
+        for w in range(0, side_length):
+            sh = h / (side_length - 1)
+            sw = w / (side_length - 1)
+            if h >= w and h <= side_length - w:
+                scaling[h, w] = sw
+            if h <= w and h <= side_length - w:
+                scaling[h, w] = sh
+            if h >= w and h >= side_length - w:
+                scaling[h, w] = 1 - sh
+            if h <= w and h >= side_length - w:
+                scaling[h, w] = 1 - sw
+
+    return 2 * scaling
+
+
+def tiles_merge(tiles, stride_size, img_size, paddings):
+    """Merges the list of tiles into one image. img_size is the original size, before
+    padding."""
+
+    _, tile_h, tile_w = tiles[0].shape
+    pad_left, pad_right, pad_top, pad_bottom = paddings
+    height = img_size[1] + pad_top + pad_bottom
+    width = img_size[2] + pad_left + pad_right
+    stride_h, stride_w = stride_size
+
+    # stride must be even
+    assert (stride_h % 2 == 0) and (stride_w % 2 == 0)
+    # stride must be greater or equal than half tile
+    assert (stride_h >= tile_h / 2) and (stride_w >= tile_w / 2)
+    # stride must be smaller or equal tile size
+    assert (stride_h <= tile_h) and (stride_w <= tile_w)
+
+    merged = np.zeros((img_size[0], height, width))
+    mask = generate_mask((tile_h, tile_w), stride_size)
+
+    h_range = ((height - tile_h) // stride_h) + 1
+    w_range = ((width - tile_w) // stride_w) + 1
+
+    idx = 0
+    for h in range(0, h_range):
+        for w in range(0, w_range):
+            h_from, h_to = h * stride_h, h * stride_h + tile_h
+            w_from, w_to = w * stride_w, w * stride_w + tile_w
+            merged[:, h_from:h_to, w_from:w_to] += tiles[idx] * mask
+            idx += 1
+
+    return merged[:, pad_top:-pad_bottom, pad_left:-pad_right]
+
+
+def tiles_split(img, tile_size, stride_size):
+    """Returns list of tiles from the given image and the padding used to fit the tiles
+    in it. Input image must have dimension C,H,W."""
+    log.debug(f"Splitting img: tile {tile_size}, stride {stride_size} ")
+    tile_h, tile_w = tile_size
+    stride_h, stride_w = stride_size
+    img_h, img_w = img.shape[0], img.shape[1]
+
+    # stride must be even
+    assert (stride_h % 2 == 0) and (stride_w % 2 == 0)
+    # stride must be greater or equal than half tile
+    assert (stride_h >= tile_h / 2) and (stride_w >= tile_w / 2)
+    # stride must be smaller or equal tile size
+    assert (stride_h <= tile_h) and (stride_w <= tile_w)
+
+    # find total height & width padding sizes
+    pad_h, pad_w = 0, 0
+    remainer_h = (img_h - tile_h) % stride_h
+    remainer_w = (img_w - tile_w) % stride_w
+    if remainer_h != 0:
+        pad_h = stride_h - remainer_h
+    if remainer_w != 0:
+        pad_w = stride_w - remainer_w
+
+    # if tile bigger than image, pad image to tile size
+    if tile_h > img_h:
+        pad_h = tile_h - img_h
+    if tile_w > img_w:
+        pad_w = tile_w - img_w
+
+    # pad image, add extra stride to padding to avoid pyramid
+    # weighting leaking onto the valid part of the picture
+    pad_left = pad_w // 2 + stride_w
+    pad_right = pad_left if pad_w % 2 == 0 else pad_left + 1
+    pad_top = pad_h // 2 + stride_h
+    pad_bottom = pad_top if pad_h % 2 == 0 else pad_top + 1
+    img = pad(img, pad_left, pad_right, pad_top, pad_bottom)
+    img_h, img_w = img.shape[1], img.shape[2]
+
+    # extract tiles
+    h_range = ((img_h - tile_h) // stride_h) + 1
+    w_range = ((img_w - tile_w) // stride_w) + 1
+    tiles = np.empty([h_range * w_range, img.shape[0], tile_h, tile_w])
+    idx = 0
+    for h in range(0, h_range):
+        for w in range(0, w_range):
+            h_from, h_to = h * stride_h, h * stride_h + tile_h
+            w_from, w_to = w * stride_w, w * stride_w + tile_w
+            tiles[idx] = img[:, h_from:h_to, w_from:w_to]
+            idx += 1
+
+    return tiles, (pad_left, pad_right, pad_top, pad_bottom)
+
+
+# endregion
+
+
+# region MODEL Utilities
+def download_antelopev2():
+    antelopev2_url = "https://drive.google.com/uc?id=18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8"
+
+    try:
+        import gdown
+
+        log.debug("Loading antelopev2 model")
+
+        dest = get_model_path("insightface")
+        archive = dest / "antelopev2.zip"
+        final_path = dest / "models" / "antelopev2"
+        if not final_path.exists():
+            log.info(f"antelopev2 not found, downloading to {dest}")
+            gdown.download(
+                antelopev2_url,
+                archive.as_posix(),
+                resume=True,
+            )
+
+            log.info(f"Unzipping antelopev2 to {final_path}")
+
+            if archive.exists():
+                # we unzip it
+                import zipfile
+
+                with zipfile.ZipFile(archive.as_posix(), "r") as zip_ref:
+                    zip_ref.extractall(final_path.parent.as_posix())
+
+    except Exception as e:
+        log.error(
+            f"Could not load or download antelopev2 model, download it manually from {antelopev2_url}"
+        )
+        raise e
+
+
+def get_model_path(fam, model=None):
+    log.debug(f"Requesting {fam} with model {model}")
+    res = None
+    if model:
+        res = folder_paths.get_full_path(fam, model)
+    else:
+        # this one can raise errors...
+        with contextlib.suppress(KeyError):
+            res = folder_paths.get_folder_paths(fam)
+
+    if res:
+        if isinstance(res, list):
+            if len(res) > 1:
+                log.warning(
+                    f"Found multiple match, we will pick the first {res[0]}\n{res}"
+                )
+            res = res[0]
+        res = Path(res)
+        log.debug(f"Resolved model path from folder_paths: {res}")
+    else:
+        res = models_dir / fam
+        if model:
+            res /= model
+
+    return res
+
+
+# endregion
+
+
+# region UV Utilities
+
+
+def create_uv_map_tensor(width=512, height=512):
+    u = torch.linspace(0.0, 1.0, steps=width)
+    v = torch.linspace(0.0, 1.0, steps=height)
+
+    U, V = torch.meshgrid(u, v)
+
+    uv_map = torch.zeros(height, width, 3, dtype=torch.float32)
+    uv_map[:, :, 0] = U.t()
+    uv_map[:, :, 1] = V.t()
+
+    return uv_map.unsqueeze(0)
+
+
+# endregion
+
+
+# region ANIMATION Utilities
+def apply_easing(value, easing_type):
+    if easing_type == "Linear":
+        return value
+
+    # Back easing functions
+    def easeInBack(t):
+        s = 1.70158
+        return t * t * ((s + 1) * t - s)
+
+    def easeOutBack(t):
+        s = 1.70158
+        return ((t - 1) * t * ((s + 1) * t + s)) + 1
+
+    def easeInOutBack(t):
+        s = 1.70158 * 1.525
+        if t < 0.5:
+            return (t * t * (t * (s + 1) - s)) * 2
+        return ((t - 2) * t * ((s + 1) * t + s) + 2) * 2
+
+    # Elastic easing functions
+    def easeInElastic(t):
+        if t == 0:
+            return 0
+        if t == 1:
+            return 1
+        p = 0.3
+        s = p / 4
+        return -(math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p))
+
+    def easeOutElastic(t):
+        if t == 0:
+            return 0
+        if t == 1:
+            return 1
+        p = 0.3
+        s = p / 4
+        return math.pow(2, -10 * t) * math.sin((t - s) * (2 * math.pi) / p) + 1
+
+    def easeInOutElastic(t):
+        if t == 0:
+            return 0
+        if t == 1:
+            return 1
+        p = 0.3 * 1.5
+        s = p / 4
+        t = t * 2
+        if t < 1:
+            return -0.5 * (
+                math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)
+            )
+        return (
+            0.5 * math.pow(2, -10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)
+            + 1
+        )
+
+    # Bounce easing functions
+    def easeInBounce(t):
+        return 1 - easeOutBounce(1 - t)
+
+    def easeOutBounce(t):
+        if t < (1 / 2.75):
+            return 7.5625 * t * t
+        elif t < (2 / 2.75):
+            t -= 1.5 / 2.75
+            return 7.5625 * t * t + 0.75
+        elif t < (2.5 / 2.75):
+            t -= 2.25 / 2.75
+            return 7.5625 * t * t + 0.9375
+        else:
+            t -= 2.625 / 2.75
+            return 7.5625 * t * t + 0.984375
+
+    def easeInOutBounce(t):
+        if t < 0.5:
+            return easeInBounce(t * 2) * 0.5
+        return easeOutBounce(t * 2 - 1) * 0.5 + 0.5
+
+    # Quart easing functions
+    def easeInQuart(t):
+        return t * t * t * t
+
+    def easeOutQuart(t):
+        t -= 1
+        return -(t**2 * t * t - 1)
+
+    def easeInOutQuart(t):
+        t *= 2
+        if t < 1:
+            return 0.5 * t * t * t * t
+        t -= 2
+        return -0.5 * (t**2 * t * t - 2)
+
+    # Cubic easing functions
+    def easeInCubic(t):
+        return t * t * t
+
+    def easeOutCubic(t):
+        t -= 1
+        return t**2 * t + 1
+
+    def easeInOutCubic(t):
+        t *= 2
+        if t < 1:
+            return 0.5 * t * t * t
+        t -= 2
+        return 0.5 * (t**2 * t + 2)
+
+    # Circ easing functions
+    def easeInCirc(t):
+        return -(math.sqrt(1 - t * t) - 1)
+
+    def easeOutCirc(t):
+        t -= 1
+        return math.sqrt(1 - t**2)
+
+    def easeInOutCirc(t):
+        t *= 2
+        if t < 1:
+            return -0.5 * (math.sqrt(1 - t**2) - 1)
+        t -= 2
+        return 0.5 * (math.sqrt(1 - t**2) + 1)
+
+    # Sine easing functions
+    def easeInSine(t):
+        return -math.cos(t * (math.pi / 2)) + 1
+
+    def easeOutSine(t):
+        return math.sin(t * (math.pi / 2))
+
+    def easeInOutSine(t):
+        return -0.5 * (math.cos(math.pi * t) - 1)
+
+    easing_functions = {
+        "Sine In": easeInSine,
+        "Sine Out": easeOutSine,
+        "Sine In/Out": easeInOutSine,
+        "Quart In": easeInQuart,
+        "Quart Out": easeOutQuart,
+        "Quart In/Out": easeInOutQuart,
+        "Cubic In": easeInCubic,
+        "Cubic Out": easeOutCubic,
+        "Cubic In/Out": easeInOutCubic,
+        "Circ In": easeInCirc,
+        "Circ Out": easeOutCirc,
+        "Circ In/Out": easeInOutCirc,
+        "Back In": easeInBack,
+        "Back Out": easeOutBack,
+        "Back In/Out": easeInOutBack,
+        "Elastic In": easeInElastic,
+        "Elastic Out": easeOutElastic,
+        "Elastic In/Out": easeInOutElastic,
+        "Bounce In": easeInBounce,
+        "Bounce Out": easeOutBounce,
+        "Bounce In/Out": easeInOutBounce,
+    }
+
+    function_ease = easing_functions.get(easing_type)
+    if function_ease:
+        return function_ease(value)
+
+    log.error(f"Unknown easing type: {easing_type}")
+    log.error(f"Available easing types: {list(easing_functions.keys())}")
+    raise ValueError(f"Unknown easing type: {easing_type}")
+
+
+# endregion
@@ -13,6 +13,10 @@ data otherwise:
 ![debug](https://github.com/melMass/comfy_mtb/assets/7041726/1f4393e4-1c3d-4807-9501-fe8888bfae25)


+**note +**
+A basic HTML note mainly to add better looking notes/instructions for workflow makers:
+![image](https://github.com/melMass/comfy_mtb/assets/7041726/2ba1f832-0044-4bad-974c-e6387981af57)
+

 ## Standalone
 These scripts can be taken and placed independently of `comfy_mtb` or any other files, mimicking what pythongosss did for their 
@@ -7,7 +7,7 @@
 *
 */

-import { app } from '/scripts/app.js'
+import { app } from '../../scripts/app.js'

 export const log = (...args) => {
  if (window.MTB?.DEBUG) {
@@ -18,6 +18,30 @@ export const log = (...args) => {
 //- WIDGET UTILS
 export const CONVERTED_TYPE = 'converted-widget'

+export const hasWidgets = (node) => {
+  if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
+    return false
+  }
+  return true
+}
+
+export const cleanupNode = (node) => {
+  if (!hasWidgets(node)) {
+    return
+  }
+
+  for (const w of node.widgets) {
+    if (w.canvas) {
+      w.canvas.remove()
+    }
+    if (w.inputEl) {
+      w.inputEl.remove()
+    }
+    // calls the widget remove callback
+    w.onRemoved?.()
+  }
+}
+
 export function offsetDOMWidget(
  widget,
  ctx,
@@ -49,7 +73,7 @@ export function offsetDOMWidget(
    position: 'absolute',
    background: !node.color ? '' : node.color,
    color: !node.color ? '' : 'white',
-    zIndex: app.graph._nodes.indexOf(node),
+    zIndex: 5, //app.graph._nodes.indexOf(node),
  })
 }

@@ -60,7 +84,7 @@ export function offsetDOMWidget(
 */
 export function getWidgetType(config) {
  // Special handling for COMBO so we restrict links based on the entries
-  let type = config[0]
+  let type = config?.[0]
  let linkType = type
  if (type instanceof Array) {
    type = 'COMBO'
@@ -68,14 +92,38 @@ export function getWidgetType(config) {
  }
  return { type, linkType }
 }
+export const setupDynamicConnections = (nodeType, prefix, inputType) => {
+  const onNodeCreated = nodeType.prototype.onNodeCreated
+  nodeType.prototype.onNodeCreated = function () {
+    const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined
+    this.addInput(`${prefix}_1`, inputType)
+    return r
+  }

+  const onConnectionsChange = nodeType.prototype.onConnectionsChange
+  nodeType.prototype.onConnectionsChange = function (
+    type,
+    index,
+    connected,
+    link_info
+  ) {
+    const r = onConnectionsChange
+      ? onConnectionsChange.apply(this, arguments)
+      : undefined
+    dynamic_connection(this, index, connected, `${prefix}_`, inputType)
+  }
+}
 export const dynamic_connection = (
  node,
  index,
  connected,
  connectionPrefix = 'input_',
-  connectionType = 'PSDLAYER'
+  connectionType = 'PSDLAYER',
+  nameArray = []
 ) => {
+  if (!node.inputs[index].name.startsWith(connectionPrefix)) {
+    return
+  }
  // remove all non connected inputs
  if (!connected && node.inputs.length > 1) {
    log(`Removing input ${index} (${node.inputs[index].name})`)
@@ -90,22 +138,24 @@ export const dynamic_connection = (

    // make inputs sequential again
    for (let i = 0; i < node.inputs.length; i++) {
-      node.inputs[i].label = `${connectionPrefix}${i + 1}`
+      const name =
+        i < nameArray.length ? nameArray[i] : `${connectionPrefix}${i + 1}`
+      node.inputs[i].label = name
+      node.inputs[i].name = name
    }
  }

  // add an extra input
  if (node.inputs[node.inputs.length - 1].link != undefined) {
-    log(
-      `Adding input ${node.inputs.length + 1} (${connectionPrefix}${
-        node.inputs.length + 1
-      })`
-    )
+    const nextIndex = node.inputs.length
+    const name =
+      nextIndex < nameArray.length
+        ? nameArray[nextIndex]
+        : `${connectionPrefix}${nextIndex + 1}`

-    node.addInput(
-      `${connectionPrefix}${node.inputs.length + 1}`,
-      connectionType
-    )
+    log(`Adding input ${nextIndex + 1} (${name})`)
+
+    node.addInput(name, connectionType)
  }
 }

@@ -284,6 +334,43 @@ function getBrightness(rgbObj) {
 }

 //- HTML / CSS UTILS
+export const loadScript = (
+  FILE_URL,
+  async = true,
+  type = 'text/javascript'
+) => {
+  return new Promise((resolve, reject) => {
+    try {
+      // Check if the script already exists
+      const existingScript = document.querySelector(`script[src="${FILE_URL}"]`)
+      if (existingScript) {
+        resolve({ status: true, message: 'Script already loaded' })
+        return
+      }
+
+      const scriptEle = document.createElement('script')
+      scriptEle.type = type
+      scriptEle.async = async
+      scriptEle.src = FILE_URL
+
+      scriptEle.addEventListener('load', (ev) => {
+        resolve({ status: true })
+      })
+
+      scriptEle.addEventListener('error', (ev) => {
+        reject({
+          status: false,
+          message: `Failed to load the script ＄{FILE_URL}`,
+        })
+      })
+
+      document.body.appendChild(scriptEle)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+
 export function defineClass(className, classStyles) {
  const styleSheets = document.styleSheets

@@ -7,17 +7,35 @@
 *
 */

-import { app } from '/scripts/app.js'
-import * as shared from '/extensions/mtb/comfy_shared.js'
-import { log } from '/extensions/mtb/comfy_shared.js'
-import { MtbWidgets } from '/extensions/mtb/mtb_widgets.js'
+import { app } from '../../scripts/app.js'
+
+import * as shared from './comfy_shared.js'
+import { log } from './comfy_shared.js'
+import { MtbWidgets } from './mtb_widgets.js'

 // TODO: respect inputs order...

+function escapeHtml(unsafe) {
+  return unsafe
+    .replace(/&/g, '&amp;')
+    .replace(/</g, '&lt;')
+    .replace(/>/g, '&gt;')
+    .replace(/"/g, '&quot;')
+    .replace(/'/g, '&#039;')
+}
 app.registerExtension({
  name: 'mtb.Debug',
  async beforeRegisterNodeDef(nodeType, nodeData, app) {
    if (nodeData.name === 'Debug (mtb)') {
+      const onNodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        const r = onNodeCreated
+          ? onNodeCreated.apply(this, arguments)
+          : undefined
+        this.addInput(`anything_1`, '*')
+        return r
+      }
+
      const onConnectionsChange = nodeType.prototype.onConnectionsChange
      nodeType.prototype.onConnectionsChange = function (
        type,
@@ -57,15 +75,18 @@ app.registerExtension({
          // const pos = this.widgets.findIndex((w) => w.name === "anything_1");
          // if (pos !== -1) {
          for (let i = 0; i < this.widgets.length; i++) {
-            this.widgets[i].onRemoved?.()
+            if (this.widgets[i].name !== 'output_to_console') {
+              this.widgets[i].onRemoved?.()
+            }
          }
-          this.widgets.length = 0
+          this.widgets.length = 1
        }
        let widgetI = 1
+
        if (message.text) {
          for (const txt of message.text) {
            const w = this.addCustomWidget(
-              MtbWidgets.DEBUG_STRING(`${prefix}_${widgetI}`, txt)
+              MtbWidgets.DEBUG_STRING(`${prefix}_${widgetI}`, escapeHtml(txt))
            )
            w.parent = this
            widgetI++
@@ -81,15 +102,17 @@ app.registerExtension({
          }
          // this.onResize?.(this.size);
          // this.resize?.(this.size)
-          this.setSize(this.computeSize())
        }

+        this.setSize(this.computeSize())
+
        this.onRemoved = function () {
          // When removing this node we need to remove the input from the DOM
          for (let y in this.widgets) {
            if (this.widgets[y].canvas) {
              this.widgets[y].canvas.remove()
            }
+            shared.cleanupNode(this)
            this.widgets[y].onRemoved?.()
          }
        }
@@ -9,8 +9,8 @@

 // forked from pysssss's imageFeed.js

-import { api } from '/scripts/api.js'
-import { app } from '/scripts/app.js'
+import { api } from '../../scripts/api.js'
+import { app } from '../../scripts/app.js'

 const styles = {
  lighbox: {
@@ -31,7 +31,7 @@ const styles = {
    background: 'none',
    border: 'none',
    color: '#fff',
-    zIndex: 9999999,
+    zIndex: 1000,
    fontSize: '30px',
    cursor: 'pointer',
    pointerEvents: 'auto',
@@ -43,7 +43,7 @@ const styles = {
    width: '100vw',
    position: 'absolute',
    bottom: 0,
-    zIndex: 9999999,
+    zIndex: 10,
    background: '#333',
    overflow: 'auto',
  },
@@ -7,13 +7,46 @@
 *
 */

-import { app } from '/scripts/app.js'
-import parseCss from '/extensions/mtb/extern/parse-css.js'
-import * as shared from '/extensions/mtb/comfy_shared.js'
-import { log } from '/extensions/mtb/comfy_shared.js'
-import { api } from '/scripts/api.js'
+// TODO: Use the builtin addDOMWidget everywhere appropriate

-const newTypes = ['BOOL', 'COLOR', 'BBOX']
+import { app } from '../../scripts/app.js'
+import { api } from '../../scripts/api.js'
+
+import parseCss from './extern/parse-css.js'
+import * as shared from './comfy_shared.js'
+import { log } from './comfy_shared.js'
+
+const newTypes = [, /*'BOOL'*/ 'COLOR', 'BBOX']
+
+const withFont = (ctx, font, cb) => {
+  const oldFont = ctx.font
+  ctx.font = font
+  cb()
+  ctx.font = oldFont
+}
+
+const calculateTextDimensions = (ctx, value, width, fontSize = 16) => {
+  const words = value.split(' ')
+  const lines = []
+  let currentLine = ''
+  for (const word of words) {
+    const testLine = currentLine.length === 0 ? word : `${currentLine} ${word}`
+    const testWidth = ctx.measureText(testLine).width
+    if (testWidth > width) {
+      lines.push(currentLine)
+      currentLine = word
+    } else {
+      currentLine = testLine
+    }
+  }
+  if (lines.length === 0) lines.push(value)
+  const textHeight = (lines.length + 1) * fontSize
+  const maxLineWidth = lines.reduce(
+    (maxWidth, line) => Math.max(maxWidth, ctx.measureText(line).width),
+    0
+  )
+  return { textHeight, maxLineWidth }
+}

 export const MtbWidgets = {
  BBOX: (key, val) => {
@@ -204,116 +237,7 @@ export const MtbWidgets = {
    widget.desc = 'Represents a Bounding Box with x, y, width, and height.'
    return widget
  },
-  BOOL: (key, val, compute = false) => {
-    /** @type {import("/types/litegraph").IWidget} */
-    const widget = {
-      name: key,
-      type: 'BOOL',
-      options: { default: false },
-      y: 0,

-      draw: function (ctx, node, widget_width, widgetY, height) {
-        const hide = this.type !== 'BOOL' && app.canvas.ds.scale > 0.5
-        if (hide) {
-          return
-        }
-        const outline_color = LiteGraph.WIDGET_OUTLINE_COLOR
-        const background_color = LiteGraph.WIDGET_BGCOLOR
-        const text_color = LiteGraph.WIDGET_TEXT_COLOR
-        const H = LiteGraph.NODE_WIDGET_HEIGHT
-        // const arrowSize = 8
-
-        let margin = 15
-        if (hide) return
-
-        let currentY = widgetY
-
-        ctx.textAlign = 'left'
-        ctx.strokeStyle = outline_color
-        ctx.fillStyle = background_color
-        ctx.beginPath()
-        // ctx.roundRect(margin, currentY, widget_width - margin * 2, H, [H * 0.5]);
-        ctx.rect(margin, currentY, H, H) // Draw checkbox square
-
-        ctx.fill()
-        ctx.stroke()
-
-        ctx.fillStyle = text_color
-        // ctx.fillText(this.label || this.name, margin * 2 + 5, currentY + H * 0.7);
-        ctx.fillText(
-          this.label || this.name,
-          H + margin * 2,
-          currentY + H * 0.7
-        )
-
-        // Draw arrow if the value is true
-        // Draw checkmark if the value is true
-        if (this.value) {
-          ctx.fillStyle = text_color
-          ctx.beginPath()
-          ctx.moveTo(margin + H * 0.15, currentY + H * 0.5)
-          ctx.lineTo(margin + H * 0.4, currentY + H * 0.8)
-          ctx.lineTo(margin + H * 0.85, currentY + H * 0.2)
-          ctx.stroke()
-        }
-      },
-      get value() {
-        return this.inputEl.value === 'true'
-      },
-      set value(x) {
-        this.inputEl.value = x
-      },
-      computeSize: function (width) {
-        return [width, 32]
-      },
-      mouse: function (event, pos, node) {
-        // let x = pos[0] - node.pos[0];
-        // let y = pos[1] - node.pos[1];
-        // let width = node.size[0];
-        // let H = LiteGraph.NODE_WIDGET_HEIGHT;
-        // let margin = 15;
-
-        // if (event.type == LiteGraph.pointerevents_method + "down") {
-        //     if (x > margin && x < widget_width - margin && y > widgetY && y < widgetY + H) {
-        //         this.value = !this.value; // Toggle checkbox value
-        //         shared.inner_value_change(this, this.value, event);
-        //         app.canvas.setDirty(true);
-        //     }
-        // }
-        if (event.type === 'pointerdown') {
-          // get widgets of type type : "COLOR"
-          const widgets = node.widgets.filter((w) => w.type === 'BOOL')
-
-          for (const w of widgets) {
-            // color picker
-            const rect = [w.last_y, w.last_y + 32]
-            if (pos[1] > rect[0] && pos[1] < rect[1]) {
-              // picker.style.position = "absolute";
-              // picker.style.left = ( pos[0]) + "px";
-              // picker.style.top = (  pos[1]) + "px";
-
-              // place at screen center
-              // picker.style.position = "absolute";
-              // picker.style.left = (window.innerWidth / 2) + "px";
-              // picker.style.top = (window.innerHeight / 2) + "px";
-              // picker.style.transform = "translate(-50%, -50%)";
-              // picker.style.zIndex = 1000;
-
-              this.value = this.value ? false : true
-            }
-          }
-        }
-      },
-    }
-
-    // create a checkbox
-    widget.inputEl = document.createElement('input')
-    widget.inputEl.type = 'checkbox'
-    widget.value = val || false
-
-    document.body.appendChild(widget.inputEl)
-    return widget
-  },
  COLOR: (key, val, compute = false) => {
    /** @type {import("/types/litegraph").IWidget} */
    const widget = {}
@@ -425,46 +349,22 @@ export const MtbWidgets = {
        // const [cw, ch] = this.computeSize(widgetWidth)
        shared.offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, height)
      },
-      computeSize: function (width) {
-        const value = this.inputEl.innerHTML
-        if (!value) {
+      computeSize(width) {
+        if (!this.value) {
          return [32, 32]
        }
        if (!width) {
-          log(`No width ${this.parent.size}`)
+          console.debug(`No width ${this.parent.size}`)
        }
-
-        const oldFont = app.ctx.font
-        app.ctx.font = `${fontSize}px monospace`
-
-        const words = value.split(' ')
-        const lines = []
-        let currentLine = ''
-        for (const word of words) {
-          const testLine =
-            currentLine.length === 0 ? word : `${currentLine} ${word}`
-
-          const testWidth = app.ctx.measureText(testLine).width
-
-          if (testWidth > width) {
-            lines.push(currentLine)
-            currentLine = word
-          } else {
-            currentLine = testLine
-          }
-        }
-        app.ctx.font = oldFont
-        if (lines.length === 0) lines.push(currentLine)
-
-        const textHeight = (lines.length + 1) * fontSize
-
-        const maxLineWidth = lines.reduce(
-          (maxWidth, line) =>
-            Math.max(maxWidth, app.ctx.measureText(line).width),
-          0
+        let dimensions
+        withFont(app.ctx, `${fontSize}px monospace`, () => {
+          dimensions = calculateTextDimensions(app.ctx, this.value, width)
+        })
+        const widgetWidth = Math.max(
+          width || this.width || 32,
+          dimensions.maxLineWidth
        )
-        const widgetWidth = Math.max(width || this.width || 32, maxLineWidth)
-        const widgetHeight = textHeight * 1.5
+        const widgetHeight = dimensions.textHeight * 1.5
        return [widgetWidth, widgetHeight]
      },
      onRemoved: function () {
@@ -472,25 +372,23 @@ export const MtbWidgets = {
          this.inputEl.remove()
        }
      },
-    }
-
-    Object.defineProperty(w, 'value', {
-      get() {
+      get value() {
        return this.inputEl.innerHTML
      },
-      set(value) {
-        this.inputEl.innerHTML = value
+      set value(val) {
+        this.inputEl.innerHTML = val
        this.parent?.setSize?.(this.parent?.computeSize())
      },
-    })
+    }

    w.inputEl = document.createElement('p')
-    w.inputEl.style.textAlign = 'center'
-    w.inputEl.style.fontSize = `${fontSize}px`
-    w.inputEl.style.color = 'var(--input-text)'
-    w.inputEl.style.lineHeight = 0
-
-    w.inputEl.style.fontFamily = 'monospace'
+    w.inputEl.style = `
+      text-align: center;
+      font-size: ${fontSize}px;
+      color: var(--input-text);
+      line-height: 0;
+      font-family: monospace;
+    `
    w.value = val
    document.body.appendChild(w.inputEl)

@@ -621,12 +519,7 @@ const mtb_widgets = {

        this.onRemoved = function () {
          // When removing this node we need to remove the input from the DOM
-          for (const w of this.widgets) {
-            if (w.canvas) {
-              w.canvas.remove()
-            }
-            w.onRemoved?.()
-          }
+          shared.cleanupNode(this)
        }
        return r
      }
@@ -670,6 +563,10 @@ const mtb_widgets = {
      }
    }

+    if (!nodeData.name.endsWith('(mtb)')) {
+      return
+    }
+
    //- Extending Python Nodes
    switch (nodeData.name) {
      case 'Psd Save (mtb)': {
@@ -759,22 +656,14 @@ const mtb_widgets = {
                i++
              }
            }
-            this.setSize?.(this.computeSize())
-            return r
-          }
-
-          const onRemoved = nodeType.prototype.onRemoved
-          nodeType.prototype.onRemoved = function (message) {
-            const r = onRemoved ? onRemoved.apply(this, message) : undefined
-            if (!this.widgets) return r
-            for (const w of this.widgets) {
-              if (w.canvas) {
-                w.canvas.remove()
-              }
-              w.onRemoved?.()
+            const onRemoved = this.onRemoved
+            this.onRemoved = () => {
+              shared.cleanupNode(this)
+              return onRemoved?.()
            }
-            return r
          }
+          this.setSize?.(this.computeSize())
+          return r
        }

        break
@@ -842,12 +731,7 @@ const mtb_widgets = {
          })

          this.onRemoved = () => {
-            for (const w of this.widgets) {
-              if (w.canvas) {
-                w.canvas.remove()
-              }
-              w.onRemoved?.()
-            }
+            shared.cleanupNode(this)
            app.canvas.setDirty(true)
          }

@@ -890,6 +774,40 @@ const mtb_widgets = {
        }
        break
      }
+      case 'Interpolate Clip Sequential (mtb)': {
+        const onNodeCreated = nodeType.prototype.onNodeCreated
+        nodeType.prototype.onNodeCreated = function () {
+          const r = onNodeCreated
+            ? onNodeCreated.apply(this, arguments)
+            : undefined
+          const addReplacement = () => {
+            const input = this.addInput(
+              `replacement_${this.widgets.length}`,
+              'STRING',
+              ''
+            )
+            console.log(input)
+            this.addWidget('STRING', `replacement_${this.widgets.length}`, '')
+          }
+          //- add
+          this.addWidget('button', '+', 'add', function (value, widget, node) {
+            console.log('Button clicked', value, widget, node)
+            addReplacement()
+          })
+          //- remove
+          this.addWidget(
+            'button',
+            '-',
+            'remove',
+            function (value, widget, node) {
+              console.log(`Button clicked: ${value}`, widget, node)
+            }
+          )
+
+          return r
+        }
+        break
+      }
      case 'Styles Loader (mtb)': {
        const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions
        nodeType.prototype.getExtraMenuOptions = function (_, options) {
@@ -965,6 +883,70 @@ const mtb_widgets = {

        break
      }
+      case 'Add To Playlist (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'video', 'VIDEO')
+        break
+      }
+      case 'Stack Images (mtb)':
+      case 'Concat Images (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'image', 'IMAGE')
+
+        break
+      }
+      case 'Batch Float Assemble (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'floats', 'FLOATS')
+        break
+      }
+      case 'Batch Merge (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'batches', 'IMAGE')
+
+        break
+      }
+      // TODO: remove this, recommend pythongoss's version that is much better
+      case 'Math Expression (mtb)': {
+        const onNodeCreated = nodeType.prototype.onNodeCreated
+        nodeType.prototype.onNodeCreated = function () {
+          const r = onNodeCreated
+            ? onNodeCreated.apply(this, arguments)
+            : undefined
+          this.addInput(`x`, '*')
+          return r
+        }
+
+        const onConnectionsChange = nodeType.prototype.onConnectionsChange
+        nodeType.prototype.onConnectionsChange = function (
+          type,
+          index,
+          connected,
+          link_info
+        ) {
+          const r = onConnectionsChange
+            ? onConnectionsChange.apply(this, arguments)
+            : undefined
+          shared.dynamic_connection(this, index, connected, 'var_', '*', [
+            'x',
+            'y',
+            'z',
+          ])
+
+          //- infer type
+          if (link_info) {
+            const fromNode = this.graph._nodes.find(
+              (otherNode) => otherNode.id == link_info.origin_id
+            )
+            const type = fromNode.outputs[link_info.origin_slot].type
+            this.inputs[index].type = type
+            // this.inputs[index].label = type.toLowerCase()
+          }
+          //- restore dynamic input
+          if (!connected) {
+            this.inputs[index].type = '*'
+            this.inputs[index].label = `number_${index + 1}`
+          }
+        }
+
+        break
+      }
      case 'Save Tensors (mtb)': {
        const onDrawBackground = nodeType.prototype.onDrawBackground
        nodeType.prototype.onDrawBackground = function (ctx, canvas) {
@@ -0,0 +1,181 @@
+import { app } from '../../scripts/app.js'
+import { api } from '../../scripts/api.js'
+import * as shared from './comfy_shared.js'
+
+class NotePlus extends LiteGraph.LGraphNode {
+  title = 'Note+ (mtb)'
+  category = 'mtb/utils'
+
+  constructor() {
+    super()
+
+    this.isVirtualNode = true
+    this.serialize_widgets = true
+
+    this.editing = false
+    this.live = true
+    this.rawVal = "<p style='color:red;font-family:monospace'\n>    Note+\n</p>"
+
+    this.calculated_height = 36
+
+    const inner = document.createElement('div')
+    inner.style.margin = '0'
+    inner.style.padding = '0'
+    this.html_widget = this.addDOMWidget('HTML', 'html', inner, {
+      setValue: (v) => {
+        // update our widget preview
+        this.html_widget.element.innerHTML = v
+        // calculate height
+        this.calculated_height = this.html_widget.element.scrollHeight + 36
+      },
+      getValue: () => this.rawVal,
+      getMinHeight: () => this.calculated_height, // (the edit button),
+    })
+
+    // console.log(`Value of HTML: ${this.html_widget.value}`)
+    this.html_widget.element.innerHTML = this.html_widget.value
+
+    //- ace based editor
+    this.addWidget('button', 'Edit', 'Edit', () => {
+      const container = document.createElement('div')
+      Object.assign(container.style, {
+        display: 'flex',
+        gap: '10px',
+      })
+
+      dialog.show('')
+      dialog.textElement.append(container)
+
+      const value = document.createElement('div')
+      value.id = 'noteplus-editor'
+      Object.assign(value.style, {
+        width: '300px',
+        height: '200px',
+        backgroundColor: 'rgb(30,30,30)',
+        color: 'whitesmoke',
+      })
+
+      container.append(value)
+
+      const live_edit = document.createElement('input')
+      live_edit.type = 'checkbox'
+      live_edit.checked = this.live
+      live_edit.onchange = () => {
+        this.live = live_edit.checked
+      }
+
+      const live_edit_label = document.createElement('label')
+      live_edit_label.textContent = 'Live Edit'
+      live_edit_label.append(live_edit)
+
+      value.after(live_edit_label)
+
+      this.setupEditor()
+      this.editor.setValue(this.html_widget.element.innerHTML)
+    })
+
+    const dialog = new app.ui.dialog.constructor()
+    dialog.element.classList.add('comfy-settings')
+
+    const closeButton = dialog.element.querySelector('button')
+    closeButton.textContent = 'CANCEL'
+    const saveButton = document.createElement('button')
+    saveButton.textContent = 'SAVE'
+    saveButton.onclick = () => {
+      this.updateHTML(this.editor.getValue())
+
+      this.editor.destroy()
+      this.editor.container.remove()
+
+      dialog.close()
+    }
+
+    closeButton.before(saveButton)
+
+    shared
+      .loadScript(
+        'https://cdn.jsdelivr.net/npm/ace-builds@1.16.0/src-min-noconflict/ace.min.js'
+      )
+      .catch((e) => {
+        console.error(e)
+      })
+  }
+
+  setupEditor() {
+    this.editor = ace.edit('noteplus-editor')
+    this.editor.setTheme('ace/theme/dracula')
+    this.editor.session.setMode('ace/mode/html')
+
+    this.editor.setShowPrintMargin(false)
+    this.editor.session.setUseWrapMode(true)
+    this.editor.renderer.setShowGutter(false)
+    this.editor.session.setTabSize(4)
+    this.editor.session.setUseSoftTabs(true)
+    this.editor.setFontSize(14)
+    this.editor.setReadOnly(false)
+    this.editor.setHighlightActiveLine(false)
+    this.editor.setShowFoldWidgets(true)
+
+    this.editor.session.on('change', (delta) => {
+      // delta.start, delta.end, delta.lines, delta.action
+      if (this.live) {
+        this.updateHTML(this.editor.getValue())
+      }
+    })
+  }
+
+  updateHTML(val) {
+    // if (CONTAINER_HTML.includes('${html}')) {
+    //   console.log('found template')
+    //   val = CONTAINER_HTML.replace('${html}', val)
+    // }
+
+    this.html_widget.value = val
+    this.rawVal = val
+
+    this.calculated_height = this.html_widget.element.scrollHeight
+
+    this.setSize(this.computeSize())
+  }
+
+  // //   onRemoved() {
+  // //     console.log('Removing', this)
+  // //     for (const w of this.widgets) {
+  // //       console.log('Removing', w)
+  // //       w.onRemove?.()
+  // //       w.onRemoved?.()
+  // //     }
+  // //   }
+}
+
+app.registerExtension({
+  name: 'mtb.noteplus',
+
+  setup() {
+    // app.ui.settings.addSetting({
+    //     id: "mtb.noteplus.Container",
+    //     name: "📦 HTML container",
+    //     type: "text",
+    //     defaultValue: "<div>${html}</div>",
+    //     tooltip:
+    //         "This defines the wrapper for the noteplus html content, use '${html}' to define the location of the placeholder",
+    //     attrs: {
+    //         style: {
+    //             fontFamily: "monospace",
+    //         },
+    //     },
+    //     onChange(value) {
+    //         if (!value) {
+    //             CONTAINER_HTML = null;
+    //             return;
+    //         }
+    //         console.log(`NOTEPLUS| value changed: ${value}`)
+    //         CONTAINER_HTML = value
+    //     },
+    // });
+  },
+
+  registerCustomNodes() {
+    LiteGraph.registerNodeType('Note Plus (mtb)', NotePlus)
+  },
+})
@@ -7,7 +7,7 @@
 *
 */

-import { app } from '/scripts/app.js'
+import { app } from '../../scripts/app.js'

 const log = (...args) => {
  if (window.MTB?.TRACE) {
Author	SHA1	Message	Date
Mel Massadian	90d96366c8	docs: 📄 add note+ screenshot	2023-12-02 19:16:35 +01:00
melMass	605c8db320	feat: 📝 add note plus future ideas: - add a markdown mode - add a separate css tab on the edit dialog	2023-12-02 19:07:32 +01:00
melMass	cf965727e8	feat: 🚧 add playlist nodes very basic for now	2023-12-01 00:50:50 +01:00
melMass	12b134ab4c	fix: 💄 register the COLOR type even for external extensions Since I register the widgets, anyone using a COLOR type in their extension and mixing it with mtb will end up with widgets, before this commit they would not have the option to turn it back and forth between input <-> widgets. related to: https://github.com/melMass/comfy_mtb/discussions/131 draft pr in bmad here: https://github.com/bmad4ever/comfyui_bmad_nodes/pull/6	2023-11-30 20:58:29 +01:00
melMass	dd27f990c7	chore: 📝 update node list	2023-11-29 15:41:47 +01:00
melMass	16c1a59312	feat: 🚨 add missing node - pickfrombatch: exctract X images from either ends of your batch - added alignement to TextToImage	2023-11-29 15:40:57 +01:00
melMass	59a361af58	fix: ✨ mask crop output Follow up of #124 Thanks to @Yurchikian	2023-11-14 21:39:09 +01:00
melMass	e4da832b99	fix: 🚑️ thread font loading This created a huge lag on first opening of the browser. The small drawback is that the webpage needs a reload for fonts to be available	2023-11-14 20:53:44 +01:00
Artem YurchenkoandmelMass	14ee9e23c0	fix: 📦 changed way of creating bbox from mask (#124 ) * fix: changed way of creating bbox from mask changed code according to https://stackoverflow.com/questions/31400769/bounding-box-of-numpy-array Also fixed processing of mask input * fix: 🐛 ambigous check --------- Co-authored-by: melMass <mel@melmassadian.com>	2023-11-14 20:46:26 +01:00
melMass	53cb503866	fix: ✨ expose invert of bboxfrommask changes the default to false related to #124	2023-11-14 18:02:04 +01:00
melMass	d5c4c5f264	fix: ✨ less strict csv parsing closes #122	2023-11-08 18:28:27 +01:00
Mel Massadian	87e301d120	merge: 🔀 pull request #109 from melMass/dev/0.2.0	2023-11-04 16:22:34 +01:00
melMass	537a0d8108	chore: ✨ update node_list	2023-11-04 16:21:36 +01:00
melMass	9afad1a168	chore: ✨ local stuff	2023-11-04 16:19:10 +01:00
melMass	142624eea6	feat: ✨ Math Expression node	2023-11-04 16:14:06 +01:00
melMass	c8658dfbdd	fix: 🐛 fit number regression closes #120	2023-11-04 16:10:52 +01:00
melMass	403903798a	Merge branch 'main' into dev/0.2.0	2023-10-29 23:49:31 +01:00
Mel Massadian	4e07450bca	fix: 🐛 remove uneeded installs	2023-10-29 23:46:20 +01:00
melMass	bcac66508d	refactor: ⚡️ small local fixes made while writting wiki	2023-10-21 03:14:12 +02:00
Mel Massadian	6b993b8407	docs: 📝 add cover image	2023-10-21 01:04:26 +02:00
melMass	049983dbe2	feat: 🚀 add optional inputs to colored image	2023-10-20 20:03:08 +02:00
melMass	255ac036ba	fix: 🐛 import issue	2023-10-20 20:02:10 +02:00
melMass	8d12b59844	fix: 🐛 wrong output for bbox	2023-10-20 20:01:28 +02:00
melMass	7812cfa3c2	Merge branch 'main' into dev/0.2.0	2023-10-12 12:44:04 +02:00
melMass	278f22c209	fix: 🚑️ fallback when symlink detection fails	2023-10-12 12:42:24 +02:00
melMass	e6f6502673	fix: ✨ handle malformed styles.csv closes #106	2023-10-10 14:41:53 +02:00
melMass	5af284067c	fix: 🐛 encoding closes #107	2023-10-10 14:40:44 +02:00
melMass	d7b8ac8e0c	feat: ✨ Add support for extra_model_paths.yaml closes #66	2023-10-10 14:31:28 +02:00
melMass	af94203d1b	feat: ✨ add batch shake applies "camera shake" using Brownian Noise	2023-10-10 11:44:14 +02:00
melMass	bb90e0415f	fix: ⚡️ add the cli deps	2023-10-09 21:40:40 +02:00
Mel Massadian	3e8c2fe789	docs: 📝 fix image size	2023-10-09 21:29:54 +02:00
Mel Massadian	3e93ea6f2c	docs: 📝 add image	2023-10-09 21:29:04 +02:00
melMass	cea0b08eb0	docs: 📝 explain optional nodes	2023-10-09 21:27:21 +02:00
melMass	5b75436610	refactor: 🗑️ remove unused code in install script	2023-10-09 21:11:00 +02:00
melMass	a798eb07d0	feat: ✨ enhance concat images Comfy added native support for that: ImageBatch (see #67) But instead of removing it, this one uses "dynamic" input length. closes #67	2023-10-09 21:03:41 +02:00
melMass	25b933c698	fix: 🚑️ check for symlink	2023-10-09 20:51:13 +02:00
melMass	5dfea51dd8	fix: 🚑️ remove problematic dependencies - Back to using requirements.txt (closes #100) - Use the web directory (closes #108) - Add support for Python 11 (closes #65) - Faceswap nodes and Film not supported anymore, check the readme closes #95 #105 #101 #99 #96 #76 #72 #64	2023-10-09 20:36:43 +02:00
melMass	f1ff9fc7c4	fix: 🐛 batch support	2023-10-09 17:40:52 +02:00
melMass	c1d42de0fc	feat: 💄 add a few more batch nodes	2023-10-09 03:56:23 +02:00
melMass	4605f74f37	fix: 🐛 automatically disable tiling if seamless is on Artifacts shows up again when using both on the VAE Decode	2023-10-09 03:44:03 +02:00
Mel Massadian	8f909864bf	docs: 📝 add the example previews from the wiki	2023-10-08 03:23:17 +02:00
Mel Massadian	4917e31c42	docs: 📝 update node list	2023-10-08 03:09:08 +02:00
melMass	cef5023efc	feat: ✨ Batch node utilities Usefull for animateDiff Updates the node list	2023-10-08 02:31:40 +02:00
melMass	bb3277d85f	feat: 🚨 Image Stack node (horizontal and vertical stack) with dynamic inputs	2023-10-06 00:52:06 +02:00
melMass	dc500b788e	fix: 🐛 debug node wouldn't work when run twice since the past fix...	2023-10-05 22:23:06 +02:00
melMass	21acc87ff0	feat: 🚀 add seamless model hack Inspired by the A111 hack and FlyingFireCo/tiled_ksampler	2023-10-05 18:28:36 +02:00
melMass	d49b2578c2	fix: ⚡️ hack to handle prompt validation I can finally reproduce :) Fixes #85	2023-10-05 00:55:37 +02:00
melMass	87b245c6a6	fix: ✨ deepbump update - fixes #102 - Add support for batch in deep bump.	2023-10-04 19:28:11 +02:00
melMass	38df58a78c	fix: 👷 user folder_paths to retrieve comfy root	2023-10-04 15:00:12 +02:00
melMass	90aee83797	fix: 🐛 typo Closes #89	2023-09-08 12:59:20 +02:00
melMass	a50b11bdaa	fix: 🐛 do not resolve symlink for "here" - Using absolute instead of resolve - Closes #90 - reorder imports (isort)	2023-09-08 12:49:43 +02:00
Michael Poutre	88a2779687	fix: ✏️ use Union to allow support for <3.10 (#91 )	2023-09-08 12:37:45 +02:00
Mel Massadian	da290dbcf2	chore: 📝 fix update issue template	2023-09-06 11:20:04 +02:00
Mel Massadian	b949bb406b	chore: 📝 update issue template	2023-09-06 11:17:40 +02:00
melMass	cdd098e102	fix: ⚡️ simplify widgets cleanup Closes #88	2023-09-06 10:53:00 +02:00
Mel Massadian	cbdb816164	merge: 🔀 pull request #86 from melMass/feature/styles-editor	2023-09-02 23:57:29 +02:00
melMass	11162b3ea7	Merge branch 'main' into feature/styles-editor	2023-09-02 23:05:55 +02:00
melMass	638498c6b4	feat: 🔧 debug handle a few more types To avoid the huge output of tensors related to #85	2023-09-02 23:05:32 +02:00
melMass	2faa2f2a14	feat: 🎨 Add an editor for the styles loader For simplicity I implemented it an endpoint for now. Closes #84	2023-09-02 21:09:37 +02:00
melMass	6a00d1da5a	feat: ✨ add a static assets path much easier to manage custom css and js on endpoints!	2023-09-02 18:22:14 +02:00
melMass	cc43654af2	fix: ✨ don't assume the install was ran I must probably check for other places too, but this directly addresses #82. Closes #82	2023-09-02 16:52:56 +02:00
Mel Massadian	e11df9d45c	docs: 📝 add some deprecation warnings and recommendations also add a link to @pennyvc 's tutorial	2023-08-26 17:27:33 +02:00
melMass	616b2bfc6c	fix: 🐛 install check string against Path	2023-08-25 22:07:01 +02:00
melMass	22cac9b2d9	fix: 🐛 properly escape paths Handle spaces in paths Partially address #73	2023-08-25 16:17:29 +02:00
Mel Massadian	bb35098c65	docs: 📝 add a reference to SlickComfy for colab	2023-08-25 14:30:34 +02:00
melMass	e2773ff22e	fix: 🐛 use relative paths in JS StableSwarm is using a reverse proxy I initially thought these import did not work in comfy! Seems like I was wrong. Closes #74	2023-08-25 13:40:23 +02:00
melMass	3b07984716	fix: 💄 BatchFromHistory when "listening" When using --listen, BatchFromHistory was trying the wrong local ip on local remotes.	2023-08-15 20:22:35 +02:00
melMass	fe8f519f88	fix: ✨ save gif widget removal fixes #63	2023-08-14 19:41:57 +02:00
melMass	a71c273baf	feat: ✨ add Interpolate Clip Sequential Still need testing but works	2023-08-13 00:47:56 +02:00
melMass	49c64c74eb	ci: 💄 encoding	2023-08-13 00:15:12 +02:00
Mel Massadian	2ecd4700d7	merge: 🔀 pull request #50 from melMass/dev/august-refactor	2023-08-12 23:56:08 +02:00
melMass	ea5d73d48c	fix: 🚀 pending fixes should be ready to go	2023-08-12 23:53:53 +02:00
melMass	30d6cfe812	fix: 🚑️ image resize infinite loop	2023-08-12 23:41:58 +02:00
melMass	610afe031f	fix: ✨ update example files	2023-08-12 23:41:24 +02:00
melMass	a4d99d966b	feat: 💫 export to prores -> export with ffmpeg	2023-08-12 00:35:02 +02:00
melMass	4fc84d615d	fix: 🐛 simplify install steps	2023-08-12 00:11:54 +02:00
melMass	8523392df7	fix: ✨ refactor	2023-08-11 22:22:07 +02:00
melMass	dbdb872b74	feat: 🔥 add any to string & refactor	2023-08-10 23:31:46 +02:00
melMass	40560f8154	fix: 🐛 debug rgba	2023-08-10 23:22:05 +02:00
melMass	e7f72f9825	fix: 🎨 rename fun to generate	2023-08-10 22:58:12 +02:00
melMass	11444662b9	fix: ✨ refactor existing	2023-08-10 22:54:19 +02:00
melMass	2eccba4e33	fix: ⚡️ move getbatchfromhistory to graphutils Fixes #59	2023-08-10 16:34:36 +02:00
melMass	5ec5511433	feat: ✨ add UI for interpolate clip sequential	2023-08-09 21:59:46 +02:00
melMass	630b492347	fix: 🚧 wip dependency installer UI Will allow to install missing deps/models from the endpoint: /mtb/status	2023-08-09 14:33:58 +02:00
melMass	4f30829e06	refactor: 🚧 tidy	2023-08-08 23:16:29 +02:00
Mel Massadian	414beb99a1	ci: 🚀 only fetch controlnet_preprocessor deps A true install seems to requires CUDA, I can probably change the image too.	2023-08-08 22:40:53 +02:00
melMass	3f14b1676d	feat: ✨ add portable reqs	2023-08-08 21:29:28 +02:00
melMass	9c2e8ac57c	Merge branch 'main' into dev/august-refactor	2023-08-08 18:10:39 +02:00
melMass	4dd5321852	fix: ⬇️ download_antelopev2 the url used in insightface returns 404. fixes #55	2023-08-07 23:53:34 +02:00
melMass	91f60d4c46	fix: 🚑️ frontend pushed too early Since I mistakenly pushed some js code from a PR some nodes weren't working anymore... This fix that and the model path for face_restore nodes if installed using the manager, with a fallback for now..	2023-08-07 21:24:23 +02:00
melMass	fb644847ca	feat: ✨ add border extension The maths are still not correct I need to debug it in isolation	2023-08-07 20:49:47 +02:00
Mel Massadian	84ac8ac852	fix: 🚑️ missing input	2023-08-07 02:59:16 +02:00
Mel Massadian	63b3aece2b	ci: 🚀 add controlnetpreprocessors to tests	2023-08-07 00:39:32 +02:00
Mel Massadian	a54d7d5346	feat: 🎨 update node list	2023-08-06 00:34:42 +02:00
melMass	13d255a730	refactor: ♻️ get batch from history	2023-08-05 13:31:54 +02:00
melMass	2bc7ae88bf	feat: ✨ use PIL for gif saving	2023-08-05 13:31:30 +02:00
melMass	0fb2d4da90	fix: 🐛 image feed zorder	2023-08-05 13:28:01 +02:00
melMass	cfb3b237cf	revertible: 💄 use BOOLEAN instead of BOOL Since this commit: https://github.com/comfyanonymous/ComfyUI/commit/9534f0f8a5a026654492da378f84d2cdc589ed01 Input <-> widget is possible on booleans. Locally I edited it but forgot about it not being in comfy This commit is reversable since I'm not yet sure of all the impacts	2023-08-05 13:08:18 +02:00
melMass	3d5075fea2	fix: 🐛 shell command bug Since we always build a string shell should always be true	2023-08-04 14:44:52 +02:00
Mel Massadian	098d74a3cd	docs: 📝 link the actual action instead of badge	2023-08-03 14:33:15 +02:00
Mel Massadian	e74314b04e	docs: 📝 add action badge	2023-08-03 14:28:38 +02:00
Mel Massadian	d4f791d7a1	ci: ✨ remove unused input	2023-08-03 14:26:02 +02:00
Mel Massadian	2ff04672da	ci: ✨ use the same cwd as manager	2023-08-03 14:22:26 +02:00
melMass	b854a302ce	fix: 🚑️ remove pipe mode from the install.py I added a `path` argument to mimic what pipe did.	2023-08-03 13:12:56 +02:00
Mel Massadian	512de6023e	feat: ✨ install fix - removed un-needed dependencies - added a ci to test comfy-embedded - fixed wheel order install	2023-08-01 03:24:27 +02:00
Mel Massadian	c5bbe83008	test: 🧪 remove sha input	2023-07-31 18:57:41 +02:00
Mel Massadian	7b3afca817	test: 🧪 ci for comfy embedded	2023-07-31 18:50:51 +02:00
Mel Massadian	bbfcb62c39	ci: 🎨 no brace glob	2023-07-30 18:09:24 +02:00
Mel Massadian	a22fd01d66	ci: 🎨 extract txt	2023-07-30 18:06:34 +02:00
Mel Massadian	8e5b7765cc	ci: 🎨 also push wheels_order to releases I will use it directly from the installer	2023-07-30 17:56:39 +02:00
melMass	36d8e6bdb0	fix: ⚡️ colab install	2023-07-30 02:55:06 +02:00
melMass	3dadc119f4	chore: 🚧 more info for bug reports	2023-07-30 02:16:12 +02:00
melMass	ffa1a87b91	fix: 🚑️ install typo	2023-07-30 02:06:41 +02:00
melMass	346ff649d5	ci: ✨ individual wheels	2023-07-30 01:41:14 +02:00